Fourier-Feature MLP Toolkit for GPU-Accelerated Cardiac-MRI 4DCMR Strain Analysis

Author

Aarnav T. Sabale, Purdue UniversityFollow
Marco A. Prado, Purdue UniversityFollow
Craig J. Goergen, Purdue UniversityFollow

Abstract

This paper explores the embedding of a Fourier-Feature—enhanced multiplayer perceptron(MLP-FEE) at the heart of a newly refactored python workflow for four-dimensional cardiac-MRI strain quantification demonstrating how a single, compact network can outperform traditional convolution and spline-based methods. The original code, capable of orientation normalization, displacement tracking, and finite-difference strain computation, has been translated and consolidated into pytorch. By injecting sinusoidal positional encodings at the network’s input layer supplied a rich set of high-frequency basis functions hence enabling multilayer MLP to resolve gradients that cubic splines and conventional CNNs typically blur or struggle with. Profiling on an Apple-silicon GPU shows interactive inference without hand-tuned CUDA alongside a network has a higher precision while trimming code complexity by roughly 50%. Qualitative comparison with finite-element ground truths confirms preservation of peak systolic strain and segment-wise GLS trends. Meanwhile, the transpilation preserves domain-specific algorithms while exposing them to modern deep-learning tooling along with swappable optimisers and augmentation strategies. By showcasing the power of Fourier features inside a lightweight MLP, this work illustrates a path for researchers to migrate legacy workflows into a GPU-accelerated, open-source ecosystem to unlock higher-resolution insights across a variety of Biomedical Engineering datasets.

Keywords

Fourier Feature MLP, JAX, GPU Acceleration, Biomedical Imaging, Machine Learning

Date of this Version

8-5-2025

Recommended Citation

M. Tancik, P.-P. Srinivasan, B. Mildenhall, S. Fridovich-Keil, N. Raghavan, U. Singhal, R. Ramamoorthi and R. Ng, “Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains,” in Advances in Neural Information Processing Systems, vol. 33, pp. 7537–7547, 2020. V. Sitzmann, J. Martel, A. Bergman, D. Lindell and G. Wetzstein, “Implicit Neural Representations with Periodic Activation Functions,” in Advances in Neural Information Processing Systems, vol. 33, pp. 7462–7473, 2020. O. Ronneberger, P. Fischer and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), LNCS, vol. 9351, pp. 234–241, 2015.