Utilizing Artificial Intelligence-Based Deformable Registration for Global and Layer-Specific Cardiac MRI Strain Analysis in Healthy Children and Young Adults.

RATIONALE AND OBJECTIVES: The absence of published reference values for multilayer-specific strain measurement using cardiac magnetic resonance (CMR) in young healthy individuals limits its use. This study aimed to establish normal global and layer-specific strain values in healthy children and young adults using a deformable registration algorithm (DRA). MATERIALS AND METHODS: A retrospective study included 131 healthy children and young adults (62 males and 69 females) with a mean age of 16.6 ± 3.9 years. CMR examinations were conducted using 1.5T scanners, and strain analysis was performed using TrufiStrain research prototype software (Siemens Healthineers, Erlangen, Germany). Global and layer-specific strain parameters were extracted from balanced Steady-state free precession cine images. Statistical analyses were conducted to evaluate the impact of demographic variables on strain measurements. RESULTS: The peak global longitudinal strain (LS) was -16.0 ± 3.0%, peak global radial strain (RS) was 29.9 ± 6.3%, and peak global circumferential strain (CS) was -17.0 ± 1.8%. Global LS differed significantly between males and females. Transmural strain analysis showed a consistent pattern of decreasing LS and CS from endocardium to epicardium, while radial strain increased. Basal-to-apical strain distribution exhibited decreasing LS and increasing CS in both global and layer-specific analysis. CONCLUSION: This study uses DRA to provide reference values for global and layer-specific strain in healthy children and young adults. The study highlights the impact of sex and age on LS and body mass index on RS. These insights are vital for future cardiac assessments in children, particularly for early detection of heart diseases.

Feature-Tracking Strain Parameters Differ Between Highly Accelerated and Conventional Acquisitions: A Multisoftware Assessment.

BACKGROUND: Cardiac magnetic resonance imaging protocols have been adapted to fit the needs for faster, more efficient acquisitions, resulting in the development of highly accelerated, compressed sensing-based (CS) sequences. The aim of this study was to evaluate intersoftware and interacquisition differences for postprocessing software applied to both CS and conventional cine sequences. MATERIALS AND METHODS: A total of 106 individuals (66 healthy volunteers, 40 patients with dilated cardiomyopathy, 51% female, 38±17 y) underwent cardiac magnetic resonance at 3T with retrospectively gated conventional cine and CS sequences. Postprocessing was performed using 2 commercially available software solutions and 1 research prototype from 3 different developers. The agreement of clinical and feature-tracking strain parameters between software solutions and acquisition types was assessed by Bland-Altmann analyses and intraclass correlation coefficients. Differences between softwares and acquisitions were assessed using Kruskal-Wallis analysis of variances. In addition, receiver operating characteristic curve-derived cutoffs were used to evaluate whether sequence-specific cutoffs influence disease classification. RESULTS: There were significant intersoftware ( P <0.002 for all except LV end-diastolic volume per body surface area) and interacquisition differences ( P <0.02 for all except end-diastolic volume per body surface area from Neosoft, left ventricular mass per body surface area from cvi42 and TrufiStrain and global circumferential strain from Neosoft). However, the intraclass correlation coefficients between acquisitions were strong-to-excellent for all parameters (all ≥0.81). In comparing individual softwares to a pooled mean, Bland-Altmann analyses revealed smaller magnitudes of bias for cine acquisition than for CS acquisition. In addition, the application of conventional cutoffs to CS measurements did not result in the false reclassification of patients. CONCLUSION: Significantly lower magnitudes of strain and volumetric parameters were observed in retrospectively gated CS acquisitions, despite strong-to-excellent agreement amongst software solutions and acquisition types. It remains important to be aware of the acquisition type in the context of follow-up examinations, where different cutoffs might lead to misclassifications.

MRI Quantification of Left Atrial Circumferential Strain in Mitral Regurgitation: A Feasibility and Reproducibility Study.

