The quantification of cardiac motion using cardiac magnetic resonance imaging (CMR) has shown promise as an early-stage marker for cardiovascular diseases. Despite the growing popularity of CMR-based myocardial strain calculations, measures of complete spatiotemporal strains (i.e., three-dimensional strains over the cardiac cycle) remain elusive. Complete spatiotemporal strain calculations are primarily hampered by poor spatial resolution, with the rapid motion of the cardiac wall also challenging the reproducibility of such strains. We hypothesize that a super-resolution reconstruction (SRR) framework that leverages combined image acquisitions at multiple orientations will enhance the reproducibility of complete spatiotemporal strain estimation. Two sets of CMR acquisitions were obtained for five wild-type mice, combining short-axis scans with radial and orthogonal long-axis scans. Super-resolution reconstruction, integrated with tissue classification, was performed to generate full four-dimensional (4D) images. The resulting enhanced and full 4D images enabled complete quantification of the motion in terms of 4D myocardial strains. Additionally, the effects of SRR in improving accurate strain measurements were evaluated using an in-silico heart phantom. The SRR framework revealed near isotropic spatial resolution, high structural similarity, and minimal loss of contrast, which led to overall improvements in strain accuracy. In essence, a comprehensive methodology was generated to quantify complete and reproducible myocardial deformation, aiding in the much-needed standardization of complete spatiotemporal strain calculations.

OBJECTIVE: Delineation variations and organ motion produce difficult-to-quantify uncertainties in planned radiation doses to targets and organs at risk. Similar to manual contouring, most automatic segmentation tools generate single delineations per structure; however, this does not indicate the range of clinically acceptable delineations. This study develops a method to generate a range of automatic cardiac structure segmentations, incorporating motion and delineation uncertainty, and evaluates the dosimetric impact in lung cancer.
METHODS: Eighteen cardiac structures were delineated using a locally developed auto-segmentation tool. It was applied to lung cancer planning CTs for 27 curative (planned dose ≥50 Gy) cases, and delineation variations were estimated by using ten mapping-atlases to provide separate substructure segmentations. Motion-related cardiac segmentation variations were estimated by auto-contouring structures on ten respiratory phases for 9/27 cases that had 4D-planning CTs. Dose volume histograms (DVHs) incorporating these variations were generated for comparison.
RESULTS: Variations in mean doses (Dmean), defined as the range in values across ten feasible auto-segmentations, were calculated for each cardiac substructure. Over the study cohort the median variations for delineation uncertainty and motion were 2.20-11.09 Gy and 0.72-4.06 Gy, respectively. As relative values, variations in Dmean were between 18.7%-65.3% and 7.8%-32.5% for delineation uncertainty and motion, respectively. Doses vary depending on the individual planned dose distribution, not simply on segmentation differences, with larger dose variations to cardiac structures lying within areas of steep dose gradient.
CONCLUSIONS: Radiotherapy dose uncertainties from delineation variations and respiratory-related heart motion were quantified using a cardiac substructure automatic segmentation tool. This predicts the \'dose range\' where doses to structures are most likely to fall, rather than single DVH curves. This enables consideration of these uncertainties in cardiotoxicity research and for future plan optimisation. The tool was designed for cardiac structures, but similar methods are potentially applicable to other OARs.

