Mecanum Wheel Robot

finite-time control law using backstepping technique to perform stabilization and trajectory tracking objectives for a FWMR system. A rigorous stability proof is presented and explicit computation of the finite-time is provided. For tracking objective, we demonstrate the results taking an S-shaped trajectory inclined towards collision avoidance applications. Simulation validation in real time using Gazebo-ROS on a Mecanum robot model is carried out which complies with the theoretical results.

Four Wheeled Mecanum Robots (Fwmrs) Exhibit The

ability to navigate freely in all directions on a flat surface. This omnidirectional mobility feature makes them widely employed in various contexts, including industrial opera- tions, constrained environments like warehouses, inspection zones, search and rescue missions, cleaning duties, and assistive robots (Zhang et al., 2023). Hence, across vari- ous sectors such as manufacturing, logistics, and unmanned warehousing, the mecanum wheel robot is renowned for its remarkable ability to execute zero-radius turns effortlessly in any direction as well as its adeptness in navigating within confined spaces (Taheri and Zhao, 2020). Its widespread adoption is a testament to its versatility and efficiency, making it a cornerstone technology in modern industrial operations. Consequently, FWMR’s have garnered signifi- cant attention in research circles. To enable the mobile robot to follow the desired trajectory accurately, it is imperative to develop precise tracking controllers. A model predictive control strategy equipped with compensation for friction is designed in (Conceição et al., 2013) to facilitate tracking objective. Output-feedback strategy tailored with continuous sliding mode controllers is employed in (Ovalle et al., 2019) for addressing the robust trajectory tracking challenges en- countered by FWMR. A sliding mode controller derived through backstepping technique tailored for robust control of FWMR is presented in (Thi et al., 2019). A variant of computed-torque control approach is developed in (Vázquez and Velasco-Villa, 2008) for addressing the tracking control objective of FWMR. Researchers have developed a dif- ferential sliding mode controller aimed at facilitating the movement of an omnidirectional mobile platform in (Viet et al., 2012). A robust adaptive terminal sliding mode control scheme is designed in (Feng and Wang, 2021) specifically to address the tracking challenges encountered by an FWMR

Orcid(S): 0000-0001-5232-1314 (A. B)

targeted for examination of aircraft skin. Regarding tran- sient performance, which aims to limit the tracking error within a specified range, a widely adopted strategy involves employing an error transformation function. This technique is illustrated in (Hu et al., 2020) to ensure the transient performance of underactuated surface vessels for executing trajectory tracking. In (Shi et al., 2020), the concept of an error transformation function is presented, which effectively converts the constrained tracking objective into an uncon- strained stabilization problem for marine surface vessel.

Additionally, in (Sui et al., 2020), a performance function based on finite-time is implemented to converge the tracking errors within a specified range in the context of tracking problems involving systems with uncertain dynamics. The conventional asymptotic stability theory can solely ensure the asymptotic stability of a control object concerning finite- time convergence. The emergence of finite-time stability theory aims to achieve stabilization within a finite duration, ensuring that the tracking error converges within a bounded interval. Initially proposed by (Bhat and Bernstein, 1998), subsequent variants of finite-time stability theory (Wang et al., 2017; Shen and Xia, 2008; Yu et al., 2005; Zhu et al., 2011; Bohn and Sanyal, 2016) have been introduced for enhanced practical application. In (Li et al., 2019b), a finite-time adaptive neural controller, with optimized design parameters, is developed to address the finite-time optimal A finite-time tracking objective is addressed for a class of nonlinear Multi-Input and Multi-Output (MIMO) systems in (Li et al., 2019a) using neural networks in an adaptive setting, whose performance depends on the number of train- ing samples considered. Despite the inherent advantages of extreme manueurability of FWMR, the practical implemen- tation or realistic simulation of the Mecanum wheel robot faces significant hurdles in control, a critical aspect marked by numerous challenges. Factors such as nonlinearity, ex- ternal perturbations, uncertainty etc., renders conventional control techniques inadequate for meeting stringent trajec- tory tracking demands. Furthermore, issues like significant drift from intended trajectories, oscillatory nature of control

Finite-Time Trajectory Tracking Of A Fwmr

signals and time delays may arise as a consequence. Conven- tional control schemes often face difficulties to effectively tackle tracking control issues under these complex practi- cal conditions resulting in inefficiencies, inaccuracies, and unreliability in handling control tasks (Tang et al., 2024).