BACKGROUND: MRI-derived left atrial (LA) longitudinal strain has been shown to be a marker for mitral regurgitation, but the utility of LA circumferential strain remains unclear. PURPOSE: To assess feasibility and reproducibility of LA circumferential strain, identify changes in mitral regurgitation patients compared to healthy volunteers, and determine strain's association with mitral regurgitation severity and cardiac function. STUDY TYPE: Retrospective. POPULATION: 52 mitral regurgitation patients, 12 healthy volunteers. FIELD STRENGTH/SEQUENCE: Steady-state free precession cine and 2D phase contrast sequences at 1.5T. ASSESSMENT: Peak LA circumferential strain was computed in each of three short-axis slices (superior, mid, and inferior) and longitudinal strain computed from long-axis slices using MRI feature-tracking software. Strain test-retest reproducibility was determined from two repeat studies in healthy volunteers. STATISTICAL TESTS: LA circumferential strain test-retest reproducibility was assessed by intra-class correlation coefficient (ICC). Strain was compared between cohorts using Student's t-test or Mann-Whitney U. Mitral regurgitation severity association with strain and LV function was assessed using Spearman correlation and multivariable regression. Significance was defined as P < 0.05. RESULTS: LA circumferential strain assessment was feasible in all subjects with moderate reproducibility in the superior (ICC = 0.74), mid LA (ICC = 0.71), and inferior LA (ICC = 0.63). In mitral regurgitation patients, LA circumferential strain was significantly lower in the superior (11.86% [6.5%,19.2%] vs. 18.73% ± 6.7%) and mid LA slices (18.41% ± 9.5% vs. 28.7% ± 10.4%) compared to healthy volunteers. Mitral regurgitation severity significantly associated with mid LA circumferential strain (ß = -0.03) and LAV significantly associated with superior LA circumferential strain (ß = -2.09), both independent of LA longitudinal strain and CO. DATA CONCLUSION: LA circumferential strain assessment is feasible with moderate reproducibility. Compared to healthy volunteers, patients had significantly lower LA circumferential strain. Mitral regurgitation severity and LAV were significantly associated with LA circumferential strain independent of LA longitudinal strain. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Measurement of Abdominal Aortic Aneurysm Strain Using MR Deformable Image Registration: Accuracy and Relationship to Recent Aneurysm Progression.

BACKGROUND: Management of asymptomatic abdominal aortic aneurysm (AAA) based on maximum aneurysm diameter and growth rate fails to preempt many ruptures. Assessment of aortic wall biomechanical properties may improve assessment of progression and rupture risk. This study aimed to assess the accuracy of AAA wall strain measured by cine magnetic resonance imaging (MRI) deformable image registration (MR strain) and investigate its relationship with recent AAA progression. METHODS: The MR strain accuracy was evaluated in silico against ground truth strain in 54 synthetic MRIs generated from a finite element model simulation of an AAA patient's abdomen for different aortic pulse pressures, tissue motions, signal intensity variations, and image noise. Evaluation included bias with 95% confidence interval (CI) and correlation analysis. Association of MR strain with AAA growth rate was assessed in 25 consecutive patients with >6 months of prior surveillance, for whom cine balanced steady-state free-precession imaging was acquired at the level of the AAA as well as the proximal, normal-caliber aorta. Univariate and multivariate regressions were used to associate growth rate with clinical variables, maximum AAA diameter (Dmax), and peak circumferential MR strain through the cardiac cycle. The MR strain interoperator variability was assessed using bias with 95% CI, intraclass correlation coefficient, and coefficient of variation. RESULTS: In silico experiments revealed an MR strain bias of 0.48% ± 0.42% and a slope of correlation to ground truth strain of 0.963. In vivo, AAA MR strain (1.2% ± 0.6%) was highly reproducible (bias ± 95% CI, 0.03% ± 0.31%; intraclass correlation coefficient, 97.8%; coefficient of variation, 7.14%) and was lower than in the nonaneurysmal aorta (2.4% ± 1.7%). Dmax (ß = 0.087) and MR strain (ß= -1.563) were both associated with AAA growth rate. The MR strain remained an independent factor associated with growth rate (ß= -0.904) after controlling for Dmax. CONCLUSIONS: Deformable image registration analysis can accurately measure the circumferential strain of the AAA wall from standard cine MRI and may offer patient-specific insight regarding AAA progression.

Deep learning-based left ventricular segmentation demonstrates improved performance on respiratory motion-resolved whole-heart reconstructions.

