Bifurcation analysis of motoneuronal excitability mechanisms under normal and ALS conditions.

Introduction: Bifurcation analysis allows the examination of steady-state, non-linear dynamics of neurons and their effects on cell firing, yet its usage in neuroscience is limited to single-compartment models of highly reduced states. This is primarily due to the difficulty in developing high-fidelity neuronal models with 3D anatomy and multiple ion channels in XPPAUT, the primary bifurcation analysis software in neuroscience. Methods: To facilitate bifurcation analysis of high-fidelity neuronal models under normal and disease conditions, we developed a multi-compartment model of a spinal motoneuron (MN) in XPPAUT and verified its firing accuracy against its original experimental data and against an anatomically detailed cell model that incorporates known MN non-linear firing mechanisms. We used the new model in XPPAUT to study the effects of somatic and dendritic ion channels on the MN bifurcation diagram under normal conditions and after amyotrophic lateral sclerosis (ALS) cellular changes. Results: Our results show that somatic small-conductance Ca2+-activated K (SK) channels and dendritic L-type Ca2+ channels have the strongest effects on the bifurcation diagram of MNs under normal conditions. Specifically, somatic SK channels extend the limit cycles and generate a subcritical Hopf bifurcation node in the V-I bifurcation diagram of the MN to replace a supercritical node Hopf node, whereas L-type Ca2+ channels shift the limit cycles to negative currents. In ALS, our results show that dendritic enlargement has opposing effects on MN excitability, has a greater overall impact than somatic enlargement, and dendritic overbranching offsets the dendritic enlargement hyperexcitability effects. Discussion: Together, the new multi-compartment model developed in XPPAUT facilitates studying neuronal excitability in health and disease using bifurcation analysis.

Spatiotemporal Mammography-based Deep Learning Model for Improved Breast Cancer Risk Prediction.

Breast cancer is one of the most prevalent cancers among women. It is the second leading cause of death in cancer-related deaths. Early detection and personalized risk assessment can reduce the mortality rate and improve survival rates. Classical risk prediction models which rely on traditional risk factors produce inconsistent results among the different populations. Thus, they are not routinely used in screening programs. Deep learning was proven to improve the results of breast cancer risk prediction. CNNs can detect risk cues from screening mammograms. However, the deep learning models utilize the spatial information of each screening mammogram independently. This study aims to further improve the risk prediction models by exploiting the spatiotemporal information in multiple screening time points. We implemented a Siamese neural network for spatiotemporal risk prediction and compared the results against CNN trained using two different time points (T1 and T2) independently. We tested our results on 191 cases, 61 of which were diagnosed with cancer. The Siamese model showed a superior AUC of 0.81 against 0.75 and 0.77 at T1 and T2 respectively. The Siamese network also exhibited higher accuracy and F1-score with values of 0.78 and 0.61 while CNNs have the same accuracy of 0.76 with an F1-score of 0.54 at T1, and 0.59 at T2. The results suggest that spatiotemporal risk prediction can be a more reliable risk assessment tool.

Bilateral Analysis Boosts the Performance of Mammography-based Deep Learning Models in Breast Cancer Risk Prediction.

Breast cancer is one of the leading causes of death among women. Early prediction of breast cancer can significantly improve the survival rates. Breast density was proven as a reliable risk factor. Deep learning models can learn subtle cues in the mammogram images. CNN models were recently shown to improve the risk discrimination in full-field mammograms. This study aims to improve risk prediction models using bilateral analysis. Bilateral analysis is the process of comparing two breasts to verify presence of anomalies. We developed a Siamese neural network to leverage the bilateral information and asymmetries between the two mammograms of the same patient. We tested our model on 271 patients and compared the results of our Siamese model against the traditional unilateral CNN model. Our results showed AUCs of 0.75 and 0.70 respectively (p = 0.0056). The Siamese model also exhibits higher sensitivity, specificity, precision, and false positive rate with values of 0.68, 0.69, 0.71, 0.31 respectively. While the CNN values were 0.61, 0.66, 0.67, 0.34 respectively. We merged both models by two techniques using pre-trained weights and weighted voting ensemble. The merging technique boosted the AUC to 0.78. The results suggest that bilateral analysis can significantly improve the risk discrimination.

