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Purpose: Respiratory motion during positron emission tomography (PET) scans can be a major detriment to image quality in oncological imaging. The impact of motion on lesion quantification and detectability can be assessed using phantoms with realistic anatomy representation and motion modeling. In this work, we develop an anthropomorphic phantom for PET imaging that combines anatomic fidelity and a realistic breathing mechanism with deformable lungs. Methods: We start from a previously developed anatomically accurate but static phantom of a human torso, and add elastic lungs with a highly controllable actuation mechanism which replicates the physics of breathing. The space outside the lungs is filled with a radioactive water solution. To maintain anatomical accuracy and realistic gamma ray attenuation in the torso, all motion mechanisms and actuators are positioned outside of the phantom compartment. The actuation mechanism can produce custom respiratory waveforms with breathing rates up to 25 breaths per minute and tidal volumes up to 1200 mL. Results: Several tests were performed to validate the performance of the phantom assembly, in which the phantom was filled with water and given respiratory waveforms to execute. All parts demonstrated expected performance. Force requirements were not exceeded and no leaks were detected, although continued use of the phantom is required to evaluate wear. The motion of the lungs was determined to be within a reasonable realistic range. Conclusions: The full mechanical design is described in this paper, as well as a software application with graphical user interface which was developed to plan and visualize respiratory patterns. Both are available online as open source files. The developed phantom will facilitate future work in evaluating the impact of respiratory motion on lesion quantification and detectability in clinical practice. [ABSTRACT FROM AUTHOR]

Purpose: A motion‐correction network for multi‐contrast brain MRI is proposed to correct in‐plane rigid motion artifacts in brain MR images using deep learning. Method: The proposed method consists of 2 parts: image alignment and motion correction. Alignment of multi‐contrast MR images is performed in an unsupervised manner by a CNN work, yielding transformation parameters to align input images in order to minimize the normalized cross‐correlation loss among multi‐contrast images. Then, fine‐tuning for image alignment is performed by maximizing the normalized mutual information. The motion correction network corrects motion artifacts in the aligned multi‐contrast images. The correction network is trained to minimize the structural similarity loss and the VGG loss in a supervised manner. All datasets of motion‐corrupted images are generated using motion simulation based on MR physics. Results: A motion‐correction network for multi‐contrast brain MRI successfully corrected artifacts of simulated motion for 4 test subjects, showing 0.96%, 7.63%, and 5.03% increases in the average structural simularity and 5.19%, 10.2%, and 7.48% increases in the average normalized mutual information for T1‐weighted, T2‐weighted, and T2‐weighted fluid‐attenuated inversion recovery images, respectively. The experimental setting with image alignment and artifact‐free input images for other contrasts shows better performances in correction of simulated motion artifacts. Furthermore, the proposed method quantitatively outperforms recent deep learning motion correction and synthesis methods. Real motion experiments from 5 healthy subjects demonstrate the potential of the proposed method for use in a clinical environment. Conclusion: A deep learning‐based motion correction method for multi‐contrast MRI was successfully developed, and experimental results demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

PurposeTo achieve whole-heart coronary magnetic resonance angiography (MRA) with (1.0 mm)(3) spatial resolution and 5 min of free-breathing scan time.MethodsWe used an electrocardiograph-gated, T2-prepared and fat-saturated balanced steady state free precession sequence with 3DPR trajectory for free-breathing data acquisition with 100% gating efficiency. For image reconstruction, we used a self-calibrating iterative SENSE scheme with integrated retrospective motion correction. We performed healthy volunteer study to compare the proposed method with motion-corrected gridding at different retrospective undersampling levels on apparent signal-to-noise ratio (aSNR) and subjective coronary artery (CA) visualization scores.ResultsCompared with gridding, the proposed method significantly improved both image quality metrics for undersampled datasets with 6000, 8000, and 10,000 projections. With as few as 10,000 projections, the proposed method yielded good CA visualization scores (3.02 of 4) and aSNR values comparable to those with 20,000 projections.ConclusionUsing the proposed method, good image quality was observed for free breathing whole-heart coronary MRA at (1.0 mm)(3) resolution with an achievable scan time of 5 min.

Accurate reconstruction of a high-resolution 3D volume of the heart is critical for comprehensive cardiac assessments. However, cardiac magnetic resonance (CMR) data is usually acquired as a stack of 2D short-axis (SAX) slices, which suffers from the inter-slice misalignment due to cardiac motion and data sparsity from large gaps between SAX slices. Therefore, we aim to propose an end-to-end deep learning (DL) model to address these two challenges simultaneously, employing specific model components for each challenge. The objective is to reconstruct a high-resolution 3D volume of the heart (V HR) from acquired CMR SAX slices (V LR). We define the transformation from V LR to V HR as a sequential process of motion correction and super-resolution. Accordingly, our DL model incorporates two distinct components. The first component conducts motion correction by predicting displacement vectors to re-position each SAX slice accurately. The second component takes the motion-corrected SAX slices from the first component and performs the super-resolution to fill the data gaps. These two components operate in a sequential way, and the entire model is trained end-to-end. Our model significantly reduced inter-slice misalignment from originally 3.33 ± 0.74 mm to 1.36 ± 0.63 mm and generated accurate high resolution 3D volumes with Dice of 0.974 ± 0.010 for left ventricle (LV) and 0.938 ± 0.017 for myocardium in a simulation dataset. When compared to the LAX contours in a real-world dataset, our model achieved Dice of 0.945 ± 0.023 for LV and 0.786 ± 0.060 for myocardium. In both datasets, our model with specific components for motion correction and super-resolution significantly enhance the performance compared to the model without such design considerations. The codes for our model are available at https://github.com/zhennongchen/CMR_MC_SR_End2End. • CMR suffers from inter-slice misalignment (motion) and low z-resolution. • An DL model for sequential motion correction and super-resolution on SAX slices. • Designs specific components for each task and integrate into an end-to-end model. • Achieves accurate 3D heart volume reconstruction in simulation and real data. [ABSTRACT FROM AUTHOR]

Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment and management of adult patients with congenital heart disease (CHD). However, conventional techniques for three-dimensional (3D) whole-heart acquisition involve long and unpredictable scan times and methods that accelerate scans via k-space undersampling often rely on long iterative reconstructions. Deep-learning-based reconstruction methods have recently attracted much interest due to their capacity to provide fast reconstructions while often outperforming existing state-of-the-art methods. In this study, we sought to adapt and validate a non-rigid motion-corrected model-based deep learning (MoCo-MoDL) reconstruction framework for 3D whole-heart MRI in a CHD patient cohort. The previously proposed deep-learning reconstruction framework MoCo-MoDL, which incorporates a non-rigid motion-estimation network and a denoising regularization network within an unrolled iterative reconstruction, was trained in an end-to-end manner using 39 CHD patient datasets. Once trained, the framework was evaluated in eight CHD patient datasets acquired with seven-fold prospective undersampling. Reconstruction quality was compared with the state-of-the-art non-rigid motion-corrected patch-based low-rank reconstruction method (NR-PROST) and against reference images (acquired with three-or-four-fold undersampling and reconstructed with NR-PROST). Seven-fold undersampled scan times were 2.1 ± 0.3 minutes and reconstruction times were ∼30 seconds, approximately 240 times faster than an NR-PROST reconstruction. Image quality comparable to the reference images was achieved using the proposed MoCo-MoDL framework, with no statistically significant differences found in any of the assessed quantitative or qualitative image quality measures. Additionally, expert image quality scores indicated the MoCo-MoDL reconstructions were consistently of a higher quality than the NR-PROST reconstructions of the same data, with the differences in 12 of the 22 scores measured for individual vascular structures found to be statistically significant. The MoCo-MoDL framework was applied to an adult CHD patient cohort, achieving good quality 3D whole-heart images from ∼2-minute scans with reconstruction times of ∼30 seconds. [Display omitted] [ABSTRACT FROM AUTHOR]