Based on an elaborative literature survey, there is not much adequate research implemented in the validation of finite- time trajectory tracking controllers in a realistic simulation environment for FWMR system. Towards this effort, our

Contributions Are As Follows:

1. We present a finite-time backstepping tracking control to track a desired non-trivial trajectory. The proposed control law is finite-time continuous and gives finite- time global convergence of states to the equilibrium.

2. A closed loop stability proof in finite-time setting with an explicit computation of finite-time is provided. 3. An estimation of upper norm bound of disturbance torque that can be tolerated with the proposed con- troller is provided and a case study associated with the same is demonstrated in the simulation analysis.

4. The proposed control algorithm is validated in rGazebo ROS realistic simulation platform inorder to demon- strate the efficiency of the controller under such practical conditions as outlined above.

5. We also present a comparison of finite-time and asymptotic control results for the FWMR carried out in Gazebo-ROS.

Organization of the paper is as follows: Section 2 and 3 discusses the mathematical preliminaries and system model respectively. The theoretical developments are presented in section 4 followed by simulation results in 5 and conclusions in section 6.

Athematical Preliminaries

In this section, we present the key elements necessary for the theoretical developments outlined in this paper. Notations: ℝ𝑛denotes the Euclidean space of dimension 𝑛. For any matrix 𝐾, 𝐾𝑇and 𝐾−1 denotes its transpose and inverse respectively. 𝜆𝑚(⋅) and 𝜆𝑀(⋅) denotes the minimum and maximum Eigen values of a given matrix respectively.

We denote ‖ ⋅‖ as the standard Euclidean 2-norm of a given vector and log(⋅) represents the logarithmic function.

⊂ℝ𝑛being An Open Neighbourhood Of The Origin And

𝑓(0) = 0. This implies that 𝑓−1(0) ≜{𝑥∈∶𝑓(𝑥) = 0} is non-empty. A 1 (continuously differentiable) function

𝑥∶→Is A Solution Of (1) On The Interval ⊂ℝ,

if 𝑥satisfies (1) ∀𝑡∈. The function 𝑓being continuous implies that ∀𝑥0 ∈, ∃𝑡0 < 0 < 𝑡1 and a solution 𝑥(⋅) of (1) defined on 𝑡∈(𝑡0, 𝑡𝑓) such that 𝑥(0) = 𝑥0. Suppose for all initial conditions, (1) possess unique solution except at the origin (Bohn and Sanyal, 2016). This implies that for every 𝑥∈⧵{0}, ∃𝑡2 > 0 and a unique solution 𝑥(⋅) of (1) defined on [0, 𝑡2) satisfying 𝑥(0) = 𝑥0.

Proposition 1. (Bhat and Bernstein, 1998) Let ⊆be an open neighbourhood of origin and let (𝑥) ∶∖{0} →ℝ+ be the finite time. Suppose the following statements hold.

Lim

𝑡→(𝑥) 𝜅𝑡(𝑥) = 0 implies finite-time convergence.

𝑥∈𝛿∖{0}, 𝜅𝑡(𝑥) ∈𝜖∀𝑡∈[0, (𝑥)]. This Yields

Lyapunov stability. Then, the origin is said to be a finite-time stable equilibrium of (1). It will be globally finite-time stable if = = ℝ𝑛.