Introduction: Deep learning (DL)-based segmentation has gained popularity for routine cardiac magnetic resonance (CMR) image analysis and in particular, delineation of left ventricular (LV) borders for LV volume determination. Free-breathing, self-navigated, whole-heart CMR exams provide high-resolution, isotropic coverage of the heart for assessment of cardiac anatomy including LV volume. The combination of whole-heart free-breathing CMR and DL-based LV segmentation has the potential to streamline the acquisition and analysis of clinical CMR exams. The purpose of this study was to compare the performance of a DL-based automatic LV segmentation network trained primarily on computed tomography (CT) images in two whole-heart CMR reconstruction methods: (1) an in-line respiratory motion-corrected (Mcorr) reconstruction and (2) an off-line, compressed sensing-based, multi-volume respiratory motion-resolved (Mres) reconstruction. Given that Mres images were shown to have greater image quality in previous studies than Mcorr images, we hypothesized that the LV volumes segmented from Mres images are closer to the manual expert-traced left ventricular endocardial border than the Mcorr images. Method: This retrospective study used 15 patients who underwent clinically indicated 1.5 T CMR exams with a prototype ECG-gated 3D radial phyllotaxis balanced steady state free precession (bSSFP) sequence. For each reconstruction method, the absolute volume difference (AVD) of the automatically and manually segmented LV volumes was used as the primary quantity to investigate whether 3D DL-based LV segmentation generalized better on Mcorr or Mres 3D whole-heart images. Additionally, we assessed the 3D Dice similarity coefficient between the manual and automatic LV masks of each reconstructed 3D whole-heart image and the sharpness of the LV myocardium-blood pool interface. A two-tail paired Student's t-test (alpha = 0.05) was used to test the significance in this study. Results & Discussion: The AVD in the respiratory Mres reconstruction was lower than the AVD in the respiratory Mcorr reconstruction: 7.73 ± 6.54 ml vs. 20.0 ± 22.4 ml, respectively (n = 15, p-value = 0.03). The 3D Dice coefficient between the DL-segmented masks and the manually segmented masks was higher for Mres images than for Mcorr images: 0.90 ± 0.02 vs. 0.87 ± 0.03 respectively, with a p-value = 0.02. Sharpness on Mres images was higher than on Mcorr images: 0.15 ± 0.05 vs. 0.12 ± 0.04, respectively, with a p-value of 0.014 (n = 15). Conclusion: We conclude that the DL-based 3D automatic LV segmentation network trained on CT images and fine-tuned on MR images generalized better on Mres images than on Mcorr images for quantifying LV volumes.

Prognostic impact of artificial intelligence-based fully automated global circumferential strain in patients undergoing stress CMR.

AIMS: To determine whether fully automated artificial intelligence-based global circumferential strain (GCS) assessed during vasodilator stress cardiovascular (CV) magnetic resonance (CMR) can provide incremental prognostic value. METHODS AND RESULTS: Between 2016 and 2018, a longitudinal study included all consecutive patients with abnormal stress CMR defined by the presence of inducible ischaemia and/or late gadolinium enhancement. Control subjects with normal stress CMR were selected using a propensity score-matching. Stress-GCS was assessed using a fully automatic machine-learning algorithm based on featured-tracking imaging from short-axis cine images. The primary outcome was the occurrence of major adverse clinical events (MACE) defined as CV mortality or nonfatal myocardial infarction. Cox regressions evaluated the association between stress-GCS and the primary outcome after adjustment for traditional prognosticators. In 2152 patients [66 ± 12 years, 77% men, 1:1 matched patients (1076 with normal and 1076 with abnormal CMR)], stress-GCS was associated with MACE [median follow-up 5.2 (4.8-5.5) years] after adjustment for risk factors in the propensity-matched population [adjusted hazard ratio (HR), 1.12 (95% CI, 1.06-1.18)], and patients with normal CMR [adjusted HR, 1.35 (95% CI, 1.19-1.53), both P < 0.001], but not in patients with abnormal CMR (P = 0.058). In patients with normal CMR, an increased stress-GCS showed the best improvement in model discrimination and reclassification above traditional and stress CMR findings (C-statistic improvement: 0.14; NRI = 0.430; IDI = 0.089, all P < 0.001; LR-test P < 0.001). CONCLUSION: Stress-GCS is not a predictor of MACE in patients with ischaemia, but has an incremental prognostic value in those with a normal CMR although the absolute event rate remains low.