Improved Centerline Extraction in Fully Automated Coronary Ostium Localization and Centerline Extraction Framework using Deep Learning.

Coronary artery extraction in cardiac CT angiography (CCTA) image volume is a necessary step for any quantitative assessment of stenoses and atherosclerotic plaque. In this work, we propose a fully automated workflow that depends on convolutional networks to extract the centerlines of the coronary arteries from CCTA image volumes, starting from identifying the ostium points and then tracking the vessel till its end based on its radius and direction. First, a regression U-Net is employed to identify the ostium points in the image volume, then these points are fed to an orientation and radius predictor CNN model to track and extract each artery till its end point. Our results show that an average of 96% of the ostium points were identified and located within less than 5mm from their true location. The coronary arteries centerlines extraction was performed with high accuracy and lower number of training parameters making it suitable for real clinical applications and continuous learning.

Improved Automatic Grading of Diabetic Retinopathy Using Deep Learning and Principal Component Analysis.

Diabetic retinopathy (DR) is one of the most common chronic diseases around the world. Early screening and diagnosis of DR patients through retinal fundus is always preferred. However, image screening and diagnosis is a highly time-consuming task for clinicians. So, there is a high need for automatic diagnosis. The objective of our study is to develop and validate a new automated deep learning-based approach for diabetic retinopathy multi-class detection and classification. In this study we evaluate the contribution of the DR features in each color channel then we pick the most significant channels and calculate their principal components (PCA) which are then fed to the deep learning model, and the grading decision is decided based on a majority voting scheme applied to the out of the deep learning model. The developed models were trained on a publicly available dataset with around 80K color fundus images and were tested on our local dataset with around 100 images. Our results show a significant improvement in DR multi-class classification with 85% accuracy, 89% sensitivity, and 96% specificity.

Detecting liver fibrosis using a machine learning-based approach to the quantification of the heart-induced deformation in tagged MR images.

Liver disease causes millions of deaths per year worldwide, and approximately half of these cases are due to cirrhosis, which is an advanced stage of liver fibrosis that can be accompanied by liver failure and portal hypertension. Early detection of liver fibrosis helps in improving its treatment and prevents its progression to cirrhosis. In this work, we present a novel noninvasive method to detect liver fibrosis from tagged MRI images using a machine learning-based approach. Specifically, coronal and sagittal tagged MRI imaging are analyzed separately to capture cardiac-induced deformation of the liver. The liver is manually delineated and a novel image feature, namely, the histogram of the peak strain (HPS) value, is computed from the segmented liver region and is used to classify the liver as being either normal or fibrotic. Classification is achieved using a support vector machine algorithm. The in vivo study included 15 healthy volunteers (10 males; age range 30-45 years) and 22 patients (15 males; age range 25-50 years) with liver fibrosis verified and graded by transient elastography, and 10 patients only had a liver biopsy and were diagnosed with a score of F3-F4. The proposed method demonstrates the usefulness and efficiency of extracting the HPS features from the sagittal slices for patients with moderate fibrosis. Cross-validation of the method showed an accuracy of 83.7% (specificity = 86.6%, sensitivity = 81.8%).

Imaging sequence for joint myocardial T1 mapping and fat/water separation.