Lemma 1 ((Bhat and Bernstein, 1998)). Let 𝑐> 0 be a

Positive Real Number, 𝛼∈(0, 1) And 𝑉∶→ℝbe A 1

function. Suppose ∃a neighborhood ⊂such that 𝑉is positive definite on anḋ 𝑉+𝑐𝑉𝛼is negative semi-definite

𝜕𝑥𝑓(𝑥). Then, The Origin Is Said To Be

finite-time stable equilibrium of (1). Furthermore, if (𝑥) is

The Finite-Time, Then (𝑥) ≤

𝑐(1−𝛼)𝑉(0)1−𝛼∀𝑥∈. Lemma 2 ((Khalil, 2002) Rayleigh-Ritz inequality). Con- sider a symmetric positive definite matrix, 𝑃∈ℝ𝑛×𝑛and let 𝜆∈ℝ𝑛be the eigenvalues of 𝑃. Then the following

∀𝑥∈ℝ𝑛,

holds, where, 𝜆𝑚(⋅) and 𝜆𝑀(⋅) denotes the minimum and maximum eigenvalues respectively. Lemma 3. Suppose 𝑎1, 𝑎2 are positive real numbers and let

+ 𝑎𝑐

2). Proof. To aid the proof, we make use of the subadditivity property of a concave function.

Subadditivity of a function: A function 𝑓∶ℝ≥0 ⊇→ℝ is said to be subadditive on if ∀𝑎1, 𝑎2 ∈such that 𝑎1 + 𝑎2 ∈, the following inequality holds: 𝑓(𝑎1 + 𝑎2) ≤ 𝑓(𝑎1) + 𝑓(𝑎2).

Concavity: Let 𝑐∈[0, 1] and 𝑎1, 𝑎2 ∈, a concave function

(2)

is subadditive. Choose 𝑎2 = 0 in (2), this yields 𝑓(𝑐𝑎1) ≥

𝑓(𝑎1) = 𝑓(𝑐(𝑎1 + 𝑎2)) ≥𝑐𝑓(𝑎1 + 𝑎2)

𝑓(𝑎2) = 𝑓((1 −𝑐)(𝑎1 + 𝑎2)) ≥(1 −𝑐)𝑓(𝑎1 + 𝑎2).

Adding The Above Two Equations Yields,

𝑓(𝑎1 + 𝑎2) ≤𝑓(𝑎1) + 𝑓(𝑎2) ⟹subadditivity.

And 𝑓(𝑎1 + 𝑎2) =

(𝑎1 + 𝑎2)𝑐, yields the proof.

Onsider A Four Wheeled Mecanum Robot (Fwmr)

model shown in Figure 1. It forms a unique type of om-

: Schematic Representation Of A Four Wheeled

Mecanum Robot. nidirectional mobile robot, characterized by a rectangular configuration with four specialized mecanum wheels placed symmetrically at the corners. Each mecanum wheels are equipped with a series of passive rollers along its circum- ference. These rollers are typically oriented with their axis of rotation at an angle of 45◦with the wheel roll axis.

This distinctive design affords FWMR an additional degree of freedom compared to the conventional wheeled mobile robots. Consequently, FWMR possess the capability to ma- neuver in any direction without necessitating reorientation.

It contains four mecanum wheels anḋ 𝜃𝑖is the angular velocity of the robot 𝐵. 𝑙1 and 𝑙2 are the lengths from the center of mass and center of wheels of FWMRs. In order to describe the pose of the vehicle, we consider two coordinate frames which are mutually orthonormal to each other. Suppose (𝑋𝐵, 𝑌𝐵) denotes coordinates of position expressed in vehicle fixed frame 𝐵located at the center of mass of the vehicle. Let (𝑋𝐼, 𝑌𝐼) be the corresponding coordinates expressed in earth fixed frame 𝐼as shown in

Figure 1. Suppose 𝑞= (𝑥, 𝑦, 𝜃) ∈⊂ℝ2 × [0, 2𝜋)

denotes the pose of the vehicle expressed in 𝐼frame and leṫ 𝑞= (̇𝑥,̇ 𝑦,̇ 𝜃) ∈ℝ3 denotes the corresponding inertial fixed velocity of the robot. The transformation which relates the

(3)

where, 𝑄∈ℝ3×3 represents the transformation matrix anḋ 𝑞𝐵represents the robot velocity in 𝐵frame. Let 𝜌= (𝜃1, 𝜃2, 𝜃3, 𝜃4) represents the angular position of the wheels.

