Dexterous Manipulation

Project Web: https://sites.google.com/view/dexterous-cable-manipulation/home Fig. 1: Our designed hand pulling the cable from right to left. (a)-(c): The hand performs a pre-grasp motion to position the cable correctly. (d): The left-side thumb and index finger grasp the cable. (e): The middle finger bends to hook the grasped cable. (f)-(h): The left-side thumb and index finger drag the cable to the left, and the right-side thumb and index finger hold the cable to prevent it from sliding back.

Abstract—Humans use their hands to dexterously manipulate cables to perform various tasks, like grasping cables, moving cables in hand without dropping them, bending the cable into a U shape for hooking and so on. Existing research that addressed cable manipulation relied on two-fingered grippers, which make it difficult to perform similar cable manipulation tasks that humans perform. This is due to the limited dexterity of a two- fingered gripper, which can only grasp and release a cable without additional manipulability. Thus, we need a multi-fingered hand, which is much more dexterous than a two-fingered gripper.

However, unlike dexterous manipulation of rigid objects, the development of dexterous cable manipulation skills in robotics remains underexplored due to the unique challenges posed by a cable’s deformability and inherent uncertainty. In addition, using a dexterous hand introduces specific difficulties in tasks, such as cable grasping, pulling, and in-hand bending, for which no dedicated task definitions, benchmarks, or evaluation metrics exist. Furthermore, we observed that most existing dexterous hands are designed with structures identical to humans’, typically featuring only one thumb, which often limits their effectiveness during dexterous cable manipulation. Lastly, existing non-task- specific methods did not have enough generalization ability to solve these cable manipulation tasks or are unsuitable due to the designed hardware. We address these three challenges in real- world dexterous cable manipulation in the following steps: (1) We first defined and organized a set of dexterous cable manipulation tasks into a comprehensive taxonomy, covering most short- horizon action primitives and long-horizon tasks for one-handed cable manipulation. This taxonomy revealed that coordination between the thumb and the index finger is critical for cable manipulation, which decomposes long-horizon tasks into simpler primitives. (2) We designed a novel five-fingered hand with 25 degrees of freedom (DoF), featuring two symmetric thumb- index configurations and a rotatable joint on each fingertip, which enables dexterous cable manipulation. (3) We developed a demonstration collection pipeline for this non-anthropomorphic hand, which is difficult to operate by previous motion capture methods. Given only one demonstration on one specific cable for each manipulation, among 8 primitives, our method achieved 88% success rate of demonstration replaying on three cables of the same material but various diameters and over 75% success rate on three cables of very different materials, stiffness, and diameters. Based on collected primitive demonstrations, we developed finite state machines (FSM) that enabled the robotic hand to execute complex long-horizon tasks without requiring prior demonstrations of the complete trajectories. Among four long-horizon and complicated manipulations, we achieved a 64% success rate of demonstration replaying on the cables of the same materials and various diameters. Our robotic hand achieved performance comparable to human baseline dexterity in both primitive actions and long-horizon tasks. Through human-guided transitions between primitives, the robotic hand demonstrates

Arxiv:2502.00396V2 [Cs.Ro] 6 Feb 2025

robust long-horizon manipulation capabilities even under diverse external disturbances. Index Terms—Manipulation taxonomy, cable manipulation, dexterous manipulation, multi-fingered hand design.

Ntroduction

The manipulation of cables, deformable linear objects in a formal description, is a fundamental task across diverse settings - from precise movements in surgical procedures to everyday handling in offices and homes, as well as critical ap- plications in manufacturing and various industries [68, 43, 58].

Previous works demonstrated that cables can be manipulated in versatile but specific ways with one or two grippers mounted on robot arms, e.g., cable insertion [61, 52, 67], cable slid- ing and following [45, 60], cable knotting [38, 29], cable untangling , cable waving [11, 51], cable routing [25, 31], cable coiling and wrapping for packing and storing [34, 33], cable shape control and planning in 2D and 3D[55, 59, 32] and so on. However, a significant gap still exists between robotic cable manipulation and human-level dexterity. We demonstrate the gap by considering a manipulation case: a human wants to insert a USB connector into a computer port, and they first grasp the middle part of USB cable, slide the cable to reach the USB port connector, adjusts the connector’s position and orientation, and finish insertion. In the above- mentioned task, humans use their dexterous hands to perform versatile manipulation skills, allowing them to handle cables in numerous ways without the need for specialized tools or being limited to particular tasks. This versatility highlights the limitations of current robotic approaches, e.g. using a robot gripper, which lack the adaptability and dexterity inherent in human hands and cannot perform either in-hand manipulation or long-horizon manipulations. Although some grippers with task-specific modifications can perform a certain dexterous cable manipulation, e.g. a gripper with tactile sensors for in-hand cable following [45, 60], such modifications highly depend on the hardware designs for specific tasks without generalization like human hands.

One straightforward way to begin to bridge the gap be- tween robot and human manipulation of cables is to analyze and understand how humans manipulate cables. Investigating robotic successes on dexterous manipulation on rigid objects demonstrates some commonalities: multi-fingered hands were mainly used with high dexterity on tasks that were already well-defined in studies on human manipulation, e.g. in-hand object reorientation [10, 9, 22], multiple object grasping , and so on. To explore how humans manipulate objects, previ- ous researchers developed taxonomies of rigid object grasping and manipulation [14, 17, 28, 4]. These taxonomies can help segment, classify, and label different types of primitives or grasping poses among datasets from humans and provide a better understanding and analysis of human behaviors, for the benefit of enlightened robotics research. By manually defining primitives, task and motion planning can be proposed to address long-horizon manipulation of rigid objects with low level primitives and high level planners [19, 35, 36, 16, 49].

Inspired by this rigid object research, for dexterous cable manipulation (DCM) we propose a cable’s dexterous manip- ulation taxonomy (Cable Dexonomy, see Section III.).

Built on the taxonomy for DCM, the second goal is to develop an improved end-effector. Human hands can perform dexterous cable manipulation in many ways without needing specific end-effectors or limitations to particular tasks. As for robotic cable manipulation, there is much previous research into cable manipulation [61, 52, 67, 45, 38, 29, 60, 50, 11, 51, 25, 34, 33, 55, 59, 32], but they all used either one or two two-fingered grippers, and most grippers grasp cables firmly without relative motions between the cable and the gripper unless released, except [45, 60], which used tactile sensors that allowed sliding between the cable and the gripper.

Using dexterous hands to manipulate cables is still under- explored, and one notable reason is both the dexterous hand and the cable have exceptionally high degree-of-freedoms (DoFs), which make the control extremely difficult.

The example in Figure 1 shows that using a multi-fingered hand to manipulate the cable has many benefits. The hand manipulates the cable in a dexterous way by first pre-grasping the cable into a more convenient position, grasping it, hooking it with the middle-finger, and performing in-hand pulling to move the cable along the hand. If a two-fingered gripper were used instead of the hand, it would be necessary to move the whole arm to perform pre-grasp and grasping and it would also need support from the table to perform the cable sliding for releasing and re-grasping. These benefits also happen in different scenarios with similar behaviors.

One notable thing about the hand shown in Figure 1 is the hand has two new features compared to anthropomorphic hands: two symmetrically placed thumbs and rotatable finger- tips. These improvements were identified as a consequence of the taxonomy study. This will be further discussed in Section Given the Cable Dexonomy and the hand designed for dexterous cable manipulation, the third problem arises: how to control the new hand to enable it to perform various DCM tasks. As far as we can tell, all previously published solutions are task-specific without having (or at least demonstrating) the potential to generalize to different tasks. Considering the enormous types of cables and associated dexterous manipu- lations, proposing new solutions for each new manipulation task is time-consuming and unrealistic. For a general-propose solution for various manipulations, either optimization-based methods or learning-based methods have demonstrated their potential [41, 40]. However, the former requires a careful construction of optimization equalities, and the latter one needs specific reward engineering and meanwhile sim-to-real transferring is another challenge [3, 22]. Recently, imitation learning-based methods have become very popular for robotic manipulation with single or dual arms, e.g. Action Chunking Transformer [64, 65], Diffusion Policy , with a multi- fingered hand and Dexterous Imitation Made Easy , See to Touch , on an anthropomorphic hand. However, these methods, requiring motion capture or teleoperation, are almost impossible to use for dexterous cable manipulation, which is a multi-contact problem and requires intensive haptic feedback.

So far, we have not seen any method that can collect in-hand demonstrations of dexterous manipulation of cables with an anthropomorphic hand, let alone with a hand whose dexterity is beyond the usual anthropomorphic design.

Therefore, to address the control problem by using imita- tion learning, we proposed a special data collection pipeline, together with a finite state machine that can execute primitives to perform a long-horizon manipulation. (See Section V.) We do not propose or implement an autonomous manip- ulation system in this paper. The goal of the experiments is to show that: 1. The proposed demonstration collection pipeline is well established and capable of replaying primitive and long-horizon actions on different cables with different configurations. 2. A good cable taxonomy could lead to a good performance of long-horizon manipulations with clearer strategies and sub-steps. 3. A good hardware design that can perform these dexterous manipulation with a high success rate.