PURPOSE: To develop and evaluate an imaging sequence to simultaneously quantify the epicardial fat volume and myocardial T1 relaxation time. METHODS: We introduced a novel simultaneous myocardial T1 mapping and fat/water separation sequence (joint T1 -fat/water separation). Dixon reconstruction is performed on a dual-echo data set to generate water/fat images. T1 maps are computed using the water images, whereas the epicardial fat volume is calculated from the fat images. A phantom experiment using vials with different T1 /T2 values and a bottle of oil was performed. Additional phantom experiment using vials of mixed fat/water was performed to show the potential of this sequence to mitigate the effect of intravoxel fat on estimated T1 maps. In vivo evaluation was performed in 17 subjects. Epicardial fat volume, native myocardial T1 measurements and precision were compared among slice-interleaved T1 mapping, Dixon, and the proposed sequence. RESULTS: In the first phantom, the proposed sequence separated oil from water vials and there were no differences in T1 of the fat-free vials (P = .1). In the second phantom, the T1 error decreased from 22%, 36%, 57%, and 73% to 8%, 9%, 16%, and 26%, respectively. In vivo there was no difference between myocardial T1 values (1067 ± 17 ms versus 1077 ± 24 ms, P = .6). The epicardial fat volume was similar for both sequences (54.3 ± 33 cm3 versus 52.4 ± 32 cm3 , P = .8). CONCLUSION: The proposed sequence provides simultaneous quantification of native myocardial T1 and epicardial fat volume. This will eliminate the need for an additional sequence in the cardiac imaging protocol if both measurements are clinically indicated.

Gray blood late gadolinium enhancement cardiovascular magnetic resonance for improved detection of myocardial scar.

BACKGROUND: Low scar-to-blood contrast in late gadolinium enhanced (LGE) MRI limits the visualization of scars adjacent to the blood pool. Nulling the blood signal improves scar detection but results in lack of contrast between myocardium and blood, which makes clinical evaluation of LGE images more difficult. METHODS: GB-LGE contrast is achieved through partial suppression of the blood signal using T2 magnetization preparation between the inversion pulse and acquisition. The timing parameters of GB-LGE sequence are determined by optimizing a cost-function representing the desired tissue contrast. The proposed 3D GB-LGE sequence was evaluated using phantoms, human subjects (n = 45) and a swine model of myocardial infarction (n = 5). Two independent readers subjectively evaluated the image quality and ability to identify and localize scarring in GB-LGE compared to black-blood LGE (BB-LGE) (i.e., with complete blood nulling) and conventional (bright-blood) LGE. RESULTS: GB-LGE contrast was successfully generated in phantoms and all in-vivo scans. The scar-to-blood contrast was improved in GB-LGE compared to conventional LGE in humans (1.1 ± 0.5 vs. 0.6 ± 0.4, P < 0.001) and in animals (1.5 ± 0.2 vs. -0.03 ± 0.2). In patients, GB-LGE detected more tissue scarring compared to BB-LGE and conventional LGE. The subjective scores of the GB-LGE ability for localizing LV scar and detecting papillary scar were improved as compared with both BB-LGE (P < 0.024) and conventional LGE (P < 0.001). In the swine infarction model, GB-LGE scores for the ability to localize LV scar scores were consistently higher than those of both BB-LGE and conventional-LGE. CONCLUSION: GB-LGE imaging improves the ability to identify and localize myocardial scarring compared to both BB-LGE and conventional LGE. Further studies are warranted to histologically validate GB-LGE.

Improved dark blood late gadolinium enhancement (DB-LGE) imaging using an optimized joint inversion preparation and T2 magnetization preparation.

PURPOSE: To develop a dark blood-late gadolinium enhancement (DB-LGE) sequence that improves scar-blood contrast and delineation of scar region. METHODS: The DB-LGE sequence uses an inversion pulse followed by T2 magnetization preparation to suppress blood and normal myocardium. Time delays inserted after preparation pulses and T2 -magnetization-prep duration are used to adjust tissue contrast. Selection of these parameters was optimized using numerical simulations and phantom experiments. We evaluated DB-LGE in 9 swine and 42 patients (56 ± 14 years, 33 male). Improvement in scar-blood contrast and overall image quality was subjectively evaluated by two independent readers (1 = poor, 4 = excellent). The signal ratios among scar, blood, and myocardium were compared. RESULTS: Simulations and phantom studies demonstrated that simultaneous nulling of myocardium and blood can be achieved by selecting appropriate timing parameters. The scar-blood contrast score was significantly higher for DB-LGE (P < 0.001) with no significant difference in overall image quality (P > 0.05). Scar-blood signal ratios for DB-LGE versus LGE were 5.0 ± 2.8 versus 1.5 ± 0.5 (P < 0.001) for patients, and 2.2 ± 0.7 versus 1.0 ± 0.4 (P = 0.0023) for animals. Scar-myocardium signal ratios were 5.7 ± 2.9 versus 6.3 ± 2.6 (P = 0.35) for patients, and 3.7 ± 1.1 versus 4.1 ± 2.0 (P = 0.60) for swine. CONCLUSIONS: The DB-LGE sequence simultaneously reduces normal myocardium and blood signal intensity, thereby enhancing scar-blood contrast while preserving scar-myocardium contrast. Magn Reson Med 79:351-360, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Improved segmented modified Look-Locker inversion recovery T1 mapping sequence in mice.