OBJECTIVE: Left ventricle (LV) regional myocardial displacement due to cardiac motion was assessed using cardiovascular magnetic resonance (CMR) cine images to establish region-specific margins for cardiac radioablation treatments.
METHODS: CMR breath-hold cine images and LV myocardial tissue contour points were analyzed for 200 subjects, including controls (n = 50) and heart failure (HF) patients with preserved ejection fraction (HFpEF, n = 50), mid-range ejection fraction (HFmrEF, n = 50), and reduced ejection fraction (HFrEF, n = 50). Contour points were divided into segments according to the 17-segment model. For each patient, contour point displacements were determined for the long-axis (all 17 segments) and short-axis (segments 1-12) directions. Mean overall, tangential (longitudinal or circumferential), and normal (radial) displacements were calculated for the 17 segments and for each segment level.
RESULTS: The greatest overall motion was observed in the control group-long axis: 4.5 ± 1.2 mm (segment 13 [apical anterior] epicardium) to 13.8 ± 3.0 mm (segment 6 [basal anterolateral] endocardium), short axis: 4.3 ± 0.8 mm (segment 9 [mid inferoseptal] epicardium) to 11.5 ± 2.3 mm (segment 1 [basal anterior] endocardium). HF patients exhibited lesser motion, with the smallest overall displacements observed in the HFrEF group-long axis: 4.3 ± 1.7 mm (segment 13 [apical anterior] epicardium) to 10.6 ± 3.4 mm (segment 6 [basal anterolateral] endocardium), short axis: 3.9 ± 1.3 mm (segment 8 [mid anteroseptal] epicardium) to 7.4 ± 2.8 mm (segment 1 [basal anterior] endocardium).
CONCLUSIONS: This analysis provides an estimate of epicardial and endocardial displacement for the 17 segments of the LV for patients with normal and impaired LV function. This reference data can be used to establish treatment planning margin guidelines for cardiac radioablation. Smaller margins may be used for patients with higher degree of impaired heart function, depending on the LV segment.

Purpose.Cardiac radiosurgery is a non-invasive treatment modality for ventricular tachycardia, where a linear accelerator is used to irradiate the arrhythmogenic region within the heart. In this work, cardiac magnetic resonance (CMR) cine images were used to quantify left ventricle (LV) segment-specific motion during the cardiac cycle and to assess potential advantages of cardiac-gated radiosurgery.Methods.CMR breath-hold cine images and LV contour points were analyzed for 50 controls and 50 heart failure patients with reduced ejection fraction (HFrEF, EF < 40%). Contour points were divided into anatomic segments according to the 17-segment model, and each segment was treated as a hypothetical treatment target. The optimum treatment window (one fifth of the cardiac cycle) was determined where segment centroid motion was minimal, then the maximum centroid displacement and treatment area were determined for the full cardiac cycle and for the treatment window. Mean centroid displacement and treatment area reductions with cardiac gating were determined for each of the 17 segments.Results.Full motion segment centroid displacements ranged between 6-14 mm (controls) and 4-11 mm (HFrEF). Full motion treatment areas ranged between 129-715 mm2(controls) and 149-766 mm2(HFrEF). With gating, centroid displacements were reduced to 1 mm (controls and HFrEF), while treatment areas were reduced to 62-349 mm2(controls) and 83-393 mm2(HFrEF). Relative treatment area reduction ranged between 38%-53% (controls) and 26%-48% (HFrEF).Conclusion.This data demonstrates that cardiac cycle motion is an important component of overall target motion and varies depending on the anatomic cardiac segment. Accounting for cardiac cycle motion, through cardiac gating, has the potential to significantly reduce treatment volumes for cardiac radiosurgery.

BACKGROUND: Despite the widespread use of cine MRI for evaluation of cardiac function, existing real-time methods do not easily enable quantification of ventricular function. Moreover, segmented cine MRI assumes periodicity of cardiac motion. We aim to develop a self-gated, cine MRI acquisition scheme with data-driven cluster-based binning of cardiac motion.
METHODS: A Cartesian golden-step balanced steady-state free precession sequence with sorted k-space ordering was designed. Image data were acquired with breath-holding. Principal component analysis and k-means clustering were used for binning of cardiac phases. Cluster compactness in the time dimension was assessed using temporal variability, and dispersion in the spatial dimension was assessed using the Caliński-Harabasz index. The proposed and the reference electrocardiogram (ECG)-gated cine methods were compared using a four-point image quality score, SNR and CNR values, and Bland-Altman analyses of ventricular function.
RESULTS: A total of 10 subjects with sinus rhythm and 8 subjects with arrhythmias underwent cardiac MRI at 3.0 T. The temporal variability was 45.6 ms (cluster) versus 24.6 ms (ECG-based) (p < 0.001), and the Caliński-Harabasz index was 59.1 ± 9.1 (cluster) versus 22.0 ± 7.1 (ECG based) (p < 0.001). In subjects with sinus rhythm, 100% of the end-systolic and end-diastolic images from both the cluster and reference approach received the highest image quality score of 4. Relative to the reference cine images, the cluster-based multiphase (cine) image quality consistently received a one-point lower score (p < 0.05), whereas the SNR and CNR values were not significantly different (p = 0.20). In cases with arrhythmias, 97.9% of the end-systolic and end-diastolic images from the cluster approach received an image quality score of 3 or more. The mean bias values for biventricular ejection fraction and volumes derived from the cluster approach versus reference cine were negligible.
CONCLUSIONS: ECG-free cine cardiac MRI with data-driven clustering for binning of cardiac motion is feasible and enables quantification of cardiac function.