4. The potential of collecting a large scale of demonstration dataset to train a learning-based manipulation policy. To conclude, this paper makes three contributions: • A proposal for a dexterous cable manipulation taxonomy (Cable Dexonomy - Section III).

• An improved multi-fingered hand with one additional thumb and rotatable fingertips in symmetric structure in- spired by Cable Dexonomy, which is capable of perform- ing various dexterous cable manipulation tasks. (Section

)

• An effective demonstration collection pipeline using the designed hand to identify and implement manipulation primitives and replaying long horizon tasks with finite state machines according to the Cable Dexonomy with unseen cables. (Section V).

Research Background

Dexterous manipulation refers to turning and shifting of objects to change their positions and orientation in the hand through the motion of the palm and fingers given a reference configuration [37, 6]. Compared to gripper-based manipulation, dexterous manipulation usually involves a multi- fingered hand to perform in-hand manipulation, including dexterous grasping , in-hand object reorientation [10, 9, 3], in-hand finger motion planning , grasped object shape

Estimation , Tossing , Catching , Multiple Object

grasping , solving Rubik’s cubes , rotating Baoding balls and so on. Another way of performing dexterous manipulation is called extrinsic dexterity . With the help of gravity or extrinsic contacts with environment, like tables or walls, end-effectors can perform more dexterous manipulation including object flipping [46, 23]. In this work, we focus on dexterous manipulation with a multi-fingered hand, but we still discussed how contacts with environment benefit in-hand dexterous manipulation.

Manipulation taxonomies have been proposed to classify different types of human grasping and manipulation, due to the many and complex manipulations possible with different objects and with different goals. Cutkosky et al. first developed a taxonomy for power and precision grasps based on the concept of virtual fingers . Feix et al. extended human grasping taxonomies into The GRASP Taxonomy with 33 different grasp types . Beyond human grasping, there are several works on taxonomies for labeling and segmentation of videos of human object manipulation [28, 4, 7, 8]. These manipulation taxonomies have two advantages: 1. a robotic manipulation benchmark, and 2. introducing long-term ma- nipulation by composition of short-term primitives.

Cable manipulation has been explored in various way, including cable insertion [61, 52, 67], cable following and sliding [45, 60], cable knotting [38, 29], cable untangling , cable waiving [11, 51], cable routing [25, 31], cable coiling and wrapping for packing and storing [34, 33], cable shape control in 2D and 3D[55, 59, 32] and so on. For these tasks, the most frequently used end-effectors are parallel jaw grippers or similar end-effectors that can firmly grasp the cables without relative sliding. However, fixed grasps of cables prevent the robot from performing DCM due to the restricted action space, She et al. used a gripper with tactile sensors to do cable following, allowing the gripper to reach one end-tip of the cable by sliding along it. Yu et al. performed a similar manipulation using the thumb and a single index finger on a multi-fingered hand equipped with tactile sensors on the fingertips, but did not further explore the use of all fingers or other tasks. For both methods, sliding the gripper along a cable, rather than pulling the cable through the hand, requires two strong assumptions: one end of the cable is fixed, and the hand is moved by the robot arm rather than staying static to perform in-hand manipulation.

In this paper, we show that using a 5-fingered hand without tactile sensors can perform not only one hand cable pulling, but also several other in-hand cable manipulation tasks.

Taxonomy Of Dexterous Cable Manipulation

This section introduces a proposed Cable Dexonomy (Dex- terous cable manipulation Taxonomy) from the human per- spective.

A. Cable Dexonomy Tree

Previously, a hand-centric manipulation taxonomy was pro- posed to classify different in-hand rigid object manipulation tasks , given 6 criteria used to define the task. There are 4 criteria shown in Figure 2: prehensile or non-prehensile, motion or no motion, in-hand or out-of-hand, and with or without support. Figure 4, shows that the fingers used during manipulation can be summarized into three types. Figure 3, displays the different goal configurations that the cable may reach during dexterous manipulation.

Next, we describe how Bullock et al’s scheme is adapted for cables and dexterous manipulators. We propose several cable-specific extensions for the criteria mentioned above: 1. Prehensile refers to having more than one contact point and having contact forces that can stabilize the cable without Fig. 2: Human DCM with Prehensile (or not), Motion between fingers and the cable (or not), In-hand or out-of-hand, and Support from the external contact (or not).

external forces like gravity or ground support . 2. Motion indicates active movement exists between the hand and the cable. 3. In-hand means the manipulation mostly happens inside the hand space between fingers and the cable, and its opposite, out-of-hand, refers to the movement of the whole arm or the wrist without considering the fingers’ actions.

4. With support means that the cable is supported by an external contact, such as lying on the table or the ground. 5. Used fingers indicates which fingers and how many are used to perform the manipulation. Two additional terms are introduced: virtual middle finger (VMF) and thumb-index combination (TIC). Virtual fingers (VF) were proposed by

Utkosky And Used In The Grasp Taxonomy , Which

refers to several fingers working together as a functional unit. More specifically, a VMF includes at least one middle finger possibly with the ring finger and the little finger, shown in Figure 4. The thumb-index combination (TIC) refers to the most frequently used two fingers among many tasks, i.e. the thumb and the index finger. [VMF+Palm] indicates using the VMF and palm to grasp the cable without the ability to control the cable’s pose (shown in Figure 4), whereas one TIC can grasp the cable with the ability to control its pose. Different from The Grasp Taxonomy, we do not assign the palm to a virtual finger. 6. Goal configuration describes the target state of the cable.

The configuration descriptions of the 4 types are: 1) pose change of the local part, 2) overall geometric shapes, 3) hand- cable relative position, and 4) topological information. Though a long thin cylinder’s motion was discussed by Bullock et al.

, cables are much more complicated than cylinders due to their relative smaller radius and possible shape deformation. ‘Pose change’ refers to a local movement of the cable in orientation or position in three dimensions. ‘Overall geometric shapes’ refers to the cable shape control problem, such as mak- ing a cable straight or into a U-shape. The ‘hand-cable relative position’ refers to how the cable is placed with respect to the hand, e.g., the cable is placed between the thumb and the index finger for future grasping. ‘Topological information’ refers to adjacency relations among intersections during knotting and untangling [48, 20]. Figure 3 shows the criteria for these four types of goal configurations. Some primitives are complicated so they may have multiple goal configurations. For example, the goal configuration of precision grasping usually contains hand-cable relative position and a Z-axis position control by lifting the cable.

B. Common Manipulation Tasks

Table I displays some common manipulation tasks and their categories according to the proposed Cable Dexonomy, together with their corresponding research which used grippers or a multi-fingerd hand that can be moved by the robot arm.

Because a jaw-parallel gripper grasps the cable firmly during Fig. 3: Four types of goal configurations. The coordinate system shown at the lower right is the same for the human hand, our rendered robot hand, and our real-world robot hand.

Fig. 4: Three types of combinations of fingers. (a) TIC: the thumb and the index finger combo. (b) VMF: virtual middle fingers can be a combination of the middle finger, the ring finger, and the little finger. (c) [VMF+Palm]: using VMF and the palm for extra grasping, can be replaced by another TIC if exists.

manipulation, a human hand can also perform this with one TIC or [VMF + Palm]. Thus, our Cable Dexonomy also covers some previous studies that used jaw-parallel grippers by clarifying the used fingers. Some examples are:

• 2D Shape Control On The Table: Yan Et Al. Used

a gripper to control the cable’s 2D shape by picking and placing. This can be performed with a TIC using a precision grasp.

• 3D shape control in the air: Yu et al. used dual grippers with two arms to control the cable’s 3D shape.

With Two Tics Or Two [Vmf + Palm] That Can Freely

move in the air, we can still perform 3D shape control. • Cable pulling: Two grippers can grasp and release the cable as an alternative way to move it to the target position, and meanwhile two TICs can perform the same behavior.

• End-tip orientation control: One gripper/TIC that is at- tached to a robot arm can freely control the orientation of the cable’s end-tip. However, if the hand base is fixed,

We Use A Tic To Grasp The Cable, And Vmf To Pivot

it by applying a force on the other side. This can be performed with a gripper loosely grasping the cable (This can be achieved by a gripper with tactile sensors .) and another additional finger to pivot the other side of the cable to rotate its end-tip.

These cases indicate that cable manipulation with one or multiple grippers mounted on a robot arm can be performed using a multi-fingered hand such as the one presented here.

Omposition Of Long-Horizon Tasks

Long-horizon tasks are those that require a sequence of short-term actions (primitives). They are mostly complex and require a high-level planner to execute primitives in the correct order [19, 8]. How to decompose a long-horizon task is crucial to solve the task. The primitive actions of the Cable Dexonomy allow the decomposition of many long-horizon tasks into a sequence of several primitives. One long-horizon task example is Cable pulling, as in Figure 5. The hand performs pre-grasp, grasping, middle-finger hooking, and cable pulling repetitively until reaching the end-tip.

