arXiv subjects

Maarten L. Terpstra

Publications and source records attributed to Maarten L. Terpstra.

4 recordsLinked to original sources

arXiv2026

Feasibility of Continuous Ventricular Volumetric Quantification in Arrhythmias using Real-Time 3D CMR-MOTUS

Conventional cardiovascular magnetic resonance (CMR) cine imaging combines data across multiple heartbeats, an assumption that can fail in arrhythmia because beat-to-beat variation causes motion artifacts and obscures functional heterogeneity. Although 2D real-time cine resolves individual beats, stacked slices are suboptimal for capturing complex 3D cardiac dynamics. We investigated continuous beat-to-beat volumetric quantification in patients with premature ventricular contractions (PVCs) using free-running 3D real-time CMR. CMR-MOTUS was extended to jointly reconstruct time-resolved 3D motion fields and a motion-corrected reference image from continuously acquired data without breath-holding or ECG gating. Data were acquired with a variable-density Cartesian trajectory and either 3D spoiled gradient-echo or balanced steady-state free-precession imaging. Ventricular volumes were obtained by propagating one manual reference-image segmentation through all reconstructed frames. The method was evaluated in a cardiac motion phantom with static ground-truth acquisitions, 10 healthy volunteers, and 10 patients with PVCs; all in vivo scans were performed after contrast administration. Phantom ejection fraction (EF) agreed closely with ground truth (17.86% versus 17.27%). Healthy volunteers showed narrow beat-to-beat EF distributions, whereas patients with PVCs showed broader and sometimes bimodal distributions. Simultaneous ECG recordings supported the temporal correspondence between volume irregularities and PVC episodes. Free-running joint 3D motion-field and image reconstruction enables continuous beat-to-beat volumetric assessment and can reveal functional heterogeneity obscured by gated or heartbeat-averaged methods. Larger studies are required to establish clinical validity and determine its role alongside standard 2D cine analysis.

physics.med-ph↗

arXiv2026

Generating synthetic computed tomography for radiotherapy: SynthRAD2025 challenge report

Radiation therapy (RT) requires precise dose delivery over multiple fractions, with CT fundamental for treatment planning due to its electron density information. Repeated CT acquisitions impose radiation exposure and logistical burdens, MRI lacks electron density, and cone-beam CT (CBCT) requires correction for dose calculation. Synthetic CT (sCT) generation addresses these by converting MRI or CBCT into CT-equivalent images with accurate Hounsfield Unit (HU) values, enabling MRI-only RT and CBCT-based adaptive workflows. Building on SynthRAD2023, SynthRAD2025 benchmarked sCT methods on 2,362 patients from five European centers across head and neck, thorax, and abdomen. Two tasks: MRI-to-CT (890 cases) and CBCT-to-CT (1,472 cases), evaluated via image similarity (MAE, PSNR, MS-SSIM), segmentation (Dice, HD95), and dosimetric metrics from photon and proton plans. With 803 participants and 12/13 valid submissions, Task 1 top performance reached MAE $64.8\pm21.3$ HU, PSNR $\sim$30 dB, MS-SSIM $\sim$0.936, Dice 0.79, photon $γ_{2\%/2\text{mm}}>98\%$, proton $γ\approx85\%$. Task 2 improved: MAE $48.3\pm13.4$ HU, PSNR 32.6 dB, MS-SSIM 0.968, Dice 0.86, photon $γ>99\%$, proton $γ\approx89\%$. Strong image--segmentation correlations ($ρ=0.78$--$0.79$) but moderate dose correlations confirmed image quality is insufficient as a dosimetric surrogate. Head-and-neck cases were most consistent; thoracic and abdominal cases showed greater variability. Residual errors at tissue interfaces propagate along beam paths, affecting proton dose more than photon. SynthRAD2025 demonstrates that deep learning yields clinically relevant sCTs, especially for CBCT-to-CT, while identifying persistent MRI-to-CT challenges and underscoring dose-based evaluation as essential for clinical validation.

physics.med-ph↗

arXiv2024

Free-Running Time-Resolved First-Pass Myocardial Perfusion Using a Multi-Scale Dynamics Decomposition: CMR-MOTUS

We present a novel approach for the reconstruction of time-resolved free-running first-pass myocardial perfusion MRI, named CMR-MOTUS. This method builds upon the MR-MOTUS framework and addresses the challenges of a contrast varying reference image. By integrating a low-rank plus sparse (L+S) decomposition, CMR-MOTUS efficiently captures both motion fields and contrast variations. This innovative technique eliminates the need for acquiring a motion-static reference image prior to the examination, thereby reducing examination time and complexity for cardiac MRI examinations. Our results demonstrate that CMR-MOTUS can successfully disentangle different dynamic components, offering high-quality motion fields and motion correct a myocardial first-pass perfusion image series.

physics.med-ph↗

arXiv2024

Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: 1) MRI-to-CT and 2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (>0.87/0.90) and gamma pass rates for photon (>98.1%/99.0%) and proton (>97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy.

physics.med-ph↗