Cine Magnetic Resonance Imaging Research Articles

Accelerated cardiac magnetic resonance imaging using deep learning for volumetric assessment in children.

Ventricular volumetry using a short-axis stack of two-dimensional (D) cine balanced steady-state free precession (bSSFP) sequences is crucial in any cardiac magnetic resonance imaging (MRI) examination. This task becomes particularly challenging in children due to multiple breath-holds. To assess the diagnostic performance of accelerated 3-RR cine MRI sequences using deep learning reconstruction compared with standard 2-D cine bSSFP sequences. Twenty-nine consecutive patients (mean age 11 ± 5, median 12, range 1-17years) undergoing cardiac MRI were scanned with a conventional segmented 2-D cine and a deep learning accelerated cine (three heartbeats) acquisition on a 1.5-tesla scanner. Short-axis volumetrics were performed (semi-)automatically in both datasets retrospectively by two experiencedreaderswho visually assessed image quality employing a 4-point grading scale. Scan times and image quality were compared using the Wilcoxon rank-sum test. Volumetrics were assessed with linear regression and Bland-Altman analyses, and measurement agreement with intraclass correlation coefficient (ICC). Mean acquisition time was significantly reduced with the 3-RR deep learning cine compared to the standard cine sequence (45.5 ± 13.8s vs. 218.3 ± 44.8s; P < 0.001). No significant differences in biventricular volumetrics were found. Left ventricular (LV) mass was increased in the deep learning cine compared with the standard cine sequence (71.4 ± 33.1g vs. 69.9 ± 32.5g; P < 0.05). All volumetric measurements had an excellent agreement with ICC > 0.9 except for ejection fraction (EF) (LVEF 0.81, RVEF 0.73). The image quality of deep learning cine images was decreased for end-diastolic and end-systolic contours, papillary muscles, and valve depiction (2.9 ± 0.5 vs. 3.5 ± 0.4; P < 0.05). Deep learning cine volumetrics did not differ significantly from standard cine results except for LV mass, which was slightly overestimated with deep learning cine. Deep learning cine sequences result in a significant reduction in scan time with only slightly lower image quality.

Estimate of the hydraulic force in the aging heart: a cardiovascular magnetic resonance imaging study

BackgroundCoupling between left ventricle (LV) and left atrium (LA) plays a central role in the process of cardiac remodeling during aging and development of cardiac disease. The hydraulic force (HyF) is related to variation in size between LV and LA. The objectives of this study were to: (1) derive an estimate of left atrioventricular HyF using cine- Magnetic Resonance Imaging (MRI) in healthy subjects with a wide age range, and (2) study its relationship with age and conventional diastolic function parameters, as estimated by reference echocardiography.MethodsWe studied 119 healthy volunteers (mean age 44 ± 17 years, 58 women) who underwent Doppler echocardiography and MRI on the same day. Conventional transmitral flow early (E) and late (A) LV filling peak velocities as well as mitral annulus diastolic longitudinal peak velocity (E’) were derived from echocardiography. MRI cine SSFP images in longitudinal two and four chamber views were acquired, and analyzed using feature tracking (FT) software. In addition to conventional LV and LA strain measurements, FT-derived LV and LA contours were further used to calculate chamber cross-sectional areas. HyF was approximated as the difference between the LV and LA maximal cross-sectional areas in the diastasis phase corresponding to the lowest LV-LA pressure gradient. Univariate and multivariate analyses while adjusting for appropriate variables were used to study the associations between HyF and age as well as diastolic function and strain indices.ResultsHyF decreased significantly with age (R²=0.34, p < 0.0001). In addition, HyF was significantly associated with conventional indices of diastolic function and LA strain: E/A: R²=0.24, p < 0.0001; E’: R²=0.24, p < 0.0001; E/E’: R²=0.12, p = 0.0004; LA conduit longitudinal strain: R²=0.27, p < 0.0001. In multivariate analysis, associations with E/A (R2 = 0.39, p = 0.03) and LA conduit strain (R2 = 0.37, p = 0.02) remained significant after adjustment for age, sex, and body mass index.ConclusionsHyF, estimated using FT contours, which are primarily used to quantify LV/LA strain on standard cardiac cine MRI, varied significantly with age in association with subclinical changes in ventricular filling. Its usefulness in cohorts of patients with left heart disease to detect LV-LA uncoupling remains to be evaluated.