TABLE I: Example tasks classified according to 6 criteria. ? for unknown solutions because knotting or untangling normally requires two hands which are much more sophisticated than the case of using one hand. TIC is the combination of the thumb and the index finger, VMF is the virtual middle finger. ∗indicates that the cited research used a moving end-effector rather than a fixed-base. † means that 3D shape control is not designed for single hand manipulation unless one end is fixed.

Topology

Fig. 5: Demonstrations of cable pulling. See the appendix for other long-horizon manipulations.

Hardware Design

Many other hands are not open-source or available for purchase yet, such as the Tesla Optimus’s hands. These hands have the closest similarity to human hands: one thumb and a subset of the remaining four fingers, and are named Anthropomorphic hands. There are also other multi-fingered hands that are not human-like. D’Claw hands have four identical index fingers, evenly distributed on a circle. Among these multi-fingered hands, the Leap hand and the D’Claw hand are often the best choices because they are cheap, open- sourced, and dexterous with high DoFs. However, the original D’Claw only has three joints on each finger, and identical fingers without any thumb make pincer grasping difficult, so we chose to use the Leap Hand as the basis for an extended hand design.

We use the same thumb and index fingers from the Leap Hand , but made several changes. Although we still use DYNAMIXEL XC330-M288-T servo motors as the actuators, Fig. 6: A top view from the hand palm side and a side view from the right thumb side of the 2-thumbed hand rendered in MuJoCo. The red dot indicates the joint positions on each identical finger. The last blue link is the rotatable fingertip.

The white areas are the finger designs from the Leap Hand. The two thumbs have symmetric structures and the other three fingers have identical structures. The hand back is mounted to the last joint of the robot arm. All fingertips are the same size with a thin layer of sponge on each.

Fig. 7: Two manipulations to display advantages of our de- signs, where each step is arranged in numerical order displayed in each image. (a) Cable pulling from left to right with the help of the additional thumb and the symmetric structure with the collected demonstration which is in a different pulling direction. (b) Z-axis orientation control in the air with the rotatable fingertips to avoid relative displacement between the cable and two fingertips which can cause cable slipping away.

we used 25 motors instead of 16 motors which the Leap Hand used. Two thumbs are designed in symmetric positions and the hand has five fingers in total. Each finger has one additional rotatable fingertip, and therefore contains five actuators.

This section answers two questions: 1. Why is the original Leap hand (and other commonly used anthropomorphic hands) not a suitable design for dexterous cable manipulation (DCM)? 2. How can the Leap style of hands be improved for better

A. Dual-Thumb

Inspired by the Cable Dexonomy in Section III, we sum- marize the frequency of used fingers in different primitives: 1. TIC is the most frequently used finger configuration, and almost all manipulations require TIC. 2. Most DCMs are symmetric about the Y-axis, e.g., the only difference between cable pulling from left to right and from right to left is the pulling direction. However, if an anthropomorphic hand that is asymmetric about the Y-axis is used, two different control policies are needed to perform the same tasks with different manipulation directions. Zhaole et. al explored this in the simulation . Another example is performing USB insertion, where the plug is on the other side of the TIC.

Here, the hand needs to flip upside down to move the TIC near to the plug, because the little finger, which grasps the cable against the palm, does not have the ability to control the cable orientation. For DCMs that are sensitive to the TIC’s location, implementing two TICs in a symmetric layout is a good solution to keep the symmetric manner of DCM (see the design in Figure 6). We also show a benefit that symmetric manipulations only require one demonstration for two opposite direction manipulations in Figure 7 a.

A notable work added an additional prosthetic thumb onto a human hand, which can affect object manipulability , including holding a cup to do in-hand liquid pouring into the grasped cup. The multi-fingered design proposed here is the first one to confirm that adding an additional thumb can help extend robotic manipulability (as contrasted to the human manipulability explored in the previous research).

B. Rotatable Fingertips

When a human hand performs a pincer grasp on a cable, the skin of the fingertips will deform to cover the surface of the cable to make a larger contact area for more robust grasping. Thus, designing a better fingertip can increase the stability of cable grasping. We add an additional rotation joint to the fingertip to make such a grasping pose available in more configuration states. When performing cable orientation control, the rotatable fingertips also help rotate the cable about the Z-axis without relative sliding between the cable and two fingertips, which cannot be performed by a cylinder-shaped fingertip. Overall, one additional joint added to each hand can provide more manipulability.

Many tactile sensors have a relatively flat surface, like GelSight . Although we do not mount any tactile sensors, our rotatable fingertip design has the potential of mounting flat tactile sensors on it. E.g. NeuralFeel rotated their ring finger’s fingertip 90 degrees towards the in-hand operation space. With rotatable fingertips, such adjustments can be performed during manipulation in real-time. In additional to Z-axis orientation control on the table, our hand performed this task in the air, shown in Figure 7 b. When rotating the cable, if there is relative displacement between the fingertip and the cable, the cable will slowly slip downward and eventually drop from the TIC. The additional rotatable joint on the Fig. 8: Two demonstrators drag the hand fingers to record the finger joint trajectories.

fingertip avoids this and provides better manipulability in such a difficult task.

Emonstration Collection Pipeline

This section introduces the proposed demonstration data collection pipeline. There are three stages: 1. human short- term primitive demonstration data collection, 2. demonstration replay, and 3. building finite-state machines for long-horizon manipulation.

A. Demonstration Collection

Since the proposed hand has a different structure than the human hand and more degrees of freedom, common demonstration methods are not applicable, like motion capture with a mocap glove or a RGBD camera [5, 42]. Instead, one or more people cooperate to gently drag the robot fingers in the same style as kinesthetic teaching of robot arms, shown in Figure 8.

The goal joint angle positions are set to be equal to the actual joint angles with a relatively low joint stiffness to make dragging easier and smoother. Once one or two fingers are temporarily fixed, we pause the collaborative mode and increase the joint stiffness to avoid unnecessary finger move- ments. During human demonstration data collections, only the joint angle trajectories are recorded. These trajectories will be used as actions in the demonstration replay later.

The advantages of using dragging for demonstrations are: 1. finer motor control recording of those dexterous manipulations than previous mocap-based methods, 2. eliminates the need for mapping from the human finger joints to the robot hand joints, and most importantly 3. it allows recording demonstrations from designs which are different from human hand structures.

Two human demonstrators are needed to drag all five fingers, one to control one pair of TIC and the middle finger, and another to control the other TIC.

B. Demonstration Replay

In this stage, the demonstration data is replayed according to the collected joint angle trajectories without any changes or human interventions to identify the successful demonstrations except for some compensation to the thumb and index finger joints due to the low maximum torque of the hand motors.

PID position control was used to make the motors follow the joint trajectories from the demonstrations, with a control rate of 30 Hz.

This includes the first and the third joints (the order is determined from the proximal to the distal on each finger) of Fig. 9: Cable Dexonomy Finite State Machine for Cable Pulling.

two index fingers and the second and the third joints of two without evaluating its robustness in other scenarios or on other cables, and only the actual joint angles and cable key point positions were recorded. Each collected human demonstration can be replayed several times with different initializations of cable shapes and positions for more data. Although we do not apply learning-based methods here, the collected data can possibly be used to train the agent with an imitation learning algorithm, such as ACT [64, 65] and Diffusion Policy , or one-shot reusable learning strategies .

The replay stage will show that the hand is capable of per- forming DCM and that a usable demonstration data collection is available (which may also be suitable for learning-based algorithms).

Ong-Horizon Manipulation

Collecting demonstrations of long-horizon manipulation is difficult, since any failure that occurs during the demonstration leads to total failure. Based on the Cable Dexonomy, long- horizon DCM instances are decomposed into a sequence of basic primitives. To reduce the collection time and increase the success rate of demonstration data collection, only short- term primitives were collected and by combining these allows long-horizon manipulations without prior full long-horizon demonstrations.

Four long-horizon tasks are considered here, each with their own Finite State Machine (FSM): 1. Cable pulling, 2. Cable U-shape bending and grasping, 3. Cable direction flipping, and 4. Cable in-hand insertion. The FSM graph of cable pulling is shown in Figure 9, and the rest FSM graphs and experiment figures can be found in the appendix and the video.

With the help of the FSM, we demonstrate that the new hand is capable of performing long-horizon tasks. The demonstra- tion data collection of long-horizon tasks is also available.

Experiments

This section presents real-world experiments applying dif- ferent primitives to different cables.

A. Experiment Setup

The hand and the camera are placed as shown in Figure 10

A. Here Are Some Details:

Workspace. The hand base is placed in a tilted pose to let the index finger and thumb finger exactly hang above the table without collision during manipulation. This can be seen in Figure 10 b. The cable was randomly placed in the in-hand space, also shown in Figure 10 b, and it is not always placed

Authors:

Peder EZ Larson 1, 2,* , Jenna ML Bernard1, James A Bankson 3, Nikolaj Bøgh 4, Robert A Bok1, Albert P. Chen 5, Charles H Cunningham 6,7, Jeremy Gordon1, Jan-Bernd Hövener 8, Christoffer Laustsen 4, Dirk Mayer 9,10, Mary A McLean11 12, Franz Schilling13, James Slater1, Jean-Luc Vanderheyden5, 14, Cornelius von Morze 15, Daniel B Vigneron1, 2, Duan Xu1, 2, and the HP 13C

94143, Usa.

