Simulia Abaqus Spinal Implant — Biomedical Instrumentation & Clinical Sensing

Abstract — Simulia Abaqus Spinal Implant

stacks, simulation worlds, reports and viva from Bangalore. Paul Friedrich1, Julia Wolleb1, Florentin Bieder1, Florian M. Thieringer1,2, Abstract. Advances in 3D printing of biocompatible materials make patient-specific implants increasingly popular. The design of these im- plants is, however, still a tedious and largely manual process. Existing ap- proaches to automate implant generation are mainly based on 3D U-Net architectures on downsampled or patch-wise data, which can result in a loss of detail or contextual information. Following the recent success of Diffusion Probabilistic Models, we propose a novel approach for implant generation based on a combination of 3D point cloud diffusion models and voxelization networks. Due to the stochastic sampling process in our diffusion model, we can propose an ensemble of different implants per defect, from which the physicians can choose the most suitable one. We evaluate our method on the SkullBreak and SkullFix datasets, generating high-quality implants and achieving competitive evaluation scores. The project page can be found at https://pfriedri.github.io/pcdiff-implant-io.

Sensor Interfacing & Analog Front-End Conditioning

Figure 1: Diagnostic Medical Waveform & Bio-Potential Clinical Trace for Simulia Abaqus Spinal Implant The design of 3D-printed patient-specific implants, commonly used in cranio- plasty and maxillofacial surgery, is a challenging and time-consuming task that is usually performed manually. To speed up the design process and enable point-of- care implant generation, approaches for automatically deriving suitable implant designs from medical images are needed. This paper presents a novel approach based on a Denoising Diffusion Probabilistic Model for 3D point clouds that reconstructs complete anatomical structures Sc from segmented CT images of subjects showing bone defects Sd. An overview of the proposed method is shown in Figure 1. Since performing the anatomy reconstruction task in a high reso- lution voxel space would be memory inefficient and computationally expensive, we propose a method that builds upon a sparse surface point cloud representa- tion of the input anatomy. This point cloud c0, which can be obtained from the defective segmentation mask Sd, serves as input to a Denoising Diffusion Proba- bilistic Model that, conditioned on this input c0, reconstructs the complete P. Friedrich et al. Fig. 1. Proposed implant generation method with shape reconstruction in point cloud space. The implant geometry I is defined as the Boolean subtraction between completed output Sc and defective input Sd in voxel space.

Bio-Signal Waveform Analysis & Simulation Findings

anatomy x0 by generating missing points ˜x0. The second network transforms the point cloud x0 back into voxel space using a Differentiable Poisson Solver . The final implant I is generated by the Boolean subtraction of the completed and defective anatomical structure. We thereby ensure a good fit at the junction between implant and skull. Our main contributions are: – We employ 3D point cloud diffusion models for an automatic patient-specific implant generation task. The stochastic sampling process of diffusion models allows for the generation of multiple anatomically reasonable implant designs per subject, from which physicians can choose the most suitable one.

Scope & Clinical Applications

• Real-time multi-parameter vital signs telemetry (ECG, Heart Rate, SpO2, Temperature, Respiration)
• Low-noise Analog Front-End (AFE) conditioning with active Driven Right Leg (DRL) 50 Hz cancellation
• Digital signal processing in MATLAB: Pan-Tompkins QRS detection, FIR/IIR notch, and Continuous Wavelet Transforms
• Finite element tissue bio-electromagnetics and orthopedic mechanical simulations in COMSOL & ANSYS
• Complete Arduino firmware .INO sketches, circuit schematics, and university dissertation reports