AI-Based Fully Automated Left Atrioventricular Coupling Index as a Prognostic Marker in Patients Undergoing Stress CMR.

BACKGROUND: The left atrioventricular coupling index (LACI) is a strong and independent predictor of heart failure (HF) in individuals without clinical cardiovascular disease. Its prognostic value is not established in patients with cardiovascular disease. OBJECTIVES: This study sought to determine in patients undergoing stress cardiac magnetic resonance (CMR) whether fully automated artificial intelligence-based LACI can provide incremental prognostic value to predict HF. METHODS: Between 2016 and 2018, the authors conducted a longitudinal study including all consecutive patients with abnormal (inducible ischemia or late gadolinium enhancement) vasodilator stress CMR. Control subjects with normal stress CMR were selected using propensity score matching. LACI was defined as the ratio of left atrial to left ventricular end-diastolic volumes. The primary outcome included hospitalization for acute HF or cardiovascular death. Cox regression was used to evaluate the association of LACI with the primary outcome after adjustment for traditional risk factors. RESULTS: In 2,134 patients (65 ± 12 years, 77% men, 1:1 matched patients [1,067 with normal and 1,067 with abnormal CMR]), LACI was positively associated with the primary outcome (median follow-up: 5.2 years [IQR: 4.8-5.5 years]) before and after adjustment for risk factors in the overall propensity-matched population (adjusted HR: 1.18 [95% CI: 1.13-1.24]), in patients with abnormal CMR (adjusted HR per 0.1% increment: 1.22 [95% CI: 1.14-1.30]), and in patients with normal CMR (adjusted HR per 0.1% increment: 1.12 [95% CI: 1.05-1.20]) (all P < 0.001). After adjustment, a higher LACI of ≥25% showed the greatest improvement in model discrimination and reclassification over and above traditional risk factors and stress CMR findings (C-index improvement: 0.16; net reclassification improvement = 0.388; integrative discrimination index = 0.153, all P < 0.001; likelihood ratio test P < 0.001). CONCLUSIONS: LACI is independently associated with hospitalization for HF and cardiovascular death in patients undergoing stress CMR, with an incremental prognostic value over traditional risk factors including inducible ischemia and late gadolinium enhancement.

Analysis of three-chamber view conventional and tagged cine MRI in patients with suspected hypertrophic cardiomyopathy.

OBJECTIVES: To investigate the potential value of adding a tagged three-chamber (3Ch) cine to clinical hypertrophic cardiomyopathy (HCM) magnetic resonance imaging (MRI) protocols, including to help distinguish HCM patients with regionally impaired cardiac function. METHODS: Forty-eight HCM patients, five patients with "septal knuckle" (SK), and 20 healthy volunteers underwent MRI at 1.5T; a tagged 3Ch cine was added to the protocol. Regional strain, myocardial wall thickness, and mitral valve leaflet lengths were measured in the 3Ch view. RESULTS: In HCM, we found a reduced tangential strain with decreased diastolic relaxation in both hypertrophied (p = 0.003) and remote segments (p = 0.035). Strain in the basal septum correlated with the length of the coaptation zone + residual leaflet (r = 0.48, p < 0.001). In the basal free wall, patients with SK had faster relaxation compared to HCM patients with septal hypertrophy. DISCUSSION: The 3Ch tagged MRI sequence provides useful information for the examination of suspected HCM patients, with minimal additional time cost. Local wall function is closely associated with morphological changes of the mitral apparatus measured in the same plane and may provide insights into mechanisms of obstruction. The additional strain information may be helpful when analyzing local myocardial wall motion patterns in the presence of SK.

MRI in cardio-oncology: A review of cardiac complications in oncologic care.

From detailed characterization of cardiac abnormalities to the assessment of cancer treatment-related cardiac dysfunction, cardiac MRI is playing a growing role in the evaluation of cardiac pathology in oncology patients. Current guidelines are now incorporating the use of MRI for the comprehensive multidisciplinary approach to cancer management, and innovative applications of MRI in research are expanding its potential to provide a powerful noninvasive tool in the arsenal against cancer. This review focuses on the application of cardiac MRI to diagnose and manage cardiovascular complications related to cancer and its treatment. Following an introduction to current cardiac MRI methods and principles, this review is divided into two sections: functional cardiovascular analysis and anatomical or tissue characterization related to cancer and cancer therapeutics. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:1349-1366.