Wheel coordinate velocities can be related to body coordi- nate velocities using simple geometrical relationship from

,

where, 𝐽is the transformation matrix. 𝑙1 and 𝑙2 are the distance of center of mass of the robot from the respective wheel centers as shown in Figure 1. Wheel coordinate

(4)

where, 𝐽† denotes the right pseudo inverse of 𝐽. Dynamical model of FWMR obtained using Lagrange D’Alembert’s formulation (Yuan et al., 2020; Huang and Lin, 2019; Zhao

, 𝐷= Diag(𝐷1, 𝐷2, 𝐷3, 𝐷4) And

Γ = 𝑚1 + 𝑚2 + 𝐼𝑚. Control input, 𝜏≜(𝜏1, 𝜏2, 𝜏3, 𝜏4) ∈ℝ4 represents the torques acting on four wheels of the robot; 𝐼𝑚and 𝐼𝑧denotes the moment of inertia of wheels and the robot respectively; 𝑚denotes mass of the robot; 𝑟is the wheel radius; 𝐷𝑖’s are viscous friction coefficients of the four wheels; 𝜏𝑑∈ℝ4 forms the external disturbances including static friction. Using kinematics (3) and (4), we

(7)

To make ease of control synthesis, we apply a transformation to convert the above four-dimensional torque expressed in wheel coordinates into body fixed torque in ℝ3. Multiply by

Which Can Be Expressed As,̈

𝑞𝐵= −𝐽𝑀−1𝐷𝐽†𝑄̇𝑞𝐵+ 𝐽𝑀−1𝜏+ 𝐽𝑀−1𝜏𝑑.

(8)

For notational simplicity, let 𝜂= 𝑞and 𝜈=̇ 𝑞𝐵. Then, the second-order unperturbed (𝜏𝑑= 0) dynamical system can be

(9)

where,̄ 𝑄= 𝑄𝑇, 𝐹(𝜂) = −𝐽𝑀−1𝐷𝐽†𝑄and̄ 𝜏= 𝐽𝑀−1𝜏. One can observe that the dimension of control input is now reduced from four to three which makes the subsequent controller synthesis easier.

These two equations governing the kinematics and dynamics of FWMR represents the complete system dynamics which serves as the starting point for the synthesis of finite-time tracking control law which is discussed in the next section.

Finite-Time Trajectory Tracking Of A Fwmr

Figure 2: Schematic representation of the proposed control architecture. Assumption 1. The desired center of mass position and orientation of FWMR, 𝜂𝑑(𝑡) is 2 with its first and second derivatives are bounded.

Problem statement 1. Consider the dynamics (9), with the feedback law̄ 𝜏. Given a desired trajectory 𝜂𝑑(𝑡) satisfying Assumption 1, our aim is to design the feedback law̄ 𝜏, such that the vehicle pose error,̃ 𝜂(𝑡) ≜𝜂(𝑡) −𝜂𝑑(𝑡) converges to the origin in finite-time.

With The Complete Dynamical Model Of Fwmr De-

scribed in section 3, designing a finite-time tracking control law using backstepping technique is outlined in this section. A schematic representation of the proposed control archi- tecture is shown in Figure 2. Let 𝜂𝑑∈denotes desired pose (position and orientation) of the vehicle in 𝐼frame.

Suppose 𝜈𝑑∈ℝ3 anḋ 𝜂𝑑∈ℝ3 be the desired velocities of the robot expressed in 𝐵and 𝐼frames respectively. We define the tracking error for pose of the vehicle as,̃ 𝜂≜ 𝜂−𝜂𝑑∈satisfying assumption 1 . Consider a Lyapunov function 𝑉1 ∶→ℝfor the configuration error subsystem,

(10)