Denmark. 5 GE Healthcare, Menlo Park, California, USA. 6 Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.

8 Section Biomedical Imaging, Molecular Imaging North Competence Center (MOIN CC), Medicine, Baltimore, MD, USA. Cambridge, United Kingdom.

14Jlvmi Consulting Llc, Dousman, Wi, Usa

#See Acknowledgements for a list of all HP 13C MRI Consensus Group Members This work was supported by the ISMRM Hyperpolarized Media MR Study Group, the ISMRM Hyperpolarization Methods & Equipment Study Group, and the Hyperpolarized MRI Technology Resource Center (NIH/NIBIB grant P41EB013598).

Abstract

MRI with hyperpolarized (HP) 13C agents, also known as HP 13C MRI, can measure processes such as localized metabolism that is altered in numerous cancers, liver, heart, kidney diseases, and more. It has been translated into human studies during the past 10 years, with recent rapid growth in studies largely based on increasing availability of hyperpolarized agent preparation methods suitable for use in humans. This paper aims to capture the current successful practices for HP MRI human studies with [1-13C]pyruvate - by far the most commonly used agent, which sits at a key metabolic junction in glycolysis. The paper is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification. In each area, we identified the key components for a successful study, summarized both published studies and current practices, and discuss evidence gaps, strengths, and limitations. This paper is the output of the “HP 13C MRI Consensus Group” as well as the ISMRM Hyperpolarized Media MR and Hyperpolarized Methods & Equipment study groups. It further aims to provide a comprehensive reference for future consensus building as the field continues to advance human studies with this metabolic imaging modality.

Keywords: Hyperpolarized MRI, metabolic imaging, carbon-13, pyruvate, dissolution dynamic

Introduction

MRI with hyperpolarized 13C agents, also known as hyperpolarized (HP) 13C MRI, has shown great potential as a novel imaging modality, particularly for its ability to probe metabolic processes in real time. The first human studies with HP [1-13C]pyruvate were performed in 2011 in prostate cancer patients (1).

Since then, there have been over 60 papers published with imaging results of human subjects from 13 different sites, with applications including prostate cancer, brain tumors, breast cancer, kidney cancer, pancreatic cancer, metastatic disease, liver disease, ischemic heart disease, diabetes and cardiomyopathies. The vast majority of these studies used [1-13C]pyruvate (1–63), where [2-13C]pyruvate (64) and 13C-urea (56) have been demonstrated too.

As clinical HP 13C MRI advances, there is a growing need to build consensus for best practices, which are critical for comparing data across sites, performing multi-site trials,deploying methods to new sites, partnering with vendors, and potentially for obtaining broader regulatory approvals.

In March 2022, we initiated an effort to build consensus within the HP 13C MRI community with this opportunity in mind, and it was greeted with strong enthusiasm. The “HP 13C MRI Consensus Group”, containing over 55 members from 27 sites, identified the area of greatest need and opportunity for consensus building to be HP [1-13C]pyruvate human

●

Pyruvate is the most mature and widely used HP agent and has the most significant translational evidence emphasizing the potential clinical impact.

●

Clinical trials, particularly multi-site trials, have the strongest need for consensus methods to ensure that data can be combined across sites. This work is a Position Paper for which the goal is to describe current successful practices and study methods for HP [1-13C]pyruvate human studies along with justification to support those practices. This is divided into four major topic areas: (1) HP 13C-pyruvate preparation, (2) MRI system setup and calibrations, (3) data acquisition and image reconstruction, and (4) data analysis and quantification (Fig. 1). The current successful practices and study methods include a literature review of published peer-reviewed journal papers showing human HP [1-13C]pyruvate study data, up to September 2022 (1–63), as well as new unpublished information from surveys of HP 13C study sites. Based on this information, we also highlight the evidence gaps, strengths, and limitations of current practices which are summarized at the end of each section.

Figure 1: Illustration of the HP 13C MRI human study process, including the 4 major areas covered in this paper: Hyperpolarized 13C-pyruvate preparation, MRI system setup and calibration, Acquisition and Reconstruction, and Data Analysis and Quantification.

Figure 2: Anatomical targets of HP [1-13C]pyruvate MRI human studies published up to September 2022.

Hyperpolarized 13C-Pyruvate Preparation

This section covers the processes for creating the HP agent, 13C pyruvate, and will include many aspects and considerations that are needed to safely and effectively prepare doses for metabolic imaging studies in human subjects. These include material, personnel, equipment and facility, fluid path preparation, quality control, and release.

It is helpful to understand that the specifications of a dose of 13C pyruvate suitable for in vivo MR HP metabolic imaging were shaped in part by early preclinical studies performed by GE HealthCare summarized in Ref. (65). In short, the safety of the two novel drug components, 13C pyruvate and the electron paramagnetic agent (EPA) AH111501, were demonstrated in those studies. The more precise formulation of the dose suitable for human use was then determined from clinical studies (66) that included two Phase 1 clinical trials in young and elderly healthy volunteers without hyperpolarization of the 13C nuclei and another Phase 1/2a dose escalation and imaging feasibility study with HP 13C pyruvate in 31 prostate cancer patients at the With the exception of the first HP 13C imaging clinical trial, which utilized a prototype device in a cleanroom (1), all HP 13C studies performed in humans to date have utilized the SPINlab polarizer (manufactured by GE HealthCare). Consequently all doses of the HP 13C pyruvate delivered by SPINlab have been produced using the “SPINlab Pharmacy Kit” that serves as the container-closure system for the various drug components (13C pyruvic acid and EPA mixture, dissolution medium, and neutralization and dilution medium) during sample polarization, dissolution and quality control (QC) processes. Thus many aspects of the HP sample preparation considerations discussed below are related to the SPINlab instrument and the consumables designed to be used with it (67).

General Considerations

While more than 860 patients or healthy subjects having been injected with HP 13C pyruvate as of January 2022 without reports of any serious adverse events (68), HP 13C pyruvate injection remains an investigational MR contrast agent and can only be administered by those with Investigational New Drug (IND) exemption from the Food and Drug Administration (FDA) in the USA, a Clinical Trial Application (CTA) in Canada, approval from National Research Ethics Committee Services in the UK, or approval from the relevant local regulatory body. Thus, methods and processes involved to produce a dose should have patient safety as the first priority. Since utilizing dissolution dynamic nuclear polarization (dissolution-DNP) for human use is still a relatively new development, there are no existing published regulatory guidelines specifically for this method.

There are two major production styles that determine how various sites approach the agent preparation. In the US, the most common approach is to rely on a sterilizing filter (“Terminal Sterilization”) to ensure sterility of the final product, akin to PET tracer production, where a starting molecule with a radioisotope is processed using various other ingredients to make the final, desired and injectable contrast agent within a necessarily short amount of time (69). For these sites, sterilization of the components and accessories upstream of this filter are not required, although many of them were manufactured and tested following Good Manufacturing Practice (GMP) or Good Laboratory Practice (GLP) requirements. The filling process is usually performed under an ISO 5 laminar flow hood, but a clean room or an isolator is not required.

This approach is typically accompanied by testing the integrity of the sterilizing filter prior to release of the dose for injection. Typically, post release endotoxin and sterility tests are performed using an aliquot reserved from each released dose.

In the UK and EU, the most common approach is to more-closely follow sterile pharmaceutical compounding guidelines (70), where all components and ingredients are required to be sterile or manufactured under GMP guidelines and are assembled and filled within a clean room environment or an isolator system (“Sterile Preparation”). Typically a batch of Pharmacy Kits for HP 13C pyruvate injection are prepared together. The sterility of the final dose is also ensured by batch validation testing, in addition to the sterility of the ingredients and the sterile compounding process. The endotoxin and sterility testing are performed for the process validation but are not performed for each injected dose.

Some institutions fill and assemble the Pharmacy Kit required for a specific study on the same day or the day prior to polarization, dissolution, and patient administration, but others have also demonstrated the feasibility of preparing a batch of kits, keeping them in a -20ºC freezer and using them over a period of a few months.

Beyond the obvious requirements that the process and the facility has to ultimately produce a dose that is safe to inject into a human, regulatory authorities will also focus on the question “Are you in control of your processes?”. To be in control of your process requires an in-depth and broad understanding of all processes involved in pre, post, and during the production process.

Personnel

It is typical and may be required to have licensed personnel involved in the production process depending on local regulations.Typically a pharmacist, radiopharmacist or other similarly qualified person (QP), in charge of the facility where the Pharmacy Kit filling and preparation is taking place, is responsible for the overall process and the release of the injectable dose.

Qualified cleanroom technicians are often involved in the Pharmacy Kit filling under the supervision of the pharmacist or QP. As is required for pharmaceutical compounding or PET tracer production, training requirements and training records for all personnel need to be maintained and available for audit by the FDA or equivalent.

