BackgroundLiver diffusion‐weighted imaging (DWI) with apparent diffusion coefficient (ADC) measurement has proven valuable in diagnosing liver diseases. In certain patient populations, a free‐breathing (FB) liver DWI approach is desirable to improve patient comfort and broaden clinical applicability. However, maintaining high image quality under FB conditions and achieving satisfactory ADC repeatability can be challenging when using routine diffusion‐weighted single‐shot echo‐planar imaging (DW‐ss‐EPI).PurposeThis study aimed to develop an advanced FB liver DWI technique based on diffusion‐weighted Propeller echo‐planar imaging (DW‐Propeller‐EPI) to achieve motion compensation, and to prospectively evaluate its performance in terms of image quality and ADC repeatability compared to standard DW‐ss‐EPI.MethodsA DW‐Propeller‐EPI pulse sequence and reconstruction pipeline was developed on a 1.5T MRI system. The pipeline incorporated a reference‐free Nyquist ghost correction method and motion compensation using correlation weighting during data reconstruction. For in vivo evaluation, participants prospectively underwent two repeated liver DWI scans using four techniques: three routine DW‐ss‐EPI sequences with different breathing controls (breath‐holding, BH; respiratory‐triggering, RT; and FB), and one FB DW‐Propeller‐EPI sequence. The raw data from the FB DW‐Propeller‐EPI scans were processed offline with the developed motion‐compensated reconstruction. Two radiologists independently rated the image quality on a 5‐point scale across five aspects (signal homogeneities in the left lobe, geometric fidelity, liver edge sharpness, vessel clarity, and overall image quality). Mean scores were compared among the four techniques using the Friedman test. The repeatability of ADC measurements was evaluated with Bland‐Altman analysis, and differences in ADC values between liver lobes and techniques were analyzed using two‐way repeated measures ANOVA.Results35 participants were enrolled, with twenty completing repeated scans. FB DW‐Propeller‐EPI demonstrated significantly higher ratings than the three routine DW‐ss‐EPI sequences across all aspects (all P < 0.001), with good interobserver agreements (ICC ranging from 0.80‐0.88). The mean geometric fidelity score of FB DW‐Propeller‐EPI was rated the highest (4.86 ± 0.33). FB DW‐Propeller‐EPI also exhibited superior ADC repeatability in both the left and right liver lobes (LOA: 0.212 × 10−3 mm2/s versus 0.255 × 10−3 mm2/s). ADC measurements were comparable between FB DW‐Propeller‐EPI and both RT or FB DW‐ss‐EPI techniques.ConclusionsThe proposed FB DW‐Propeller‐EPI technique enables high‐quality FB liver DWI with satisfactory ADC repeatability, outperforming conventional DW‐ss‐EPI in image quality and ADC repeatability. This advancement addresses the limitations of routine DW‐ss‐EPI in FB scenarios, offering a promising solution for clinical applications in challenging population.

Vision-based displacement estimation of short- to medium-span bridges using two-stage UAV motion correction

Abstract Lake surface fluxes provide important information about the lake's thermal environment. To capture their spatial variations, a ship serves as an excellent platform for applying the eddy correlation (EC) method. Although ship‐based EC measurements have been conducted over the ocean, this has not been the case over lake surfaces. Ship‐based measurements in a lake differ from those over the ocean in terms of the freedom to select the ship, route, and operation, as well as the wave regime, creating measurement conditions that have not been addressed in ocean studies. Thus, 10‐day EC flux measurements on the highly maneuverable yet stable research ship NIES ' 94 were conducted in Lake Kasumigaura (surface area of 172 km 2 ), which facilitated extensive data analysis on the ship's motion and fluxes under various conditions. The results indicated that the ship's motion differs greatly depending on the ship's shape and dimensions, and that a larger fluctuation in roll and pitch angles propagates into a larger error of the vertical wind velocity measurements. The motion correction was found necessary for momentum fluxes, while it is preferable but may not be essential under favorable conditions for scalar fluxes. Comparisons between the fluxes obtained from the EC method and those from the bulk method showed that the ship's speed and direction and wave height have minimal impact on the agreement, reflecting the use of a stable ship and lower wave height in our study, leading to small ship motion in Lake Kasumigaura compared to the ocean.