Free-running cardiac and respiratory motion-resolved whole-heart five-dimensional (5D) cardiovascular magnetic resonance (CMR) can reduce scan planning and provide a means of evaluating respiratory-driven changes in clinical parameters of interest. However, respiratory-resolved imaging can be limited by user-defined parameters which create trade-offs between residual artifact and motion blur. In this work, we develop and validate strategies for both correction of intra-bin and compensation of inter-bin respiratory motion to improve the quality of 5D CMR. Each component of the reconstruction framework was systematically validated and compared to the previously established 5D approach using simulated free-running data (N = 50) and a cohort of 32 patients with congenital heart disease. The impact of intra-bin respiratory motion correction was evaluated in terms of image sharpness while inter-bin respiratory motion compensation was evaluated in terms of reconstruction error, compression of respiratory motion, and image sharpness. The full reconstruction framework (intra-acquisition correction and inter-acquisition compensation of respiratory motion [IIMC] 5D) was evaluated in terms of image sharpness and scoring of image quality by expert reviewers. Intra-bin motion correction provides significantly (p < 0.001) sharper images for both simulated and patient data. Inter-bin motion compensation results in significant (p < 0.001) lower reconstruction error, lower motion compression, and higher sharpness in both simulated (10/11) and patient (9/11) data. The combined framework resulted in significantly (p < 0.001) sharper IIMC 5D reconstructions (End-expiration (End-Exp): 0.45 ± 0.09, End-inspiration (End-Ins): 0.46 ± 0.10) relative to the previously established 5D implementation (End-Exp: 0.43 ± 0.08, End-Ins: 0.39 ± 0.09). Similarly, image scoring by three expert reviewers was significantly (p < 0.001) higher using IIMC 5D (End-Exp: 3.39 ± 0.44, End-Ins: 3.32 ± 0.45) relative to 5D images (End-Exp: 3.02 ± 0.54, End-Ins: 2.45 ± 0.52). The proposed IIMC reconstruction significantly improves the quality of 5D whole-heart MRI. This may be exploited for higher resolution or abbreviated scanning. Further investigation of the diagnostic impact of this framework and comparison to gold standards is needed to understand its full clinical utility, including exploration of respiratory-driven changes in physiological measurements of interest. [Display omitted] [ABSTRACT FROM AUTHOR]

PurposeTo incorporate a newly developed shape-based motion estimation scheme into magnetic resonance urography (MRU) and verify its efficacy in facilitating quantitative functional analysis.MethodsThe authors propose a motion compensation scheme in MRU that consists of three sequential modules: MRU image acquisition, motion compensation, and quantitative functional analysis. They designed two sets of complementary experiments to evaluate the performance of the proposed method. In the first experiment, dynamic contrast enhanced (DCE) MR images were acquired from three sedated subjects, from which clinically valid estimates were derived and served as the "ground truth." Physiologically sound motion was then simulated to synthesize image sequences influenced by respiratory motion. Quantitative assessment and comparison were performed on functional estimates of Patlak number, glomerular filtration rate, and Patlak differential renal function without and with motion compensation against the ground truth. In the second experiment, the authors acquired a temporal series of noncontrast MR images under free breathing from a healthy adult subject. The performance of the proposed method on compensating real motion was evaluated by comparing the standard deviation of the obtained temporal intensity curves before and after motion compensation.ResultsOn DCE-MR images with simulated motion, the generated relative enhancement curves exhibited large perturbations and the Patlak numbers of the left and right kidney were significantly underestimated up to 35% and 34%, respectively, compared with the ground truth. After motion compensation, the relative enhancement curves exhibited much less perturbations and Patlak estimation errors reduced within 3% and 4% for the left and right kidneys, respectively. On clinical free-breathing MR images, the temporal intensity curves exhibited significantly reduced variations after motion compensation, with standard deviation decreased from 30.3 and 38.2 to 8.3 and 11.7 within two manually selected regions of interest, respectively.ConclusionsThe developed motion compensation method has demonstrated its ability to facilitate quantitative MRU functional analysis, with improved accuracy of pharmacokinetic modeling and quantitative parameter estimations. Future work will consider performing more intensive clinical verifications with sophisticated pharmacokinetic models and generalizing the proposed method to other quantitative DCE analysis, such as on liver or prostate function.

ObjectivesContrast-enhanced ultrasound (CEUS) cines of focal liver lesions (FLLs) can be quantitatively analyzed to measure tumor perfusion on a pixel-by-pixel basis for diagnostic indication. However, CEUS cines acquired freehand and during free breathing cause nonuniform in-plane and out-of-plane motion from frame to frame. These motions create fluctuations in the time-intensity curves (TICs), reducing the accuracy of quantitative measurements. Out-of-plane motion cannot be corrected by image registration in 2-dimensional CEUS and degrades the quality of in-plane motion correction (IPMC). A 2-tier IPMC strategy and adaptive out-of-plane motion filter (OPMF) are proposed to provide a stable correction of nonuniform motion to reduce the impact of motion on quantitative analyses.Materials and methodsA total of 22 cines of FLLs were imaged with dual B-mode and contrast specific imaging to acquire a 3-minute TIC. B-mode images were analyzed for motion, and the motion correction was applied to both B-mode and contrast images. For IPMC, the main reference frame was automatically selected for each cine, and subreference frames were selected in each respiratory cycle and sequentially registered toward the main reference frame. All other frames were sequentially registered toward the local subreference frame. Four OPMFs were developed and tested: subsample normalized correlation (NC), subsample sum of absolute differences, mean frame NC, and histogram. The frames that were most dissimilar to the OPMF reference frame using 1 of the 4 above criteria in each respiratory cycle were adaptively removed by thresholding against the low-pass filter of the similarity curve. Out-of-plane motion filter was quantitatively evaluated by an out-of-plane motion metric (OPMM) that measured normalized variance in the high-pass filtered TIC within the tumor region-of-interest with low OPMM being the goal. Results for IPMC and OPMF were qualitatively evaluated by 2 blinded observers who ranked the motion in the cines before and after various combinations of motion correction steps.ResultsQuantitative measurements showed that 2-tier IPMC and OPMF improved imaging stability. With IPMC, the NC B-mode metric increased from 0.504 ± 0.149 to 0.585 ± 0.145 over all cines (P < 0.001). Two-tier IPMC also produced better fits on the contrast-specific TIC than industry standard IPMC techniques did (P < 0.02). In-plane motion correction and OPMF were shown to improve goodness of fit for pixel-by-pixel analysis (P < 0.001). Out-of-plane motion filter reduced variance in the contrast-specific signal as shown by a median decrease of 49.8% in the OPMM. Two-tier IPMC and OPMF were also shown to qualitatively reduce motion. Observers consistently ranked cines with IPMC higher than the same cine before IPMC (P < 0.001) as well as ranked cines with OPMF higher than when they were uncorrected.ConclusionThe 2-tier sequential IPMC and adaptive OPMF significantly reduced motion in 3-minute CEUS cines of FLLs, thereby overcoming the challenges of drift and irregular breathing motion in long cines. The 2-tier IPMC strategy provided stable motion correction tolerant of out-of-plane motion throughout the cine by sequentially registering subreference frames that bypassed the motion cycles, thereby overcoming the lack of a nearly stationary reference point in long cines. Out-of-plane motion filter reduced apparent motion by adaptively removing frames imaged off-plane from the automatically selected OPMF reference frame, thereby tolerating nonuniform breathing motion. Selection of the best OPMF by minimizing OPMM effectively reduced motion under a wide variety of motion patterns applicable to clinical CEUS. These semiautomated processes only required user input for region-of-interest selection and can improve the accuracy of quantitative perfusion measurements.

PurposeTo implement a nonrigid autofocus motion correction technique to improve respiratory motion correction of free-breathing whole-heart coronary magnetic resonance angiography acquisitions using an image-navigated 3D cones sequence.Methods2D image navigators acquired every heartbeat are used to measure superior-inferior, anterior-posterior, and right-left translation of the heart during a free-breathing coronary magnetic resonance angiography scan using a 3D cones readout trajectory. Various tidal respiratory motion patterns are modeled by independently scaling the three measured displacement trajectories. These scaled motion trajectories are used for 3D translational compensation of the acquired data, and a bank of motion-compensated images is reconstructed. From this bank, a gradient entropy focusing metric is used to generate a nonrigid motion-corrected image on a pixel-by-pixel basis. The performance of the autofocus motion correction technique is compared with rigid-body translational correction and no correction in phantom, volunteer, and patient studies.ResultsNonrigid autofocus motion correction yields improved image quality compared to rigid-body-corrected images and uncorrected images. Quantitative vessel sharpness measurements indicate superiority of the proposed technique in 14 out of 15 coronary segments from three patient and two volunteer studies.ConclusionThe proposed technique corrects nonrigid motion artifacts in free-breathing 3D cones acquisitions, improving image quality compared to rigid-body motion correction.

Abstract In this study, we propose a deep-learning-based method to correct motion artifacts in optical resolution photoacoustic microscopy (OR-PAM). The method is a convolutional neural network that establishes an end-to-end map from input raw data with motion artifacts to output corrected images. First, we performed simulation studies to evaluate the feasibility and effectiveness of the proposed method. Second, we employed this method to process images of rat brain vessels with multiple motion artifacts to evaluate its performance for in vivo applications. The results demonstrate that this method works well for both large blood vessels and capillary networks. In comparison with traditional methods, the proposed method in this study can be easily modified to satisfy different scenarios of motion corrections in OR-PAM by revising the training sets.