Equipment And Facility

The facility and all equipment need to have standard operating procedures (SOPs) that describe how equipment is used, maintained, and calibrated to comply with relevant legislation. Currently, almost all the filling of the Pharmacy Kit takes place within a compounding laminar flow hood or isolator (typically ISO 5). At some sites, the filling is conducted within a cleanroom, while at others, it is conducted in a dedicated non-cleanroom space, reflecting differences in cleanroom approach and specifications between regulators worldwide (71). Some equipment or facilities, such as the compounding hood or cleanroom, may require external certified laboratories for testing.

Material Handling

Material handling guidelines (69,70) require SOPs detailing a system to track all of the materials involved in the HP production process for a particular patient dose, similar to current good manufacturing practice (cGMP) requirements for material handling for drug compounding. This includes acceptance standards, storage conditions, amount used in the patient dose for each ingredient and materials used in the assembly of the fluid path and Pharmacy Kit. Currently some users choose to open and inspect and sometimes modify the Pharmacy Kits upon arrival, but some users keep them in the sealed packaging until they are required for dose preparation.

Pharmacy Kit Filling And Assembling

As required by an IND or its equivalent, the preparation of the doses of HP 13C agent are detailed in the Chemistry, Manufacturing, and Control (CMC) section of an applicable regulatory submission; an example of this has been made available (72). It describes the processes of filling the Pharmacy Kit with the different components that make up the final drug product, and of assembling the final kit for either storage or immediate use in the polarizer. Special attention should be given to the laser welding process in order to satisfy installation qualification (IQ) and operational qualification (OQ). Typically, the final developed process is validated by process qualification (PQ) runs, during which 3 or more Pharmacy Kits are filled and used and the final HP 13C products are tested for endotoxin and sterility and to confirm that they meet the dose specifications for injections (usually including pyruvate concentration, residual EPA concentration, pH, liquid state polarization level and dose temperature). The data from 3 consecutive PQ runs are submitted as part of the IND submission (or its equivalent), and are often also reviewed by the Institutional Review Board (IRB) where the studies are conducted.

Quality Control And Dose Release

The quality control (QC) and dose release can be separated into two aspects: one is the QC and release of the filled Pharmacy Kit, and second is the QC and release of the HP 13C agent for injection, after polarization and dissolution. For institutions filling a batch of kits and storing them to use over a period of time, typically the batch can be released based on initial validation, environmental monitoring data from the day of kit production, and if filters are used during preparation of any of the components, filter integrity testing. But in some cases one or more kits are used for validation before the batch of kits are released for future use. For institutions that fill only the kits required for specific studies shortly before the experiment, the filled kits often do not go through separate release tests before they are used.

The quality control of the HP 13C pyruvate solution post dissolution is primarily performed to ensure that the agent meets the dose specifications (Table 1) before it is administered to the subject. These specifications target both safety (pH, residual EPA, temperature) and efficacy (pyruvate concentration, polarization, volume). Typically, the pyruvate concentration, residual EPA concentration, pH, dose temperature, dose volume, and liquid state polarization are measured by the QC accessory associated with the SPINlab polarizer. Some users perform a secondary measurement for one of the parameters, such as pH, using a different instrument or pH paper. For sites that do not go through a separate release testing process for batch filled kits, the integrity of the sterilization assurance filter, a part of the Pharmacy Kit, is typically tested as a part of the dose release. It is also common for these users to preserve an aliquot of the final HP 13C pyruvate solution for post-release endotoxin and sterility testing. This testing cannot be completed fast enough to test an individual dose prior to injection, but this is why other processes such as PQ runs and validation testing are done to minimize the chance a subject could be injected with a contaminated dose.

The Final Dose Release And Injection

should be done under the supervision of a licensed professional, based on local regulations.

Some Key Challenges

Many of the challenges associated with HP 13C pyruvate preparation can be attributed to the conditions required for the dissolution-DNP method of high magnetic field (~3-7 T) and very low temperature (~1 K) during polarization, with pressurized and superheated water necessary for the rapid dissolution event. These extreme conditions are quite challenging for the design of the container-closure and fluid path system. In particular, the cryogenic temperature in the polarizer requires special attention to any moisture or ambient (moist) air introduced into that portion of the fluid path, which can form an ice block at ~1 K. This ice can lead to flow restriction during the dissolution event and reduce the strength of the laser welded bond between the cryovial and its cap. This can ultimately produce failures in the dissolution step, including variations in final pyruvate concentration and pH that may fail to meet QC release criteria as well as fluid path ruptures that provide no available dose and result in polarizer down-time.

The polarization of the HP 13C pyruvate sample decays quickly over the span of a few minutes after dissolution, and thus the process of dissolution, QC for release, and injection should be completed as fast as possible to preserve the high polarization level achieved. Any delays in the preparation process, such as transportation time or equipment malfunction, can significantly reduce the final polarization and result in lower quality imaging data.

Current Practices

A summary of data collected from all sites performing clinical trials with HP 13C-pyruvate is shown in Fig. 3 and Table 1, including the specification of the final dose and how the quality control and release of the final dose are performed. There is a split in the Production Style, described in the General Considerations section above, with 8/13 sites using Sterile Preparation versus 5/13 using Terminal Sterilization. While many of the dose specifications show notable differences in acceptable ranges, all of these variations listed in tables have been successfully and safely been used to perform HP 13C pyruvate studies in humans. Their differences depend on the institutions’ preferences, resources and their particular regulatory situation. There is high similarity in pyruvate ranges, temperature ranges, EPA limits, and volume limits. There is modest variability in pH ranges and large variability in the endotoxin test limit. There is a 3-fold difference in acceptable polarization levels, which are measured to ensure a futile dose is not injected since the polarization is directly proportional to SNR. This reflects the decision by several sites to believe that useful data can be still be obtained with suboptimal polarizations.

Figure 3: Hyperpolarized agent preparation methods reported by sites currently performing HP

In House

Table 1: HP 13C-pyruvate preparation parameters, methods, and dose specifications used for quality control testing and release as well as validation. These were obtained from a survey of all sites performing clinical trials with HP [1-13C]pyruvate. The parameters used for product release are noted in bold text, otherwise these parameters are measured for batch validation or other QC measurements. The endotoxin and sterility testing are performed during process validation of the batch and/or post-injection, and largely depends on the agent production approach.

Summary

The overall safety record of HP 13C-pyruvate has been very strong, and the SPINlab hyperpolarizer has proven to provide high polarizations at human sized doses while meeting numerous QC and release criteria. A weakness remains the failure modes of the SPINlab Phamacy Kits (e.g. ice blocks, path ruptures), which are placed under extreme requirements particularly during dissolution. The preparation process still requires a high degree of expertise.

Therefore, there is a significant need to improve the reliability, robustness, and ease of operation for generating HP 13C-pyruvate doses for human studies. Furthermore, there is a divide between manufacturing and sterile compounding style preparation as well as other site-specific practices, resulting in variations in SOPs and justification required to relevant regulatory bodies. There have also been no comparisons between these approaches. It is also unclear what release criteria and QC parameters are truly required to ensure patient safety.

However, all of the reported methods are acceptable and approved by the appropriate regulatory authorities, and have led to the rapid expansion of successful human studies in recent years.

Mri System Setup And Calibrations

This section covers the MRI system setup, including the imaging system, RF coils, phantoms, and prescan calibration methods.

Imaging System

The main prerequisite for a given MRI scanner to be capable of supporting studies with HP 13C is its “broadband” capability to transmit and receive radiofrequency (RF) signal at the frequency of 13C, which is around 4 times lower than 1H. This does not come as a default on clinical MR devices. The transmit power of the broadband amplifier should also be sufficient to support the intended flip angle and RF pulse shape with the employed transmission RF coil(s) for 13C. Most studies to date use relatively low flip angles (< 90 degrees) for HP 13C in order to preserve polarization for time-resolved imaging. The capability to receive 13C signal on multiple channels is also desirable to increase SNR, as discussed further in the “RF coils” section.

The choice of magnetic field strength is primarily dependent on the metabolites’ frequency separation due to chemical shift dispersion and 1H imaging. High field strengths do not enhance hyperpolarized 13C signal as they do for 1H because the signal strength in a HP experiment relies on manipulating the population of quantum energy states outside of the MRI scanner.

However, the injected HP 13C-pyruvate and its metabolic products have greater frequency separation at higher fields, and it may thus be easier to separate and quantify these resonances at higher fields. This comes at the cost of a reduction in the achievable T2* and often reduced T1. As the initial polarization is independent of the imaging field strength it has been proposed that the increased T2* at 1.5T can potentially be exploited to increase SNR by adapting the acquisition bandwidth or reduce off-resonance imaging effects in cases when the decay of the transverse magnetization is dominated by T2* (73). In practice, 3T has been used in all published human 13C-pyruvate studies surveyed (Supporting Table S1), and comprises the majority of scanners currently in use for human studies (Table 3). A field strength of 3T is well-suited for 1H MRI anatomical reference and correlative imaging.