To implement and evaluate the feasibility of a Pilot Tone (PT)-based prospective gating and tracking technique, which uses a long short-term memory (LSTM) neural network to predict respiratory motion from PT signals. A subject-specific calibration scan consisting of 100 ECG-triggered single-shot images was performed. Respiratory motion was estimated from the images and PT data were processed to extract the respiratory component. The LSTM model was trained to predict respiratory motion from PT signals. During respiratory-corrected scans, PROMPT-predicted slice-shifting parameters were used to update gating information and slice position for each heartbeat before image acquisition. The method was retrospectively evaluated in 12 healthy volunteers, comparing LSTM with linear and polynomial regression models using normalized root mean square error in decibels (NRMSEdB) and mean absolute error (MAE). PROMPT was then implemented in late gadolinium enhancement (LGE) and compared with free-breathing retrospective gating in 14 patients. Residual in-plane motion was calculated to assess performance. The LSTM model achieved an NRMSEdB of -7.20 dB and an MAE of 1.97 mm between predicted and actual motion, demonstrating significantly higher accuracy than either regression models (p < 0.05). The residual in-plane motion in PROMPT-LGE was significantly lower than in FB-LGE (1.22 ± 0.38 mm vs. 1.35 ± 0.48 mm, p = 0.033). The proposed respiratory motion correction approach was successfully implemented. The LSTM-based predictive model outperformed linear and polynomial regression models. In LGE imaging, PROMPT significantly reduced in-plane motion and showed potential for limiting through-plane motion compared to the clinical protocol.

Precise quantification of myocardial blood flow (MBF) and flow reserve (MFR) in 18F-flurpiridaz PET significantly relies on motion correction (MC). However, the manual frame-by-frame correction leads to significant inter-observer variability, time-consuming, and requires significant experience. We propose a deep learning (DL) framework for automatic MC of 18F-flurpiridaz PET. The method employs a 3D-ResNet based architecture that takes 3D PET volumes and outputs motion vectors. It was validated using 5-fold cross-validation on data from 32-sites of a Phase-III clinical trial (NCT01347710). Manual corrections from two experienced operators served as ground truth, and data augmentation using simulated vectors enhanced training robustness. The study compared the DL approach to both manual and standard non-AI automatic MC methods, assessing agreement and diagnostic accuracy using minimal segmental stress MBF and MFR. The area under the receiver operating characteristic curves (AUC) for significant CAD were comparable between DL-MC stress MBF, manual-MC stress MBF from Operators (AUC = 0.897, 0.892 and 0.889, respectively; p > 0.05), standard non-AI automatic MC (AUC = 0.877; p > 0.05) and significantly higher than No-MC (AUC = 0.835; p < 0.05). Similar findings were observed with MFR. The 95% confidence limits for agreement with the operator were ± 0.49 (mean difference = 0.00) for MFR and ± 0.24ml/g/min (mean difference = 0.00) for stress MBF. DL-MC is significantly faster but diagnostically comparable to manual-MC. The quantitative results obtained with DL-MC for stress MBF and MFR are in excellent agreement with those manually corrected by experienced operators compared to standard non-AI automatic MC in patients undergoing 18F-flurpiridaz PET-MPI.

Super-MoCo-MoDL: A combined super-resolution and motion-corrected undersampled deep-learning reconstruction framework for 3D whole-heart cardiac MRI.

To compare the free-breathing motion-corrected (MOCO) sequence with the segmented breath-hold (BH) sequence in subjects undergoing late gadolinium enhancement (LGE) scans of Cardiac MRI. Retrospectively enrolled 71 consecutive subjects underwent MOCO-LGE and BH-LGE sequences in random order. Semi-quantitative image quality (IQ) scores, image contrast-to-noise ratios (CNRs), myocardial scar size, and thickness of pericardial effusion were evaluated. Compared to BH-LGE images, MOCO-LGE images showed improved CNR of the scar to the remote myocardium (11.46 [6.43,16.82] vs. 7.94 [4.36,12.65], p < 0.001) as well as higher IQ scores (4.25 ± 0.60 vs. 3.70 ± 0.88, p < 0.001). More subepicardial scar was seen in MOCO-LGE than BH-LGE images (83.6% vs. 43.3%, p < 0.001) in lateral wall, and larger scar size was measured by MOCO-LGE than BH-LGE images by using 3-standard deviation (SD) method (24.39 [16.90,33.02] vs. 15.62 [10.67,24.76], p < 0.001) and 5-SD method (8.44 [4.89, 13.53] vs. 4.30 [1.63,8.21], p < 0.001). More and thicker epicardial effusions were seen in MOCO-LGE than BH-LGE images (81.7% vs. 60.6%, p = 0.001, and 9.16 ± 4.44 mm vs. 6.24 ± 4.16 mm, p < 0.001). MOCO-LGE images demonstrate superior quality compared to BH-LGE images. However, MOCO-LGE images have a tendency to overestimate the extent of subepicardial scar and pericardial effusion. Our study demonstrates that MOCO-LGE images tend to overestimate the extent of subepicardial scar and pericardial effusion in comparison to BH-LGE images, suggesting potential diagnostic misconceptions and an area for improvement.