Cardiac motion causes unpredictable signal loss in respiratory-triggered diffusion-weighted magnetic resonance imaging (DWI) of the liver, especially inside the left lobe. The left liver lobe may thus be frequently neglected in the clinical evaluation of liver DWI. In this work, a data-driven algorithm that relies on the statistics of the signal in the left liver lobe to mitigate the motion-induced signal loss is presented. The proposed data-driven algorithm utilizes the exclusion of severely corrupted images with subsequent spatially dependent image scaling based on a signal-loss model to correctly combine the multi-average diffusion-weighted images. The signal in the left liver lobe is restored and the liver signal is more homogeneous after applying the proposed algorithm. Furthermore, overestimation of the apparent diffusion coefficient (ADC) in the left liver lobe is reduced. The proposed algorithm can therefore contribute to reduce the motion-induced bias in DWI of the liver and help to increase the diagnostic value of DWI in the left liver lobe.

BACKGROUND: Recent improvements in CT detector technology have led to smaller detector pixels resolving frequencies beyond 20 lp/cm and enabled ultra-high-resolution CT. Silicon-based photon-counting detector (PCD) CT is one such technology that promises improved spatial and spectral resolution. However, when the detector pixel sizes are reduced, the impact of cardiac motion on CT images becomes more pronounced. Here, we investigated the effects cardiac motion on the image quality of a clinical prototype Si-PCD scanner in a dynamic heart phantom.
METHODS: A series of 3D-printed vessels were created to simulate coronary arteries with diameter in the 1-3.5 ​mm range. Four coronary stents were set inside the d ​= ​3.5 ​mm vessels and all vessels were filled with contrast agents and were placed inside a dynamic cardiac phantom. The phantom was scanned in motion (60 bpm) and at rest on a prototype clinical Si-PCD CT scanner in 8-bin spectral UHR mode. Virtual monoenergetic images (VMI) were generated at 70 ​keV and CT number accuracy and effective spatial resolution (blooming) of rest and motion VMIs were compared.
RESULTS: Linear regression analysis of CT numbers showed excellent agreement (r ​> ​0.99) between rest and motion. We did not observe a significant difference (p ​> ​0.48) in estimating free lumen diameters. Differences in in-stent lumen diameter and stent strut thickness were non-significant with maximum mean difference of approximately 70 ​μm.
CONCLUSIONS: We found no significant degradation in CT number accuracy or spatial resolution due to cardiac motion. The results demonstrate the potential of spectral UHR coronary CT angiography enabled by Si-PCD.

This study assessed the impact of cardiac motion and in-vessel attenuation on coronary artery calcium (CAC) scoring using virtual non-iodine (VNI) against virtual non-contrast (VNC) reconstructions on photon-counting detector CT. Two artificial vessels containing calcifications and different in-vessel attenuations (500, 800HU) were scanned without (static) and with cardiac motion (60, 80, 100 beats per minute [bpm]). Images were post-processed using a VNC and VNI algorithm at 70 keV and quantum iterative reconstruction (QIR) strength 2. Calcium mass, Agatston scores, cardiac motion susceptibility (CMS)-indices were compared to physical mass, static scores as well as between reconstructions, heart rates and in-vessel attenuations. VNI scores decreased with rising heart rate (p < 0.01) and showed less underestimation than VNC scores (p < 0.001). Only VNI scores were similar to the physical mass at static measurements, and to static scores at 60 bpm. Agatston scores using VNI were similar to static scores at 60 and 80 bpm. Standard deviation of CMS-indices was lower for VNI-based than for VNC-based CAC scoring. VNI scores were higher at 500 than 800HU (p < 0.001) and higher than VNC scores (p < 0.001) with VNI scores at 500 HU showing the lowest deviation from the physical reference. VNI-based CAC quantification is influenced by cardiac motion and in-vessel attenuation, but least when measuring Agatston scores, where it outperforms VNC-based CAC scoring.