Estimate of the hydraulic force in the aging heart: a cardiovascular magnetic resonance imaging study

BackgroundCoupling between left ventricle (LV) and left atrium (LA) plays a central role in the process of cardiac remodeling during aging and development of cardiac disease. The hydraulic force (HyF) is related to variation in size between LV and LA. The objectives of this study were to: (1) derive an estimate of left atrioventricular HyF using cine- Magnetic Resonance Imaging (MRI) in healthy subjects with a wide age range, and (2) study its relationship with age and conventional diastolic function parameters, as estimated by reference echocardiography.MethodsWe studied 119 healthy volunteers (mean age 44 ± 17 years, 58 women) who underwent Doppler echocardiography and MRI on the same day. Conventional transmitral flow early (E) and late (A) LV filling peak velocities as well as mitral annulus diastolic longitudinal peak velocity (E’) were derived from echocardiography. MRI cine SSFP images in longitudinal two and four chamber views were acquired, and analyzed using feature tracking (FT) software. In addition to conventional LV and LA strain measurements, FT-derived LV and LA contours were further used to calculate chamber cross-sectional areas. HyF was approximated as the difference between the LV and LA maximal cross-sectional areas in the diastasis phase corresponding to the lowest LV-LA pressure gradient. Univariate and multivariate analyses while adjusting for appropriate variables were used to study the associations between HyF and age as well as diastolic function and strain indices.ResultsHyF decreased significantly with age (R²=0.34, p < 0.0001). In addition, HyF was significantly associated with conventional indices of diastolic function and LA strain: E/A: R²=0.24, p < 0.0001; E’: R²=0.24, p < 0.0001; E/E’: R²=0.12, p = 0.0004; LA conduit longitudinal strain: R²=0.27, p < 0.0001. In multivariate analysis, associations with E/A (R2 = 0.39, p = 0.03) and LA conduit strain (R2 = 0.37, p = 0.02) remained significant after adjustment for age, sex, and body mass index.ConclusionsHyF, estimated using FT contours, which are primarily used to quantify LV/LA strain on standard cardiac cine MRI, varied significantly with age in association with subclinical changes in ventricular filling. Its usefulness in cohorts of patients with left heart disease to detect LV-LA uncoupling remains to be evaluated.

Accelerated cardiac cine magnetic resonance imaging using deep low-rank plus sparse network: validation in patients.

Accurate and reproducible assessment of left ventricular (LV) volumes is important in managing various cardiac conditions. However, patients are required to hold their breath multiple times during data acquisition, which may result in discomfort and restrict cardiac motion, potentially compromising the accuracy of the detected results. Accelerated imaging techniques can help reduce the number of breath holds needed, potentially improving patient comfort and the reliability of the LV assessment. This study aimed to prospectively evaluate the feasibility and accuracy of LV assessment with a model-based low-rank plus sparse network (L+S-Net) for accelerated magnetic resonance (MR) cine imaging. Fourty-one patients with different cardiac conditions were recruited in this study. Both accelerated MR cine imaging with L+S-Net and traditional electrocardiogram (ECG)-gated segmented cine were performed for each patient. Subjective image quality (IQ) score and quantitative LV volume function parameters were measured and compared between L+S-Net and traditional standards. The IQ score and LV volume measurements of cardiovascular magnetic resonance (CMR) images reconstructed by L+S-Net and standard cine were compared by paired t-test. The acquisition time of the two methods was also calculated. In a quantitative analysis, L+S-Net and standard cine yielded similar measurements for all parameters of LV function (ejection fraction: 35±22 for standard vs. 33±23 for L+S-Net), although L+S-Net had slightly lower IQ scores than standard cine CMR (4.2±0.5 for L+S-Net vs. 4.8±0.4 for standard cine; P<0.001). The mean acquisition time of L+S-Net and standard cine was 0.83±0.08 vs. 6.35±0.78 s per slice (P<0.001). Assessment of LV function with L+S-Net at 3.0 T yields comparable results to the reference standard, albeit with a reduced acquisition time. This feature enhances the clinical applicability of the L+S-Net approach, helping alleviate patient discomfort and motion artifacts that may arise due to prolonged acquisition time.

