Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure.

Biological sex is a crucial variable in neuroscience studies where sex differences have been documented across cognitive functions and neuropsychiatric disorders. While gross statistical differences have been previously documented in macroscopic brain structure such as cortical thickness or region size, less is understood about sex-related cellular-level microstructural differences which could provide insight into brain health and disease. Studying these microstructural differences between men and women paves the way for understanding brain disorders and diseases that manifest differently in different sexes. Diffusion MRI is an important in vivo, non-invasive methodology that provides a window into brain tissue microstructure. Our study develops multiple end-to-end classification models that accurately estimates the sex of a subject using volumetric diffusion MRI data and uses these models to identify white matter regions that differ the most between men and women. 471 male and 560 female healthy subjects (age range, 22-37 years) from the Human Connectome Project are included. Fractional anisotropy, mean diffusivity and mean kurtosis are used to capture brain tissue microstructure characteristics. Diffusion parametric maps are registered to a standard template to reduce bias that can arise from macroscopic anatomical differences like brain size and contour. This study employ three major model architectures: 2D convolutional neural networks, 3D convolutional neural networks and Vision Transformer (with self-supervised pretraining). Our results show that all 3 models achieve high sex classification performance (test AUC 0.92-0.98) across all diffusion metrics indicating definitive differences in white matter tissue microstructure between males and females. We further use complementary model architectures to inform about the pattern of detected microstructural differences and the influence of short-range versus long-range interactions. Occlusion analysis together with Wilcoxon signed-rank test is used to determine which white matter regions contribute most to sex classification. The results indicate that sex-related differences manifest in both local features as well as global features / longer-distance interactions of tissue microstructure. Our highly consistent findings across models provides new insight supporting differences between male and female brain cellular-level tissue organization particularly in the central white matter.

Functional Connectivity Changes on Resting-State fMRI after Mild Traumatic Brain Injury: A Systematic Review.

BACKGROUND: Mild traumatic brain injury is theorized to cause widespread functional changes to the brain. Resting-state fMRI may be able to measure functional connectivity changes after traumatic brain injury, but resting-state fMRI studies are heterogeneous, using numerous techniques to study ROIs across various resting-state networks. PURPOSE: We systematically reviewed the literature to ascertain whether adult patients who have experienced mild traumatic brain injury show consistent functional connectivity changes on resting-state -fMRI, compared with healthy patients. DATA SOURCES: We used 5 databases (PubMed, EMBASE, Cochrane Central, Scopus, Web of Science). STUDY SELECTION: Five databases (PubMed, EMBASE, Cochrane Central, Scopus, and Web of Science) were searched for research published since 2010. Search strategies used keywords of "functional MR imaging" and "mild traumatic brain injury" as well as related terms. All results were screened at the abstract and title levels by 4 reviewers according to predefined inclusion and exclusion criteria. For full-text inclusion, each study was evaluated independently by 2 reviewers, with discordant screening settled by consensus. DATA ANALYSIS: Data regarding article characteristics, cohort demographics, fMRI scan parameters, data analysis processing software, atlas used, data characteristics, and statistical analysis information were extracted. DATA SYNTHESIS: Across 66 studies, 80 areas were analyzed 239 times for at least 1 time point, most commonly using independent component analysis. The most analyzed areas and networks were the whole brain, the default mode network, and the salience network. Reported functional connectivity changes varied, though there may be a slight trend toward decreased whole-brain functional connectivity within 1 month of traumatic brain injury and there may be differences based on the time since injury. LIMITATIONS: Studies of military, sports-related traumatic brain injury, and pediatric patients were excluded. Due to the high number of relevant studies and data heterogeneity, we could not be as granular in the analysis as we would have liked. CONCLUSIONS: Reported functional connectivity changes varied, even within the same region and network, at least partially reflecting differences in technical parameters, preprocessing software, and analysis methods as well as probable differences in individual injury. There is a need for novel rs-fMRI techniques that better capture subject-specific functional connectivity changes.

