Combining Prospective and Retrospective Motion Correction, Using the Scout Accelerated Motion Estimation and Reduction (SAMER) Framework, for Rapid and Motion-Robust 2D TSE Imaging.

Prospective motion correction with data reacquisition based on fat navigators for T1-weighted brain MRI at 3T.

Combining Clinical LAFOV PET/CT with a Digital Twin Providing Motion-Free Ground Truth Reveals Quantitative Trade-offs in Respiratory Motion Correction

Purpose: Respiratory motion remains a major source of quantitative bias in PET and becomes increasingly relevant for high-sensitivity long axial field-of-view (LAFOV) PET/CT. Although numerous respiratory motion correction (MoCo) methods have been proposed, their quantitative accuracy cannot be established clinically because a patient-specific motion-free reference is fundamentally unavailable in vivo. This study combined clinical PET imaging with a digital twin, a realistic representation of both the PET/CT system and the patient, to objectively validate respiratory MoCo against a corresponding motion-free reference. Methods: Twenty patients (10 [18F]FDG with predominantly pulmonary lesions and 10 [18F]SiFAlin-TATE with predominantly hepatic lesions; total 135 lesions) were analyzed. The digital twin combined a validated LAFOV PET/CT simulation model with an anatomically realistic phantom containing 14 lung and liver lesions, two patient-derived respiratory patterns, and respiratory motion amplitudes of 2 and 3 cm, generating patient-like datasets with corresponding motion-free references. Data-driven and image-based MoCo were evaluated using lesion morphology, SUVmean, SUVmax, and metabolic tumor volume (MTV). Results: In patients, data-driven MoCo produced larger SUVmean increases than image-based MoCo for liver (48.1{+/-}18.9% vs. 17.0 {+/-} 12.0%; p<0.01), lower-lung (32.5{+/-}21.2% vs. 16.3{+/-}15.6%, p=0.06), and upper-lung lesions (28.4{+/-}32.0% vs. 10.4 {+/-} 17.2%; p<0.01), with similar findings for SUVmax and larger MTV reductions. Simulation revealed marked motion-induced SUVmean underestimation before correction, particularly in liver (-31.2{+/-}6.8%) and lower lung (-15.5{+/-}13.9%). Relative to the motion-free reference, data-driven MoCo most accurately recovered hepatic uptake (4.3{+/-}11.7% vs. -10.0 {+/-} 9.2%; p=0.01) but overestimated pulmonary uptake (lower lung: 19.8{+/-}16.3% vs. -1.6 {+/-} 10.2%; p=0.02). SUVmax showed the same regional behavior, whereas image-based MoCo yielded MTV estimates closer to the reference. Quantitative recovery was largely independent of respiratory pattern, while larger motion amplitudes mainly affected image-based MoCo. Conclusion: Combining clinical PET with a realistic digital twin and corresponding motion-free ground truth enabled objective validation of respiratory MoCo beyond conventional clinical evaluation. Larger correction-induced quantitative changes should not be equated with greater quantitative accuracy. Instead, MoCo performance was region- and metric-dependent, highlighting the value of ground-truth-based validation for developing and benchmarking respiratory motion correction and quantitative PET on LAFOV PET/CT systems.

[Motion parameter decoupling and motion constraint-driven optimization for correcting rigid motion artifacts in cone-beam computed tomography].

The proposed rigid motion artifact correction algorithm demonstrates good performance in estimating motion trajectories and compensating for image artifacts, thus providing a viable and robust solution for suppressing rigid motion artifacts in clinical CBCT imaging.

Evaluation of an iterative motion-correction algorithm for hepatic cone-beam CT during transarterial interventions.

PURPOSE To evaluate an iterative motion-correction algorithm for periinterventional hepatic cone-beam computed tomography (CBCT) regarding its efficacy in reducing motion artifacts, improving vessel depiction, and enhancing diagnostic confidence during transarterial interventions. MATERIALS AND METHODS This retrospective single-centre study included 69 CBCT datasets from 69 patients undergoing TACE or SIRT between 2018 and 2021. One CBCT dataset per patient served as the unit of analysis. Each dataset was post-processed with a motion-correction algorithm at 100-iteration increments (It0 = Baseline; It100-It1000). Three interventional radiologists independently assessed motion artifacts (MA) and vessel depiction (VD) using a 5-point Likert scale (higher ratings representing better image quality). Newly generated artifacts (NGA) were assessed on a 0-5 scale ("0″ representing no evidence of NGA, higher ratings representing stronger artifacts). Target lesion (TL) and vessel tree (VT) visibility were defined as yes/no. Non-parametric tests were applied, including Friedman and Wilcoxon signed-rank tests for ordinal ratings and Cochran's Q and McNemar tests for binary visibility outcomes. Interrater reliability was determined using Fleiss' kappa. RESULTS Compared with baseline datasets, median ratings for motion artifacts and vessel depiction improved from 3 (IQR 1-2) to 4 (IQR 1) at iteration levels 200-300 (all p ≤ 0.001). Target lesion and vessel tree visibility increased from baseline values of 42-44% and 30-41% to peak rates of 65-75% and 54-64%, respectively, with significant differences across iteration levels for all readers (all p < 0.001). Beyond 500 iterations, diagnostic quality declined due to progressively increasing NGA. The proportion of non-diagnostic datasets increased continuously with higher iteration levels, ranging from 1.4% at It300 to 88.4% at It1000. Interrater agreement across parameters was moderate to substantial (κ = 0.40-0.70). CONCLUSION Iterative motion correction significantly improves image quality in hepatic CBCT during transarterial interventions at moderate iteration levels (200-300). Excessive iteration introduces new artifacts, underscoring the importance of optimizing mid-range iteration settings to balance motion correction and artifact generation.