OBJECT: To develop and evaluate a 2D modified Look-Locker (MOLLI) for high-resolution T1 mapping in mice using a 3T MRI scanner. MATERIALS AND METHODS: To allow high-resolution T1 mapping in mice at high heart rates a multi-shot ECG-triggered 2D MOLLI sequence was developed. In the proposed T1 mapping sequence the optimal number of sampling points and pause cardiac cycles following an initial adiabatic inversion pulse was investigated in a phantom. Seven native control and eight mice, 3 days post myocardial infarction (MI) after administration of gadolinium were scanned. Two experienced readers graded the visual T1 map quality. RESULTS: In T1 phantoms, there were no significant differences (<0.4% error) between 12, 15 and 20 pause cardiac cycles (p = 0.1, 0.2 and 0.6 respectively) for 8 acquisition cardiac cycles for 600bpm in comparison to the conventional inversion recovery spin echo T1 mapping sequence for short T1's (<600 ms). Subsequently, all in-vivo scans were performed with 8 data acquisitions and 12 pause cardiac cycles to minimize scan time. The mean native T1 value of myocardium in control animal was 820.5±52 ms. The post-contrast T1 measured 3 days after MI in scar was 264±59 ms and in healthy myocardium was 512±62 ms. The Bland-Altman analysis revealed mean difference of only -1.06% of infarct size percentage between T1 maps and LGE. CONCLUSIONS: A multi-shot 2D MOLLI sequence has been presented that allows reliable measurement of high spatial resolution T1 maps in mice for heart rates up to 600bpm.

Clinical performance of high-resolution late gadolinium enhancement imaging with compressed sensing.

PURPOSE: To evaluate diagnostic image quality of 3D late gadolinium enhancement (LGE) with high isotropic spatial resolution (∼1.4 mm3 ) images reconstructed from randomly undersampled k-space using LOw-dimensional-structure Self-learning and Thresholding (LOST). MATERIALS AND METHODS: We prospectively enrolled 270 patients (181 men; 55 ± 14 years) referred for myocardial viability assessment. 3D LGE with isotropic spatial resolution of 1.4 ± 0.1 mm3 was acquired at 1.5T using a LOST acceleration rate of 3 to 5. In a subset of 121 patients, 3D LGE or phase-sensitive LGE were acquired with parallel imaging with an acceleration rate of 2 for comparison. Two readers evaluated image quality using a scale of 1 (poor) to 4 (excellent) and assessed for scar presence. The McNemar test statistic was used to compare the proportion of detected scar between the two sequences. We assessed the association between image quality and characteristics (age, gender, torso dimension, weight, heart rate), using generalized linear models. RESULTS: Overall, LGE detection proportions for 3D LGE with LOST were similar between readers 1 and 2 (16.30% vs. 18.15%). For image quality, readers gave 85.9% and 80.0%, respectively, for images categorized as good or excellent. Overall proportion of scar presence was not statistically different from conventional 3D LGE (28% vs. 33% [P = 0.17] for reader 1 and 26% vs. 31% [P = 0.37] for reader 2). Increasing subject heart rate was associated with lower image quality (estimated slope = -0.009 (P = 0.001)). CONCLUSION: High-resolution 3D LGE with LOST yields good to excellent image quality in >80% of patients and identifies patients with LV scar at the same rate as conventional 3D LGE. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1829-1838.

Relationship between native papillary muscle T1 time and severity of functional mitral regurgitation in patients with non-ischemic dilated cardiomyopathy.