Callosal Interhemispheric Communication in Mild Traumatic Brain Injury: A Mediation Analysis on WM Microstructure Effects.

BACKGROUND AND PURPOSE: Because the corpus callosum connects the left and right hemispheres and a variety of WM bundles across the brain in complex ways, damage to the neighboring WM microstructure may specifically disrupt interhemispheric communication through the corpus callosum following mild traumatic brain injury. Here we use a mediation framework to investigate how callosal interhemispheric communication is affected by WM microstructure in mild traumatic brain injury. MATERIALS AND METHODS: Multishell diffusion MR imaging was performed on 23 patients with mild traumatic brain injury within 1 month of injury and 17 healthy controls, deriving 11 diffusion metrics, including DTI, diffusional kurtosis imaging, and compartment-specific standard model parameters. Interhemispheric processing speed was assessed using the interhemispheric speed of processing task (IHSPT) by measuring the latency between word presentation to the 2 hemivisual fields and oral word articulation. Mediation analysis was performed to assess the indirect effect of neighboring WM microstructures on the relationship between the corpus callosum and IHSPT performance. In addition, we conducted a univariate correlation analysis to investigate the direct association between callosal microstructures and IHSPT performance as well as a multivariate regression analysis to jointly evaluate both callosal and neighboring WM microstructures in association with IHSPT scores for each group. RESULTS: Several significant mediators in the relationships between callosal microstructure and IHSPT performance were found in healthy controls. However, patients with mild traumatic brain injury appeared to lose such normal associations when microstructural changes occurred compared with healthy controls. CONCLUSIONS: This study investigates the effects of neighboring WM microstructure on callosal interhemispheric communication in healthy controls and patients with mild traumatic brain injury, highlighting that neighboring noncallosal WM microstructures are involved in callosal interhemispheric communication and information transfer. Further longitudinal studies may provide insight into the temporal dynamics of interhemispheric recovery following mild traumatic brain injury.

A Comprehensive and Broad Approach to Resting-State Functional Connectivity in Adult Patients with Mild Traumatic Brain Injury.

BACKGROUND AND PURPOSE: Several recent works using resting-state fMRI suggest possible alterations of resting-state functional connectivity after mild traumatic brain injury. However, the literature is plagued by various analysis approaches and small study cohorts, resulting in an inconsistent array of reported findings. In this study, we aimed to investigate differences in whole-brain resting-state functional connectivity between adult patients with mild traumatic brain injury within 1 month of injury and healthy control subjects using several comprehensive resting-state functional connectivity measurement methods and analyses. MATERIALS AND METHODS: A total of 123 subjects (72 patients with mild traumatic brain injury and 51 healthy controls) were included. A standard fMRI preprocessing pipeline was used. ROI/seed-based analyses were conducted using 4 standard brain parcellation methods, and the independent component analysis method was applied to measure resting-state functional connectivity. The fractional amplitude of low-frequency fluctuations was also measured. Group comparisons were performed on all measurements with appropriate whole-brain multilevel statistical analysis and correction. RESULTS: There were no significant differences in age, sex, education, and hand preference between groups as well as no significant correlation between all measurements and these potential confounders. We found that each resting-state functional connectivity measurement revealed various regions or connections that were different between groups. However, after we corrected for multiple comparisons, the results showed no statistically significant differences between groups in terms of resting-state functional connectivity across methods and analyses. CONCLUSIONS: Although previous studies point to multiple regions and networks as possible mild traumatic brain injury biomarkers, this study shows that the effect of mild injury on brain resting-state functional connectivity has not survived after rigorous statistical correction. A further study using subject-level connectivity analyses may be necessary due to both subtle and variable effects of mild traumatic brain injury on brain functional connectivity across individuals.

Assessment of Precision and Accuracy of Brain White Matter Microstructure using Combined Diffusion MRI and Relaxometry.

