Intern_MGHs on Bughht Blog

ACDC Dynamic B0 Field Control — Gordon Conference Showcase

hhong6@mgh.harvard.edu (Haotian Hong) — Thu, 09 Jul 2026 15:09:46 +0800

ACDC Dynamic B0 Field Control: Applications & Showcase

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Related Repositories:

bughht/ACDC_Optim — Shim waveform optimization solvers
bughht/concomitant_sim — Concomitant field simulation toolkit

1. Short-Term Eddy Current Compensation: Correcting EPI Edge Ghosts

The Problem

In single-shot EPI, rapidly switching gradient waveforms induce short-term eddy currents (decay $\tau \sim 1$–$10$ ms) in the cryostat and surrounding conductors. These eddy currents create a time-varying $B_0$ offset during the EPI readout train that manifests as Nyquist (edge) ghosts — shifted half-FOV replicas of the image superimposed on the primary image.

Concomitant Field Simulation

hhong6@mgh.harvard.edu (Haotian Hong) — Sat, 20 Dec 2025 15:09:46 +0800

MRI Concomitant Field Simulation Tool

GitHub Repository: bughht/concomitant_sim

concomitant_sim 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.

Theoretical Modeling

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:

ACDC Shim Waveform Optimization

hhong6@mgh.harvard.edu (Haotian Hong) — Mon, 15 Dec 2025 15:09:46 +0800

Shim Waveform Optimization: Method Notes

GitHub Repository: bughht/ACDC_Optim

We solve for shim coil currents that cancel a measured (or predicted) $B_0$ bias field, subject to per-coil amplitude and total-current hardware limits. The codebase (ACDC_optimization.py) provides four solvers for this problem, chosen according to whether the currents are static or time-varying, and whether the amplifier chain’s temporal response (SIRF) needs to be modeled:

Naming scheme: solve_shim_<scope>_<algorithm>.

Function	Use case	Method
`solve_shim_static_qp`	Single time frame	Exact QP (`quadprog`, Goldfarb–Idnani)
`solve_shim_waveform_qp`	Waveform, no SIRF coupling ($\mathbf{C}=\mathbf{I}$)	Exact QP per time point, parallelized (`joblib`)
`solve_shim_waveform_fista`	Waveform, with SIRF coupling	FISTA (NumPy, FFT-based `scipy.signal.convolve`, or legacy dense $\mathbf{C}$)
`solve_shim_waveform_fista_torch`	Waveform, with SIRF, large $T$ / GPU	FISTA (PyTorch, FFT-based `conv1d`, or legacy dense $\mathbf{C}$)

When the SIRF convolution can be neglected, per-time-point QP gives the exact convex optimum and is used directly (solve_shim_waveform_qp). FISTA is only needed once the temporal convolution matrix $\mathbf{C}$ couples time points together, since that removes the block-diagonal structure that makes per-frame QP tractable.