BACKGROUND: Functional mitral regurgitation is one of the severe complications of non-ischemic dilated cardiomyopathy (DCM). Non-contrast native T1 mapping has emerged as a non-invasive method to evaluate myocardial fibrosis. We sought to evaluate the potential relationship between papillary muscle T1 time and mitral regurgitation in DCM patients. METHODS: Forty DCM patients (55 ± 13 years) and 20 healthy adult control subjects (54 ± 13 years) were studied. Native T1 mapping was performed using a slice interleaved T1 mapping sequence (STONE) which enables acquisition of 5 slices in the short-axis plane within a 90 s free-breathing scan. We measured papillary muscle diameter, length and shortening. DCM patients were allocated into 2 groups based on the presence or absence of functional mitral regurgitation. RESULTS: Papillary muscle T1 time was significantly elevated in DCM patients with mitral regurgitation (n = 22) in comparison to those without mitral regurgitation (n = 18) (anterior papillary muscle: 1127 ± 36 msec vs 1063 ± 16 msec, p < 0.05; posterior papillary muscle: 1124 ± 30 msec vs 1062 ± 19 msec, p < 0.05), but LV T1 time was similar (1129 ± 38 msec vs 1134 ± 58 msec, p = 0.93). Multivariate linear regression analysis showed that papillary muscle native T1 time (ß = 0.10, 95 % CI: 0.05-0.17, p < 0.05) is significantly correlated with mitral regurgitant fraction. Elevated papillary muscle T1 time was associated with larger diameter, longer length and decreased papillary muscle shortening (all p values <0.05). CONCLUSIONS: In DCM, papillary muscle native T1 time is significantly elevated and related to mitral regurgitant fraction.

Comparison of spoiled gradient echo and steady-state free-precession imaging for native myocardial T1 mapping using the slice-interleaved T1 mapping (STONE) sequence.

Cardiac T1 mapping allows non-invasive imaging of interstitial diffuse fibrosis. Myocardial T1 is commonly calculated by voxel-wise fitting of the images acquired using balanced steady-state free precession (SSFP) after an inversion pulse. However, SSFP imaging is sensitive to B1 and B0 imperfection, which may result in additional artifacts. A gradient echo (GRE) imaging sequence has been used for myocardial T1 mapping; however, its use has been limited to higher magnetic field to compensate for the lower signal-to-noise ratio (SNR) of GRE versus SSFP imaging. A slice-interleaved T1 mapping (STONE) sequence with SSFP readout (STONE-SSFP) has been recently proposed for native myocardial T1 mapping, which allows longer recovery of magnetization (>8 R-R) after each inversion pulse. In this study, we hypothesize that a longer recovery allows higher SNR and enables native myocardial T1 mapping using STONE with GRE imaging readout (STONE-GRE) at 1.5T. Numerical simulations and phantom and in vivo imaging were performed to compare the performance of STONE-GRE and STONE-SSFP for native myocardial T1 mapping at 1.5T. In numerical simulations, STONE-SSFP shows sensitivity to both T2 and off resonance. Despite the insensitivity of GRE imaging to T2 , STONE-GRE remains sensitive to T2 due to the dependence of the inversion pulse performance on T2 . In the phantom study, STONE-GRE had inferior accuracy and precision and similar repeatability as compared with STONE-SSFP. In in vivo studies, STONE-GRE and STONE-SSFP had similar myocardial native T1 times, precisions, repeatabilities and subjective T1 map qualities. Despite the lower SNR of the GRE imaging readout compared with SSFP, STONE-GRE provides similar native myocardial T1 measurements, precision, repeatability, and subjective image quality when compared with STONE-SSFP at 1.5T.

Myocardial Native T1 Time in Patients With Hypertrophic Cardiomyopathy.

