Cine Magnetic Resonance Imaging Research Articles

Abstract 4117594: Fetal Synthetic Glucocorticoid (sGC) Exposure Results in Later Life Cardiac Dysfunction and Ventricular Remodeling in Male Baboon Offspring

The “theory of developmental origins of health and disease” posits that stress in critical developmental periods will program life-course health and may affect cardiovascular (CV) health. Mothers at risk for premature delivery receive synthetic glucocorticoids (sGC) to enhance fetal lung maturation and manage fetal respiratory distress, but sGC exposure is linked to long-term CV dysfunction in offspring. Four groups of male baboons were studied: sGC exposed in utero at middle age (sGC-MA, 13.8±0.2Y), older sGC exposed in utero (sGC-O, 17.1±0.4Y), middle age unexposed (CTR-MA, 13.5±0.6Y), and older, unexposed baboons (CTR-O, 17.4±0.4Y). Gated, breath-hold, cine cardiac magnetic resonance imaging was performed at 3 Tesla (Siemens TIM Trio) to assess left ventricular (LV) function and myocardial strain, analyzed using cvi42® software. Mitochondrial electron transport chain (ETC) complex activity was analyzed using LV tissue from sGC-O and CTR-O euthanized baboons. Protein quantification was used to measure mitochondrial function and RNA-seq assessed differential gene expression and gene ontology (GO) enrichment. Unpaired t-tests were used. Data are reported as mean ± SD. Stroke volume and ejection fraction were similar in the elderly groups (4 sGC-O, 8 CTR-O). However, higher end-systolic and end-diastolic sphericity indexes (SI) and shorter LV axis lengths indicated remodeling. Also, global longitudinal and radial strains were reduced (both p<0.04) in sGC-O vs. CTR-O. Reduced LV cardiac output (p=0.005) was found in 5 sGC-MA animals vs 4 sGC-O animals, but not in 5 CTR-MA vs 8 CTR-O. In sGC-MA vs sGC-O, but not CTR-MA vs CTR-O, significant increases in SI (p=0.001), radial strain (p=0.02), and longitudinal (p=0.03) strain were found, but LV axis length (p=0.03) was reduced. LV mitochondrial analysis in 4 sGC-O vs 4 CTR-O myocardial samples revealed 33.5% less ETC activity (p=0.002) and 19.8% less complex I expression (p=0.048) in sGC. LV RNA-seq identified 63 upregulated and 20 downregulated genes in the sGC-O samples. GO pathway enrichment included changes in heart contraction, wounding responses, and leukocyte regulation pathways. These findings indicate that fetal sGC exposure developmentally programs offspring with LV remodeling and long-term cardiac and gene expression changes in male baboons, the closest available experimental nonhuman primate species to man. Future studies on females will probe sexual dimorphism in developmental programming.

Imaging error reduction in radial cine-MRI with deep learning-based intra-frame motion compensation

Objective.Radial cine-MRI allows for sliding window reconstruction at nearly arbitrary frame rate, promising high-speed imaging for intra-fractional motion monitoring in magnetic resonance guided radiotherapy. However, motion within the reconstruction window may determine the location of the reconstructed target to deviate from the true real-time position (target positioning errors), particularly in cases of fast breathing or for anatomical structures affected by the heartbeat. In this work, we present a proof-of-concept study aiming to enhance radial cine-MR imaging by implementing deep-learning-based intra-frame motion compensation techniques.Approach.A novel network (TransSin-UNet) was proposed to continuously estimate the final-position image of the target, corresponding to end of the frame acquisition. Within the radial k-space reconstruction window, the spatial-temporal dependencies among the sinogram representation of the spokes were modeled by a transformer encoder subnetwork, followed by a UNet subnetwork operating in the spatial domain for pixel-level fine-tuning. By simulating motion-dependent radial sampling with (tiny) golden angles, we generated datasets from 25 4D digital anthropomorphic lung cancer phantoms. The network was then trained and extensively evaluated across datasets characterized by varying azimuthal radial profile increments.Main Results.The method required additional 4.8 ms per frame over the conventional approach involving direct image reconstruction with motion-corrupted spokes. TransSin-UNet outperformed architectures relying solely on transformer encoders or UNets across all the comparative evaluations, leading to a noticeable enhancement in image quality and target positioning accuracy. The normalized root mean-squared error decreased by 50% from the initial value of 0.188 on average, whereas the mean Dice similarity coefficient of the gross tumor volume increased from 85.1% to 96.2% in the investigated cases. Furthermore, the final-positions of anatomical structures undergoing substantial intra-frame deformations were precisely derived.Significance.The proposed approach enables an effective intra-frame motion compensation, offering an opportunity to reduce errors in radial cine-MR imaging for real-time motion management.