Inter-scanner super-resolution of 3D cine MRI using a transfer-learning network for MRgRT

Objective. Deep-learning networks for super-resolution (SR) reconstruction enhance the spatial-resolution of 3D magnetic resonance imaging (MRI) for MR-guided radiotherapy (MRgRT). However, variations between MRI scanners and patients impact the quality of SR for real-time 3D low-resolution (LR) cine MRI. In this study, we present a personalized super-resolution (psSR) network that incorporates transfer-learning to overcome the challenges in inter-scanner SR of 3D cine MRI. Approach: Development of the proposed psSR network comprises two-stages: (1) a cohort-specific SR (csSR) network using clinical patient datasets, and (2) a psSR network using transfer-learning to target datasets. The csSR network was developed by training on breath-hold and respiratory-gated high-resolution (HR) 3D MRIs and their k-space down-sampled LR MRIs from 53 thoracoabdominal patients scanned at 1.5 T. The psSR network was developed through transfer-learning to retrain the csSR network using a single breath-hold HR MRI and a corresponding 3D cine MRI from 5 healthy volunteers scanned at 0.55 T. Image quality was evaluated using the peak-signal-noise-ratio (PSNR) and the structure-similarity-index-measure (SSIM). The clinical feasibility was assessed by liver contouring on the psSR MRI using an auto-segmentation network and quantified using the dice-similarity-coefficient (DSC). Results. Mean PSNR and SSIM values of psSR MRIs were increased by 57.2% (13.8–21.7) and 94.7% (0.38–0.74) compared to cine MRIs, with the reference 0.55 T breath-hold HR MRI. In the contour evaluation, DSC was increased by 15% (0.79–0.91). Average time consumed for transfer-learning was 90 s, psSR was 4.51 ms per volume, and auto-segmentation was 210 ms, respectively. Significance. The proposed psSR reconstruction substantially increased image and segmentation quality of cine MRI in an average of 215 ms across the scanners and patients with less than 2 min of prerequisite transfer-learning. This approach would be effective in overcoming cohort- and scanner-dependency of deep-learning for MRgRT.

Multi-Modal in Vitro Experiments Mimicking the Flow Through a Mitral Heart Valve Phantom.

Fluid-structure interaction (FSI) models are more commonly applied in medical research as computational power is increasing. However, understanding the accuracy of FSI models is crucial, especially in the context of heart valve disease in patient-specific models. Therefore, this study aimed to create a multi-modal benchmarking data set for cardiac-inspired FSI models, based on clinically important parameters, such as the pressure, velocity, and valve opening, with an in vitro phantom setup. An in vitro setup was developed with a 3D-printed phantom mimicking the left heart, including a deforming mitral valve. A range of pulsatile flows were created with a computer-controlled motor-and-pump setup. Catheter pressure measurements, magnetic resonance imaging (MRI), and echocardiography (Echo) imaging were used to measure pressure and velocity in the domain. Furthermore, the valve opening was quantified based on cine MRI and Echo images. The experimental setup, with 0.5% cycle-to-cycle variation, was successfully built and six different flow cases were investigated. Higher velocity through the mitral valve was observed for increased cardiac output. The pressure difference across the valve also followed this trend. The flow in the phantom was qualitatively assessed by the velocity profile in the ventricle and by streamlines obtained from 4D phase-contrast MRI. A multi-modal set of data for validation of FSI models has been created, based on parameters relevant for diagnosis of heart valve disease. All data is publicly available for future development of computational heart valve models.

Exercise MR of Skeletal Muscles, the Heart, and the Brain.