Abstract Background Validating new techniques for fetal cardiovascular magnetic resonance (CMR) is challenging due to random fetal movement that precludes repeat measurements. Consequently, fetal CMR development has been largely performed using physical phantoms or postnatal volunteers. In this work, we present an open-source simulation designed to aid in the development and validation of new approaches for fetal CMR. Our approach, fetal extended Cardiac-Torso cardiovascular magnetic resonance imaging (Fetal XCMR), builds on established methods for simulating CMR acquisitions but is tailored toward the dynamic physiology of the fetal heart and body. We present comparisons between the Fetal XCMR phantom and data acquired in utero, resulting in image quality, anatomy, tissue signals and contrast. Methods Existing extended Cardiac-Torso models are modified to create maternal and fetal anatomy, combined according to simulated motion, mapped to CMR contrast, and converted to CMR data. To provide a comparison between the proposed simulation and experimental fetal CMR images acquired in utero, images from a typical scan of a pregnant woman are included and simulated acquisitions were generated using matching CMR parameters, motion and noise levels. Three reconstruction (static, real-time, and CINE), and two motion estimation methods (translational motion, fetal heart rate) from data acquired in transverse, sagittal, coronal, and short-axis planes of the fetal heart were performed to compare to in utero acquisitions and demonstrate feasibility of the proposed simulation framework. Results Overall, CMR contrast, morphologies, and relative proportions of the maternal and fetal anatomy are well represented by the Fetal XCMR images when comparing the simulation to static images acquired in utero. Additionally, visualization of maternal respiratory and fetal cardiac motion is comparable between Fetal XCMR and in utero real-time images. Finally, high quality CINE image reconstructions provide excellent delineation of fetal cardiac anatomy and temporal dynamics for both data types. Conclusion The fetal CMR phantom provides a new method for evaluating fetal CMR acquisition and reconstruction methods by simulating the underlying anatomy and physiology. As the field of fetal CMR continues to grow, new methods will become available and require careful validation. The fetal CMR phantom is therefore a powerful and convenient tool in the continued development of fetal cardiac imaging.

Techniques for calcium imaging were first achieved in the mid-1970s, whilst tools to analyse these markers of cellular activity are still being developed and improved. For image analysis, custom tools were developed within labs and until relatively recently, software packages were not widely available between researchers. We will discuss some of the most popular, alongside our preferred, methods for calcium imaging analysis that are now widely available and describe why these protocols are so effective. We will also describe some of the newest innovations in the field that are likely to benefit researchers, particularly as calcium imaging is often an inherently low signal-to-noise method. Although calcium imaging analysis has seen recent advances, particularly following the rise of machine learning, we will end by highlighting the outstanding requirements and questions that hinder further progress, and pose the question of how far we have come in the past sixty years and what can be expected for future development in the field.

Motion correction is an important issue in ophthalmic optical coherence tomography (OCT), and can improve the ability of data sets to reflect the physiological structures of tissues and make visualization and subsequent analysis easier. In this study, we present a novel method to correct the cross‐sectional motion artifacts in retinal OCT volumes. Motion along the x‐direction (fast‐scan direction) is corrected through the normalized cross‐correlation algorithm, while axial motion compensation is performed using the polynomial fitting method on the inner segment/outer segment (IS/OS) layer segmented by the shortest path faster algorithm (SPFA). The results of volunteers with central serous chorioretinopathy demonstrate that the proposed method effectively corrects motion artifacts in OCT volumes and may have potential application value in the evaluation of ophthalmic diseases such as diabetic retinopathy, glaucoma and age‐related macular degeneration. [ABSTRACT FROM AUTHOR]

Purpose: Gastrointestinal motion patterns such as peristalsis and segmental contractions can alter the shape and position of the stomach and intestines with respect to other irradiated organs during radiation therapy. Unfortunately, these deformations are concealed by conventional four‐dimensional (4D)‐MRI techniques, which were developed to visualize respiratory motion by binning acquired data into respiratory motion states without considering the phases of GI motion. We present a method to reconstruct breathing‐compensated images showing the phases of periodic gastric motion and study the effect of this motion on regional anatomical structures. Methods: Sixty‐seven DCE‐MRI examinations were performed on patients undergoing MRI simulation for hepatocellular carcinoma using a golden‐angle stack‐of‐stars sequence that collected 2000 radial spokes over 5 min. The collected data were reconstructed using a method with integrated respiratory motion correction into a time series of 3D image volumes without visible breathing motion. From this series, a gastric motion signal was extracted by temporal filtering of time–intensity curves in the stomach. Using this motion signal, breathing‐corrected back‐projection images were sorted according to the gastric phase and reconstructed into 21 gastric motion state images showing the phases of gastric motion. Results: Reconstructed image volumes showed gastric motion states clearly with no visible breathing motion or related artifacts. The mean frequency of the gastric motion signal was 3 cycles/min with a standard deviation of 0.27 cycles/min. Conclusions: Periodic gastrointestinal motion can be visualized without confounding respiratory motion using the presented GI 4D MRI technique. GI 4D MRIs may help define internal target volumes for treatment planning, aid in planning organ at risk volume definition, or support motion model development for gastrointestinal motion tracking algorithms for real‐time MR‐guided radiation therapy. [ABSTRACT FROM AUTHOR]

This article presents a motion compensation procedure that significantly improves the accuracy of synthetic aperture tensor velocity estimates for row–column arrays. The proposed motion compensation scheme reduces motion effects by moving the image coordinates with the velocity field during summation of low-resolution volumes. The velocity field is estimated using a transverse oscillation cross-correlation estimator, and each image coordinate’s local tensor velocity is determined by upsampling the field using spline interpolation. The motion compensation procedure is validated using Field II simulations and flow measurements acquired using a 3-MHz row–column addressed probe and the research scanner SARUS. For a peak velocity of 25 cm/s, a pulse repetition frequency of 2 kHz, and a beam-to-flow angle of 60°, the proposed motion compensation procedure was able to reduce the relative bias from −27.0% to −9.4% and the standard deviation from 8.6% to 8.1%. In simulations performed with a pulse repetition frequency of 10 kHz, the proposed method reduces the bias in all cases with beam-to-flow angles of 60° and 75° and peak velocities between 10 and 150 cm/s. [ABSTRACT FROM AUTHOR]

Long acquisition times due to intrinsically low signal‐to‐noise ratio and the need for highly homogeneous B0 field make MRS particularly susceptible to motion or scanner instability compared with MRI. Motion‐induced changes in both localization and shimming (ie B0 homogeneity) degrade MRS data quality. To mitigate the effects of motion three approaches can be employed: (1) subject immobilization, (2) retrospective correction, and (3) prospective real‐time correction using internal and/or external tracking methods. Prospective real‐time correction methods can simultaneously update localization and the B0 field to improve MRS data quality. While localization errors can be corrected with both internal (navigators) and external (optical camera, NMR probes) tracking methods, the B0 field correction requires internal navigator methods to measure the B0 field inside the imaged volume and the possibility to update the scanner shim hardware in real time. Internal and external tracking can rapidly update the MRS localization with submillimeter and subdegree precision, while scanner frequency and first‐order shims of scanner hardware can be updated by internal methods every sequence repetition. These approaches are most well developed for neuroimaging, for which rigid transformation is primarily applicable. Real‐time correction greatly improves the stability of MRS acquisition and quantification, as shown in clinical studies on subjects prone to motion, including children and patients with movement disorders, enabling robust measurement of metabolite signals including those with low concentrations, such as gamma‐aminobutyric acid and glutathione. Thus, motion correction is recommended for MRS users and calls for tighter integration and wider availability of such methods by MR scanner manufacturers. [ABSTRACT FROM AUTHOR]