Analysis of sagittal plane cine magnetic resonance imaging for measurement of pancreatic tumor residual motion during breath hold and evaluation of gating margins used in radiotherapy treatment.

In pancreatic radiotherapy, residual tumor motion during treatment increases the risk of toxicity. Cine imaging acquired during magnetic resonance guided radiotherapy (MRgRT) enables real-time treatment gating in response to anatomical motion, which can reduce this risk; however, treatment gating can negatively impact the efficiency of treatment. This study aimed to quantify the extent of residual tumor motion during breath hold and evaluate the appropriateness of the treatment gating margins used in current clinical practice. Cine imaging acquired during pancreatic MRgRT of 11 patients on the ViewRay MRIdian was analyzed. The total duration of treatment analyzed was 12 h 13 min. Improved methods for processing and analyzing cine imaging were developed: breath holds were systematically separated with frequency analysis, residual motion was measured with consideration of both the tracking structure contour and centroid, and residual motion measurements were supported by phantom measurements of image scaling, resolution, and noise. Residual motion was measured at angles 0°, 45°, 90°, and 135° to the superior-inferior (SI) direction. Total residual motion was measured by combining directional measurements. The minimum tracking structure displacement resolvable through cine imaging was found to be 1.5mm; therefore, residual motion analysis was limited to 1.5mm spatial resolution. Total residual motion was contained within margins ±1.5, ±3, and ±4.5mm with mean percentage frequencies of 97.0%, 91.1%, and 67.8%. Most residual motion was observed in the SI direction, and significantly more residual motion was measured for the tracking structure contour than the centroid. The results demonstrate that patients are largely able to maintain breath hold positions to within a 3mm margin, thus provide evidence that supports the use of a 3mm gating margin in clinical practice. Residual motion frequently exceeded 1.5mm so a reduction in gating margin would have an undesirable impact on treatment efficiency.

Effect of empagliflozin on epicardial adipose tissue volume in patients with type 2 diabetes, or prediabetes, and heart failure with reduced ejection fraction (SUGAR-DM-HF)

Abstract Background Epicardial adipose tissue (EAT) is postulated to be linked to the pathophysiology of heart failure with reduced ejection fraction (HFrEF), acting both as a biomarker and a potential therapeutic target. Investigating changes in EAT volume following treatment with sodium-glucose cotransporter 2 (SGLT2) inhibitor could provide valuable mechanistic insights. Purpose This posthoc substudy of the SUGAR-DM-HF trial aimed to assess the effect of the SGLT2 inhibitor empagliflozin on EAT volume. Methods Within a multicentre, randomised, double-blind, placebo-controlled trial, the effects of empagliflozin were evaluated in patients with HFrEF (New York Heart Association functional class II-IV, left ventricular ejection fraction (LVEF) ≤40%), and type 2 diabetes or prediabetes. Patients with atrial fibrillation/flutter were excluded to avoid image degradation. Patients were randomly assigned 1:1 to empagliflozin 10mg daily or placebo. EAT volume was quantified using cardiovascular magnetic resonance (CMR) cine imaging at baseline and week 36, and reported as: (a) total EAT volume (mL); (b) EAT volume indexed to body surface area (mL/m2); (c) % change from baseline. Observers were blinded to subject identification, scan date, clinical data, and randomization arm. Scans with major artefacts were not analysed. Results One hundred five patients were randomly assigned: mean age 68.7 (SD, 11.1) years, 77 (73.3%) male, 82 (78.1%) diabetes and 23 (21.9%) prediabetes, mean LVEF 32.5% (9.8%). Baseline EAT volume was available in 101 patients (median [IQR] 85 [67-104] mL and 41 [34-52] mL/m2). Paired baseline and 36-week EAT volume was available in 90 patients. Higher baseline total EAT volume was associated with male gender, higher body mass index, coronary artery disease, elevated serum creatinine, higher left ventricular end-diastolic volume, and higher left ventricular mass (TABLE). EAT volume changed by -2.7 mL / -1.3 mL/m2 / -3.1% in the empagliflozin group versus 0.3 mL / 0.3 mL/m2 / 1.5% in the placebo group (between-group difference -3.4 mL, 95% CI -6.7 to -0.1; p=0.045 and -1.7 mL/m2, 95% CI -3.4 to -0.04; p=0.045) at 36 weeks (FIGURE). The beneficial reduction in left ventricular end-systolic volume indexed to body surface area observed with empagliflozin treatment was not modified by baseline total or indexed EAT volumes (p interaction=0.31 and 0.45, respectively). Conclusion Empagliflozin treatment significantly reduced EAT volume. This may represent an additional benefit of SGLT2 inhibition in HFrEF.