Magnetic resonance imaging is inherently non-invasive, and thus an ideal technique for probing living biological matter. The low sensitivity and prolonged data acquisition time, coupled with stringent magnetic field homogeneity requirements and spatial constraints inside the magnet, make the technique under-utilised for the study of live and freely moving model organisms. We introduce a new method for performing MRI of a live insect that is moving on a treadmill. The tethered insect, positioned on a treadmill inside an RF volume coil, provides a controlled environment for studying the organism, and maintains the spatial consistency for an MRI excitation slice, thereby limiting any residual motion artifacts within the slice or MRI field-of-view, and thus making the problem manageable with motion correction techniques available in clinical MRI research. We address the particular case of semi-periodic abdominal motion of the insect, and its effect on MRI reconstruction. An MR compatible optical imaging system has been integrated with the high-field magnet, in conjunction with a computer vision algorithm for extracting the real-time motion information, with the added advantage of phenotypic characterisation of the behaving organism. The motion information, with a prospective triggering system, has been used for the acquisition of spatially consistent k-space lines, thereby reducing artifacts due to the gross body motion of the walking insect.

Rapid and reliable identification of large vessel occlusions and critical stenoses is essential for guiding treatment in acute ischemic stroke. Conventional MR angiography (MRA) and PET protocols are constrained by trade-offs among acquisition time, spatial resolution, and motion tolerance. A multimodal MR–PET angiography reconstruction framework is introduced that integrates joint Hankel-structured sparsity with topology-preserving multitask learning to overcome these limitations. High-resolution time-of-flight MRA and perfusion-sensitive PET volumes are reconstructed from undersampled data using a cross-modal low-rank Hankel prior coupled to a super-resolution generator optimized with adversarial, perceptual, and pixel-wise losses. Vesselness filtering and centerline continuity terms enforce preservation of fine arterial topology, while learned k-space and sinogram sampling concentrate measurements within vascular territories. Motion correction, blind deblurring, and modality-specific denoising are embedded to improve robustness under clinical conditions. A multitask output head estimates occlusion probability, stenosis localization, and collateral flow, with hypoperfusion mapping generated for dynamic PET. Evaluation on clinical and synthetically undersampled MR–PET studies demonstrated consistent improvements over MR-only, PET-only, and conventional fusion methods. The framework achieved higher image quality (MRA PSNR gains up to 3.7 dB and SSIM improvements of 0.042), reduced vascular topology breaks by over 20%, and improved large vessel occlusion detection by nearly 10% in AUROC, while maintaining at least a 40% reduction in sampling. These findings demonstrate that embedding vascular-aware priors within a joint Hankel–sparse MR–PET framework enables accelerated acquisition with clinically relevant benefits for early stroke assessment.