Stronger and more rapidly slewing magnetic field gradients support more rapid spatial encoding, particularly for metabolite-specific single-shot imaging using echo-planar imaging (EPI) or spiral imaging (See “Acquisition and Reconstruction”). Although the spatial resolution acquired for HP 13C imaging is typically much coarser than for 1H MRI, the factor of ~4 in gyromagnetic ratio leads to the same reduction factor in performance of the gradient system, so 13C experiments are potentially more limited by gradient hardware performance. To date, all human studies have used the commercially-available integrated gradient systems provided in clinical MRI scanners.

Optimization of scanner design has understandably focused on minimization of artifacts in 1H MRI, where devices such as room lights, the gradient amplifiers, and the motors driving the patient bed are checked to ensure that they do not produce RF interference at the 1H frequency, but artifacts may arise at other frequencies. Eddy current compensation is also not always appropriately adjusted for nuclei at other frequencies (74). In order to optimize for 13C, many sites have performed checks on phantoms for RF interference, gradient artifacts, and eddy currents (74), including the use of post-hoc gradient impulse response function characterisation and correction, and some vendors have fixed these issues as well.

Rf Coils

For HP 13C imaging studies in humans, RF coils for both 1H and 13C nuclei are needed, with 1H MRI providing an anatomical reference for registration and optional additional multiparametric MRI readouts. At the Larmor frequency of 13C nuclei, the relative contributions from coil noise compared to sample noise increase compared to 1H (73,75), although sample noise still is likely the dominant contributor for human-sized coils at 32.1MHz - the resonance frequency of 13C nuclei at 3T.

The key requirement for human 13C-pyruvate RF coils are that the coil geometry and sensitive volume must cover the volume of interest in the subject. Table 2 and Figure 4 shows coil configurations that have been used and optimized for applications in different anatomic regions.

Volume resonators are most commonly used for transmit, as they surround the subject to

Provide B1 Transmit Across The Fov (B1

+). While 1H relies on a large birdcage (“body”) coil built into the scanner, 13C transmit coils must be placed inside the bore. This takes up valuable space within the magnet, and also has led to the use of designs with relatively inhomogeneous

B1

+. Many human studies have used Helmholz pair resonators for transmit, including the “clamshell coil”, which has a notably inhomogeneous B1

+ Profile But Has Been Used Because Of

relatively easy integration into the scanner bore. B1

+ Variation Results In Variations In The Flip

angles that control the use of the hyperpolarized magnetization and creates errors in common HP metrics (9,76). The exception are head coils, where birdcage designs with highly

Homogeneous B1

+ can be placed around the head while easily fitting inside the bore. As with 1H MRI, higher SNR can typically be achieved by smaller receive coil elements, such as surface coils or phased arrays, and the majority of 13C receive coils used have layouts similar to 1H phased arrays.

RF coil quality control is important to ensure proper functioning of the coils to provide consistent imaging quality, especially with limited natural abundance 13C signal in vivo. It typically involves 1) a physical integrity check of the coil cables and connectors and 2) phantom SNR tests to check the coil’s performance and to monitor it over time (see Phantoms below). An useful reference for RF coil quality control is outlined in the MRI accreditation program of the American College of Radiology (77) and can be adapted for 13C coils.

Notably, configurations for brain and prostate studies used dual-tuned 1H/13C coil designs, which greatly simplify workflow and registration of 1H and 13C images, as no switching of coils is needed.

(1)

Table 2: RF coil configurations reported for human HP [1-13C]pyruvate studies.

Tx = Transmit

coil, RX = receive coil. The commonly used “clamshell” TX coil is a Helmholz pair design. For 1H RF configurations, all used the Body coil for TX unless otherwise noted, and “repositioned” indicates the 13C coil was removed for 1H imaging. One representative reference is listed for each configuration. The RF coil configurations reported in the reviewed papers are shown in Supporting Table S1.

Figure 4: Examples of RF coil configurations used for human HP [1-13C]pyruvate brain studies. (A,B) 13C Clamshell TX (Helmholz pair) and 2× 4-channel paddle RX arrays. (C) 13C Birdcage volume TX and 32-channel RX array (RX array slides into TX coil). (D) 13C Birdcage volume TX and 24-channel RX array, combined with a 1H 8-channel RX array. Image reproduced with permission from Ref (16).

Phantoms

Since hyperpolarized magnetization is non-renewable, phantoms containing 13C nuclei are important to: 1) test the multi-nuclear capabilities of the imaging system, including all parts of the signal excitation and receive chain; 2) perform calibration measurements before a scan with hyperpolarized nuclei; and 3) perform necessary pre-scan adjustments (see “Prescan Calibration” section). The phantoms currently in use are listed in Table 3. Their composition must provide sufficient 13C signal, with additional considerations of conductivity, stability, chemical shift(s) present, potential for dynamic imaging, and cost. The phantom geometries are typically either compact, in order to be used alongside the subject during a HP scan, or large enough to mimic the inner volume of a RF coil for system testing.

One popular compact design contains enriched 13C-urea at high concentration, typically 8 M, which provides a single resonance, placed inside a small container ~1 mL. The most common recipe mixes 13C-urea in a 90% water/10% glycerol solution, with glycerol used to increase the urea solubility and doping with a Gd-based contrast agent to shorten T1 which increases the potential SNR per unit time. For example, when Dotarem is added at a 3:1000 volume ratio the 13C-urea T1 is around 500 ms and T2 is around 100 ms. However, when testing pulse sequences influenced by T1 and T2, doping should be used carefully. This phantom is suitable for frequency calibration, transmit gain calibration, sequence testing, and as a fiducial marker when placed next to a patient. However, enriched 13C-urea has a relatively high cost compared to natural abundance compounds.

For larger volumes (>100 ml), the phantoms most often used contain undiluted ethylene glycol, glycerol, or dimethyl silicone. These compounds have sufficiently high carbon concentrations to provide sufficient 13C signal even with the 1.1% natural abundance of 13C. These larger phantoms matching the inner volume of an RF coil are useful for coil testing, including transmit

+) And Receive (B1

-) coil profile mapping, as well as to mimic acquisitions using in vivo FOV requirements. In this case, size and conductivity should match the expected subject size in order to mimic coil loading and get a realistic estimation of B1+. Large-volume natural abundance urea phantoms have also been used by some sites, but suffer from higher conductivity compared to biological tissues. Typically, it is easier to increase the conductivity and hence coil loading of the non-conductive phantom by adding NaCl to match physiological loading (16,78).

Dynamic phantoms that aim to mimic metabolite kinetics have also been developed (79–81), and have the potential to more closely mimic the HP experiment, but so far these are not widely used.

Prescan Calibration

Prior to performing an MRI acquisition, the so-called prescan procedure is used to set the shim parameters to maximize B0 homogeneity over the field of view (FOV) or a specific region of interest (ROI), the scanner center frequency (CF), the RF transmit gain, and the receiver gain.

While this calibration procedure is usually automated for 1H, the lack of sufficient natural abundance 13C signal prevents use of automated methods. (Although natural abundance 13C lipid signal has been detected, there are so far no reports on using this signal for prescan.) Table 3 shows current practices across sites.

Maximizing B0 homogeneity is independent of the nucleus and is therefore performed prior to 13C imaging using the 1H water signal and existing shimming tools, such as by a standard automated process (“Auto Shimming”) or using high order shimming routines. Similarly, the 13C CF can be calculated from the 1H CF using a predetermined scaling factor that depends on the target chemical shift (82). Another common approach used is to have a small, high-concentration 13C phantom, e.g. 8M 13C-urea, integrated in the RF coil or placed next to the scan subject (1). The reference frequency can also be based on real-time measurements after the HP injection but prior to imaging (83). Both the CF and B0 shimming are critical when using spectrally-selective RF pulses, as inmetabolite-specific imaging methods, where the desired excitation bandwidths are typically very narrow and frequency offsets can lead to a failure mode that is only apparent after injection.

The calibration of the RF transmit power is typically performed on a small, high-concentration 13C phantom placed near the region of interest during the scan or on a large 13C phantom of similar size and coil loading as the subject, prior to the subject scan. Reference power is often done by sweeping the power in a pulse-acquire sequence (53,62), or the Bloch-Siegert method (52,84). When using a small phantom, the location of the phantom, B1

+ Inhomogeneity As Well

as any shielding effects, e.g., when the phantom is integrated into a coil (1), may degrade the accuracy. Other methods include real-time Bloch-Siegert method measurements after the HP injection (83), and using the stronger natural abundance 23Na signal that is close enough to the 13C resonance frequency to be detected by 13C coils (82).

The receiver gain is predetermined, either systematically based on independent phantom measurements and assuming the dose and polarization of the HP compound is known prior to injection, or based on past HP imaging studies.

Power [Kw]

Phantom(s) - during study Phantom(s) - before study 13C Frequency

8

13C-bicarbonate doped with dimethyl silicone, various

Power [Kw]

Phantom(s) - during study Phantom(s) - before study 13C Frequency

Maximum Values

Table 3: Summary of the imaging systems, phantoms, and prescan procedures used at sites currently performing HP 13C-pyruvate human studies. These were obtained from a survey of all sites performing clinical trials with HP [1-13C]pyruvate. *Previously performed studies with a Siemens 3T Tim Trio. The imaging systems, phantoms, and prescan procedures reported in the reviewed papers are shown in Supporting Table S1.