Joint modeling of diffusion and relaxation has seen growing interest due to its potential to provide complementary information about tissue microstructure. For brain white matter, we designed an optimal diffusion-relaxometry MRI protocol that samples multiple b-values, B-tensor shapes, and echo times (TE). This variable-TE protocol (27 min) has as subsets a fixed-TE protocol (15 min) and a 2-shell dMRI protocol (7 min), both characterizing diffusion only. We assessed the sensitivity, specificity and reproducibility of these protocols with synthetic experiments and in six healthy volunteers. Compared with the fixed-TE protocol, the variable-TE protocol enables estimation of free water fractions while also capturing compartmental T2 relaxation times. Jointly measuring diffusion and relaxation offers increased sensitivity and specificity to microstructure parameters in brain white matter with voxelwise coefficients of variation below 10%.

Microstructurally Informed Subject-Specific Parcellation of the Corpus Callosum using Axonal Water Fraction.

The corpus callosum (CC) is the most important interhemispheric white matter (WM) structure composed of several anatomically and functionally distinct WM tracts. Resolving these tracts is a challenge since the callosum appears relatively homogenous in conventional structural imaging. Commonly used callosal parcellation methods such as the Hofer/Frahm scheme rely on rigid geometric guidelines to separate the substructures that are limited to consider individual variation. Here we present a novel subject-specific and microstructurally-informed method for callosal parcellation based on axonal water fraction (ƒ) known as a diffusion metric reflective of axon caliber and density. We studied 30 healthy subjects from the Human Connectome Project (HCP) dataset with multi-shell diffusion MRI. The biophysical parameter ƒ was derived from compartment-specific WM modeling. Inflection points were identified where there were concavity changes in ƒ across the CC to delineate callosal subregions. We observed relatively higher ƒ in anterior and posterior areas consisting of a greater number of small diameter fibers and lower ƒ in posterior body areas of the CC consisting of a greater number of large diameter fibers. Based on degree of change in ƒ along the callosum, seven callosal subregions can be consistently delineated for each individual. We observe that ƒ can capture differences in underlying tissue microstructures and seven subregions can be identified across CC. Therefore, this method provides microstructurally informed callosal parcellation in a subject-specific way, allowing for more accurate analysis in the corpus callosum.

Identifying relevant diffusion MRI microstructure biomarkers relating to exposure to repeated head impacts in contact sport athletes.

PURPOSE: Repeated head impacts (RHI) without concussion may cause long-term sequelae. A growing array of diffusion MRI metrics exist, both empiric and modeled and it is hard to know which are potentially important biomarkers. Common conventional statistical methods fail to consider interactions between metrics and rely on group-level comparisons. This study uses a classification pipeline as a means towards identifying important diffusion metrics associated with subconcussive RHI. METHODS: 36 collegiate contact sport athletes and 45 non-contact sport controls from FITBIR CARE were included. Regional/whole brain WM statistics were computed from 7 diffusion metrics. Wrapper-based feature selection was applied to 5 classifiers representing a range of learning capacities. Best 2 classifiers were interpreted to identify the most RHI-related diffusion metrics. RESULTS: Mean diffusivity (MD) and mean kurtosis (MK) are found to be the most important metrics for discriminating between athletes with and without RHI exposure history. Regional features outperformed global statistics. Linear approaches outperformed non-linear approaches with good generalizability (test AUC 0.80-0.81). CONCLUSION: Feature selection and classification identifies diffusion metrics that characterize subconcussive RHI. Linear classifiers yield the best performance and mean diffusion, tissue microstructure complexity, and radial extra-axonal compartment diffusion (MD, MK, De,⊥) are found to be the most influential metrics. This work provides proof of concept that applying such approach to small, multidimensional dataset can be successful given attention to optimizing learning capacity without overfitting and serves an example of methods that lead to better understanding of the myriad of diffusion metrics as they relate to injury and disease.

MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features.