Magnetic resonance (MR) imaging (MRI) is routinely used to evaluate organ morphology and pathology in the human body at rest or in combination with pharmacological stress as an exercise surrogate. With MR during actual physical exercise, we can assess functional characteristics of tissues and organs under real-life stress conditions. This is particularly relevant in patients with limited exercise capacity or exercise intolerance, and where complaints typically present only during physical activity, such as in neuromuscular disorders, inherited metabolic diseases, and heart failure. This review describes practical and physiological aspects of exercise MR of skeletal muscles, the heart, and the brain. The acute effects of physical exercise on these organs are addressed in the light of various dynamic quantitative MR readouts, including phosphorus-31 MR spectroscopy (31P-MRS) of tissue energy metabolism, phase-contrast MRI of blood flow and muscle contraction, real-time cine MRI of cardiac performance, and arterial spin labeling MRI of muscle and brain perfusion. Exercise MR will help advancing our understanding of underlying mechanisms that contribute to exercise intolerance, which often proceed structural and anatomical changes in disease. Its potential to detect disease-driven alterations in organ function, perfusion, and metabolism under physiological stress renders exercise MR stress testing a powerful noninvasive imaging modality to aid in disease diagnosis and risk stratification. Although not yet integrated in most clinical workflows, and while some applications still require thorough validation, exercise MR has established itself as a comprehensive and versatile modality for characterizing physiology in health and disease in a noninvasive and quantitative way. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.

A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance.

A challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists, including cardiologists, radiologists, and geneticists. This variability in the interpretation and classification of LVH leads to inconsistent diagnoses. LVH, HCM, and Fabry cardiomyopathy can be differentiated using T1 mapping on cardiac magnetic resonance imaging (MRI). However, differentiation between HCM and Fabry cardiomyopathy using echocardiography or MRI cine images is challenging for cardiologists. Our proposed system named the MRI short-axis view left ventricular hypertrophy classifier (MSLVHC) is a high-accuracy standardized imaging classification model developed using AI and trained on MRI short-axis (SAX) view cine images to distinguish between HCM and Fabry disease. The model achieved impressive performance, with an F1-score of 0.846, an accuracy of 0.909, and an AUC of 0.914 when tested on the Taipei Veterans General Hospital (TVGH) dataset. Additionally, a single-blinding study and external testing using data from the Taichung Veterans General Hospital (TCVGH) demonstrated the reliability and effectiveness of the model, achieving an F1-score of 0.727, an accuracy of 0.806, and an AUC of 0.918, demonstrating the model's reliability and usefulness. This AI model holds promise as a valuable tool for assisting specialists in diagnosing LVH diseases.

Feasibility of Continuous Bronchoscopy During Exercise in the assessment of large airway movement in healthy subjects.

Excessive dynamic airway collapse (EDAC) is a recognized cause of exertional dyspnea arising due to invagination of the trachea and/ or main bronchi. EDAC is typically assessed by evaluating large airway movement with forced expiratory maneuvers. This differs from the respiratory response to exercise hyperpnea. We aimed to evaluate large airway movement during physical activity, with continuous bronchoscopy during exercise (CBE), in healthy subjects and compare findings with resting bronchoscopic maneuvers and imaging techniques. Twenty-eight individuals were recruited to complete two visits including treadmill-based CBE, to voluntary exhaustion and cine magnetic resonance imaging (MRI) with forced expiratory maneuvers at rest. 25 subjects (aged 29 (26 - 33) years, 52% female) completed the study (n=2 withdrew before bronchoscopy, and one was unable to tolerate insertion of bronchoscope). The majority (76%) achieved a peak heart rate of >90% predicted during CBE. The procedure was prematurely terminated in five subjects (n=3; elevated blood pressure and n=2; minor oxygen desaturation). The CBE assessment enabled adequate tracheal visualization in all cases. Excessive dynamic airway collapse (tracheal collapse ≥50%) was identified in 16 subjects (64%) on MRI, and in 6 (24%) individuals during resting bronchoscopy, but in no cases with CBE. No serious adverse events were reported, but minor adverse events were evident. The CBE procedure permits visualization of large airway movement during physical activity. In healthy subjects, there was no evidence of EDAC during strenuous exercise, despite evidence during forced maneuvers on imaging, thus challenging conventional approaches to diagnosis.

3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR).