Pancreas deformation in the presence of tumors using feature tracking from free-breathing XD-GRASP MRI.

BACKGROUND: Quantifying the biomechanical properties of pancreatic tumors could potentially help with assessment of tumor aggressiveness, prognosis, and prediction of therapy response. PURPOSE: To quantify respiratory-induced deformation in the pancreas and pancreatic lesions using XD-GRASP (eXtra-Dimensional Golden-angle RAdial Sparse Parallel), MRI. STUDY TYPE: Retrospective study where patients undergoing clinically indicated abdominal MRI which included free-breathing radial T1 -weighted (T1 W) imaging were studied. SUBJECTS: Thirty-two patients (12 male and 20 female) including nine with pancreatic lesions constituted our study cohort. FIELD STRENGTH/SEQUENCE: 3.0 T with T1 WI contrast-enhanced gradient echo radial free-breathing acquisition. ASSESSMENT: Using the XD-GRASP imaging technique, the acquired free-breathing radial data were sorted and binned into 10 consecutive respiratory motion states that were jointly reconstructed. 3D deformation fields along the respiratory dimension were computed using an optical flow method and were analyzed in the pancreas. STATISTICAL TESTS: The Wilcoxon signed-rank test was used to assess the difference in average displacement across pancreatic regions, while the Wilcoxon rank-sum test was used for displacement differences between patients with and without tumors. The interclass correlation coefficient (ICC) was computed to assess consistency between observers for each image quality measure. RESULTS: There was a significantly larger displacement in the pancreatic tail compared with the head (8.2 ± 3.7 mm > 5.8 ± 2.4 mm; P < 0.001) and body regions (8.2 ± 3.7 mm > 6.6 ± 2.9 mm; P < 0.001). Furthermore, there was reduced normalized average displacement in patients with pancreatic lesions compared with subjects without lesions (0.33 ± 0.1 < 0.69 ± 0.26, P < 0.001 for the head; 0.30 ± 0.1 < 0.84 ± 0.31, P < 0.001 for the body; and 0.44 ± 0.31 < 1.08 ± 0.53, P < 0.001 for the tail, respectively). DATA CONCLUSION: Free-breathing respiratory motion-sorted XD-GRASP MRI has the potential to noninvasively characterize the biomechanical properties of the pancreas by quantifying breathing-induced mechanical displacement. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1633-1640.

Two-dimensional XD-GRASP provides better image quality than conventional 2D cardiac cine MRI for patients who cannot suspend respiration.

OBJECTIVES: Residual respiratory motion degrades image quality in conventional cardiac cine MRI (CCMRI). We evaluated whether a free-breathing (FB) radial imaging CCMRI sequence with compressed sensing reconstruction [extradimensional (e.g. cardiac and respiratory phases) golden-angle radial sparse parallel, or XD-GRASP] could provide better image quality than a conventional Cartesian breath-held (BH) sequence in an unselected population of patients undergoing clinical CCMRI. MATERIALS AND METHODS: One hundred one patients who underwent BH and FB imaging in a midventricular short-axis plane at a matching location were included. Visual and quantitative image analysis was performed by two blinded experienced readers, using a five-point qualitative scale to score overall image quality and visual signal-to-noise ratio (SNR) grade, with measures of noise and sharpness. End-diastolic and end-systolic left ventricular areas were also measured and compared for both BH and FB images. RESULTS: Image quality was generally better with the BH cines (overall quality grade for BH vs FB images 4 vs 2.9, p < 0.001; noise 0.06 vs 0.08 p < 0.001; SNR grade 4.1 vs 3, p < 0.001), except for sharpness (p = 0.48). There were no significant differences between BH and FB images regarding end-diastolic or end-systolic areas (p = 0.35 and p = 0.12). Eighteen of the 101 patients had poor BH image quality (grade 1 or 2). In this subgroup, the quality of the FB images was better (p = 0.0032), as was the SNR grade (p = 0.003), but there were no significant differences regarding noise and sharpness (p = 0.45 and p = 0.47). CONCLUSION: Although FB XD-GRASP CCMRI was visually inferior to conventional BH CCMRI in general, it provided improved image quality in the subgroup of patients with respiratory-motion-induced artifacts on BH images.