In hypertrophic cardiomyopathy (HC), there are significant variations in left ventricular (LV) wall thickness and fibrosis, which necessitates a volumetric coverage. Slice-interleaved T1 (STONE) mapping sequence allows for the assessment of native T1 time with complete coverage of LV myocardium. The aims of this study were to evaluate spatial heterogeneity of native T1 time in patients with HC. Twenty-nine patients with HC (55 ± 16 years) and 15 healthy adult control subjects (46 ± 19 years) were studied. Native T1 mapping was performed using STONE sequence which enables acquisition of 5 slices in the short-axis plane within a 90 seconds free-breathing scan. We measured LV native T1 time and maximum LV wall thickness in each 16 segments from 3 slices (basal, midventricular and apical slice). Late gadolinium enhanced (LGE) magnetic resonance imaging was acquired to assess the presence of myocardial enhancement. In patients with HC, LV native T1 time was significantly elevated compared with healthy controls, regardless of the presence or absence of LGE (mean native T1 time; LGE positive segments from HC, 1,141 ± 46 ms; LGE negative segments from HC, 1,114 ± 56 ms; segments from healthy controls, 1,065 ± 35 ms, p <0.001). Elevation of native T1 time was defined as >1,135 ms, which was +2SD of native T1 time by STONE sequence in healthy controls. A total of 120 of 405 (30%) LGE negative segments from patients with HC showed elevated native T1 time. Prevalence of segments with elevated native T1 time for basal, midventricular, and apical slice was 29%, 25%, 38%, respectively. Significant correlation was found between LV wall thickness and LV native T1 time (y = 0.029 × -22.6, p <0.001 by Spearman's correlation coefficient). In conclusion, substantial number of segments without LGE showed elevation of native T1 time, and whole-heart T1 mapping revealed heterogeneity of myocardial native T1 time in patients with HC.

Reproducibility of myocardial T1 and T2 relaxation time measurement using slice-interleaved T1 and T2 mapping sequences.

PURPOSE: To assess measurement reproducibility and image quality of myocardial T1 and T2 maps using free-breathing slice-interleaved T1 and T2 mapping sequences at 1.5 Tesla (T). MATERIALS AND METHODS: Eleven healthy subjects (33 ± 16 years; 6 males) underwent a slice-interleaved T1 and T2 mapping test/retest cardiac MR study at 1.5T on 2 days. For each day, subjects were imaged in two sessions with removal out of the magnet and repositioning before the subsequent session. We studied measurement reproducibility as well as the required sample size for sufficient statistical power to detect a predefined change in T1 and T2 . In a separate prospective study, we assessed T1 and T2 map image quality in 241 patients (54 ± 15 years; 73 women) with known/suspected cardiovascular disease referred for clinical cardiac MR. A subjective quality score was used to assess a segment-based image quality. RESULTS: In the healthy cohort, the slice-interleaved T1 measurements were highly reproducible, with global coefficients of variation (CVs) of 2.4% between subjects, 2.1% between days, and 1.7% between sessions. Slice-interleaved T2 mapping sequences provided similar reproducibility with global CVs of 7.2% between subjects, 6.3% between days, and 5.0 between sessions. A lower variability resulted in a reduction of the required number of subjects to achieve a certain statistical power when compared with other T1 mapping sequences. In the subjective image quality assessment, >80% of myocardial segments had interpretable data. CONCLUSION: Slice-interleaved T1 and T2 mapping sequences yield highly reproducible T1 and T2 measurements with >80% of interpretable myocardial segments. J. Magn. Reson. Imaging 2016;44:1159-1167.

Joint myocardial T1 and T2 mapping using a combination of saturation recovery and T2 -preparation.

PURPOSE: To develop a heart-rate independent breath-held joint T1 -T2 mapping sequence for accurate simultaneous estimation of coregistered myocardial T1 and T2 maps. METHODS: A novel preparation scheme combining both a saturation pulse and T2 -preparation in a single R-R interval is introduced. The time between these two pulses, as well as the duration of the T2 -preparation is varied in each heartbeat, acquiring images with different T1 and T2 weightings, and no magnetization dependence on previous images. Inherently coregistered T1 and T2 maps are calculated from these images. Phantom imaging is performed to compare the proposed maps with spin echo references. In vivo imaging is performed in ten subjects, comparing the accuracy and precision of the proposed technique to existing myocardial T1 and T2 mapping sequences of the same duration. RESULTS: Phantom experiments show that the proposed technique provides accurate quantification of T1 and T2 values over a wide-range (T1 : 260 ms to 1460 ms, T2 : 40 ms to 200 ms). In vivo imaging shows that the proposed sequence quantifies T1 and T2 values similar to a saturation-based T1 mapping and a conventional breath-hold T2 mapping sequence, respectively. CONCLUSION: The proposed sequence allows joint estimation of accurate and coregistered quantitative myocardial T1 and T2 maps in a single breath-hold. Magn Reson Med 76:888-896, 2016. © 2015 Wiley Periodicals, Inc.