In this paper, we propose bag of adversarial features (BAFs) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRIs) (obtained within one month of injury) by incorporating unsupervised feature learning techniques. MTBI is a growing public health problem with an estimated incidence of over 1.7 million people annually in USA. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. Unlike most of the previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task. A major challenge in this field thus far is the relatively small number of subjects available for training. This makes it difficult to use an end-to-end convolutional neural network to directly classify a subject from MRIs. To overcome this challenge, we first apply an adversarial auto-encoder (with convolutional structure) to learn patch-level features, from overlapping image patches extracted from different brain regions. We then aggregate these features through a bag-of-words approach. We perform an extensive experimental study on a dataset of 227 subjects (including 109 MTBI patients, and 118 age and sex-matched healthy controls) and compare the bag-of-deep-features with several previous approaches. Our experimental results show that the BAF significantly outperforms earlier works relying on the mean values of MR metrics in selected brain regions.

A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI.

Mild traumatic brain injury is a growing public health problem with an estimated incidence of over 1.7 million people annually in US. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. This work aims to directly use diffusion MR images obtained within one month of trauma to detect injury, by incorporating deep learning techniques. To overcome the challenge due to limited training data, we describe each brain region using the bag of word representation, which specifies the distribution of representative patch patterns. We apply a convolutional auto-encoder to learn the patch-level features, from overlapping image patches extracted from the MR images, to learn features from diffusion MR images of brain using an unsupervised approach. Our experimental results show that the bag of word representation using patch level features learnt by the auto encoder provides similar performance as that using the raw patch patterns, both significantly outperform earlier work relying on the mean values of MR metrics in selected brain regions.

Working Memory And Brain Tissue Microstructure: White Matter Tract Integrity Based On Multi-Shell Diffusion MRI.

Working memory is a complex cognitive process at the intersection of sensory processing, learning, and short-term memory and also has a general executive attention component. Impaired working memory is associated with a range of neurological and psychiatric disorders, but very little is known about how working memory relates to underlying white matter (WM) microstructure. In this study, we investigate the association between WM microstructure and performance on working memory tasks in healthy adults (right-handed, native English speakers). We combine compartment specific WM tract integrity (WMTI) metrics derived from multi-shell diffusion MRI as well as diffusion tensor/kurtosis imaging (DTI/DKI) metrics with Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) subtests tapping auditory working memory. WMTI is a novel tool that helps us describe the microstructural characteristics in both the intra- and extra-axonal environments of WM such as axonal water fraction (AWF), intra-axonal diffusivity, extra-axonal axial and radial diffusivities, allowing a more biophysical interpretation of WM changes. We demonstrate significant positive correlations between AWF and letter-number sequencing (LNS), suggesting that higher AWF with better performance on complex, more demanding auditory working memory tasks goes along with greater axonal volume and greater myelination in specific regions, causing efficient and faster information process.

White Matter Tract Integrity: An Indicator of Axonal Pathology after Mild Traumatic Brain Injury.

We seek to elucidate the underlying pathophysiology of injury sustained after mild traumatic brain injury (mTBI) using multi-shell diffusion magnetic resonance imaging, deriving compartment-specific white matter tract integrity (WMTI) metrics. WMTI allows a more biophysical interpretation of white matter (WM) changes by describing microstructural characteristics in both intra- and extra-axonal environments. Thirty-two patients with mTBI within 30 days of injury and 21 age- and sex-matched controls were imaged on a 3 Tesla magnetic resonance scanner. Multi-shell diffusion acquisition was performed with five b-values (250-2500 sec/mm2) along 6-60 diffusion encoding directions. Tract-based spatial statistics (TBSS) was used with family-wise error (FWE) correction for multiple comparisons. TBSS results demonstrated focally lower intra-axonal diffusivity (Daxon) in mTBI patients in the splenium of the corpus callosum (sCC; p < 0.05, FWE-corrected). The area under the curve value for Daxon was 0.76 with a low sensitivity of 46.9% but 100% specificity. These results indicate that Daxon may be a useful imaging biomarker highly specific for mTBI-related WM injury. The observed decrease in Daxon suggests restriction of the diffusion along the axons occurring shortly after injury.