Magnetic resonance imaging of myocardial strain: A review of current approaches.

Contraction of the heart is central to its purpose of pumping blood around the body. While simple global function measures (such as the ejection fraction) are most commonly used in the clinical assessment of cardiac function, MRI also provides a range of approaches for quantitatively characterizing regional cardiac function, including the local deformation (or strain) within the heart wall. While they have been around for some years, these methods are still undergoing further technical development, and they have had relatively little clinical evaluation. However, they can provide potentially useful new ways to assess cardiac function, which may be able to contribute to better classification and treatment of heart disease. This article provides some basic background on the physical and physiological factors that determine the motion of the heart, in health and disease and then reviews some of the ways that MRI methods are being developed to image and quantify strain within the myocardium. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1263-1280.

Automatic MRI segmentation of para-pharyngeal fat pads using interactive visual feature space analysis for classification.

BACKGROUND: Obstructive sleep apnea (OSA) is a public health problem. Detailed analysis of the para-pharyngeal fat pads can help us to understand the pathogenesis of OSA and may mediate the intervention of this sleeping disorder. A reliable and automatic para-pharyngeal fat pads segmentation technique plays a vital role in investigating larger data bases to identify the anatomic risk factors for the OSA. METHODS: Our research aims to develop a context-based automatic segmentation algorithm to delineate the fat pads from magnetic resonance images in a population-based study. Our segmentation pipeline involves texture analysis, connected component analysis, object-based image analysis, and supervised classification using an interactive visual analysis tool to segregate fat pads from other structures automatically. RESULTS: We developed a fully automatic segmentation technique that does not need any user interaction to extract fat pads. Our algorithm is fast enough that we can apply it to population-based epidemiological studies that provide a large amount of data. We evaluated our approach qualitatively on thirty datasets and quantitatively against the ground truths of ten datasets resulting in an average of approximately 78% detected volume fraction and a 79% Dice coefficient, which is within the range of the inter-observer variation of manual segmentation results. CONCLUSION: The suggested method produces sufficiently accurate results and has potential to be applied for the study of large data to understand the pathogenesis of the OSA syndrome.

Multi-cycle Reconstruction of Cardiac MRI for the Analysis of Inter-ventricular Septum Motion During Free Breathing.

Small variations in left-ventricular preload due to respiration produce measurable changes in cardiac function in normal subjects. We show that this mechanism is altered in patients with reduced ejection fraction (EF), hypertrophy, or volume-loaded right ventricle (RV). We propose a multi-dimensional retrospective image reconstruction, based on an adaptive, soft classification of data into respiratory and cardiac phases, to study these effects.

Use of self-gated radial cardiovascular magnetic resonance to detect and classify arrhythmias (atrial fibrillation and premature ventricular contraction).

BACKGROUND: Arrhythmia can significantly alter the image quality of cardiovascular magnetic resonance (CMR); automatic detection and sorting of the most frequent types of arrhythmias during the CMR acquisition could potentially improve image quality. New CMR techniques, such as non-Cartesian CMR, can allow self-gating: from cardiac motion-related signal changes, we can detect cardiac cycles without an electrocardiogram. We can further use this data to obtain a surrogate for RR intervals (valley intervals: VV). Our purpose was to evaluate the feasibility of an automated method for classification of non-arrhythmic (NA) (regular cycles) and arrhythmic patients (A) (irregular cycles), and for sorting of common arrhythmia patterns between atrial fibrillation (AF) and premature ventricular contraction (PVC), using the cardiac motion-related signal obtained during self-gated free-breathing radial cardiac cine CMR with compressed sensing reconstruction (XD-GRASP). METHODS: One hundred eleven patients underwent cardiac XD-GRASP CMR between October 2015 and February 2016; 33 were included for retrospective analysis with the proposed method (6 AF, 8 PVC, 19 NA; by recent ECG). We analyzed the VV, using pooled statistics (histograms) and sequential analysis (Poincaré plots), including the median (medVV), the weighted mean (meanVV), the total number of VV values (VVval), and the total range (VVTR) and half range (VVHR) of the cumulative frequency distribution of VV, including the median to half range (medVV/VVHR) and the half range to total range (VVHR/VVTR) ratios. We designed a simple algorithm for using the VV results to differentiate A from NA, and AF from PVC. RESULTS: Between NA and A, meanVV, VVval, VVTR, VVHR, medVV/VVHR and VVHR/VVTR ratios were significantly different (p values = 0.00014, 0.0027, 0.000028, 5×10-9, 0.002, respectively). Between AF and PVC, meanVV, VVval and medVV/VVHR ratio were significantly different (p values = 0.018, 0.007, 0.044, respectively). Using our algorithm, sensitivity, specificity, and accuracy were 93 %, 95 % and 94 % to discriminate between NA and A, and 83 %, 71 %, and 77 % to discriminate between AF and PVC, respectively; areas under the ROC curve were 0.93 and 0.89. CONCLUSIONS: Our study shows we can reliably detect arrhythmias and differentiate AF from PVC, using self-gated cardiac cine XD-GRASP CMR.