The temporomandibular joint (TMJ) is typically involved in 45–87% of children with Juvenile Idiopathic Arthritis (JIA). Accurate diagnosis of JIA is difficult as various clinical tests, including MRI, disagree. The purpose of this study is to optimize the methodological aspects of Dynamic Contrast Enhanced (DCE) MRI of the TMJ in children. In this cross-sectional study, including data from 73 JIA affected children, aged 6–15 years, effects of motion correction, sampling rate and parametric modelling on DCE-MRI data is investigated. Consensus among three radiologists determined the regions of interest. Quantitative perfusion parameters were estimated using four perfusion models; the Adiabatic Approximation to Tissue Homogeneity (AATH), Distributed Capillary Adiabatic Tissue Homogeneity (DCATH), Gamma Capillary Transit Time (GCTT) and Two Compartment Exchange (2CXM) models. Effects of motion correction were evaluated by a sum of least squares between corrected raw data and the GCTT model. The effect of systematically down sampling the raw data was tested. The sum of least squares was computed across all pharmacokinetic models. Relative difference perfusion parameters between the left and right TMJ were used for an unsupervised k-means based stratification of the data based on a principal component analysis, as well as for a supervised random forest classification. Diagnostic sensitivity and specificity were computed relative to structural image scorings. Paired sample t -tests, as well as ANOVA tests, were used (significant threshold: p < 0.05) with Tukeys post hoc test. High-level elastic motion correction provides the best least square fit to the GCTT model (percental improvement: 72–84%). A 4 s sampling rate captures more of the potentially disease relevant signal variations. The various parametric models all leave comparable residues (relative standard deviation: 3.4%). In further evaluation of DCE-MRI as a potential diagnostic tool for JIA a high-level elastic motion correction scheme should be adopted, with a sampling rate of at least 4 s. Results suggest that DCE-MRI data can be a valuable part in JIA diagnostics in the TMJ. • A postprocessing pipeline for DCE-MRI of the TMJ is evaluated for the first time. • High-level elastic motion correction should be applied to DCE data from the TMJ. • DCE data from the TMJ should be acquired with a sampling rate higher than 4 s. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this retrospective study was to evaluate the feasibility of a motion correction 3D reconstruction prototype technique for C-arm computed tomography (CACT). Material and Methods: We included 65 consecutive CACTs acquired during transarterial chemoembolization of 54 patients (47 m,7f; 67 ± 11.3 years). All original raw datasets (CACTOrg) underwent reconstruction with and without volume punching of high-contrast objects using a 3D image reconstruction software to compensate for motion (CACTMC_bone;CACTMC_no bone). Subsequently, the effect on image quality (IQ) was evaluated using objective (image sharpness metric) and subjective criteria. Subjective criteria were defined by vessel geometry, overall IQ, delineation of tumor feeders, the presence of foreign material-induced artifacts and need for additional imaging, assessed by two independent readers on a 3-(vessel geometry and overall IQ) or 2-point scale, respectively. Friedman rank-sum test and post hoc analysis in form of pairwise Wilcoxon signed-rank test were computed and inter-observer agreement analyzed using kappa test. Results: Objective IQ as defined by an image sharpness metric, increased from 273.5 ± 28 (CACTOrg) to 328.5 ± 55.1 (CACTMC_bone) and 331 ± 57.8 (CACTMC_no bone; all p < 0.0001). These results could largely be confirmed by the subjective analysis, which demonstrated predominantly good and moderate inter-observer agreement, with best agreement for CACTMC_no bone in all categories (e.g., vessel geometry: CACTOrg: κ = 0.51, CACTMC_bone: κ = 0.42, CACTMC_no bone: κ = 0.69). Conclusion: The application of a motion correction algorithm was feasible for all data sets and led to an increase in both objective and subjective IQ parameters. Level of Evidence: 3 [ABSTRACT FROM AUTHOR]

Purpose: To explore the impact of temporal motion‐induced coil sensitivity changes on CEST‐MRI at 7T and its correction using interleaved volumetric EPI navigators, which are applied for real‐time motion correction. Methods: Five healthy volunteers were scanned via CEST. A 4‐fold correction pipeline allowed the mitigation of (1) motion, (2) motion‐induced coil sensitivity variations, ΔB1‐, (3) motion‐induced static magnetic field inhomogeneities, ΔB0, and (4) spatially varying transmit RF field fluctuations, ΔB1+. Four CEST measurements were performed per session. For the first 2, motion correction was turned OFF and then ON in absence of voluntary motion, whereas in the other 2 controlled head rotations were performed. During post‐processing ΔB1‐ was removed additionally for the motion‐corrected cases, resulting in a total of 6 scenarios to be compared. In all cases, retrospective ∆B0 and ‐ΔB1+ corrections were performed to compute artifact‐free magnetization transfer ratio maps with asymmetric analysis (MTRasym). Results: Dynamic ΔB1‐ correction successfully mitigated signal deviations caused by head motion. In 2 frontal lobe regions of volunteer 4, induced relative signal errors of 10.9% and 3.9% were reduced to 1.1% and 1.0% after correction. In the right frontal lobe, the motion‐corrected MTRasym contrast deviated 0.92%, 1.21%, and 2.97% relative to the static case for Δω = 1, 2, 3 ± 0.25 ppm. The additional application of ΔB1‐ correction reduced these deviations to 0.10%, 0.14%, and 0.42%. The fully corrected MTRasym values were highly consistent between measurements with and without intended head rotations. Conclusion: Temporal ΔB1‐ cause significant CEST quantification bias. The presented correction pipeline including the proposed retrospective ΔB1‐ correction significantly reduced motion‐related artifacts on CEST‐MRI. [ABSTRACT FROM AUTHOR]

Background: Radial self-navigated (RSN) whole-heart coronary cardiovascular magnetic resonance angiography (CCMRA) is a free-breathing technique that estimates and corrects for respiratory motion. However, RSN has been limited to a 1D rigid correction which is often insufficient for patients with complex respiratory patterns. The goal of this work is therefore to improve the robustness and quality of 3D radial CCMRA by incorporating both 3D motion information and nonrigid intra-acquisition correction of the data into a framework called focused navigation (fNAV). Methods: We applied fNAV to 500 data sets from a numerical simulation, 22 healthy subjects, and 549 cardiac patients. In each of these cohorts we compared fNAV to RSN and respiratory resolved extradimensional golden-angle radial sparse parallel (XD-GRASP) reconstructions of the same data. Reconstruction times for each method were recorded. Motion estimate accuracy was measured as the correlation between fNAV and ground truth for simulations, and fNAV and image registration for in vivo data. Percent vessel sharpness was measured in all simulated data sets and healthy subjects, and a subset of patients. Finally, subjective image quality analysis was performed by a blinded expert reviewer who chose the best image for each in vivo data set and scored on a Likert scale 0–4 in a subset of patients by two reviewers in consensus. Results: The reconstruction time for fNAV images was significantly higher than RSN (6.1 ± 2.1 min vs 1.4 ± 0.3, min, p < 0.025) but significantly lower than XD-GRASP (25.6 ± 7.1, min, p < 0.025). Overall, there is high correlation between the fNAV and reference displacement estimates across all data sets (0.73 ± 0.29). For simulated data, healthy subjects, and patients, fNAV lead to significantly sharper coronary arteries than all other reconstruction methods (p < 0.01). Finally, in a blinded evaluation by an expert reviewer fNAV was chosen as the best image in 444 out of 571 data sets (78%; p < 0.001) and consensus grades of fNAV images (2.6 ± 0.6) were significantly higher (p < 0.05) than uncorrected (1.7 ± 0.7), RSN (1.9 ± 0.6), and XD-GRASP (1.8 ± 0.8). Conclusion: fNAV is a promising technique for improving the quality of RSN free-breathing 3D whole-heart CCMRA. This novel approach to respiratory self-navigation can derive 3D nonrigid motion estimations from an acquired 1D signal yielding statistically significant improvement in image sharpness relative to 1D translational correction as well as XD-GRASP reconstructions. Further study of the diagnostic impact of this technique is therefore warranted to evaluate its full clinical utility. [ABSTRACT FROM AUTHOR]

Purpose: Respiratory motion of patients during positron emission tomography (PET)/computed tomography (CT) imaging affects both image quality and quantitative accuracy. Hardware‐based motion estimation, which is the current clinical standard, requires initial setup, maintenance, and calibration of the equipment, and can be associated with patient discomfort. Data‐driven techniques are an active area of research with limited exploration into lesion‐specific motion estimation. This paper introduces a time‐of‐flight (TOF)‐weighted positron emission particle tracking (PEPT) algorithm that facilitates lesion‐specific respiratory motion estimation from raw listmode PET data. Methods: The TOF‐PEPT algorithm was implemented and investigated under different scenarios: (a) a phantom study with a point source and an Anzai band for respiratory motion tracking; (b) a phantom study with a point source only, no Anzai band; (c) two clinical studies with point sources and the Anzai band; (d) two clinical studies with point sources only, no Anzai band; and (e) two clinical studies using lesions/internal regions instead of point sources and no Anzai band. For studies with radioactive point sources, they were placed on patients during PET/CT imaging. The motion tracking was performed using a preselected region of interest (ROI), manually drawn around point sources or lesions on reconstructed images. The extracted motion signals were compared with the Anzai band when applicable. For the purposes of additional comparison, a center‐of‐mass (COM) algorithm was implemented both with and without the use of TOF information. Using the motion estimate from each method, amplitude‐based gating was applied, and gated images were reconstructed. Results: The TOF‐PEPT algorithm is shown to successfully determine the respiratory motion for both phantom and clinical studies. The derived motion signals correlated well with the Anzai band; correlation coefficients of 0.99 and 0.94‐0.97 were obtained for the phantom study and the clinical studies, respectively. TOF‐PEPT was found to be 13–38% better correlated with the Anzai results than the COM methods. Maximum Standardized Uptake Values (SUVs) were used to quantitatively compare the reconstructed‐gated images. In comparison with the ungated image, a 14–39% increase in the max SUV across several lesion areas and an 8.7% increase in the max SUV on the tracked lesion area were observed in the gated images based on TOF‐PEPT. The distinct presence of lesions with reduced blurring effect and generally sharper images were readily apparent in all clinical studies. In addition, max SUVs were found to be 4–10% higher in the TOF‐PEPT‐based gated images than in those based on Anzai and COM methods. Conclusion: A PEPT‐ based algorithm has been presented for determining movement due to respiratory motion during PET/CT imaging. Gating based on the motion estimate is shown to quantifiably improve the image quality in both a controlled point source phantom study and in clinical data patient studies. The algorithm has the potential to facilitate true motion correction where the reconstruction algorithm can use all data available. [ABSTRACT FROM AUTHOR]