Left ventricular native T1 time and the risk of atrial fibrillation recurrence after pulmonary vein isolation in patients with paroxysmal atrial fibrillation.

BACKGROUND: Native T1 mapping has emerged as a noninvasive non-contrast magnetic resonance imaging (MRI) method to assess for diffuse myocardial fibrosis. However, LV native T1 time in AF patients and its clinical relevance are unclear. METHODS: Fifty paroxysmal AF patients referred for PVI (60 ± 8 years, 37 male) and 11 healthy control subjects (57 ± 8 years, 10 male) were studied. All patients were in sinus rhythm during the MRI scan. Native T1 mapping images were acquired using a Modified Look-Locker imaging (MOLLI) sequence in 3 short-axis planes (basal, mid and apical slices) using an electrocardiogram triggered single-shot acquisition with a balanced steady-state free precession readout. Late gadolinium enhanced (LGE) MRI was acquired to evaluate for LV myocardial scar. RESULTS: LV ejection fraction was similar between groups (AF: 61 ± 6%; controls: 60 ± 6%, p=0.75). No LV myocardial scar was observed in any patient on LGE. Myocardial native T1 time was greater in AF patients (1099 ± 52 vs 1042 ± 20 msec, p<0.001). During a median follow-up period of 326 days, 18 of 50 (36%) patients experienced recurrence of AF. Multivariate Cox proportional hazard analysis identified elevated native T1 time as an independent predictor of recurrence of AF (HR: 6.53, 95% CI: 1.25-34.3, p=0.026). CONCLUSIONS: There are differences in the native LV myocardial T1 time between AF patients with preserved LV function referred for PVI and normal controls. Native T1 time is an independent predictor of recurrence of AF after PVI in patients with paroxysmal AF.

Free-breathing slice-interleaved myocardial T2 mapping with slice-selective T2 magnetization preparation.

PURPOSE: To develop and evaluate a free-breathing slice-interleaved T2 mapping sequence by proposing a new slice-selective T2 magnetization preparation (T2 prep) sequence that allows interleaved data acquisition for different slices in subsequent heartbeats. METHODS: We developed a slice-selective T2 prep for myocardial T2 mapping by adding slice-selective gradients to a conventional single-slice T2 prep sequence. In this sequence, five slices are acquired during five consecutive heartbeats, each using a slice-selective T2 prep. The scheme was repeated four times using different T2 prep echo times. We compared the performance of the proposed slice-interleaved T2 mapping sequence and the conventional single-slice T2 mapping sequence in term of accuracy, precision, and reproducibility using phantom experiments and in vivo imaging in 10 healthy subjects. We also evaluated the feasibility of the proposed sequence in 28 patients with cardiovascular disease, and the quality of the maps was scored subjectively. Furthermore, we investigated the impact of through-plane motion by comparing T2 measurements acquired during end-systole versus mid-diastole. RESULTS: T2 measurements using a slice-interleaved T2 mapping sequence were correlated with a spin echo (r(2) = 0.88) and single-slice T2 mapping sequence (r(2) = 0.98). The mean myocardial T2 values were correlated between slice-interleaved (48 ms) and single-slice (51 ms) T2 mapping sequences. Subjective scores of T2 map quality were good to excellent in 81% of the maps in patients. There was no difference in T2 measurements between end-systole versus mid-diastole. CONCLUSIONS: The proposed free-breathing slice-interleaved T2 mapping sequence allows T2 measurements of five left ventricular slices in 20 heartbeats with similar reproducibility and precision as the single-slice T2 mapping sequence but with a four-fold reduction in acquisition time. Magn Reson Med 76:555-565, 2016. © 2015 Wiley Periodicals, Inc.