Localized Quadratic RF Encoded Spin-Echo With Spiral-PRIME Reconstruction: A Practical Alternative to 3D FSE for High-Resolution Volumetric Brain MRI.

PURPOSE To extend localized quadratic (LQ) RF encoded spin-echo imaging with acquisition and reconstruction strategies that improve efficiency and artifact robustness, positioning it as a practical alternative to 3D FSE for high-resolution volumetric brain MRI. METHODS The framework integrates (1) additional gradient-echo readouts for simultaneous T2*w or PDw with T2w without prolonging scan time, (2) an in-plane sampling scheme that distributes arms across shot/trajectory types to maximize k-space coverage and render phase inconsistencies as incoherent residue, (3) sliding-slice encoding to disperse through-plane artifacts, (4) a novel loop-ordering to avoid repeated startup cycles and improve motion robustness, and (5) a hybrid 2D/3D Physics-based Reconstruction with Iterative Model-based Enhancement (spiral-PRIME) performing deblurring, fat-water separation, and 3D wavelet denoising. Healthy-volunteer imaging (3 T) was compared with fully sampled LQ and conventional 3D FSE using peak signal-to-noise ratio, structural similarity index measure, pseudo-replica SNR gain, and qualitative review. RESULTS Sliding-slice with loop-ordering reduced through-plane artifacts and improved temporal efficiency, while in-plane sampling dispersed trajectory inconsistencies as incoherent noise. Spiral-PRIME suppressed undersampling artifacts and noise while preserving fine structure. Relative to spiral-SENSE, spiral-PRIME achieved consistent T2w SNR gains of ∼30%-40% in WM/GM and substantially higher gains for T2*w, with CNR improvements most pronounced for GM-WM. Despite R ≈ 2.33 undersampling, reconstructions closely matched fully sampled references and delivered quality comparable to or exceeding 3D FSE. CONCLUSION LQ spin-echo with spiral-PRIME enables efficient, multi-contrast volumetric brain imaging with robust artifact suppression and clinically meaningful SNR/CNR gains, supporting its potential as a practical alternative to 3D FSE.

S2V-DREME: a time-resolved slice-to-volume MR image reconstruction framework with dynamic reconstruction and motion estimation

Objective. Existing volumetric magnetic resonance imaging (MRI) techniques are constrained by the trade-off between acquisition time and image quality, limiting accuracy in motion-impacted sites such as the liver. To enable fast, better-quality volumetric imaging with sufficient spatiotemporal resolution, we developed a time-resolved volumetric MRI technique that recovers 3D volumes from acquired 2D MR slices for real-time 3D anatomy and motion tracking. Approach. 2D MR slices dynamically acquired in time and space were mapped to time-resolved 3D MRIs using a one-shot slice-to-volume framework, S2V-DREME. The model jointly estimates a reference 3D MRI and time-resolved deformation vector fields (DVFs) that warp the reference volume into dynamic 3D MRIs. The reference volume is represented by a spatial implicit neural representation (INR), while the DVFs are derived via low-rank motion modeling. Motion basis components (MBCs) are generated by a spline-enhanced INR (SINR)-based motion generator, with coefficients inferred by a feature-wise linear modulation-based motion encoder. A progressive optimization strategy sequentially initializes the spatial INR and MBCs before joint optimization. The loss function integrates slice data fidelity, total variation regularization, MBC normalization, and DVF smoothness constraints. Main results. S2V-DREME generates time-resolved volumetric MRIs from 2D MR slice inputs. It was evaluated on digital phantom extended cardiac torso (XCAT), physical phantom, and human studies. In XCAT, it accurately captured regular and irregular motion during dynamic reconstruction (training stage, Dice similarity coefficient (DSC)/COME: 0.92 ± 0.03/0.98 ± 0.43 mm) and real-time motion estimation (testing stage, DSC/COME: 0.91 ± 0.02/0.99 ± 0.73 mm). Physical phantom experiments achieved a mean COME of 1.16 mm, and human studies demonstrated the feasibility of time-resolved 3D reconstruction from orthogonal-view and single-view slice acquisitions. Significance. By combining a novel step-and-shoot acquisition protocol with motion-compensated one-shot learning, S2V-DREME enables accurate time-resolved volumetric MRI reconstruction and motion tracking from cineslices, with strong potential for rapid volumetric imaging and real-time MR-guided adaptive radiotherapy.