Purpose: The purpose is to develop a retrospective correction for subtle slice‐to‐slice positional inconsistencies that can occur when overlapped slices are acquired for super resolution in T2‐weighted spin‐echo multislice imaging. Methods: Spin‐echo acquisition of overlapped slices is typically done using multiple passes. After the passes are assembled into the final slice set, consecutive slices are correlated due to their overlap. Cross correlation was used to measure slice‐to‐slice displacement. After Z‐dependent filtering to preserve true object shape, the displacements were used to correct slice position. The method was tested in a phantom moved slowly (0.16‐0.63 mm/pass) under computer control and in vivo in 16 patients having prostate MRI. Results: Over the motion range, the correlation method had an accuracy within 0.03 mm/pass and precision ± 0.20 mm (ie, subpixel). Corrected images visually resemble the true object. Over the patient studies, the mean range of motion in the anterior–posterior direction was 1.63 mm. Motion‐corrected axial images and the sagittal reformats were evaluated as significantly superior over those formed without motion correction. Conclusion: The retrospective correlation‐based motion‐correction method provides significant improvement in the slice‐to‐slice registration necessary for effective super resolution using overlapped slices. [ABSTRACT FROM AUTHOR]

Background: Phase contrast MRI in the great vessels is a potential clinical tool for managing fetal pathologies. One challenge is the uncontrollable fetal motion, potentially corrupting flow quantifications. Purpose To demonstrate improvements in fetal blood flow quantification in great vessels using retrospectively motion‐corrected golden‐angle radial phase contrast MRI relative to Cartesian phase contrast MRI. Study Type: Method comparison. Phantom/Subjects: Computer simulation. Seventeen pregnant volunteers. Field Strength/Sequence: 1.5T and 3T. Cartesian and golden‐angle radial phase contrast MRI. Assessment Through computer simulations, radial (with and without retrospective motion correction) and Cartesian phase contrast MRI were compared using flow deviations. in vivo Cartesian and radial phase contrast MRI measurements and reconstruction qualities were compared in pregnancies. Cartesian data were reconstructed into gated reconstructions (CINEs) after cardiac gating with metric optimized gating (MOG). For radial data, real‐time reconstructions were performed for motion correction and MOG followed by CINE reconstructions. Statistical Tests: Wilcoxon signed‐rank test. Linear regression. Bland–Altman plots. Student's t‐test. Results: Simulations showed significant improvements (P < 0.05) in flow accuracy and reconstruction quality with motion correction ([mean/peak] flow errors with ±5 mm motion corruption: Cartesian [35 ± 1/115 ± 7] mL/s, motion uncorrected radial [25 ± 1/75 ± 2] mL/s and motion‐corrected radial [1.0 ± 0.5/−5 ± 1] mL/s). in vivo Cartesian reconstructions without motion correction had lower quality than the motion‐corrected radial reconstructions (P < 0.05). Across all fetal mean flow measurements, the bias [limits of agreement] between the two measurements were −0.2 [−76, 75] mL/min/kg, while the linear regression coefficients were (Mradial = 0.81 × MCartesian + 29.8 [mL/min/kg], r2 = 0.67). The corresponding measures for the peak fetal flows were −23 [−214, 167] mL/min/kg and (Pradial = 0.95 × PCartesian–1.2 [mL/min/kg], r2 = 0.80). Cartesian reconstructions of low quality showed significantly higher estimated mean and peak (P < 0.05) flows than the corresponding radial reconstructions. Data Conclusion: Simulations showed that radial phase contrast MRI with motion compensation improved flow accuracy. For fetal measurements, motion‐corrected radial reconstructions showed better image quality than, and different flow values from, Cartesian reconstructions. Level of Evidence 1. Technical Efficacy Stage 1. J. MAGN. RESON. IMAGING 2021;53:540–551. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this work is to describe a T1‐weighted fluid‐attenuated inversion recovery (FLAIR) sequence that is able to produce sharp magnetic resonance images even if the subject is moving their head throughout the acquisition. Methods: The robustness to motion artifacts and retrospective motion correction capabilities of the PROPELLER (periodically rotated overlapping parallel lines with enhanced reconstruction) trajectory were combined with prospective motion correction. The prospective correction was done using an intelligent marker attached to the subject. This marker wirelessly synchronizes to the pulse sequence to measure the directionality and magnitude of the magnetic fields present in the MRI machine during a short navigator, thus enabling it to determine its position and orientation in the scanner coordinate frame. Three approaches to incorporating the marker‐navigator into the PROPELLER sequence were evaluated. The specific absorption rate, and subsequent scan time, of the T1‐weighted FLAIR PROPELLER sequence, was reduced using a variable refocusing flip‐angle scheme. Evaluations of motion correction performance were done with 4 volunteers and 3 types of head motion. Results: During minimal out‐of‐plane movement, retrospective PROPELLER correction performed similarly to the prospective correction. However, the prospective clearly outperformed the retrospective correction when there was out‐of‐plane motion. Finally, the combination of retrospective and prospective correction produced the sharpest images even during large continuous motion. Conclusion: Prospective motion correction of a PROPELLER sequence makes it possible to handle continuous, large, and high‐speed head motions with only minor reductions in image quality. [ABSTRACT FROM AUTHOR]

Purpose: Respiratory motion in cardiovascular MRI presents a challenging problem with many potential solutions. Current approaches require breath‐holds, apply retrospective image registration, or significantly increase scan time by respiratory gating. Myocardial T1 and T2 mapping techniques are particularly sensitive to motion as they require multiple source images to be accurately aligned prior to the estimation of tissue relaxation. We propose a patient‐specific prospective motion correction (PROCO) strategy that corrects respiratory motion on the fly with the goal of reducing the spatial variation of myocardial parametric mapping techniques. Methods: A rapid, patient‐specific training scan was performed to characterize respiration‐induced motion of the heart relative to a diaphragmatic navigator, and a parametric mapping pulse sequence utilized the resulting motion model to prospectively update the scan plane in real‐time. Midventricular short‐axis T1 and T2 maps were acquired under breath‐hold or free‐breathing conditions with and without PROCO in 7 healthy volunteers and 3 patients. T1 and T2 were measured in 6 segments and compared to reference standard breath‐hold measurements using Bland‐Altman analysis. Results: PROCO significantly reduced the spatial variation of parametric maps acquired during free‐breathing, producing limits of agreement of −47.16 to 30.98 ms (T1) and −1.35 to 4.02 ms (T2), compared to −67.77 to 74.34 ms (T1) and −2.21 to 5.62 ms (T2) for free‐breathing acquisition without PROCO. Conclusion: Patient‐specific respiratory PROCO method significantly reduced the spatial variation of myocardial T1 and T2 mapping, while allowing for 100% efficient free‐breathing acquisitions. [ABSTRACT FROM AUTHOR]

Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously deteriorate the image quality. Various prospective and retrospective methods have been proposed for MRI motion correction, among which deep learning approaches have achieved state-of-the-art motion correction performance. This survey paper aims to provide a comprehensive review of deep learning-based MRI motion correction methods. Neural networks used for motion artifacts reduction and motion estimation in the image domain or frequency domain are detailed. Furthermore, besides motion-corrected MRI reconstruction, how estimated motion is applied in other downstream tasks is briefly introduced, aiming to strengthen the interaction between different research areas. Finally, we identify current limitations and point out future directions of deep learning-based MRI motion correction., (© 2024. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