Summary

Commercially available 3T MRI systems are by far the most commonly used for human HP 13C-pyruvate studies, although a systematic investigation of the impact of B0 has only recently been investigated (73). The multi-nuclear RF transmit and receive chain has proven sufficient for current acquisition strategies, although many sites have observed artifacts due to RF interference, gradient interference, and residual eddy currents when operating at the 13C frequency. A variety of 13C RF coils, tailored for numerous anatomical targets, have been successfully demonstrated, with the main limitation that most transmit coils take up a lot of additional space inside the bore and provide relatively inhomogeneous B1

+ Profiles. The

phantoms used have converged into generally 2 categories - small phantoms containing 13C-enriched compounds that can be used during the study and human-sized phantoms containing compounds with high carbon concentrations but without 13C enrichment that are used to test and calibrate the coils. There are no standardized compositions or geometry, and dynamic phantoms that recapitulate in vivo kinetics would be desirable but are still an emerging area. Prescan calibration procedures were not well defined in most publications, so we surveyed individual sites to determine current practices. Calibration procedures for the B0 field (13C CF and shimming) for most sites take advantage of 1H signal and methods, while methods

For Calibration Of B1

+ is more variable across sites, likely a reflection of remaining challenges in how to perform this calibration. Standardization of both phantoms and calibration procedures would synergistically improve the robustness and reproducibility of HP 13C studies.

Acquisition And Reconstruction

Data acquisition strategies in human HP [1-13C]pyruvate MRI studies must account for multiple chemical shifts, efficiently utilize the non-renewable HP magnetization, and acquire data quickly relative to metabolism and relaxation decay processes. These studies require spectral encoding to separate metabolites, necessitating pulse sequences that efficiently encode up to 5D data (3 spatial + 1 spectral + 1 temporal dimension). RF pulses must efficiently sample without immediately saturating the non-renewable HP magnetization, and sequences must acquire data quickly and be robust to both experimental and physiologic variation (e.g. B1

+ Inhomogeneity,

variation in perfusion) to ensure reproducibility and minimize scan-to-scan variability. This section covers current successful practices for data acquisition in human [1-13C]pyruvate studies, and accompanying 1H imaging, from different anatomic regions, including scan parameters and image reconstruction.

Acquisition And Reconstruction Methods

The acquisition methods used in human [1-13C]pyruvate studies can be classified into 3 categories: 1) MR spectroscopy or MR spectroscopic imaging (“MRS/I”), 2) chemical shift encoding methods, and 3) metabolite-specific imaging (Fig. 5).

Mrs/I Methods Specifically

resolve a spectrum that can be analyzed to extract expected as well as unexpected resonances, making this approach very robust. It was used in many initial studies (1).

Chemical Shift

encoding methods, most commonly the Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) method, use imaging sequences acquired with multiple TEs and rely on a model-based separation of expected chemical shifts (85).

Metabolite-specific imaging methods use specialized RF pulses that are spatially and spectrally selective to excite individual metabolites which are then typically imaged with fast k-space trajectories such as echo planar imaging (EPI) or spirals (86).

Their Application To Different

organ systems is described below. The image reconstruction methods used in human [1-13C]pyruvate studies have typically been conventional methods (e.g. FFT, non-uniform FFT, or equivalent). The incorporation of accelerated imaging and advanced reconstruction methods including parallel imaging (4,57,87) and compressed sensing (7) has also been applied in human studies for improved spatial resolution, temporal resolution and coverage, but have the potential for additional artifacts as well as SNR losses due to ill-conditioning of the reconstruction (e.g. g-factor).

The Majority Of

published studies do not use accelerated imaging indicating the resolution and coverage achievable without acceleration is currently adequate for successful data collection. Performing coil combination, even with fully sampled data has also been shown to have specific challenges for HP human images: using naive sum-of-squares methods suffer from high noise amplification in the relatively low SNR regime of HP [1-13C]pyruvate (compared to 1H), motivating several HP 13C-specific methods that include data-driven coil sensitivity estimation which have shown obvious improvements over sum-of-squares (11).

More recently denoising techniques have been applied as post-processing of human HP data(41,42,44). The techniques applied are based on spatial-temporal singular value decomposition for unsupervised estimation of signal and noise components. They have shown improvements in apparent SNR in the brain and liver, while care must be taken to choose parameters such as the rank threshold to avoid oversmoothing and overfitting to the estimated signal components.

Prostate Studies

Prostate cancer was the first human application of HP [1-13C]pyruvate (1), and data was acquired with MRS/I methods: 1D dynamic MRS, single-slice 2D dynamic echo-planar spectroscopic imaging (EPSI), and single time point 3D EPSI. Advances in imaging strategies led to the development and application of new acquisition schemes, including undersampled 3D EPSI with compressed-sensing (7), model-based chemical shift encoding methods that use a priori information (47,59), and metabolite-specific EPI (10), all of which can provide volumetric whole-organ coverage and dynamic acquisitions.

The pyruvate bolus arrival in the prostate can vary by ± 10 s between patients, necessitating dynamic imaging to reliably and consistently capture the pyruvate bolus (18). For this reason, all currently ongoing studies acquire dynamic data. While MRS/I, chemical shift encoding, and metabolite-specific imaging can all achieve dynamic imaging, chemical shift encoding and metabolite-specific imaging provide greater dynamic and volumetric coverage (85). For scan prescriptions, the FOV is designed to provide full prostate coverage and typically to match the orientation of the anatomic imaging used for registration. Flip angles used in current studies are constant through time, as quantification with a variable-through-time flip scheme is highly sensitive to bolus timing (8) and errors in the RF transmit (B1 +) field (76).

Heart Studies

Data acquisition methods for 13C imaging in the heart must be designed to meet the demands of significant cardiac motion and blood flow. To cope with the periodic cardiac motion, most human heart studies to date used gating to the diastolic window, the longest cardiac cycle interval, which has reduced motion (2,22,28,30,35,36,38,45,52). The duration of the diastolic window limits the available data sampling time, making cardiac acquisitions the most time-constrained of the HP 13C MRI applications. The most common acquisition approach is metabolite-specific imaging with spiral k-space trajectories (2). Their single-shot imaging capability makes these methods particularly robust to motion effects. Furthermore, spiral k-space trajectories provide rapid k-space coverage and relatively benign flow and motion artifacts. The majority of studies have used 2D multi-slice acquisitions, but 3D encoding has also been used successfully (35).

Brain Studies

For HP 13C MRI of the human brain, the majority of studies have also used 2D (slice selective) acquisitions (10–12,14,16,28,33,40,41,44,51,53,60), with a trend toward volumetric coverage using 2D multi-slice metabolite-specific imaging. 3D metabolite-specific imaging of the whole brain, with phase encoding of the slice direction (34,57), has been shown to provide similar SNR efficiency (88) compared with multislice imaging. A number of studies have employed MRS/I (5,6,29,31–33,50,55) resulting in a spectrum from each voxel, which has the advantage of not requiring a priori information about which peaks to encode. This was important in early brain studies when it was not known which peaks would be detectable. Chemical shift encoding, using a set of images with different echo times and an iterative reconstruction of the individual resonances (i.e. the IDEAL approach (85)), has also been used (12,49,54), with the drawback that coverage in the slice direction was limited due to the time required to acquire multiple echo time images.

Abdomen And Breast Studies

The fundamental approaches to data acquisition and reconstruction in the abdomen and breast are largely similar to the aforementioned applications, but demand attention to particular challenges associated with these anatomic regions, especially relating to respiratory motion.

Although it has been shown that a basic 2D MRSI approach based on phase encoding and FID readout can be successfully applied for HP 13C imaging in breast (15) and kidney (13), major advantages in terms of spatiotemporal resolution and coverage have been realized using tailored approaches based on metabolite-specific imaging (43,62) and chemical shift encoding (43), which have facilitated multi-slice or 3D dynamic acquisitions over large FOVs in the abdomen (4,37,46).

The significant respiratory motion encountered in these regions can directly blur 13C images, and has further favored these rapid acquisition strategies. Motion also degrades B0 homogeneity, which can shift frequency-selective excitation profiles and introduce artifacts into rapid imaging readouts. This makes accurate determination of the acquisition center frequency and shimming essential in these regions which often cover large FOVs. (See “Prescan Calibration” section for more information). In some studies, breath-holding was used to minimize motion effects and enforce frame-to-frame data consistency (42). A pragmatic and reasonably effective approach for dealing with respiratory motion during 13C data acquisition is an initial breath-hold (as long as can be tolerated), followed by free-breathing (46,62).

1H Imaging

Collection of 1H imaging data is essential both for prescribing the 13C acquisition and for interpretation of the resulting 13C data. Multi-planar 1H scouts are acquired prior to 13C acquisition to enable graphical prescription of the 13C imaging region. All human HP 13C-pyruvate imaging studies acquire conventional MRI scans (e.g. T1- and T2-weighted volumes) for anatomic reference, aiming to cover at least the full 13C FOV. Acquiring these anatomic scans as close as possible to the time of 13C imaging (immediately before or after) minimizes potential misregistration between the data sets. Depending on the application, other advanced 1H sequences are also acquired (e.g. diffusion-weighted imaging for cancer imaging).