Next we choose the virtual control, Ψ(̃𝜂) ∈1 for position

(11)

where, 𝐾𝜂∈ℝ3×3 is a symmetric positive definite matrix and the parameter 𝛼is further chosen (refer, Lemma 4) to ensure the boundedness of feedback torque input designed through backstepping procedure. We denote the virtual ve- locity error for velocity subsystem as, 𝑧2 ≜𝜈−Ψ(̃𝜂). Thus in terms of transformed error coordinates, the system (9) can

𝜂= 𝑄(𝑧2 + Ψ(̃𝜂)) −̇ 𝜂𝑑,̇

𝑧2 = 𝐹(̃𝜂)(𝑧2 + Ψ(̃𝜂)) −̇ Ψ(̃𝜂) +̄ 𝜏.

(12)

Suppose 𝜆𝑚(𝐾𝜂) denotes the minimum eigenvalue of 𝐾𝜂,

Therefore, By

making use of Lemma 1, configuration error subsystem,̃ 𝜂 is finite-time stable about the origin with the convergence

, 𝜂

denotes the finite-time for configuration error subsystem and 𝑉1(̃𝜂(0)) represents the Lyapunov function evaluated at 𝑡= 0.

Emma 4. Range Of 𝛼is 1

2 < 𝛼< 1 for the system (12). Proof. To see this, consider the derivative of virtual error

)

. Under the limiting case,̃ 𝜂→0 and 𝑧2 →0 on the functioṅ Ψ(̃𝜂), L-Hospital’s rule is applied on the second term of (14).

Finite-Time Trajectory Tracking Of A Fwmr

From this, and (11) yields the range of 𝛼> 0, while the limiting case applied on the fourth and fifth terms of (14)

Stability Analysis

Next, we explain the development of proposed feed- back torque designed using backstepping principles and the closed loop stability analysis with the controller yielding finite-time global convergence of states to the equilibrium.

Theorem 5. Consider the reference trajectory 𝜂𝑑(𝑡) ∈ satisfying Assumption 1, and the transformed closed loop dynamical model of FWMR (12). With the feedback torque

(1−𝛼) , (15)

where, 𝐾𝑧∈ℝ3×3 is a positive definite matrix anḋ Ψ(̃𝜂) is defined in (14), the resulting closed loop system (12) is globally finite-time stable around the equilibrium (̃𝜂, 𝑧2) = (0, 0). Consequently, the states (𝜂, 𝜈) will converge to the desired trajectory (𝜂𝑑, 𝜈𝑑).

Proof. To obtain backstepping controller, consider the Lya- punov function for velocity error subsystem 𝑧2 as 𝑉2 ∶ℝ3 →

𝑉2 =𝑧𝑇

(𝐹(̃𝜂)(𝑧2 + Ψ(̃𝜂)) +̄ 𝜏−̇ Ψ(̃𝜂)).

(16)

Next we choose a Lyapunov function for the total system, 𝑉∶× ℝ3 →ℝas 𝑉(̃𝜂, 𝑧2) = 𝑉1(̃𝜂) + 𝑉2(𝑧2). Taking the

‖‖𝑧2‖‖

(1−𝛼) . From Lemma 4, feedback torque (15) is bounded for the pre- scribed range of 𝛼. Suppose, 𝜆𝑚(𝐾𝑧) denotes the minimum

,

where, in the last inequality, taking 𝑐2 = 𝜆𝑚(𝐾𝑧)2𝛽and

),

where, it is straightforward to see that the above inequality will be always satisfied with the choice of 𝑐= min(𝑐1, 𝑐2) for any initial states (̃𝜂(0), 𝜈(0)). Next, we make use of Lemma

) ≤−𝑐(𝑉1 +

𝑉2)𝛽≤−𝑐𝑉𝛽. Therefore, by using Lemma 1, the overall system represented by (12) with the closed loop feedback torque (17) is globally finite-time stable with the guaranteed

(19)

where, 𝑉0 ≜𝑉(̃𝜂(0), 𝑧2(0)) is the Lyapunov function evalu- ated at time 𝑡= 0. This yields the proof. Corollary 1. Control law (15) is continuous and gives finite- time global convergence of the states to the equilibrium for