Objective. 3D cine-magnetic resonance imaging (cine-MRI) can capture images of the human body volume with high spatial and temporal resolutions to study anatomical dynamics. However, the reconstruction of 3D cine-MRI is challenged by highly under-sampled k-space data in each dynamic (cine) frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly under-sampled data.Approach. STINR-MR used a joint reconstruction and deformable registration approach to achieve a high acceleration factor for cine volumetric imaging. It addressed the ill-posed spatiotemporal reconstruction problem by solving a reference-frame 3D MR image and a corresponding motion model that deforms the reference frame to each cine frame. The reference-frame 3D MR image was reconstructed as a spatial implicit neural representation (INR) network, which learns the mapping from input 3D spatial coordinates to corresponding MR values. The dynamic motion model was constructed via a temporal INR, as well as basis deformation vector fields (DVFs) extracted from prior/onboard 4D-MRIs using principal component analysis. The learned temporal INR encodes input time points and outputs corresponding weighting factors to combine the basis DVFs into time-resolved motion fields that represent cine-frame-specific dynamics. STINR-MR was evaluated using MR data simulated from the 4D extended cardiac-torso (XCAT) digital phantom, as well as two MR datasets acquired clinically from human subjects. Its reconstruction accuracy was also compared with that of the model-based non-rigid motion estimation method (MR-MOTUS) and a deep learning-based method (TEMPEST).Main results. STINR-MR can reconstruct 3D cine-MR images with high temporal (<100 ms) and spatial (3 mm) resolutions. Compared with MR-MOTUS and TEMPEST, STINR-MR consistently reconstructed images with better image quality and fewer artifacts and achieved superior tumor localization accuracy via the solved dynamic DVFs. For the XCAT study, STINR reconstructed the tumors to a mean ± SD center-of-mass error of 0.9 ± 0.4 mm, compared to 3.4 ± 1.0 mm of the MR-MOTUS method. The high-frame-rate reconstruction capability of STINR-MR allows different irregular motion patterns to be accurately captured.Significance. STINR-MR provides a lightweight and efficient framework for accurate 3D cine-MRI reconstruction. It is a 'one-shot' method that does not require external data for pre-training, allowing it to avoid generalizability issues typically encountered in deep learning-based methods.

Ablation-induced left atrial mechanical dysfunction recovers in weeks after ablation.

The immediate impact of catheter ablation on left atrial mechanical function and the timeline for its recovery in patients undergoing ablation for atrial fibrillation (AF) remain uncertain. The mechanical function response to catheter ablation in patients with different AF types is poorly understood. A total of 113 AF patients were included in this retrospective study. Each patient had three magnetic resonance imaging (MRI) studies in sinus rhythm: one pre-ablation, one immediate post-ablation (within 2days after ablation), and one post-ablation follow-up MRI (≤ 3months). We used feature tracking in the MRI cine images to determine peak longitudinal atrial strain (PLAS). We evaluated the change in strain from pre-ablation, immediately after ablation to post-ablation follow-up in a short-term study (< 50days) and a 3-month study (3months after ablation). The PLAS exhibited a notable reduction immediately after ablation, compared to both pre-ablation levels and those observed in follow-up studies conducted at short-term (11.1 ± 9.0days) and 3-month (69.6 ± 39.6days) intervals. However, there was no difference between follow-up and pre-ablation PLAS. The PLAS returned to 95% pre-ablation level within 10days. Paroxysmal AF patients had significantly higher pre-ablation PLAS than persistent AF patients in pre-ablation MRIs. Both type AF patients had significantly lower immediate post-ablation PLAS compared with pre-ablation and post-ablation PLAS. The present study suggested a significant drop in PLAS immediately after ablation. Left atrial mechanical function recovered within 10days after ablation. The drop in PLAS did not show a substantial difference between paroxysmal and persistent AF patients.

Convolutional LSTM model for cine image prediction of abdominal motion.