Analyzing myocardial torsion based on tissue phase mapping cardiovascular magnetic resonance.

BACKGROUND: The purpose of this work is to analyze differences in left ventricular torsion between volunteers and patients with non-ischemic cardiomyopathy based on tissue phase mapping (TPM) cardiovascular magnetic resonance (CMR). METHODS: TPM was performed on 27 patients with non-ischemic cardiomyopathy and 14 normal volunteers. Patients underwent a standard CMR including late gadolinium enhancement (LGE) for the assessment of myocardial scar and ECG-gated cine CMR for global cardiac function. TPM was acquired in short-axis orientation at base, mid, and apex for all subjects. After evaluation by experienced observers, the patients were divided in subgroups according to the presence or absence of LGE (LGE+/LGE-), local wall motion abnormalities (WM+/WM-), and having a preserved (≥50%) or reduced (<50%) ejection fraction (EF+/EF-). TPM data was semi-automatically segmented and global LV torsion was computed for each cardiac time frame for endocardial and epicardial layers, and for the entire myocardium. RESULTS: Maximum myocardial torsion was significantly lower for patients with reduced EF compared to controls (0.21 ± 0.15°/mm vs. 0.36 ± 0.11°/mm, p = 0.018), but also for patients with wall motion abnormalities (0.21 ± 0.13°/mm vs. 0.36 ± 0.11°/mm, p = 0.004). Global myocardial torsion showed a positive correlation (r = 0.54, p < 0.001) with EF. Moreover, endocardial torsion was significantly higher than epicardial torsion for EF+ subjects (0.56 ± 0.33°/mm vs. 0.34 ± 0.18°/mm, p = 0.039) and for volunteers (0.46 ± 0.16°/mm vs. 0.30 ± 0.09°/mm, p = 0.004). The difference in maximum torsion between endo- and epicardial layers was positively correlated with EF (r = 0.47, p = 0.002) and age (r = 0.37, p = 0.016) for all subjects. CONCLUSIONS: TPM can be used to detect significant differences in LV torsion in patients with reduced EF and in the presence of local wall motion abnormalities. We were able to quantify torsion differences between the endocardium and epicardium, which vary between patient subgroups and are correlated to age and EF.

Principles and methods for automatic and semi-automatic tissue segmentation in MRI data.

The development of magnetic resonance imaging (MRI) revolutionized both the medical and scientific worlds. A large variety of MRI options have generated a huge amount of image data to interpret. The investigation of a specific tissue in 3D or 4D MR images can be facilitated by image processing techniques, such as segmentation and registration. In this work, we provide a brief review of the principles and methods that are commonly applied to achieve superior tissue segmentation results in MRI. The impacts of MR image acquisition on segmentation outcome and the principles of selecting and exploiting segmentation techniques tailored for specific tissue identification tasks are discussed. In the end, two exemplary applications, breast and fibroglandular tissue segmentation in MRI and myocardium segmentation in short-axis cine and real-time MRI, are discussed to explain the typical challenges that can be posed in practical segmentation tasks in MRI data. The corresponding solutions that are adopted to deal with these challenges of the two practical segmentation tasks are thoroughly reviewed.