𝛼∈( 1

2, 1]. Proof. Continuity of the control law (15) for 𝛼∈( 1

, 1)

can be seen directly from Lemma 4. In order to show that the continuity also holds for 𝛼= 1, evaluate the partial derivative of control input̄ 𝜏(15) with respect to 𝛼and then equate it to the limiting value of 𝛼= 1. Computing the partial derivative of (15) with respect to 𝛼yields,

Finite-Time Trajectory Tracking Of A Fwmr

−̄𝑄𝑇𝐾𝜂̃𝜂̃𝜂𝑇̇̃ 𝜂(𝛼−1)‖̃𝜂‖𝛼−3 −𝐾𝑧𝑧2‖𝑧2‖𝛼−1) .

)

−̄ 𝑄𝑇𝐾𝜂̃𝜂̃𝜂𝑇̇̃ 𝜂((𝛼−1)‖̃𝜂‖𝛼−3 log(‖̃𝜂‖) + ‖̃𝜂‖𝛼−3)

−𝐾𝑧𝑧2‖𝑧2‖𝛼−1 Log(‖𝑧2‖)

Subsequently, evaluating the above derivative for the limit

−̄ 𝑄𝑇𝐾𝜂̇̃𝜂−𝐾𝑧𝑧2 Log(‖𝑧2‖),

which shows that the control law is continuous in the range

, 1) And It Also Holds When 𝛼= 1. This Signifies

that the vector field on × ℝ3 given by the dynamics (12) under the influence of control torque (15) is continuous with respect to the parameter 𝛼in the prescribed range

( 1

2, 1]. Therefore, the flow of closed loop feedback system is

, 1]. Hence, Integrating This

with the global finite-time convergence result of Theorem 5 yields the proof. Note that, 𝛼= 1 represents the asymptotic controller. We analyzed the continuity of̄ 𝜏at 𝛼= 1 to express it within a general framework such that it can be derived as a subcase of the proposed finite-time controller.

Robustness Analysis

For the unperturbed system (12), we have proved the finite-time global convergence of system states to the desired equilibrium. For demonstrating the robustness of the pro- posed controller in the presence of disturbances, we recast the dynamical equation which follows from (8) as,̇̃

(20)

where,̄ 𝜏𝑑denotes the constant bounded disturbance torque acting on the system. We explicitly provide an expression for the maximum norm bound of the disturbance torque expressed using a desired side of neighbourhood about the equilibrium point that can be tolerated for the system states to yield the finite-time global convergence result elucidated in Theorem 5.

Corollary 2. Consider the closed loop perturbed system (20) with the control torque (15). Suppose, ⊂× ℝ3 be a closed neighbourhood of the origin defined by,

‖̃𝜂‖ ≤Δ̃𝜂, And ‖𝑧2‖ ≤Δ𝑧2,

where, Δ̃𝜂and Δ𝑧2 are bounded finite positive constants that defines the size of neighbourhood . If the maximum norm

(21)

then the closed loop feedback system (20) converges to the neighbourhood . Proof. The proof follows from the Lyapunov stability anal- ysis discussed in Theorem 5. Taking the time derivative oḟ 𝑉(̃𝜂, 𝑧2) =̇ 𝑉1(̃𝜂)+̇𝑉2(𝑧2) along the perturbed dynamics (20) under the influence of control torque (15) yields,̇

(1−𝛼) + 𝑧𝑇

2̄ 𝜏𝑑. Compared to the unperturbed dynamical system, the extra term in the time derivative of 𝑉(̃𝜂, 𝑧2) arises as a con- sequence of the disturbance torque affecting the system.

Taking the upper bound on the extra term containing the

T Can Be Further Expressed As,̇

𝑉(̃𝜂, 𝑧2) ≤−𝜆𝑚(𝐾𝜂)‖̃𝜂‖(1+𝛼)−𝜆𝑚(𝐾𝑧2)‖𝑧2‖(1+𝛼)+Δ𝑧2Υ. Consequently, evaluating the upper bound oḟ 𝑉(̃𝜂, 𝑧2) along

𝑧2

+ Δ𝑧2Υ. Hence, along the boundary of , the time derivative of Lyapunov function 𝑉(̃𝜂, 𝑧2) will be negative definite if the

(22)

is satisfied, which is sufficient to ensure that the trajec- tories originating from outside and in the domain of convergence of the equilibrium will eventually converge to . Further, equation (21) follows from (22) and hence the proof.