Proton resonance frequency shift (PRFS) MR thermometry is the most common method used in clinical thermal treatments because of its fast acquisition and high sensitivity to temperature. However, motion is the biggest obstacle in PRFS MR thermometry for monitoring thermal treatment in moving organs. This challenge arises because of the introduction of phase errors into the PRFS calculation through multiple methods, such as image misregistration, susceptibility changes in the magnetic field, and intraframe motion during MRI acquisition. Various approaches for motion correction have been developed for real-time, motion-robust, and volumetric MR thermometry. However, current technologies have inherent trade-offs among volume coverage, processing time, and temperature accuracy. These tradeoffs should be considered and chosen according to the thermal treatment application. In hyperthermia treatment, precise temperature measurements are of increased importance rather than the requirement for exceedingly high temporal resolution. In contrast, ablation procedures require robust temporal resolution to accurately capture a rapid temperature rise. This paper presents a comprehensive review of current cutting-edge MRI techniques for motion-robust MR thermometry, and recommends which techniques are better suited for each thermal treatment. We expect that this study will help discern the selection of motion-robust MR thermometry strategies and inspire the development of motion-robust volumetric MR thermometry for practical use in clinics., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Diffusion magnetic resonance imaging (dMRI) is pivotal for probing the microstructure of the rapidly-developing fetal brain. However, fetal motion during scans and its interaction with magnetic field inhomogeneities result in artifacts and data scattering across spatial and angular domains. The effects of those artifacts are more pronounced in high-angular resolution fetal dMRI, where signal-to-noise ratio is very low. Those effects lead to biased estimates and compromise the consistency and reliability of dMRI analysis. This work presents HAITCH, the first and the only publicly available tool to correct and reconstruct multi-shell high-angular resolution fetal dMRI data. HAITCH offers several technical advances that include a blip-reversed dual-echo acquisition for dynamic distortion correction, advanced motion correction for model-free and robust reconstruction, optimized multi-shell design for enhanced information capture and increased tolerance to motion, and outlier detection for improved reconstruction fidelity. The framework is open-source, flexible, and can be used to process any type of fetal dMRI data including single-echo or single-shell acquisitions, but is most effective when used with multi-shell multi-echo fetal dMRI data that cannot be processed with any of the existing tools. Validation experiments on real fetal dMRI scans demonstrate significant improvements and accurate correction across diverse fetal ages and motion levels. HAITCH successfully removes artifacts and reconstructs high-fidelity fetal dMRI data suitable for advanced diffusion modeling, including fiber orientation distribution function estimation. These advancements pave the way for more reliable analysis of the fetal brain microstructure and tractography under challenging imaging conditions.

Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in deep learning for markerless head pose estimation have yet to be applied to this problem because of the sub-millimetre spatial resolution required for motion correction. In the present work, two optical tracking systems are described for the development and training of a neural network: one marker-based system (a testing platform for measuring ground truth head pose) with high tracking fidelity to act as the training labels, and one markerless deep-learning-based system using images of the markerless head as input to the network. The markerless system has the potential to overcome issues of marker occlusion, insufficient rigid attachment of the marker, lengthy calibration times, and unequal performance across degrees of freedom (DOF), all of which hamper the adoption of marker-based solutions in the clinic. Detail is provided on the development of a custom moiré-enhanced fiducial marker for use as ground truth and on the calibration procedure for both optical tracking systems. Additionally, the development of a synthetic head pose dataset is described for the proof of concept and initial pre-training of a simple convolutional neural network. Results indicate that the ground truth system has been sufficiently calibrated and can track head pose with an error of <1 mm and <1°. Tracking data of a healthy, adult participant are shown. Pre-training results show that the average root-mean-squared error across the 6 DOF is 0.13 and 0.36 (mm or degrees) on a head model included and excluded from the training dataset, respectively. Overall, this work indicates excellent feasibility of the deep-learning-based approach and will enable future work in training and testing on a real dataset in the MRI environment.

Accurately measuring renal function is crucial for pediatric patients with kidney conditions. Traditional methods have limitations, but dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides a safe and efficient approach for detailed anatomical evaluation and renal function assessment. However, motion artifacts during DCE-MRI can degrade image quality and introduce misalignments, leading to unreliable results. This study introduces a motion-compensated reconstruction technique for DCE-MRI data acquired using golden-angle radial sampling. Our proposed method achieves three key objectives: (1) identifying and removing corrupted data (outliers) using a Gaussian process model fitting with a k -space center navigator, (2) efficiently clustering the data into motion phases and performing interphase registration, and (3) utilizing a novel formulation of motion-compensated radial reconstruction. We applied the proposed motion correction (MoCo) method to DCE-MRI data affected by varying degrees of motion, including both respiratory and bulk motion. We compared the outcomes with those obtained from the conventional radial reconstruction. Our evaluation encompassed assessing the quality of images, concentration curves, and tracer kinetic model fitting, and estimating renal function. The proposed MoCo reconstruction improved the temporal signal-to-noise ratio for all subjects, with a 21.8% increase on average, while total variation values of the aorta, right, and left kidney concentration were improved for each subject, with 32.5%, 41.3%, and 42.9% increases on average, respectively. Furthermore, evaluation of tracer kinetic model fitting indicated that the median standard deviation of the estimated filtration rate ( σ F T ), mean normalized root-mean-squared error (nRMSE), and chi-square goodness-of-fit of tracer kinetic model fit were decreased from 0.10 to 0.04, 0.27 to 0.24, and, 0.43 to 0.27, respectively. The proposed MoCo technique enabled more reliable renal function assessment and improved image quality for detailed anatomical evaluation in the case of bulk and respiratory motion during the acquisition of DCE-MRI., (© 2024 John Wiley & Sons Ltd.)

Rationale and Objectives: Assess the feasibility of a prototype image reconstruction algorithm in correcting motion artifacts in cone-beam computed tomography (CBCT) scans of interventional instruments in the lung., Materials and Methods: First, phantom experiments were performed to assess the algorithm, using the Xsight lung phantom with custom inserts containing straight or curved catheters. During scanning, the inserts moved in a continuous sinusoidal or breath-hold mimicking pattern, with varying amplitudes and frequencies. Subsequently, the algorithm was applied to CBCT data from navigation bronchoscopy procedures. The algorithm's performance was assessed quantitatively via edge-sharpness measurements and qualitatively by three specialists., Results: In the phantom study, the algorithm improved sharpness in 13 out of 14 continuous sinusoidal motion and five out of seven breath-hold mimicking scans, with more significant effects at larger motion amplitudes. Analysis of 27 clinical scans showed that the motion corrected reconstructions had significantly sharper edges than standard reconstructions (2.81 (2.24-6.46) vs. 2.80 (2.16-4.75), p = 0.003). These results were consistent with the qualitative assessment, which showed higher scores in the sharpness of bronchoscope-tissue interface and catheter-tissue interface in the motion-corrected reconstructions. However, the tumor demarcation ratings were inconsistent between raters, and the overall image quality of the new reconstructions was rated lower., Conclusion: Our findings suggest that applying the new prototype algorithm for motion correction in CBCT images is feasible. The algorithm improved the sharpness of medical instruments in CBCT scans obtained during diagnostic navigation bronchoscopy procedures, which was demonstrated both quantitatively and qualitatively., Competing Interests: Declaration of Competing Interest J.J.F. and M.M.R. have received a research grant from Siemens Healthineers, which was paid to the institute, without restrictions regarding publications or data. R.L.J.V. declares that his department has a research contract with Siemens Healthineers. P.F. is an employee of Siemens Healthcare GmbH. His contribution to this research was primarily in providing technical expertise and ensuring the accuracy of the technical content in the paper. All other authors declare no conflict of interest that could have affected the work reported in this paper. Siemens Healthineers had no role in data collection, data analysis, interpretation of the results, or the decision to submit for publication., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Participant movement is a major source of artifacts in functional near-infrared spectroscopy (fNIRS) experiments. Mitigating the impact of motion artifacts (MAs) is crucial to estimate brain activity robustly. Here, we suggest and evaluate a novel application of the nonlinear Hammerstein-Wiener model to estimate and mitigate MAs in fNIRS signals from direct-movement recordings through IMU sensors mounted on the participant's head (head-IMU) and the fNIRS probe (probe-IMU). To this end, we analyzed the hemodynamic responses of single-channel oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) signals from 17 participants who performed a hand tapping task with different levels of concurrent head movement. Additionally, the tapping task was performed without head movements to estimate the ground-truth brain activation. We compared the performance of our novel approach with the probe-IMU and head-IMU to eight established methods (PCA, tPCA, spline, spline Savitzky-Golay, wavelet, CBSI, RLOESS, and WCBSI) on four quality metrics: SNR, △AUC, RMSE, and R. Our proposed nonlinear Hammerstein-Wiener method achieved the best SNR increase ( p < 0.001) among all methods. Visual inspection revealed that our approach mitigated MA contaminations that other techniques could not remove effectively. MA correction quality was comparable with head- and probe-IMUs.