Increased afterload in aortic stenosis (AS) induces left ventricle (LV) remodeling to preserve a normal ejection fraction. This compensatory response can become maladaptive and manifest with motion abnormality. It is a clinical challenge to identify contractile and relaxation dysfunction during early subclinical stage to prevent irreversible deterioration.
To evaluate the changes of regional wall dynamics in 3D + time domain as remodeling progresses in AS.
Retrospective.
A total of 31 AS patients with reduced and preserved ejection fraction (14 AS_rEF: 7 male, 66.5 [7.8] years old; 17 AS_pEF: 12 male, 67.0 [6.0] years old) and 15 healthy (6 male, 61.0 [7.0] years old).
1.5 T Magnetic resonance imaging/steady state free precession and late-gadolinium enhancement sequences.
Individual LV models were reconstructed in 3D + time domain and motion metrics including wall thickening (TI), dyssynchrony index (DI), contraction rate (CR), and relaxation rate (RR) were automatically extracted and associated with the presence of scarring and remodeling.
Shapiro-Wilk: data normality; Kruskal-Wallis: significant difference (P < 0.05); ICC and CV: variability; Mann-Whitney: effect size.
AS_rEF group shows distinct deterioration of cardiac motions compared to AS_pEF and healthy groups (TIAS_rEF : 0.92 [0.85] mm, TIAS_pEF : 5.13 [1.99] mm, TIhealthy : 3.61 [1.09] mm, ES: 0.48-0.83; DIAS_rEF : 17.11 [7.89]%, DIAS_pEF : 6.39 [4.04]%, DIhealthy : 5.71 [1.87]%, ES: 0.32-0.85; CRAS_rEF : 8.69 [6.11] mm/second, CRAS_pEF : 16.48 [6.70] mm/second, CRhealthy : 10.82 [4.57] mm/second, ES: 0.29-0.60; RRAS_rEF : 8.45 [4.84] mm/second; RRAS_pEF : 13.49 [8.56] mm/second, RRhealthy : 9.31 [2.48] mm/second, ES: 0.14-0.43). The difference in the motion metrics between healthy and AS_pEF groups were insignificant (P-value = 0.16-0.72). AS_rEF group was dominated by eccentric hypertrophy (47.1%) with concomitant scarring. Conversely, AS_pEF group was dominated by concentric remodeling and hypertrophy (71.4%), which could demonstrate hyperkinesia with slight wall dyssynchrony than healthy. Dysfunction of LV mechanics corresponded to the presence of myocardial scarring (54.9% in AS), which reverted the compensatory mechanisms initiated and performed by LV remodeling.
The proposed 3D + time modeling technique may distinguish regional motion abnormalities between AS_pEF, AS_rEF, and healthy cohorts, aiding clinical diagnosis and monitoring of AS progression. Subclinical myocardial dysfunction is evident in early AS despite of normal EF.
4 TECHNICAL EFFICACY: Stage 1.

Numerous clinical investigations require understanding changes in anatomical shape over time, such as in dynamic organ cycle characterization or longitudinal analyses (e.g., for disease progression). Spatiotemporal statistical shape modeling (SSM) allows for quantifying and evaluating dynamic shape variation with respect to a cohort or population of interest. Existing data-driven SSM approaches leverage information theory to capture population-level shape variations by learning correspondence-based (landmark) representations of shapes directly from data using entropy-based optimization schemes. These approaches assume sample independence and thus are unsuitable for sequential dynamic shape observations. Previous methods for adapting entropy-based SSM optimization schemes for the spatiotemporal case either utilize a cross-sectional design (ignoring within-subject correlation) or impose other limiting assumptions, such as the linearity of shape dynamics. Here, we present a principled approach to spatiotemporal SSM that relaxes these assumptions to correctly capture population-level shape variation over time. We propose to incorporate modeling the underlying time dependency into correspondence optimization via a regularized principal component polynomial regression. This approach is flexible enough to capture non-linear temporal dynamics while encoding population-specific spatial regularity. We demonstrate our method\'s efficacy on synthetic data and left atrium segmented from cardiac MRI scans. Our approach better captures the population modes of variation and a statistically significant time dependency than existing methods.