Jul 2026· Journal of Medical Imaging· Vol 13 6, pp.
062206
· 0 citations
Medicine
TL;DR
This work provides an openly released resource consisting of tracked real motion and pregenerated synthetic motion, along with a pretrained variational autoencoder (VAE) to generate larger ground-truth datasets, intended to support reproducible development, benchmarking, and comparison of head motion estimation methods in medical imaging modalities.
Abstract
Purpose
Rigid head motion during interventional C-arm cone-beam CT (CBCT) is a major source of image degradation. Learning-based motion estimation requires realistic training data, but ground-truth motion is scarce, limiting direct validation of compensation trajectories. We address this gap with an open resource consisting of tracked real motion and pregenerated synthetic motion, along with a pretrained variational autoencoder (VAE) to generate larger ground-truth datasets.
Approach
Using stereo optical tracking, we recorded rigid 6-DoF head motion trajectories from 25 volunteers lying head-first supine on an examination table, resembling a clinical setting. After data preprocessing, we trained a VAE on 120 sequences of 10 s at 30 Hz. Motion is represented in patient-centered coordinates to support transformation to arbitrary scan geometries. Similarity between measured and generated data is assessed via distributional distances, correlation metrics, low-dimensional embeddings, and a posthoc analysis of the learned latent space.
Results
Evaluated based on 120 generated sequences, the trained VAE is capable of producing diverse 6-DoF trajectories that preserve real-world data correlation structure. Distributional and frequency-domain metrics, along with t-SNE embeddings, show overlap between real and synthetic samples without evidence of mode collapse or training data replication.
Conclusions
This work provides an openly released resource comprising measured trajectories, a synthetic dataset, and pretrained VAE weights together with full training and evaluation code, combining rigid 6-DoF head motion measured in a realistic C-arm setting with a retrainable generative model. It is intended to support reproducible development, benchmarking, and comparison of head motion estimation methods in medical imaging modalities.
TLCE-morph, a Tri-Path Lie Convolution Encoder-based learning framework for deformable respiratory motion correction in thoracic PET, achieves the most favorable overall quantitative performance and shows more consistent local structural recovery in representative motion-sensitive regions.
Hui Zhou, Longxi He, Siyu Wang et al.· Frontiers of Physics· 0 citations
The benefits of combining prospective and retrospective motion correction, where the Scout Accelerated Motion Estimation and Reduction (SAMER) technique is utilized for on-the-fly motion estimation with field-of-view (FoV) updates along with retrospective correction of potential residual motion, are demonstrated.
Hongli Fan, B. Clifford, Michael Koenig et al.· Magnetic Resonance in Medici...· 0 citations
OBJECTIVE
Supervised synthetic computed tomography (sCT) generation from cone-beam CT (CBCT) requires spatially registered training pairs, yet perfect registration between separately acquired scans is unattainable. This registration bias propagates into trained models and corrupts intensity-based evaluation, so higher benchmark scores may reward reproduction of registration artifacts over anatomical fidelity. We propose physics-based CBCT simulation for geometrically aligned training pairs by construction, with bias-robust geometric metrics. Approach:A framework simulated pelvic CBCT from fan-beam CT, modeling respiratory motion, X-ray scatter, and noise to yield aligned simulated-CBCT/CT pairs. On a clinical gynecological dataset (deformable registration) and the SynthRAD2023 pelvic dataset (rigid registration), sCT models trained on simulated data were compared against models trained on real pairs, a finetuned variant, and CycleGAN and RegGAN baselines. Evaluation combined intensity metrics (MAE, PSNR, SSIM) with geometric alignment metrics (normalized mutual information, NMI; correlation coefficient, CC) against input CBCT. Downstream segmentation of bladder, rectum and bowel bag was assessed in two modes: an sCT cascade applying a CT-trained model to sCT outputs, and direct segmentation by a model trained on simulated CBCT, plus a physics ablation and five-observer quality assessment. Main results:Simulation-trained models achieved higher geometric alignment than real-trained models (cross-dataset NMI 0.31 vs 0.22) despite lower intensity scores. Intensity metrics correlated inversely with observer ratings under deformable registration, whereas NMI consistently predicted clinical preference (clinical ρ = 0.29, SynthRAD ρ = 0.31). Observers preferred simulation-trained outputs in 87% of cases. In the sCT cascade, simulation-trained models improved segmentation (DSC 0.91/0.86/0.54 vs 0.84/0.77/0.04), while direct simulation-trained CBCT segmentation reached 0.92/0.87/0.83, exceeding a phantom-based baseline on the bowel bag. Significance:Physics-based simulation eliminates registration bias at its source, and downstream segmentation provides a task-based measure of sCT conversion quality that intensity metrics miss. Geometric fidelity, not intensity agreement with biased ground truth, aligns with the spatial-accuracy requirements of adaptive radiotherapy.