BackgroundPrevious studies have shown that family relationships are closely related to internet gaming disorder (IGD) in adolescents. There are also neurological mechanisms that correlate spontaneous brain activity with adolescent family relationships and IGD. Therefore, this study explores the mediating role of spontaneous brain activity between family relationships and IGD in adolescents.MethodsThis study used a cross-sectional design. It recruited 52 IGD adolescents as the IGD group and 51 normal adolescents as the healthy control (HC) group. The study included adolescents aged 12–18 years, with a male-to-female ratio of 45:7 in the IGD group and 41:10 in the HC group. The diagnosis of IGD was established based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnostic criteria. The Young Internet Addiction Scale (YIAS) was used to assess the severity of internet addiction among adolescents, and the Chinese version of the Family Environment Scale (FES-CV) was used to evaluate family relationships. Resting-state functional MRI was utilized to assess adolescents’ spontaneous brain activity, specifically measuring the mean amplitude of low-frequency fluctuation (mALFF) and functional connectivity (FC). MRI data were collected using a Siemens Magnetom Prisma 3.0T MRI scanner. The specific scan sequence and parameters are as follows: BOLD sequence, repetition time (TR) = 1000 ms, echo time (TE) = 30 ms, flip angle = 70°, slice thickness = 2.2 mm, number of slices = 52, matrix size = 64 × 64, voxel size = 3 mm × 3 mm × 3 mm, scan duration = 360 s. The steps of data preprocessing included data format conversion, exclusion of time points, slice timing correction, head motion correction, spatial normalization, removal of linear drift, regression of covariates, band-pass filtering, and data cleaning. Statistical analysis was conducted using SPSS 22.0. Gaussian random field (GRF) correction was applied in the DPABI toolbox, with voxel-level p < 0.005 and cluster-level p < 0.05 indicating statistical significance. Pearson correlation was employed to analyze the relationships between various scales and between these scales and brain spontaneous activity. Mediation analysis was performed using the SPSS Process program.Results(1) Compared to the HC group, the IGD group had significantly higher YIAS scores (t = 21.571, P < 0.05). For the FES-CV scores, the IGD group scored higher in conflict (t = 4.228, P < 0.05) and lower in cohesion (t=-3.768, P < 0.05), with statistically significant differences. (2) Pearson correlation analysis revealed a negative correlation between cohesion in family relationships and middle frontal gyrus (MFG) (r = − 0.443, p < 0.01), a negative correlation between cohesion and internet addiction (r = − 0.474, p < 0.01), and a positive correlation between internet addiction and MFG (r = 0.557, p < 0.01) in the IGD group. The MFG mediated the relationship between family relationships and internet addiction, with a mediation effect value of -0.183 (95% CI=-0.403 ~ -0.037), accounting for 38.77% of the total effect (-0.183/-0.472). (3) In the IGD group, the FC value from the MFG to the right insula was positively correlated with conflict in family relationships (r = 0.349, P < 0.05).ConclusionFamily relationships are related to internet addiction in adolescents, and the MFG mediates this relationship. Additionally, the FC value from the MFG to the right insula in the IGD group is positively correlated with conflict. This suggests that the MFG may serve as a neurobiological marker through which family relationships influence IGD development in adolescents.

Accurate quantification of extracellular volume (ECV) and fractional myocardial blood volume (fMBV) in cardiac magnetic resonance (CMR) relies on precise alignment between precontrast and postcontrast images. Variable image contrast often undermines conventional motion correction, causing misalignment due to respiration or cardiac motion. Herein, we present a registration approach that accounts for varying image contrast levels and cardiac motion to achieve more precise and high-quality quantitative cardiac mapping. Patients with suspected myocardial diseases underwent cardiac MRI with Gadavist (0.1 mmol/kg, n = 11) and ferumoxytol (4.0 mg/kg cumulative, n = 9) enhancement for ECV and fMBV measurements, respectively. T1 maps were generated using the MOLLI sequence. To remove contrast variations across different inversion times and contrast doses, precontrast and postcontrast MOLLI images were grouped and processed using correlation-weighted representations based on the myocardium and blood pool signals. Groupwise registration is performed based on the maximization of mutual information. The image registration accuracy and mapping precision of the proposed method were assessed relative to those of conventional methods. ResultsCompared with the conventional groupwise registration approach, the proposed decontrasted approach showed superior alignment between images of different contrasts, as evidenced by the higher Dice scores (mean 0.77 vs. 0.69, p < 0.001). It also eliminated artifacts commonly observed owing to image misalignment (all 11 cases showed improvement). Improved myocardial mapping precision was observed for both ECV (median coefficient of variation, 0.14 vs. 0.27; p < 0.001) and fMBV (median coefficient of variation, 0.59 vs. 0.71; p < 0.001). It also reduced individual myocardial segmental variations in the ECV (5.8 to 3.58, p < 0.001) and fMBV maps (9.86 to 7.93, p < 0.001). Overall, decontrasted image registration improves the precision of contrast-enhanced myocardial parametric mapping by reducing the misalignment between multicontrast images. This framework may be extended to other postprocessing tasks in cardiac MRI that involve variable image contrasts.