Long-term clinical-pathologic results of enzyme replacement therapy in pre-hypertrophic fabry disease cardiomyopathy

Abstract Background Limited ability of ERT in removing Gb-3 from cardiomyocytes is recognized for advanced FDCM. Pre-hypertrophic FDCM is believed to be cured or stabilized by ERT. However, no pathologic confirmation is available. We report here in the long-term clinical-pathologic impact of ERT on pre-hypertrophic FDCM. Purpose Long-term clinical-pathologic impact of ERT on pre-hypertrophic FDCM. Methods Fifteen Fabry patients with LVMWT ≤ 10.5 mm at CMR required EMB because of angina and/or ventricular arrhythmias. EMB showed coronary small vessel disease in angina cohort and vacuoles in smooth muscle cells and cardiomyocytes around 20% of cell surface containing myelin bodies at electron microscopy. Patients received α-agalsidase in 8 and β-agalsidase in 7 cases. Both groups referred symptoms improvement except 2 patients treated with α- and β-agalsidase respectively. After ERT administration ranging from 4 and 20 years all patients had control CMR and LVEMB because of persistence of symptoms or patients enquiry on disease resolution. Results In 13 asymptomatic FDCM patients, LVMWT and LV-mass, cardiomyocyte diameter, vacuole surface/cell surface ratio and vessels remained unchanged or minimally increased (LV-mass augmented by < 2%) even after 20 years observation and storage material was still present at electron microscopy. In 2 symptomatic patients, FDCM progressed with larger and more engulfed by Gb-3 myocytes being associated to myocardial virus-negative lymphocytic inflammation. Conclusions ERT stabilizes storage deposits and myocyte dimensions in 87% of patients with pre-hypertrophic FDCM. Gb-3 is never completely removed even after long-term treatment. Immune-mediated myocardial inflammation can overlap limiting ERT activity. Figure Legend Patient 11 (24-year-old man) with genetic and biopsy-proven diagnosis of FDCM before and after 20 years ERT. CineMR (A,B) and LGE (C,D) images, performed at baseline (A,C) and 20 years after ERT (B,D) show normal LV wall thickness (8mm and 10 mm) and absence of fibrotic areas. Panel E and F represent semithin section of Epon-embedded EMB samples, stained with basic fuchsin, showing mildly hypertrophied cardiomyocytes containing storage material that result unmodified after long-term ERT administration. Panel G and H are ultrastructural evidence of storage material to consist of myelin bodies (panel G) that remain unchanged after ERT (Panel H).

Dynamic MRI interpolation in temporal direction using an unsupervised generative model