Objective.In this study, we tackle the challenge of latency in magnetic resonance linear accelerator (MR-Linac) systems, which compromises target coverage accuracy in gated real-time radiotherapy. Our focus is on enhancing motion prediction precision in abdominal organs to address this issue. We developed a convolutional long short-term memory (convLSTM) model, utilizing 2D cine magnetic resonance (cine-MR) imaging for this purpose.Approach.Our model, featuring a sequence-to-one architecture with six input frames and one output frame, employs structural similarity index measure (SSIM) as loss function. Data was gathered from 17 cine-MRI datasets using the Philips Ingenia MR-sim system and an Elekta Unity MR-Linac equivalent sequence, focusing on regions of interest (ROIs) like the stomach, liver, pancreas, and kidney. The datasets varied in duration from 1 to 10 min.Main results.The study comprised three main phases: hyperparameter optimization, individual training, and transfer learning with or without fine-tuning. Hyperparameters were initially optimized to construct the most effective model. Then, the model was individually applied to each dataset to predict images four frames ahead (1.24-3.28 s). We evaluated the model's performance using metrics such as SSIM, normalized mean square error, normalized correlation coefficient, and peak signal-to-noise ratio, specifically for ROIs with target motion. The average SSIM values achieved were 0.54, 0.64, 0.77, and 0.66 for the stomach, liver, kidney, and pancreas, respectively. In the transfer learning phase with fine-tuning, the model showed improved SSIM values of 0.69 for the liver and 0.78 for the kidney, compared to 0.64 and 0.37 without fine-tuning.Significance. The study's significant contribution is demonstrating the convLSTM model's ability to accurately predict motion for multiple abdominal organs using a Unity-equivalent MR sequence. This advancement is key in mitigating latency issues in MR-Linac radiotherapy, potentially improving the precision and effectiveness of real-time treatment for abdominal cancers.

Analysis of left ventricle regional myocardial motion for cardiac radioablation: Left ventricular motion analysis.

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. 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. The greatest overall motion was observed in the control group-long axis: 4.5±1.2mm (segment 13 [apical anterior] epicardium) to 13.8±3.0mm (segment 6 [basal anterolateral] endocardium), short axis: 4.3±0.8mm (segment 9 [mid inferoseptal] epicardium) to 11.5±2.3mm (segment 1 [basal anterior] endocardium). HF patients exhibited lesser motion, with the smallest overall displacements observed in the HFrEF group-long axis: 4.3±1.7mm (segment 13 [apical anterior] epicardium) to 10.6±3.4mm (segment 6 [basal anterolateral] endocardium), short axis: 3.9±1.3mm (segment 8 [mid anteroseptal] epicardium) to 7.4±2.8mm (segment 1 [basal anterior] endocardium). 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.

Assessment and validation of glottic motion using cone-beam CT and real-time cine MRI

PurposeThis study aimed to assess the margin for the planning target volume (PTV) using the Van Herk formula. We then validated the proposed margin by real-time magnetic resonance imaging (MRI).MethodsAn analysis of cone-beam computed tomography (CBCT) data from early glottic cancer patients was performed to evaluate organ motion. Deformed clinical target volumes (CTV) after rigid registration were acquired using the Velocity program (Varian Medical Systems, Palo Alto, CA, USA). Systematic (Σ) and random errors (σ) were evaluated. The margin for the PTV was defined as 2.5 Σ + 0.7 σ according to the Van Herk formula. To validate this margin, we accrued healthy volunteers. Sagittal real-time cine MRI was conducted using the ViewRay system (ViewRay Inc., Oakwood Village, OH, USA). Within the obtained sagittal images, the vocal cord was delineated. The movement of the vocal cord was summed up and considered as the internal target volume (ITV). We then assessed the degree of overlap between the ITV and the PTV (vocal cord plus margins) by calculating the volume overlap ratio, represented as (ITV∩PTV)/ITV.ResultsCBCTs of 17 early glottic patients were analyzed. Σ and σ were 0.55 and 0.57 for left–right (LR), 0.70 and 0.60 for anterior–posterior (AP), and 1.84 and 1.04 for superior–inferior (SI), respectively. The calculated margin was 1.8 mm (LR), 2.2 mm (AP), and 5.3 mm (SI). Four healthy volunteers participated for validation. A margin of 3 mm (AP) and 5 mm (SI) was applied to the vocal cord as the PTV. The average volume overlap ratio between ITV and PTV was 0.92 (range 0.85–0.99) without swallowing and 0.77 (range 0.70–0.88) with swallowing.ConclusionBy evaluating organ motion by using CBCT, the margin was 1.8 (LR), 2.2 (AP), and 5.3 mm (SI). The margin acquired using CBCT fitted well in real-time cine MRI. Given that swallowing during radiotherapy can result in a substantial displacement, it is crucial to consider strategies aimed at minimizing swallowing and related motion.