The accurate positional measurement of Supernova (SN) 1987A is important for determining the kick velocity of its compact object and the velocities of the ejecta and various shock components. In this work, we perform absolute astrometry to determine the position of SN 1987A. We used multi--epoch Hubble Space Telescope imaging to model the early ejecta and the equatorial ring (ER). We combined our measurements and obtained the celestial coordinates in the International Celestial Reference System (ICRS) by registering the observations onto Gaia Data Release 3. The final average position of the different measurements is $ α = 5^ h m s δ = -69^ ̧irc $ (ICRS J2016). The early ejecta position is located 14 mas south and 16 mas east of the ER center, with the offset being significant at 96% confidence. The offset may be due to instrument and/or filter--dependent systematics and registration uncertainties, though an intrinsic explosion offset relative to the ER remains possible. Image registration with proper motion corrections yields similar astrometry and a source proper motion of $ μ_ ̊m east (≡ PM_ α ) = 1.60 ± 0.15 mas yr^ $ and $ μ_ north (≡ PM_δ ) = 0.44 ± 0.09 mas yr^ $, in agreement with the typical local motion of the Large Magellanic Cloud. The absolute positional uncertainty of 21 mas adds a systematic uncertainty to the sky--plane kick velocity of $ (t/40 yr km s $, where t is the time since the explosion. Comparing the location of the compact source observed with JWST to our updated position implies a sky--plane kick of $ 399±148 km s^ $ and a 3D kick of $ 472±126 km s^ $, which is consistent with previous estimates.

Abstract BACKGROUND Conventional Magnetic Resonance Imaging (MRI) struggles to distinguish tumor recurrence from non-neoplastic treatment-related changes (TRC) in patients with brain tumors. Amide Proton Transfer-weighted (APTw) imaging is a molecular Chemical Exchange Saturation Transfer (CEST) -based MRI technique to detect changes in mobile protein content, elevated in brain tumors. Standard APTw-CEST relies on Magnetization Transfer Ratio Asymmetry (MTRasym) metrics, which can be confounded by peritumoral edema. The purpose of this study was to optimize APTw-CEST imaging by applying fluid-suppression post-processing metrics to enhance contrast between tumor tissue and surrounding edema. METHODS Following IRB approval, conventional APTw-CEST protocol (2D fast spin-echo; 2μT saturation power; 2s saturation time; 1.6×2.2 mm² spatial resolution; 30 offsets; GE 3T PET/MRI system) was acquired in patients with glioma (N=5) or brain metastases (N=3) following treatment when the MRI was equivocal for tumor recurrence vs TRC. The Olea Sphere 3.0 software suite was employed for data denoising, motion correction, and B0 correction. Finally, conventional APTw maps were generated using the MTRasym metric, and fluid-suppressed APTw maps were computed using the MTRRex metric, both quantified at 3.5 ppm relative to the water resonance. All results were validated with pathological tissue confirmation. RESULTS APTw-CEST imaging of patients with path-proven tumor recurrence (N=4) achieved a mean %APTw-CESTΔ=2.17 (range: 1.68 to 2.7). Application of fluid-suppressed APT-CEST in the same patients yielded a mean %APTw-CESTΔ =1.43 (range: 1.14 to 2.13). Patients with path proven TRC (N=4) had a non-fluid suppressed %APTw-CESTΔ mean=0.18 (range: -0.27 to 0.76) and fluid-suppressed % APTw-CESTΔ mean=0.18 (range -0.17 to 0.63). An optimal fluid suppressed %APT-CESTΔ cutoff of 1.1 allowed full discernment of tumor relapse from TRC. CONCLUSION Fluid-suppressed APTw-CEST imaging in a small sample of patients with malignant brain tumors enhanced image quality, reduced peritumoral edema, and aided in differentiation between progression and treatment-induced changes. This advancement can enhance clinical utility of APTw-CEST, providing a more reliable approach for evaluating brain tumor recurrence.