PurposeCardiac cine magnetic resonance imaging (MRI) is an important tool in assessing dynamic heart function. However, this technique requires long acquisition time and long breath holds, which presents difficulties. The aim of this study is to propose an unsupervised neural network framework that can perform cardiac cine interpolation in time, so that we can increase the temporal resolution of cardiac cine without increasing acquisition time. MethodsIn this study, a subject-specific unsupervised generative neural network is designed to perform temporal interpolation for cardiac cine MRI. The network takes in a 2D latent vector in which each element corresponds to one cardiac phase in the cardiac cycle and then the network outputs the cardiac cine images which are acquired on the scanner. After the training of the generative network, we can interpolate the 2D latent vector and input the interpolated latent vector into the network and the network will output the frame-interpolated cine images. The results of the proposed cine interpolation neural network (CINN) framework are compared quantitatively and qualitatively with other state-of-the-art methods, the ground truth training cine frames, and the ground truth frames removed from the original acquisition. Signal-to-noise ratio (SNR), structural similarity index measures (SSIM), peak signal-to-noise ratio (PSNR), strain analysis, as well as the sharpness calculated using the Tenengrad algorithm were used for image quality assessment. ResultsAs shown quantitatively and qualitatively, the proposed framework learns the generative task well and hence performs the temporal interpolation task well. Furthermore, both quantitative and qualitative comparison studies show the effectiveness of the proposed framework in cardiac cine interpolation in time. ConclusionThe proposed generative model can effectively learn the generative task and perform high quality cardiac cine interpolation in time.

Cine-MRI and T1TSE Sequence for Mediastinal Mass.

Background/Objectives: Contrast-enhanced computed tomography (CT) is the standard radiologic examination for evaluating the extent of mediastinal tumors. If tumor infiltration into the large central thoracic vessels, the pericardium, or the myocardium is suspected, cine magnetic resonance imaging (cine-MRI) can provide additional valuable information. Methods: We conducted a retrospective study of patients with mediastinal tumors who were staged with CT, cine-MRI, and a T1-weighted turbo spin echo (T1TSE) prior to surgical resection. Imaging was re-evaluated regarding tumor infiltration into the pericardium, myocardium, superior vena cava, aorta, pulmonary arteries, and atria and compared with intraoperative findings and postoperative histopathological reports (gold standard). Unclear CT findings were further investigated. Results: Forty-seven patients (29 female and 18 male patients; median age: 58 years) met the inclusion criteria. Cine-MRI was able to predict infiltration of the aorta in 86%, pulmonary arteries in 85%, and atria in 80% of unclear CT cases. Aortic tumor infiltration in unclear CT cases was significantly more often correctly diagnosed with cine-MRI than with T1TSE sequence. Conclusions: Additional cine-MRI is of crucial benefit in unclear CT cases. We recommend performing cine-MRI if infiltration into the large central vessels and atria is suspected. T1TSE sequence is of very limited additional value.

Image-Based Computational Fluid Dynamics to Compare Two Repair Techniques for Mitral Valve Prolapse.

OBJECTIVE: The treatment of mitral valve prolapse involves two distinct repair techniques: chordal replacement (Neochordae technique) and leaflet resection (Resection technique). However, there is still a debate in the literature about which is the optimal one. In this context, we performed an image-based computational fluid dynamic study to evaluate blood dynamics in the two surgical techniques. METHODS: We considered a healthy subject (H) and two patients (N and R) who underwent surgery for prolapse of the posterior leaflet and were operated with the Neochordae and Resection technique, respectively. Computational Fluid Dynamics (CFD) was employed with prescribed motion of the entire left heart coming from cine-MRI images, with a Large Eddy Simulation model to describe the transition to turbulence and a resistive method for managing valve dynamics. We created three different virtual scenarios where the operated mitral valves were inserted in the same left heart geometry of the healthy subject to study the differences attributed only to the two techniques. RESULTS: We compared the three scenarios by quantitatively analyzing ventricular velocity patterns and pressures, transition to turbulence, and the ventricle ability to prevent thrombi formation. From these results, we found that the operative techniques affected the ventricular blood dynamics in different ways, with variations attributed to the reduced mobility of the Resection posterior leaflet. Specifically, the Resection technique resulted in turbulent forces, related with the risk of hemolysis formation, up to 640 Pa, while the other two scenarios exhibited a maximum of 240 Pa. Moreover, in correspondence of the ventricular apex, the Resection technique reduced the areas with low velocity to 15%, whereas the healthy case and the Neochordae case maintained these areas at 30 and 48%, respectively. Our findings suggest that the Neochordae technique developed a more physiological flow with respect to the Resection technique. CONCLUSION: Resection technique gives rise to a different direction of the mitral jet during diastole increasing the ability to washout the ventricular apex preventing from thrombi formation, but at the same time it promotes turbulence formation that is associated with ventricular effort and risk of hemolysis.

Role of Cine-Magnetic Resonance Imaging in the Assessment of Mediastinal Masses with Uncertain/Equivocal Findings from Pre-Operative Computed Tomography Scanning.