Characterization of real-time cine MR imaging distortion on 0.35 T MRgRT with concentric cine imaging QA phantom

Objective. Real-time MRgRT uses 2D-cine imaging for target tracking and motion evaluation. Rotation of gantry induced B 0 off-resonance, resulting in image artifacts and imaging isocenter-shift precluding MR-guided arc therapy. Standard MRI phantoms designed for higher resolution images face challenges when low-resolution cine imaging is needed to achieve high frame rates. This work aimed to examine the spatial accuracy including geometric distortion and isocenter shift in real-time during gantry rotation on a 0.35 T MR-Linac using the concentric Cine imaging quality assurance (QA) phantom and its associated image analysis software. Approach. The Cine imaging QA phantom consists of two concentric shells of low-T1 mineral oil and a central alignment structure. The phantom was scanned on three different MRI systems; 0.55 T Siemens Free.Max, 1.5 T Philips Ingenia, and 0.35 T ViewRay MRIdian MR-Linac using 2D balanced steady-state free precession (bSSFP) imaging sequence. In addition, bSSFP cine MRI with the banding artifact correction was tested on 0.35 T ViewRay MR-Linac. Images from the MR-Linac were acquired with the Linac gantry stationary and rotating from gantry 300°→ 0° and vice versa. Three orthogonal image planes were scanned excluding the 1.5 T Philips Ingenia, where only the axial plane was scanned. The image analysis software calculated the distortion values as well as the isocenter position for each cine frame. Main results. The geometric distortion of cine imaging on MRIs and MR-Linac at gantry stationary are within 1 mm while the substantial geometric distortion of 2 and 2.2 mm were observed on 0.35 T MR-Linac while rotating the gantry clockwise (300°→ 0°) and counterclockwise 0°→ 300° respectively. The average imaging isocenter shift was 0.1 mm for both MRIs and the static gantry and imaging isocenter shift of ≤1.5 mm was observed during the gantry rotation. The imaging isocenter shift decreased by 1 ± 0.2 mm clockwise and counterclockwise with B 0 compensation. Significance. The concentric Cine imaging QA phantom and its associated software effectively demonstrate the image distortion on real-time cine imaging on regular MRIs and 0.35 T MR-Linac. The results of significant geometric distortion with a rotating gantry in the MR-Linac system require further investigation to alleviate the extent of the image distortion.

STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution

Cardiac cine magnetic resonance imaging (MRI) is a commonly used clinical tool for evaluating cardiac function and morphology. However, its diagnostic accuracy may be compromised by the low spatial resolution. Current methods for cine MRI super-resolution reconstruction still have limitations. They typically rely on 3D convolutional neural networks or recurrent neural networks, which may not effectively capture long-range or non-local features due to their limited receptive fields. Optical flow estimators are also commonly used to align neighboring frames, which may cause information loss and inaccurate motion estimation. Additionally, pre-warping strategies may involve interpolation, leading to potential loss of texture details and complicated anatomical structures. To overcome these challenges, we propose a novel Spatial-Temporal Attention-Guided Dual-Path Network (STADNet) for cardiac cine MRI super-resolution. We utilize transformers to model long-range dependencies in cardiac cine MR images and design a cross-frame attention module in the location-aware spatial path, which enhances the spatial details of the current frame by using complementary information from neighboring frames. We also introduce a recurrent flow-enhanced attention module in the motion-aware temporal path that exploits the correlation between cine MRI frames and extracts the motion information of the heart. Experimental results demonstrate that STADNet outperforms SOTA approaches and has significant potential for clinical practice.

Patient-Specific Haemodynamic Analysis of Virtual Grafting Strategies in Type-B Aortic Dissection: Impact of Compliance Mismatch