Rehabilitation exercise assessment plays a crucial role in patient recovery, particularly for individuals recovering from injuries, surgeries, or illnesses affecting mobility. In this paper, we propose a novel approach for the assessment of skeleton-based data recorded from rehabilitation exercise, where we present the data as points on symmetric positive definite (SPD) manifold. Our method addresses the limitations of traditional Euclidean-based approaches by leveraging the SPD manifold's ability to preserve motion variations and spatial relationships. We propose a novel framework leveraging SPD manifold to preserve the intrinsic geometry of human motion and capture nonlinear variations in complex movements. By embedding motion data into SPD manifolds, we integrate unsupervised K-Nearest Neighbors (KNN) with Riemannian geometry for precise classification of correct and incorrect movements. We further develop a Tangent Space Linear SPD Support Vector Machine (SVM), optimized via stochastic gradient descent (SGD) in the tangent space at the identity matrix. Additionally, a tailored neural network architecture with multi-scale feature extraction enhances movement assessment by capturing hierarchical patterns in vectorized SPD data. Our specialized neural network, designed for vectorized SPD data, outperforms state-of-the-art methods on three benchmark datasets: Kimore, UI-PRMD, and EHE. In cross-subject evaluations, accuracy improves to 92.40% (UI-PRMD), 85.18% (Kimore), and 87.59% (EHE), with even greater improvements in random train-test splits. Although the proposed method involves a high parameter count while reducing computational complexity in terms of floating-point operations, literature suggests that certain concepts and objects recur across diverse mathematical domains, often carrying significant implications. Additionally, manifold transformations in data representation effectively capture the intrinsic geometric structure. Furthermore, our training process is faster than state-of-the-art methods, leading to quicker model convergence and reduced computational overhead without compromising accuracy. These results highlight the potential of SPD manifolds for accurate, reliable rehabilitation assessment.

To investigate how rigid head motion interacts with 3D MRI k-space sampling strategies and to introduce motion-sampling plots as a framework for predicting motion artifacts. We evaluated a range of motion-sampling combinations across three sampling trajectories (Cartesian, stack-of-stars, kooshball) in both simulation and in vivo. Experiments included shifting motion states in k-space, changing the direction of motion with regards to the sampling, and varying the magnitude of motion. In vivo experiments were conducted on healthy volunteers mimicking patient motion while wearing a real-time pose-tracking device. Motion-sampling plots were used to map motion states directly onto k-space and assess their relationship to artifact appearance. Nine categories of motion artifacts were identified. The severity and nature of artifacts were found to depend heavily on the k-space distribution of motion states. Motion-sampling plots were seen to work as guides in predicting artifact appearance. In vivo findings supported simulation results. Artifacts were especially pronounced when motion discontinuities occurred near the center of k-space or aligned with slow phase-encoding directions. Motion-sampling plots offer an effective way to visualize and interpret motion artifacts in 3D MRI, providing insight beyond traditional motion-time plots. This framework enables systematic evaluation of motion robustness and can guide the development and validation of motion correction techniques. We propose practical recommendations for motion experiment design to improve reproducibility and benchmarking in MRI research.

Cryo-electron tomography is a powerful tool to visualize heterogeneous samples, with one major application being structural characterization of pleomorphic viruses. In recent years, subtomogram averaging of viral glycoproteins has emerged as a method to directly visualize these crucial proteins on the surface of intact virions. One important target is the hemagglutinin (HA) glycoprotein of influenza virus, which densely covers the viral envelope and is responsible for influenza receptor binding and membrane fusion. While subtomogram averages of influenza HA have been reported, their resolutions have been limited due to the low signal-to-noise ratio inherent to cryoET as well as the manual effort required to analyze heterogenous influenza virions. Presented here is a cryoET analysis pipeline that integrates several software packages to analyze tomographic data of influenza virions efficiently and robustly. This protocol describes the structural determination of HA from influenza virions, through steps from initial motion correction to final model building. Following this pipeline, a HA reconstruction at 6.0 Å resolution was obtained from two cryoET datasets collected from the A/Puerto Rico/8/34 (PR8) influenza strain.

Artificial intelligence-generated content (AIGC) has shown remarkable performance in nuclear medicine imaging (NMI), offering cost-effective software solutions for tasks such as image enhancement, motion correction, and attenuation correction. However, these advancements come with the risk of hallucinations, generating realistic yet factually incorrect content. Hallucinations can misrepresent anatomic and functional information, compromising diagnostic accuracy and clinical trust. This paper presents a comprehensive perspective on hallucination-related challenges in AIGC for NMI, introducing the DREAM report, which covers recommendations for definition, representative examples, detection and evaluation metrics, and attributions and mitigation strategies. This position statement paper aims to initiate a common understanding for discussions and future research toward enhancing AIGC applications in NMI, thereby supporting their safe and effective deployment in clinical practice.