Magnetic resonance (MR) acquisitions of the torso are frequently affected by respiratory motion with detrimental effects on signal quality. The motion of organs inside the body is typically decoupled from surface motion and is best captured using rapid MR imaging (MRI). We propose a pipeline for prospective motion correction of the target organ using MR image navigators providing absolute motion estimates in millimeters. Our method is designed to feature multi-nuclear interleaving for non-proton MR acquisitions and to tolerate local transmit coils with inhomogeneous field and sensitivity distributions. OpenCV object tracking was introduced for rapid estimation of in-plane displacements in 2D MR images. A full three-dimensional translation vector was derived by combining displacements from slices of multiple and arbitrary orientations. The pipeline was implemented on 3 T and 7 T MR scanners and tested in phantoms and volunteers. Fast motion handling was achieved with low-resolution 2D MR image navigators and direct implementation of OpenCV into the MR scanner's reconstruction pipeline. Motion-phantom measurements demonstrate high tracking precision and accuracy with minor processing latency. The feasibility of the pipeline for reliable in-vivo motion extraction was shown on heart and kidney data. Organ motion was manually assessed by independent operators to quantify tracking performance. Object tracking performed convincingly on 7774 navigator images from phantom scans and different organs in volunteers. In particular the kernelized correlation filter (KCF) achieved similar accuracy (74%) as scored from inter-operator comparison (82%) while processing at a rate of over 100 frames per second. We conclude that fast 2D MR navigator images and computer vision object tracking can be used for accurate and rapid prospective motion correction. This and the modular structure of the pipeline allows for the proposed method to be used in imaging of moving organs and in challenging applications like cardiac magnetic resonance spectroscopy (MRS) or magnetic resonance imaging (MRI) guided radiotherapy., (© 2024. The Author(s).)

Background and Objective: Gait disorders stemming from brain lesions or chemical imbalances, pose significant challenges for patients. Proposed treatments encompass medication, deep brain stimulation, physiotherapy, and visual stimulation. Music, with its harmonious structures, serves as a continuous reference, synchronizing muscle activities through neural connections between hearing and motor functions, can show promise in gait disorder management. This study explores the influence of heightened music rhythm on young healthy participants' gait cadence in three conditions: FeedForward (independent rhythm), FeedBack (cadence-synced rhythm), and Adaptive (cadence-controlled musical experience). The objective is to increase gait cadence through rhythm modulation during walking., Method: The study involved 18 young healthy participants (13 males and 5 females) who did not have any gait or hearing disorders. Each participant completed the gait task in the three aforementioned conditions. Each condition was comprised of three sessions: 1) Baseline, where participants walked while listening to the original music; 2) Intervention, changing the music rhythm to affect the gait cadence; and 3) Realign, replaying the original music and measuring the durability of the effect of the Intervention session. The measurement tool was a pair of footwear equipped with push-button switches that transmited the foot-to-ground contact to the LabVIEW® software, all designed by the research team. Repeated measures of ANOVA was employed to evaluate the impact of the sessions and conditions., Results: In all three conditions, there was a significant effect of music on increasing gait cadence during Intervention and Realign sessions (p < 0.001). Additionally, the immediate impact of music on gait cadence in the Adaptive condition was superior to the other conditions., Conclusion: The study findings indicate that increasing the rhythm of music during walking has a significant impact on gait cadence among young healthy participants. This effect remained significant even after realigning the music to normal. It could be harnessed to support the rehabilitation of individuals with movement disorders characterized by a decrease in movement speed, such as Parkinson's disease. Moreover, the results indicate that the Adaptive method showed promising outcomes, suggesting its potential for further exploration as an effective means to control gait cadence., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: Subject movement during the MR examination is inevitable and causes not only image artifacts but also deteriorates the homogeneity of the main magnetic field (B 0 ), which is a prerequisite for high quality data. Thus, characterization of changes to B 0 , for example induced by patient movement, is important for MR applications that are prone to B 0 inhomogeneities., Methods: We propose a deep learning based method to predict such changes within the brain from the change of the head position to facilitate retrospective or even real-time correction. A 3D U-net was trained on in vivo gradient-echo brain 7T MRI data. The input consisted of B 0 maps and anatomical images at an initial position, and anatomical images at a different head position (obtained by applying a rigid-body transformation on the initial anatomical image). The output consisted of B 0 maps at the new head positions. We further fine-trained the network weights to each subject by measuring a limited number of head positions of the given subject, and trained the U-net with these data., Results: Our approach was compared to established dynamic B 0 field mapping via interleaved navigators, which suffer from limited spatial resolution and the need for undesirable sequence modifications. Qualitative and quantitative comparison showed similar performance between an interleaved navigator-equivalent method and proposed method., Conclusion: It is feasible to predict B 0 maps from rigid subject movement and, when combined with external tracking hardware, this information could be used to improve the quality of MR acquisitions without the use of navigators., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: Navigator-based correction of rigid-body motion reconciling high precision with minimal acquisition, minimal calibration and simple, fast processing., Methods: A short orbital navigator (2.3 ms) is inserted in a three-dimensional (3D) gradient echo sequence for human head imaging. Head rotation and translation are determined by linear regression based on a complex-valued model built either from three reference navigators or in a reference-less fashion, from the first actual navigator. Optionally, the model is expanded by global phase and field offset. Run-time scan correction on this basis establishes servo control that maintains validity of the linear picture by keeping its expansion point stable in the head frame of reference. The technique is assessed in a phantom and demonstrated by motion-corrected imaging in vivo., Results: The proposed approach is found to establish stable motion control both with and without reference acquisition. In a phantom, it is shown to accurately detect motion mimicked by rotation of scan geometry as well as change in global B 0 . It is demonstrated to converge to accurate motion estimates after perturbation well beyond the linear signal range. In vivo, servo navigation achieved motion detection with precision in the single-digit range of micrometers and millidegrees. Involuntary and intentional motion in the range of several millimeters were successfully corrected, achieving excellent image quality., Conclusion: The combination of linear regression and feedback control enables prospective motion correction for head imaging with high precision and accuracy, short navigator readouts, fast run-time computation, and minimal demand for reference data., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Diffusion MRI offers a unique probe into neural microstructure and connectivity in the developing brain. However, analysis of neonatal brain imaging data is complicated by inevitable subject motion, leading to a series of scattered slices that need to be aligned within and across diffusion-weighted contrasts. Here, we develop a reconstruction method for scattered slice multi-shell high angular resolution diffusion imaging (HARDI) data, jointly estimating an uncorrupted data representation and motion parameters at the slice or multiband excitation level. The reconstruction relies on data-driven representation of multi-shell HARDI data using a bespoke spherical harmonics and radial decomposition (SHARD), which avoids imposing model assumptions, thus facilitating to compare various microstructure imaging methods in the reconstructed output. Furthermore, the proposed framework integrates slice-level outlier rejection, distortion correction, and slice profile correction. We evaluate the method in the neonatal cohort of the developing Human Connectome Project (650 scans). Validation experiments demonstrate accurate slice-level motion correction across the age range and across the range of motion in the population. Results in the neonatal data show successful reconstruction even in severely motion-corrupted subjects. In addition, we illustrate how local tissue modelling can extract advanced microstructure features such as orientation distribution functions from the motion-corrected reconstructions.

Sub-millimeter imaging at 7T has opened new possibilities for qualitatively and quantitatively studying brain structure as it evolves throughout the life span. However, subject motion introduces image blurring on the order of magnitude of the spatial resolution and is thus detrimental to image quality. Such motion can be corrected for, but widespread application has not yet been achieved and quantitative evaluation is lacking. This raises a need to quantitatively measure image sharpness throughout the brain. We propose a method to quantify sharpness of brain structures at sub-voxel resolution, and use it to assess to what extent limited motion is related to image sharpness.The method was evaluated in a cohort of 24 healthy volunteers with a wide and uniform age range, aiming to arrive at results that largely generalize to larger populations. Using 3D fat-excited motion navigators, quantitative R1, R2* and Quantitative Susceptibility Maps and T1-weighted images were retrospectively corrected for motion. Sharpness was quantified in all modalities for selected regions of interest (ROI) by fitting the sigmoidally shaped error function to data within locally homogeneous clusters. A strong, almost linear correlation between motion and sharpness improvement was observed, and motion correction significantly improved sharpness. Overall, the Full Width at Half Maximum reduced from 0.88 mm to 0.70 mm after motion correction, equivalent to a 2.0 times smaller voxel volume. Motion and sharpness were not found to correlate with the age of study participants. We conclude that in our data, motion correction using fat navigators is overall able to restore the measured sharpness to the imaging resolution, irrespective of the amount of motion observed during scanning.