Remark 1. It is important to note that, within a closed neighborhood of the equilibrium (origin), the desired size of , as characterized by Δ̃𝜂and Δ𝑧2, will be less than unity. According to (21), in the finite-time scenario where

, The Maximum Disturbance That Can Be Tolerated

is greater in comparison with the asymptotic case where 𝛼= 1. This highlights the improved robustness capabilities of the proposed finite-time controller. In the next section, we carry out a case study including tracking the given mobile robot along a straight-line path with a humped obstacle placed along the path in order to illustrate this situation in our simulations. This impediment influences the system as an external disturbance. Therefore, in order for the system to go along the path, it must resist the extra disturbance torque.

Simulation, Case Studies And Discussions

In this section, we present the real-time simulation which captures the hardware implementation of FWMR system. For this, we make use of Gazebo-ROS simulation platform.

ROS is an open-source middleware employed in the de- velopment of robot software. Gazebo is an efficient real- time robotic simulator equipped with a complete toolbox of development libraries and cloud services to enable the simulation of physical designs in more realistic environ- have used Nexus 4WD Mecanum Simulator (nex): A ROS Repository for the robot description and gazebo simulation of the 4WD mecanum wheel robot. It contains two packages: Nexus 4wd mecanum description (having URDF model and robot meshes) and nexus_4wd_mecanuum_gazebo (having launch files and controller scripts written in python), which we have configured according to our objectives. The pack- age nexus_4wd_mecanuum_description contains the launch files which loads the nexus 4WD mecanum robot description parameter and the robot state publisher. Gazebo plugin for simulating the controller and launch files for running the simulation are contained in nexus_4wd_mecanuum_gazebo.

Real-time simulation results are visualized using plot jug- gler: an application to plot logged data. Gazebo-ROS ar- chitecture encompassing all these are outlined in Figure 3.

Communication of messages in Gazebo-ROS through the

Figure 3: Gazebo-Ros Architecture

ROS subscriber and publisher nodes are shown in ROS computation graph (Figure 4) generated through GUI plugin, rqt_graph.

Figure 4: Ros Computation Graph

which subscribes to odometry messages from Gazebo plu- gins to gather vehicle pose and velocity informations and Figure 5: Closed loop control architecture of the system a publisher node, ∕cmd_vel which publishes velocity com- mand (translational velocities along 𝑥and 𝑦directions and yaw rate about 𝑧-direction) as a twist message which forms the actuation signal to the robot model. The /gazebo_gui topic is specifically associated with the graphical user inter- face (GUI) of Gazebo. This topic is used to communicate in- formation between ROS and the Gazebo GUI. It can be used for various purposes, such as sending commands to control the simulation, receiving sensor data from simulated robots, or updating the visualization of the simulation environment in the GUI. A closed loop control architecture with the designed controller is depicted in Figure 5. The subscriber node block receives the measurement signal (vehicle pose and velocities) from Gazebo mobile robot URDF model by subscribing to ∕odometry topic. These robot states and velocities along with the desired states are fed to the finite- time backstepping ROS controller block which computes the necessary control torques. These torques are then incorpo- rated to update the position and velocity states in discrete- setting (choosing a sampling time of 0.01s) approximated using Euler’s backward difference scheme. The computed torque signals are transformed to required motor velocities using DC motor dynamics generating the motor control velocities which are then published through command ve- locity topic, ∕cmd_vel. These command velocities form the actuation signal for the Gazebo URDF mobile robot model.