Diffusion MR Imaging in Mild Traumatic Brain Injury.

Remarkable advances have been made in the last decade in the use of diffusion MR imaging to study mild traumatic brain injury (mTBI). Diffusion imaging shows differences between mTBI patients and healthy control groups in multiple different metrics using a variety of techniques, supporting the notion that there are microstructural injuries in mTBI patients that radiologists have been insensitive to. Future areas of discovery in diffusion MR imaging and mTBI include larger longitudinal studies to better understand the evolution of the injury and unravel the biophysical meaning that the detected changes in diffusion MR imaging represent.

Quantitative Perfusion Analysis of First-Pass Contrast Enhancement Kinetics: Application to MRI of Myocardial Perfusion in Coronary Artery Disease.

PURPOSE: Perfusion analysis from first-pass contrast enhancement kinetics requires modeling tissue contrast exchange. This study presents a new approach for numerical implementation of the tissue homogeneity model, incorporating flexible distance steps along the capillary (NTHf). METHODS: The proposed NTHf model considers contrast exchange in fluid packets flowing along the capillary, incorporating flexible distance steps, thus allowing more efficient and stable calculations of the transit of tracer through the tissue. We prospectively studied 8 patients (62 ± 13 years old) with suspected CAD, who underwent first-pass perfusion CMR imaging at rest and stress prior to angiography. Myocardial blood flow (MBF) and myocardial perfusion reserve index (MPRI) were estimated using both the NTHf and the conventional adiabatic approximation of the TH models. Coronary artery lesions detected at angiography were clinically assigned to one of three categories of stenosis severity ('insignificant', 'mild to moderate' and 'severe') and related to corresponding myocardial territories. RESULTS: The mean MBF (ml/g/min) at rest/stress and MPRI were 0.80 ± 0.33/1.25 ± 0.45 and 1.68 ± 0.54 in the insignificant regions, 0.74 ± 0.21/1.09 ± 0.28 and 1.54 ± 0.46 in the mild to moderate regions, and 0.79 ± 0.28/0.63 ± 0.34 and 0.85 ± 0.48 in the severe regions, respectively. The correlation coefficients of MBFs at rest/stress and MPRI between the NTHf and AATH models were r = 0.97/0.93 and r = 0.91, respectively. CONCLUSIONS: The proposed NTHf model allows efficient quantitative analysis of the transit of tracer through tissue, particularly at higher flow. Results of initial application to MRI of myocardial perfusion in CAD are encouraging.

Magnetization-tagged MRI is a simple method for predicting liver fibrosis.

BACKGROUND/AIMS: To assess the usefulness of magnetization-tagged magnetic resonance imaging (MRI) in quantifying cardiac-induced liver motion and deformation in order to predict liver fibrosis. METHODS: This retrospective study included 85 patients who underwent liver MRI including magnetization-tagged sequences from April 2010 to August 2010. Tagged images were acquired in three coronal and three sagittal planes encompassing both the liver and heart. A Gabor filter bank was used to measure the maximum value of displacement (MaxDisp) and the maximum and minimum values of principal strains (MaxP1 and MinP2, respectively). Patients were divided into three groups (no fibrosis, mild-to-moderate fibrosis, and significant fibrosis) based on their aspartate-aminotransferase-to-platelet ratio index (APRI) score. Group comparisons were made using ANOVA tests. RESULTS: The patients were divided into three groups according to APRI scores: no fibrosis (≤ 0.5; n=41), moderate fibrosis (0.5-1.5; n=23), and significant fibrosis (>1.5; n=21). The values of MaxDisp were 2.9 ± 0.9 (mean ± SD), 2.3 ± 0.7, and 2.1 ± 0.6 in the no fibrosis, moderate fibrosis, and significant fibrosis groups, respectively (P<0.001); the corresponding values of MaxP1 were 0.05 ± 0.2, 0.04 ± 0.02, and 0.03 ± 0.01, respectively (P=0.002), while those of MinP2 were -0.07 ± 0.02, -0.05 ± 0.02, and -0.04 ± 0.01, respectively (P<0.001). CONCLUSIONS: Tagge d MRI to quantify cardiac-induced liver motion can be easily incorporated in routine liver MRI and may represent a helpful complementary tool in the diagnosis of early liver fibrosis.