Abstract Introduction High reproducibility is critical for ensuring the confidence needed to use functional magnetic resonance imaging (fMRI) activation maps for presurgical planning. Methods In this study, the comparison of different motion correction methods, spatial smoothing methods, regression methods, and thresholding methods was performed to see whether specific data processing methods can be employed to improve the reproducibility of single‐subject fMRI activation. Three test–retest metrics were used: the percent difference in activation volume (PDAV), the difference in the center of mass (DCM), and the Dice Similarity Coefficient (DSC). Results The PDAV was minimized when using little or no spatial smoothing and AMPLE thresholding. The DCM was minimized when using affine motion correction and little or no spatial smoothing. The DSC was improved when using affine motion correction and generous spatial smoothing. However, it is believed that the overlap metric may be unsuitable for testing fMRI reproducibility. Conclusion Processing methods to improve fMRI reproducibility were determined. Importantly, the processing methods needed to improve reproducibility were dependent on the fMRI activation metric of interest.

Functional connectivity is derived from inter-regional correlations in spontaneous fluctuations of brain activity, and can be represented in terms of complete graphs with continuous (real-valued) edges. The structure of functional connectivity networks is strongly affected by signal processing procedures to remove the effects of motion, physiological noise and other sources of experimental error. However, in the absence of an established ground truth, it is difficult to determine the optimal procedure, and no consensus has been reached on the most effective approach to remove nuisance signals without unduly affecting the network intrinsic structural features. Here, we use a novel information-theoretic approach, based on von Neumann entropy, which provides a measure of information encoded in the networks at different scales. We also define a measure of distance between networks, based on information divergence, and optimal null models appropriate for the description of functional connectivity networks, to test for the presence of nontrivial structural patterns that are not the result of simple local constraints. This formalism enables a scale-resolved analysis of the distance between a functional connectivity network and its maximally random counterpart, thus providing a means to assess the effects of noise and image processing on network structure. We apply this novel approach to address a few open questions in the analysis of brain functional connectivity networks. Specifically, we demonstrate a strongly beneficial effect of network sparsification by removal of the weakest links, and the existence of an optimal threshold that maximizes the ability to extract information on large-scale network structures. Additionally, we investigate the effects of different degrees of motion at different scales, and compare the most popular processing pipelines designed to mitigate its deleterious effect on functional connectivity networks. We show that network sparsification, in combination with motion correction algorithms, dramatically improves detection of large scale network structure.

Optical mapping is a high-resolution fluorescence imaging technique, that uses voltage- or calcium-sensitive dyes to visualize electrical excitation waves on the heart surface. However, optical mapping is very susceptible to the motion of cardiac tissue, which results in so-called motion artifacts in the fluorescence signal. To avoid motion artifacts, contractions of the heart muscle are typically suppressed using pharmacological excitation-contraction uncoupling agents, such as Blebbistatin. The use of pharmacological agents, however, may influence cardiac electrophysiology. Recently, it has been shown that numerical motion tracking can significantly reduce motion-related artifacts in optical mapping, enabling the simultaneous optical measurement of cardiac electrophysiology and mechanics. Here, we combine ratiometric optical mapping with numerical motion tracking to further enhance the robustness and accuracy of these measurements. We evaluate the method's performance by imaging and comparing cardiac restitution and ventricular fibrillation (VF) dynamics in contracting, non-working vs. Blebbistatin-arrested Langendorff-perfused rabbit hearts (N = 10). We found action potential durations (APD) to be, on average, 25 ± 5% shorter in contracting hearts compared to hearts uncoupled with Blebbistatin. The relative shortening of the APD was found to be larger at higher frequencies. VF was found to be significantly accelerated in contracting hearts, i.e., 9 ± 2Hz with Blebbistatin and 15 ± 4Hz without Blebbistatin, and maintained a broader frequency spectrum. In contracting hearts, the average number of phase singularities was NPS = 11 ± 4 compared to NPS = 6 ± 3 with Blebbistatin during VF on the anterior ventricular surface. VF inducibility was reduced with Blebbistatin. We found the effect of Blebbistatin to be concentration-dependent and reversible by washout. Aside from the electrophysiological characterization, we also measured and analyzed cardiac motion. Our findings may have implications for the interpretation of optical mapping data, and highlight that physiological conditions, such as oxygenation and metabolic demand, must be carefully considered in ex vivo imaging experiments.

Test-retest of automated image segmentation algorithms (FSL FAST, FSL FIRST, and FREESURFER) are computed on magnetic resonance images from 12 unsedated children aged 9.4±2.6 years ([min,max] ​= ​[6.5 years, 13.8 years]) using different approaches to motion correction (prospective versus retrospective). The prospective technique, PROMO MPRAGE, dynamically estimates motion using specially acquired navigator images and adjusts the remaining acquisition accordingly, whereas the retrospective technique, MPnRAGE, uses a self-navigation property to retrospectively estimate and account for motion during image reconstruction. To increase the likelihood and range of motions, participants heads were not stabilized with padding during repeated scans. When motion was negligible both techniques had similar performance. When motion was not negligible, the automated image segmentation and anatomical labeling software tools showed the most consistent performance with the retrospectively corrected MPnRAGE technique (≥80% volume overlaps for 15 of 16 regions for FIRST and FREESURFER, with greater than 90% volume overlaps for 12 regions with FIRST and 11 regions with FREESURFER). Prospectively corrected MPRAGE with linear view-ordering also demonstrated lower performance than MPnRAGE without retrospective motion correction.

A super-resolution (SR) technique is proposed for imaging myocardial fiber architecture with cardiac magnetic resonance. Images were acquired with a motion-compensated cardiac diffusion tensor imaging (cDTI) sequence. The heart left ventricle was covered with three stacks of thick slices, in short axis, horizontal and vertical long axes orientations, respectively. The three low-resolution stacks (2 × 2 × 8 mm3) were combined into an isotropic volume (2 × 2 × 2 mm3) by a super-resolution reconstruction. For in vivo measurements, each slice was acquired during a breath-hold period. Bulk motion was corrected by optimizing a similarity metric between intensity profiles from all intersecting slices in the dataset. The benefit of the proposed approach was evaluated using a numerical heart phantom, a physical helicoidal phantom with artificial fibers, and six healthy subjects. The SR technique showed improved results compared to the native scans, in terms of image quality and cDTI metrics. In particular, the myocardial helix angle (HA) was more accurately estimated in the physical phantom (HA = 41.5° ± 1.1°, with the ground truth being 42.0°). In vivo, it resulted in a sharper rate of change of HA across the myocardial wall (−0.993°/% ± 0.007°/% against −0.873°/% ± 0.010°/%).

Abstract Purpose To image multidimensional flow in fetuses using golden-angle radial phase contrast cardiovascular magnetic resonance (PC-CMR) with motion correction and retrospective gating. Methods A novel PC-CMR method was developed using an ungated golden-angle radial acquisition with continuously incremented velocity encoding. Healthy subjects (n = 5, 27 ± 3 years, males) and pregnant females (n = 5, 34 ± 2 weeks gestation) were imaged at 3 T using the proposed sequence. Real-time reconstructions were first performed for retrospective motion correction and cardiac gating (using metric optimized gating, MOG). CINE reconstructions of multidimensional flow were then performed using the corrected and gated data. Results In adults, flows obtained using the proposed method agreed strongly with those obtained using a conventionally gated Cartesian acquisition. Across the five adults, bias and limits of agreement were − 1.0 cm/s and [− 5.1, 3.2] cm/s for mean velocities and − 1.1 cm/s and [− 6.5, 4.3] cm/s for peak velocities. Temporal correlation between corresponding waveforms was also high (R~ 0.98). Calculated timing errors between MOG and pulse-gating RR intervals were low (~ 20 ms). First insights into multidimensional fetal blood flows were achieved. Inter-subject consistency in fetal descending aortic flows (n = 3) was strong with an average velocity of 27.1 ± 0.4 cm/s, peak systolic velocity of 70.0 ± 1.8 cm/s and an intra-class correlation coefficient of 0.95 between the velocity waveforms. In one fetal case, high flow waveform reproducibility was demonstrated in the ascending aorta (R = 0.97) and main pulmonary artery (R = 0.99). Conclusion Multidimensional PC-CMR of fetal flow was developed and validated, incorporating retrospective motion compensation and cardiac gating. Using this method, the first quantification and visualization of multidimensional fetal blood flow was achieved using CMR.