Malignant neoplasms originating from or involving the mediastinum represent a diagnostic and therapeutic challenge when they are in contact with nearby cardiovascular structures. We aimed to test the diagnostic accuracy of cine-magnetic resonance imaging (cine-MRI) in detecting the infiltration of cardiovascular structures in cases with uncertain or equivocal findings from contrast-enhanced Computed Tomography (CT) scanning. Fifty patients affected by tumors with a suspected invasion of mediastinal cardiovascular structures at the pre-operative chest CT scan stage underwent cine-MRI before surgery at our Institution. Intraoperative findings and the histological post-surgical report were used as a reference standard to define infiltration. Inter- and intra-observer agreement for CT scans and cine-MRI were also computed over a homogenous sample of 14 patients. Cine-MRI had a higher negative predictive value (93% vs. 54%, p < 0.001) than CT scans, higher sensitivity (91% vs. 16%, p < 0.001), as well as greater accuracy (66% vs. 50%, p < 0.001) in detecting cardiovascular invasion. Cine-MRI also showed better inter- and intra-observer agreement for infiltration detection. Cine-MRI outperforms conventional contrast-enhanced chest CT scans in the preoperative assessment of cardiovascular infiltration by mediastinal or pulmonary tumors, making it a useful imaging modality in the preoperative staging and evaluation of patients with equivocal findings at the chest CT scan stage.

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

BackgroundVentricular 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.ObjectiveTo assess the diagnostic performance of accelerated 3-RR cine MRI sequences using deep learning reconstruction compared with standard 2-D cine bSSFP sequences.Material and methodsTwenty-nine consecutive patients (mean age 11 ± 5, median 12, range 1–17 years) 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 experienced readers who 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).ResultsMean acquisition time was significantly reduced with the 3-RR deep learning cine compared to the standard cine sequence (45.5 ± 13.8 s vs. 218.3 ± 44.8 s; 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 ± 33.1 g vs. 69.9 ± 32.5 g; 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).ConclusionDeep 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.Graphical

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.

Cardiac disease discrimination from 3D-convolutional kinematic patterns on cine-MRI sequences.

Cine-MRI (cine-magnetic resonance imaging) sequences are a key diagnostic tool to visualize anatomical information, allowing experts to localize and determine suspicious pathologies. Nonetheless, such analysis remains subjective and prone to diagnosis errors. To develop a binary and multi-class classification considering various cardiac conditions using a spatiotemporal model that highlights kinematic movements to characterize each disease. This research focuses on a 3D convolutional representation to characterize cardiac kinematic patterns during the cardiac cycle, which may be associated with pathologies. The kinematic maps are obtained from the apparent velocity maps computed from a dense optical flow strategy. Then, a 3D convolutional scheme learns to differentiate pathologies from kinematic maps. The proposed strategy was validated with respect to the capability to discriminate among myocardial infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, abnormal right ventricle, and normal cardiac sequences. The proposed method achieves an average accuracy of 78.00% and a F1 score of 75.55%. Likewise, the approach achieved 92.31% accuracy for binary classification between pathologies and control cases. The proposed method can support the identification of kinematically abnormal patterns associated with a pathological condition. The resultant descriptor, learned from the 3D convolutional net, preserves detailed spatiotemporal correlations and could emerge as possible digital biomarkers of cardiac diseases.

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.

MULTIMODAL LEARNING TO IMPROVE CARDIAC LATE MECHANICAL ACTIVATION DETECTION FROM CINE MR IMAGES.

This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. More specifically, we develop a joint learning network that for the first time leverages the accuracy and reproducibility of myocardial strains obtained from Displacement Encoding with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic resonance (CMR) imaging in late mechanical activation (LMA) detection. An image registration network is utilized to acquire the knowledge of cardiac motions, an important feature estimator of strain values, from standard cine CMRs. Our framework consists of two major components: (i) a DENSE-supervised strain network leveraging latent motion features learned from a registration network to predict myocardial strains; and (ii) a LMA network taking advantage of the predicted strain for effective LMA detection. Experimental results show that our proposed work substantially improves the performance of strain analysis and LMA detection from cine CMR images, aligning more closely with the achievements of DENSE.

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.