https://bughht.github.io/tags/pytorch/Recent content in PyTorch on Bughht BlogHugozh-CNhhong6@mgh.harvard.edu (Haotian Hong)hhong6@mgh.harvard.edu (Haotian Hong)Sat, 20 Dec 2025 15:09:46 +0800https://bughht.github.io/intern_mgh/concomitant_sim/Sat, 20 Dec 2025 15:09:46 +0800hhong6@mgh.harvard.edu (Haotian Hong)https://bughht.github.io/intern_mgh/concomitant_sim/<h1 id="mri-concomitant-field-simulation-tool">MRI Concomitant Field Simulation Tool</h1> <blockquote> <p><strong>GitHub Repository</strong>: <a href="https://github.com/bughht/concomitant_sim"target="_blank" rel="external nofollow noopener noreferrer">bughht/concomitant_sim</a></p> </blockquote> <p><strong><code>concomitant_sim</code></strong> is a fast, backend-agnostic Python package for simulating the phase accumulation caused by concomitant fields (Maxwell Terms) in MRI. It enables high-performance computations on both CPU and GPU by natively supporting NumPy arrays, PyTorch tensors, and CuPy arrays. The library uses an intelligent dispatching mechanism to adapt array operations to your inputs seamlessly.</p> <hr> <h2 id="theoretical-modeling">Theoretical Modeling</h2> <p>The phase accumulation $\Phi_c$ caused by concomitant fields is modeled by integrating the magnetic field variation $\Delta B_c$ over time. Following the standard framework established by Bernstein et al. (1998), for a system with primary linear gradients, the concomitant field $B_c$ is approximated by second-order spatial terms:</p>