Track density imaging of hypertrophic olivary degeneration from multiple sclerosis plaque.

A 32-year-old female with relapsing-remitting multiple sclerosis (MS) presented with severe new onset ataxia and diplopia. MRI showed a new inflammatory MS lesion that involved the right dorsal pons and extended into the adjacent superior cerebellar peduncle. The patient improved with aggressive immunotherapy; however, repeat MRI 3 months later revealed a new non-enhancing lesion in the left inferior medullary olive. The differential diagnosis for this new lesion included an MS lesion vs hypertrophic olivary degeneration, with infarct or neoplasm as the less likely considerations. We used track density imaging, which provides unprecedented anatomic details based on probabilistic tractography streamlines, to demonstrate apparent changes in the integrity of the dentato-rubro-olivary pathway (Guillain-Mollaret triangle) that were consistent with the diagnosis of hypertrophic olivary degeneration from the antecedent MS lesion involving the right superior cerebellar peduncle. Further medical therapy was avoided, and follow-up MRI 1 year later showed interval involution of the left olivary lesion. This case demonstrates the potential clinical utility of using track density imaging to detect lesion-induced alterations in brainstem connectivity and characterize neurodegeneration in patients.

Preliminary Assessment of Tricuspid Valve Annular Velocity Parameters by Cardiac Magnetic Resonance Imaging in Adults with a Volume-Overloaded Right Ventricle: Comparison of Unrepaired Atrial Septal Defect and Repaired Tetralogy of Fallot.

The aim is to compare tricuspid valve (TV) atrioventricular junction (AVJ) annular motion parameters in unrepaired atrial septal defect (ASD) and repaired Tetralogy of Fallot (TOF) by cardiac magnetic resonance (CMR) imaging. We retrospectively reviewed CMR studies performed between November 2007 and November 2013 in patients 16-45 years of age with unrepaired ASD (with or without partial anomalous pulmonary venous return) and with repaired TOF, who had previous infundibulotomy, but have not undergone pulmonary valve replacement. Longitudinal motion of lateral TV in four-chamber view cine image was tracked through the cardiac cycle with custom software. Twenty TOF patients and 12 ASD patients were included, and values were compared with 80 controls. Right ventricular end-diastolic volume index and right ventricular end-systolic volume index were similar in the ASD and TOF groups and were significantly higher in both groups than in controls. Maximum displacement of the TV in systole, velocity at half-maximal displacement during systole, and velocity at half-maximal displacement during early diastole were all significantly lower in the TOF group than the ASD group [1.39 ± 0.47 vs. 2.21 ± 0.46 (cm, p < 0.01), 5.9 ± 2.1 vs. 10.1 ± 2.3 (cm/s, p < 0.01), and 7.7 ± 2.6 vs. 10.9 ± 3.1 (cm/s, p < 0.05)]. TOF patients have diminished early diastolic TV AVJ velocity compared to patients with an unrepaired ASD, despite similar RV volumes. This observation could suggest diastolic dysfunction or cardiac mechanics unique to the postoperative, volume-overloaded right ventricle in patients with repaired TOF.

Evaluation of diastolic function by three-dimensional volume tracking of the mitral annulus with cardiovascular magnetic resonance: comparison with tissue Doppler imaging.