When contrast-enhanced data is acquired, it is done after 13C imaging, as paramagnetic contrast agents will accelerate 13C relaxation.

Reported Study Parameters

Figures 5 and 6, and Supporting Table S2 shows the reported acquisition study parameters for human HP [1-13C]pyruvate studies published as of September 2022. Figure 5 shows a mixture of MRS/I, metabolite-specific imaging, and chemical shift encoding methods have been successfully used, where spectroscopy-based methods have become less prevalent in recent studies. Figure 6 shows the acquisition timing, including the important start time and interval/temporal resolution, is quite variable across studies.

Figure 5: Acquisition methods used in published HP [1-13C]pyruvate human studies published up to September 2022, classified into: MR spectroscopy and spectroscopy imaging (MRS/I); chemical shift encoding methods, such as IDEAL, that use multiple TEs and model-based reconstructions; and metabolite-specific imaging methods that use spectrally-selective excitation to image a single resonance at a time.

Figure 6: Temporal acquisition characteristics reported in HP [1-13C]pyruvate human studies published up to September 2022. (a) Reported referencing of acquisition start times.

(B)

Acquisition start times reported when using dynamic imaging and when timing was reported relative to the end of the injection. (c) Temporal resolutions. “Not Applicable” indicates dynamic imaging was not used.

Summary

Three general categories of acquisition strategies have been used successfully for human HP 13C-pyruvate studies: MRS/I, model-based chemical shift encoding (e.g. IDEAL) methods, and metabolite-specific imaging methods. These have enabled successful studies in the prostate, heart, brain, abdomen, and breast. Recent studies increasingly have used the imaging-based strategies of metabolite-specific imaging and chemical shift encoding which are the fastest methods, although a heads-to–head comparison between techniques has not been performed.

Metabolite-specific imaging is quite popular because of its speed and compatibility with single-shot imaging, but is sensitive to B0 field variations and thus requires careful calibrations. Nearly all studies surveyed acquired data dynamically, allowing measurement of the bolus and metabolite kinetics. The exact timings and associated flip angles vary quite widely across reported studies, with no consensus yet as to how to choose these parameters. Image reconstruction is typically done directly using Fourier Transform methods, and accelerated imaging strategies are uncommon.

Data Analysis And Quantification

This section covers the analysis of data from human HP [1-13C]pyruvate studies, including modeling and metrics, visualization, as well as considerations for how to store data and metadata. Depending on study design, the analysis may need to give quantitative or semi-quantitative output reflecting a biological process or may just reflect a contrast between different regions of interest for quantitative evaluation.

Metrics

Figure 7: HP [1-13C]pyruvate raw data (A) have typically been quantified using four categories of metrics depending on the acquisition. Data acquired as a single time point are often quantified using normalized metabolite images or metabolite ratios (B). Dynamic data can be quantified using normalized metabolite images or metabolite ratios (B), or with metabolite timings such as time-to-peak (TTP) or pharmacokinetic (PK) models (C). The latter two require the data to be time-resolved. [1-13C]alanine and 13C-bicarbonate are analyzed similarly to [1-13C]lactate but omitted here for display.

Metabolite images are commonly used as summary metrics for HP MRI data, often including some form of normalization as well as summed over time as an area under the time curve (AUC) (17). These are analogous to the visual evaluation that is most used for routine clinical work (89,90). In these metabolite images, we expect that the [1-13C]pyruvate AUC signal is predominantly weighted towards perfusion and uptake, while [1-13C]lactate, [1-13C]alanine and 13C-bicarbonate AUCs represent metabolic conversion. The strength of this approach lies in its simplicity and relatively few underlying assumptions. Limitations to the use of single-metabolite images or AUCs include sensitivity to inhomogeneous coil profiles (57,87,91), the acquisition strategy and acquisition parameters, pyruvate polarization and concentration level, and signal relaxation rates (92). Further, the reader must be careful to interpret all the images in conjunction to better understand the underlying biology; for example, increased [1-13C]lactate in the presence of decreased [1-13C]pyruvate delivery can have a very different meaning compared to increased [1-13C]lactate with increased [1-13C]pyruvate delivery.

In an attempt to address variations in coil sensitivity, polarization level, and pyruvate delivery, AUC images are often computed by normalizing to a specified parameter, such as the maximum pyruvate or average lactate signals, or presented as a ratio such as lactate/pyruvate or divided by “total Carbon” - the sum total of HP 13C signal observed across all metabolites. The AUC ratios between metabolites and pyruvate are proportional to the corresponding forward kinetic rates (81,93), but are not directly comparable to rate constants when magnetization loss rates (e.g. relaxation and losses due to signal excitation) differ between studies. Similarly, the ratios between the produced metabolites (e.g. bicarbonate/lactate) can reflect the balance between downstream metabolic pathways (12,55). Care must be taken to consider how AUC images are calculated and normalized before comparing values between studies.

To further quantify the interpretation, pharmacokinetic (PK) modeling approaches were developed to compute the apparent kinetics of pyruvate-to-metabolite exchange (92,94–99). These yield semi-quantitative to quantitative apparent rate constants, given in s-1. Some models require a vascular input function, while others avoid this requirement (95). PK models can explicitly account for acquisition-specific details such as excitation angle and repetition time, and thus may reduce the effects of these details on quantification. An input-less model, provided in the Hyperpolarized-MRI-Toolbox (https://github.com/LarsonLab/hyperpolarized-mri-toolbox) (100) and thus frequently employed for human data, has been shown to fit well and robustly to prostate and brain data (8,20). PK models are quantitative in nature, arguably provide more relevant biological information (8,20), and appear to be reproducible across sites (51). However, rate constants derived from PK models are still apparent rates, and likely do not reflect a single biological characteristic.

Some additional considerations include whether complex or magnitude data is used, as the noise behaviors will impact the analysis differently. Additionally, cut-off thresholds or other criteria may be used to identify and avoid voxels with insufficient SNR before analysis to improve robustness (20,41).

Regardless of the analysis approach, the underlying biology is not always clearly represented by the data; instead, the metrics may be influenced by perfusion, barrier permeability, intercellular shuttles, enzyme activities, co-substrate concentrations, or combinations thereof, depending on the organ and disease of interest (19,43,94,101–103). This may be addressed by incorporating complementary information. As an example, HP 13C pyruvate data is influenced by perfusion, and thus addition of perfusion MRI could be important for interpretation (98,104,105).

All the methods outlined above have been explored in clinical studies, described in Supporting Table 3 and summarized in Figure 8. As of September 2022, approximately 52% of studies involving human subjects report rate constants derived from a PK model with a few different models reported. A nearly equal fraction (51%) of the studies report AUC ratio values.

Approximately 66% of these studies report metabolite-specific images or AUC values. About 40% report SNR values; this metric is particularly frequent in manuscripts that describe technical developments for clinical HP MRI. Approximately 16% of these studies summarize model-free metrics, and 10% report measurements from a single timepoint. Most studies report a combination of quantities.

Figure 8: Reported metrics used for analysis in HP [1-13C]pyruvate human studies published up to September 2022.

Visualization

A wide variety of approaches have been used for visualizing data from human HP 13C-MRI studies. The challenges and practical considerations are: 1) choosing the appropriate metrics to display, 2) how to encode the parameters (e.g. the colormap), and 3) choosing how to provide anatomical context and other multi-parametric data. The choice of visualization also depends on the goal which could be for diagnostic interpretation, but also quality control, reproducibility among readers and publication.

Metrics

The choice of HP 13C metrics is described in detail above. At this stage in HP 13C development where there is no standardized metric, often a combination of metabolite images and ratios or PK model parameters are shown.

Parameter Encoding

The mapping function chosen should provide an adequate, often quantitative, impression of the parameter mapped. There is a consensus in the visualization field that perceptually uniform maps are best suited to visualize continuous parameters, like the greyscale typically used by radiologists as well as other monochrome (black to blue) and color ranges (fire-type, rainbow-type) (106,107). Multi-color heatmaps have been the most frequently employed method for HP 13C data, while greyscale has infrequently been used but it ensures there is no coloring-based bias as well as facilitating later reuse (Fig. 9a). Among the color schemes employed in the clinical HP 13C literature, fire-type scheme seems to be the most common [similar to “Plasma” or “Inferno” in matplotlib.org]. Next most commonly employed is the rainbow-type scheme [similar to “Rainbow” in matplotlib.org].

Anatomical Context

HP MRI faces the challenge that it does not necessarily depict the anatomical features, similar to PET, and thus requires an anatomical reference. Most often, a grayscale anatomical image is overlaid with a HP colormap (Fig. 9c,d). This approach is very intuitive, but can skew perception as the grey-scale anatomical reference may affect the brightness of the HP data (e.g. signal in the skull). This bias does not occur when showing adjacent maps (Fig. 9a, b). Here, anatomical outlines may help to provide reference (Fig. 9b).

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