This completes the closed loop control architecture. Remark 2. Gazebo-ROS provides built-in support for pub- lishing velocities, making it straight forward to interface controller and evaluate it’s performance within a ROS ecosystem. The command velocities will be converted into torques by means of a plugin for force-based move. It will generate output torque and forces to be applied to the base link of the robot, using velocity commands as input. Our future work will involve implementing the developed control strategy on a physical FWMR system where, direct torque commands will be applied for real-world validation.

Now, We Present The Gazebo-Ros Simulation Results

with the developed finite-time backstepping controller (15). We present the comparative assessment of finite-time and

Quantitative Comparison: Case 1

asymptotic controllers for achieving stabilization and track- ing control objectives. Asymptotic feedback law can be ob- tained by setting 𝛼=1 in (15). Two scenarios are presented: a point stabilization problem and a tracking control problem.

Ase 1: Point Stabilization

Here, we consider the problem of stabilization of sys- tem states and velocities to origin from an arbitrary initial condition. Though this is a sub case of tracking it is not trivial since we carry out this objective in our simulation for demonstrating few analytical results on the effect of tuning parameters such as control gains and finite-time parameter 𝛼on system performance. Initial errors for system states is chosen as: (̃𝜂(0), 𝑧2(0)) = (5, −4, 𝜋∕4, 1, 0.5, −0.5). For these initial conditions, theoretically calculated guaranteed finite-time from (19) is around 6.4031s. Figures 6 to 8 shows the position and orientation stabilization errors. The practical value of average finite-time calculated for pose sta- bilization (|̃𝜂| ≤10−6) is about 4.24s for finite-time case. For asymptotic case, the average convergence time for the same was found to be around 10.03s. The error in velocities, 𝑧2 for subsystem 2 is shown in Figures 9 to 11. Practical value of average convergence time for |𝑧2| ≤10−6 is obtained around 4.51s while for the same convergence criteria, it is obtained around 12.62s for the asymptotic case. This clearly indicates the faster convergence capabilities of the proposed finite- time strategy in contrast with the conventional asymptotic feedback control law. Figure 12 shows the control inputs (forces and torque) applied to the base link of the robot which becomes zero at steady state once the states converges to the desired values. A quantitative comparison in terms of convergence time and control effort is provided in Table 1.

𝑘=2 |̄𝜏𝑘(𝑖) −̄ 𝜏𝑘−1(𝑖)|,

where 𝑖= 1 to 3 and 𝑁is the number of samples) as a measure for quantitative evaluation of control effort. This is indicative of control signal fluctuations which should be as low as possible to prevent actuator wear and tear. TV measure is found to be low for finite-time case compared to the asymptotic case thereby indicating the efficiency of our proposed controller in terms of actuation.

5.1.1. Guidelines for tuning the controller weights 𝐾𝜂

And 𝐾𝑧

We present the analytical results to examine how varying the tuning parameters 𝐾𝜂and 𝐾𝑧impacts the system per- formance measures (the finite-time) and TV. Specifically, we characterize the tuning gains 𝐾𝜂and 𝐾𝑧in terms of the parameters 𝜆1 and 𝜆2 as follows: 𝐾𝜂= diag(𝜆1, 𝜆1, 0.5𝜆1) and 𝐾𝑧= diag(𝜆2, 𝜆2, 0.5𝜆2). Figure 13 illustrates a surface plot showing how and TV vary with changes in 𝜆1 and 𝜆2.

Figure 6: Case 1: Pose error̃ 𝜂(1) corresponding to 𝑥-position with time; convergence time for |̃𝜂(1)| ≤10−6 is obtained around 4.35s and 10.2s for finite-time and asymptotic cases respectively.

Figure 7: Case 1: Pose error̃ 𝜂(2) corresponding to 𝑦-position with time; convergence time for |̃𝜂(2)| ≤10−6 is obtained around 4.24s and 10.1s for finite-time and asymptotic cases respectively.

Figure 8: Case 1: Pose error̃ 𝜂(3) corresponding to yaw angle with time; convergence time for |̃𝜂(3)| ≤10−6 is obtained around 4.12s and 9.8s for finite-time and asymptotic cases respectively.

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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