BACKGROUND: Measurement of mitral annulus (MA) dynamics is an important component of the evaluation of left ventricular (LV) diastolic function; MA velocities are commonly measured using tissue Doppler imaging (TDI). This study aimed to examine the clinical potential of a semi-automated cardiovascular magnetic resonance (CMR) technique for quantifying global LV diastolic function, using 3D volume tracking of the MA with conventional cine-CMR images. METHODS: 124 consecutive patients with normal ejection fraction underwent both clinically indicated transthoracic echocardiography (TTE) and CMR within 2 months. Interpolated 3D reconstruction of the MA over time was performed with semi-automated atrioventricular junction (AVJ) tracking in long-axis cine-CMR images, producing an MA sweep volume over the cardiac cycle. CMR-based diastolic function was evaluated, using the following parameters: peak volume sweep rates in early diastole (PSRE) and atrial systole (PSRA), PSRE/PSRA ratio, deceleration time of sweep volume (DTSV), and 50% diastolic sweep volume recovery time (DSVRT50); these were compared with TTE diastolic measurements. RESULTS: Patients with TTE-based diastolic dysfunction (n = 62) showed significantly different normalized MA sweep volume profiles compared to those with TTE-based normal diastolic function (n = 62), including a lower PSRE (5.25 ± 1.38 s-1 vs. 7.72 ± 1.7 s-1), a higher PSRA (6.56 ± 1.99 s-1 vs. 4.67 ± 1.38 s-1), a lower PSRE/PSRA ratio (0.9 ± 0.44 vs. 1.82 ± 0.69), a longer DTSV (144 ± 55 ms vs. 96 ± 37 ms), and a longer DSVRT50 (25.0 ± 11.0% vs. 15.6 ± 4.0%) (all p < 0.05). CMR diastolic parameters were independent predictors of TTE-based diastolic dysfunction after adjusting for left ventricular hypertrophy, hypertension, and coronary artery disease. Good correlations were observed between CMR PSRE/PSRA and early-to-late diastolic annular velocity ratios (e'/a') measured by TDI (r = 0.756 to 0.828, p < 0.001). CONCLUSIONS: 3D MA sweep volumes generated by semi-automated AVJ tracking in routinely acquired CMR images yielded diastolic parameters that were effective in identifying patients with diastolic dysfunction when correlated with TTE-based variables.

A novel and practical cardiovascular magnetic resonance method to quantify mitral annular excursion and recoil applied to hypertrophic cardiomyopathy.

BACKGROUND: We have developed a novel and practical cardiovascular magnetic resonance (CMR) technique to evaluate left ventricular (LV) mitral annular motion by tracking the atrioventricular junction (AVJ). To test AVJ motion analysis as a metric for LV function, we compared AVJ motion variables between patients with hypertrophic cardiomyopathy (HCM), a group with recognized systolic and diastolic dysfunction, and healthy volunteers. METHODS: We retrospectively evaluated 24 HCM patients with normal ejection fractions (EF) and 14 healthy volunteers. Using the 4-chamber view cine images, we tracked the longitudinal motion of the lateral and septal AVJ at 25 time points during the cardiac cycle. Based on AVJ displacement versus time, we calculated maximum AVJ displacement (MD) and velocity in early diastole (MVED), velocity in diastasis (VDS) and the composite index VDS/MVED. RESULTS: Patients with HCM showed significantly slower median lateral and septal AVJ recoil velocities during early diastole, but faster velocities in diastasis. We observed a 16-fold difference in VDS/MVED at the lateral AVJ [median 0.141, interquartile range (IQR) 0.073, 0.166 versus 0.009 IQR -0.006, 0.037, P < 0.001]. Patients with HCM also demonstrated significantly less mitral annular excursion at both the septal and lateral AVJ. Performed offline, AVJ motion analysis took approximately 10 minutes per subject. CONCLUSIONS: Atrioventricular junction motion analysis provides a practical and novel CMR method to assess mitral annular motion. In this proof of concept study we found highly statistically significant differences in mitral annular excursion and recoil between HCM patients and healthy volunteers.