IntroductionCompliance mismatch between the aortic wall and Dacron Grafts is a clinical problem concerning aortic haemodynamics and morphological degeneration. The aortic stiffness introduced by grafts can lead to an increased left ventricular (LV) afterload. This study quantifies the impact of compliance mismatch by virtually testing different Type-B aortic dissection (TBAD) surgical grafting strategies in patient-specific, compliant computational fluid dynamics (CFD) simulations.Materials and MethodsA post-operative case of TBAD was segmented from computed tomography angiography data. Three virtual surgeries were generated using different grafts; two additional cases with compliant grafts were assessed. Compliant CFD simulations were performed using a patient-specific inlet flow rate and three-element Windkessel outlet boundary conditions informed by 2D-Flow MRI data. The wall compliance was calibrated using Cine-MRI images. Pressure, wall shear stress (WSS) indices and energy loss (EL) were computed.ResultsIncreased aortic stiffness and longer grafts increased aortic pressure and EL. Implementing a compliant graft matching the aortic compliance of the patient reduced the pulse pressure by 11% and EL by 4%. The endothelial cell activation potential (ECAP) differed the most within the aneurysm, where the maximum percentage difference between the reference case and the mid (MDA) and complete (CDA) descending aorta replacements increased by 16% and 20%, respectively.ConclusionThis study suggests that by minimising graft length and matching its compliance to the native aorta whilst aligning with surgical requirements, the risk of LV hypertrophy may be reduced. This provides evidence that compliance-matching grafts may enhance patient outcomes.

Left ventricle segment-specific motion assessment for cardiac-gated radiosurgery.

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.

Technical note: Minimizing CIED artifacts on a 0.35 T MRI-Linac using deep learning.

Artifacts from implantable cardioverter defibrillators (ICDs) are a challenge to magnetic resonance imaging (MRI)-guided radiotherapy (MRgRT). This study tested an unsupervised generative adversarial network to mitigate ICD artifacts in balanced steady-state free precession (bSSFP) cine MRIs and improve image quality and tracking performance for MRgRT. Fourteen healthy volunteers (Group A) were scanned on a 0.35 T MRI-Linac with and without an MR conditional ICD taped to their left pectoral to simulate an implanted ICD. bSSFP MRI data from 12 of the volunteers were used to train a CycleGAN model to reduce ICD artifacts. The data from the remaining two volunteers were used for testing. In addition, the dataset was reorganized three times using a Leave-One-Out scheme. Tracking metrics [Dice similarity coefficient (DSC), target registration error (TRE), and 95 percentile Hausdorff distance (95% HD)] were evaluated for whole-heart contours. Image quality metrics [normalized root mean square error (nRMSE), peak signal-to-noise ratio (PSNR), and multiscale structural similarity (MS-SSIM) scores] were evaluated. The technique was also tested qualitatively on three additional ICD datasets (Group B) including a patient with an implanted ICD. For the whole-heart contour with CycleGAN reconstruction: 1) Mean DSC rose from 0.910 to 0.935; 2) Mean TRE dropped from 4.488 to 2.877mm; and 3) Mean 95% HD dropped from 10.236 to 7.700mm. For the whole-body slice with CycleGAN reconstruction: 1) Mean nRMSE dropped from 0.644 to 0.420; 2) Mean MS-SSIM rose from 0.779 to 0.819; and 3) Mean PSNR rose from 18.744 to 22.368. The three Group B datasets evaluated qualitatively displayed a reduction in ICD artifacts in the heart. CycleGAN-generated reconstructions significantly improved both tracking and image quality metrics when used to mitigate artifacts from ICDs.

Prognostic significance of left atrial strain combined with left ventricular strain using cardiac magnetic resonance feature tracking in patients with heart failure with preserved ejection fraction.

We aimed to evaluate the prognostic value of left ventricular global longitudinal strain (LVGLS) and left atrial strain (LAS) obtained from magnetic resonance imaging (MRI) feature tracking in patients with heart failure with preserved ejection fraction (HFpEF). We retrospectively enrolled consecutive patients with HFpEF admitted to our hospital who underwent cardiac MRI. LVGLS and LAS were obtained from cine MRI by feature tracking. The end point was defined as a composite of all-cause death, myocardial infarction, and hospitalization due to decompensated HF. One-hundred patients with HFpEF were enrolled. Mean LVGLS and LAS were -13.7 ± 3.7% and 22.5 ± 11.6%, respectively. During follow-up of 4.4 ± 1.9years, 24 events occurred. Multivariate Cox proportional hazards model analysis demonstrated LAS was independently associated with adverse cardiac events. Kaplan-Meier curve analysis revealed that the patients with both LVGLS and LAS worse than the median (LVGLS ≥ -12.2% and LAS ≤ 13.8%) had a significantly lower event-free rate compared to those with preserved strain (Log-rank P < 0.001). Simultaneous assessment of LVGLS and LAS using MRI was useful for risk stratification in the patients with HFpEF.