Gymnastics is a form of physical activity that combines gymnastic movements with musical rhythms to improve physical fitness, discipline, and teamwork. Socialization activities are carried out through the planning stage, material delivery, movement demonstrations, and direct practice by students. Basic movements in gymnastics are very important in efforts to instill the correct basic movements in movement. Basic movements The basic movements that are most popular are movements using objects or tools, commonly called basic manipulative movements. The results of the study showed that students were highly enthusiastic about participating in gymnastics, were able to understand and imitate the series of movements correctly, and showed increased coordination and cooperation. In addition, this activity contributed to instilling the values ​​of discipline, togetherness, and a spirit of physical fitness in the school environment. Thus, gymnastics socialization can be an alternative extracurricular activity that is beneficial for character development and student health.

Introduction: The image quality of coronary CT angiography (CCTA) has improved considerably; however, it remains suboptimal in certain cases, particularly in patients with atrial fibrillation (AF) due to motion artifacts. Recently, deep learning-based reconstruction techniques have emerged to address this limitation. Purpose: To evaluate the image quality of motion-corrected CCTA using CLEAR Motion (Canon Medical Systems Corporation), a deep learning-based motion correction algorithm, compared with conventional reconstruction in AF patients. Methods: CLEAR Motion was installed on our CT system for clinical use. This algorithm applies Partial Angle Reconstructions (PARs) to estimate motion vector fields for the three major epicardial vessels, enabling motion correction and improved image quality. The motion vector fields are incorporated into backprojection, reducing motion artifacts through deep learning. We retrospectively analyzed consecutive AF patients who underwent CCTA between July 2023 and May 2024, excluding those with prior percutaneous coronary intervention or coronary artery bypass grafting. Image quality was assessed segmentally using a five-point scale (1–5) based on the American Heart Association and Society of Cardiovascular Computed Tomography guidelines by a blinded cardiologist. Results: We evaluated 1,162 coronary segments from 80 patients (mean age 67.9 ± 9.5 years; 78% male; median heart rate 70 bpm). CLEAR Motion significantly improved image quality compared with conventional reconstruction (4.1 ± 0.5 vs. 3.9 ± 0.6; P &lt; 0.001). Image quality improved across all segments, with the most pronounced enhancement observed in the mid-right coronary artery (RCA), where motion artifacts are typically most severe. In contrast, side branches showed the least improvement, likely due to the algorithm's focus on the three major epicardial coronary arteries. Patients were stratified by R–R interval into low-rate (&gt;854 msec) and high-rate (&lt;854 msec) groups. Image quality improved significantly in both groups (low-rate: 4.0 ± 0.5 vs. 3.9 ± 0.5, P &lt; 0.001; high-rate: 4.1 ± 0.5 vs. 3.9 ± 0.6, P &lt; 0.001), with a significantly greater improvement in image quality scores in the high-rate group (P &lt; 0.001, two-way repeated measures ANOVA). Conclusions: CLEAR Motion significantly enhances image quality in AF patients, particularly in the mid-RCA and in those with higher heart rates.

To develop a framework (ACROBATIC) for correcting motion in 3D radial fetal MRI. Data were simulated (N = 200) and acquired in utero (N = 11, gestational age: 32 ± 2 weeks). Motion due to maternal respiration was estimated by extracting a self-gating signal and applying focused navigation. Bulk motion was estimated by splitting the acquisition into sequential bins, reconstructing 3D volumes and applying rigid image registration. These combined motion estimates were used to correct k-space. Self-gating signals were compared to ground truth in simulations and an external sensor in utero. The cumulative position error (CPE) measured the accuracy of motion estimations relative to ground truth in simulations and relative sharpness measured the corresponding impact on image quality for both simulations and in utero data. An expert reviewer performed a blinded ranking of in utero images including uncorrected and corrected data. Self-gating signals correlated strongly with ground truth for simulations (R = 0.97 ± 0.01) and a external sensor for in utero data (R = 0.75 ± 0.23). CPE decreased significantly using ACROBATIC (uncorrected: 13.33[12.73-14.05], corrected: 2.20[1.80-2.61]). Relative image sharpness increased with ACROBATIC for both simulated (4.51[2.89-5.79]) and in utero data (1.12[0.77-1.44]) consistent with expert ranking where ACROBATIC images were given the best rank in the majority of cases. ACROBATIC enables motion correction in 3D radial fetal MRI. Correction of displacement due to maternal respiration and bulk motion results in improved image quality in simulations and in utero. Comparison to 2D frameworks are now warranted to establish the added diagnostic value of this approach.