L. Zimmermann, Michael Rauter, Martin Buschmann et al.· Physics in Medicine and Biol...· 0 citations
Four-dimensional cone beam CT (4D CBCT) is important for image-guided radiation therapy of thoracic cancers, but its use is limited by long scan times, causing high patient dose and motion/sparse-sampling artifacts. We propose a deep learning method for motion-resolved 4D CBCT reconstruction from conventional free-breathing scans, without a respiratory signal or explicit projection binning. Our CNN takes free-breathing 3D CBCT projections as input and predicts a static volume at maximum inhalation plus ten displacement vector fields (DVFs) spanning a breathing cycle. The network extends U-Net: the encoder acts on filtered projection stacks, the decoder acts in the volume domain, and skip connections are replaced with non-trainable back-projection functions at multiple resolutions to transfer features between domains. The model is trained on simulated CBCT scans and evaluated on 11 unseen simulated patients and 13 clinical free-breathing scans. Two additional models (60 s and 6 s scans) were evaluated by clinical experts on three and two scans, comparing single phases of our 4D reconstruction to reference 3D SART-TV images for tumor and esophagus visibility. Experts preferred our method for tumor visibility (59% vs. 36% no preference, 5% reference) and esophagus visibility (47% vs. 42%, 11%). On simulated data, image quality matched SART-TV (mean RMSE: -1.19 HU, PSNR: +0.09 dB, SSIM: -0.009) while enabling 4D reconstruction. On clinical scans, our method showed sharper dynamic structures (e.g., diaphragm) and fewer motion streak artifacts than traditional reconstruction. This non-patient-specific CNN predicts static volumes and full 4D respiratory motion models from a single free-breathing scan, without a respiratory surrogate or projection binning, reducing motion artifacts while adding motion-modeling capability.
Ivo Herzig, P. Paysan, Daniel Barco et al.· 0 citations
Patient motion remains a source of image degradation in brain MRI, leading to signal loss, blurring, and geometric distortion that compromise quantitative analysis. Existing deep learning methods for motion correction typically rely on paired clean-corrupted data or k-space acquisitions, which are rarely available in clinical settings. We propose SSRL-MAR, a motion artifact-aware unpaired representation learning framework for motion artifact reduction that requires neither paired training data nor explicit motion labels. SSRL-MAR employed a three-stage training strategy: (1) contrastive learning on 3D patches to extract motion representations by contrasting clean and synthetically corrupted images, (2) a motion artifact-aware synthesis network to generate motion artifacts from clean scans, and (3) a motion artifact-aware generator to restore clean volumes using the learned degrader for self-supervised supervision. On in-silico dataset, SSRL-MAR achieved PSNR 23.81dB, SSIM 91.55%, and NMSE 0.79%. On in-vivo MR-ART dataset, the pretrained model reduced motion distortion, and unsupervised domain adaptation further improved anatomical fidelity. Against a source-only supervised model trained on the same simulated pairs, SSRL-MAR improved PSNR by up to 2.0 dB on MR-ART after unsupervised domain adaptation, and remained within 0.25-0.47 dB of an oracle supervised model that requires real paired data unavailable in practice. At the milder motion level, volumetric error in structures such as the corpus callosum and ventricular system decreased by more than 50%, confirming improved neuroanatomical consistency. These results indicate that SSRL-MAR provides a robust and scalable image-domain solution for 3D brain MRI motion correction, enabling reliable structural quantification in large-scale neuroimaging studies without requiring prospectively acquired pairs or acquisition-specific calibration.
M. Safari, Shansong Wang, Zach Eidex et al.· IEEE Transactions on Medical...· 0 citations
While image quality metrics suggested superior overall correction with MOS, an in vivo framework provided a more detailed characterization of in vivo performance differences, Notably, the framework detected a subtle improvement in FatNav performance with neck masking, an effect uncaptured by conventional image quality metrics.
Zakaria Zariry, Frank Lamberton, Robert Frost et al.· NMR in Biomedicine· 0 citations
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