Native Myocardial T1 as a Biomarker of Cardiac Structure in Non-Ischemic Cardiomyopathy.

Diffuse myocardial fibrosis is involved in the pathology of nonischemic cardiomyopathy (NIC). Recently, the application of native (noncontrast) myocardial T1 measurement has been proposed as a method for characterizing diffuse interstitial fibrosis. To determine the association of native T1 with myocardial structure and function, we prospectively studied 39 patients with NIC (defined as left ventricular ejection fraction (LVEF) ≤ 50% without cardiac magnetic resonance (CMR) evidence of previous infarction) and 27 subjects with normal LVEF without known overt cardiovascular disease. T1, T2, and extracellular volume fraction (ECV) were determined over 16 segments across the base, mid, and apical left ventricular (LV). NIC participants (57 ± 15 years) were predominantly men (74%), with a mean LVEF 34 ± 10%. Subjects with NIC had a greater native T1 (1,131 ± 51 vs 1,069 ± 29 ms; p <0.0001), a greater ECV (0.28 ± 0.04 vs 0.25 ± 0.02, p = 0.002), and a longer myocardial T2 (52 ± 8 vs 47 ± 5 ms; p = 0.02). After multivariate adjustment, a lower global native T1 time in NIC was associated with a greater LVEF (ß = -0.59, p = 0.0003), greater right ventricular ejection fraction (ß = -0.47, p = 0.006), and smaller left atrial volume index (ß = 0.51, p = 0.001). The regional distribution of native myocardial T1 was similar in patients with and without NIC. In NIC, native myocardial T1 is elevated in all myocardial segments, suggesting a global (not regional) abnormality of myocardial tissue composition. In conclusion, native T1 may represent a rapid, noncontrast alternative to ECV for delineating myocardial tissue remodeling in NIC.

Impact of motion correction on reproducibility and spatial variability of quantitative myocardial T2 mapping.

BACKGROUND: To evaluate and quantify the impact of a novel image-based motion correction technique in myocardial T2 mapping in terms of measurement reproducibility and spatial variability. METHODS: Twelve healthy adult subjects were imaged using breath-hold (BH), free breathing (FB), and free breathing with respiratory navigator gating (FB + NAV) myocardial T2 mapping sequences. Fifty patients referred for clinical CMR were imaged using the FB + NAV sequence. All sequences used a T2 prepared (T2prep) steady-state free precession acquisition. In-plane myocardial motion was corrected using an adaptive registration of varying contrast-weighted images for improved tissue characterization (ARCTIC). DICE similarity coefficient (DSC) and myocardial boundary errors (MBE) were measured to quantify the motion estimation accuracy in healthy subjects. T2 mapping reproducibility and spatial variability were evaluated in healthy subjects using 5 repetitions of the FB + NAV sequence with either 4 or 20 T2prep echo times (TE). Subjective T2 map quality was assessed in patients by an experienced reader using a 4-point scale (1-non diagnostic, 4-excellent). RESULTS: ARCTIC led to increased DSC in BH data (0.85 ± 0.08 vs. 0.90 ± 0.02, p = 0.007), FB data (0.78 ± 0.13 vs. 0.90 ± 0.21, p < 0.001), and FB + NAV data (0.86 ± 0.05 vs. 0.90 ± 0.02, p = 0.002), and reduced MBE in BH data (0.90 ± 0.40 vs. 0.64 ± 0.19 mm, p = 0.005), FB data (1.21 ± 0.65 vs. 0.63 ± 0.10 mm, p < 0.001), and FB + NAV data (0.81 ± 0.21 vs. 0.63 ± 0.08 mm, p < 0.001). Improved reproducibility (4TE: 5.3 ± 2.5 ms vs. 4.0 ± 1.5 ms, p = 0.016; 20TE: 3.9 ± 2.3 ms vs. 2.2 ± 0.5 ms, p = 0.002), reduced spatial variability (4TE: 12.8 ± 3.5 ms vs. 10.3 ± 2.5 ms, p < 0.001; 20TE: 9.7 ± 3.5 ms vs. 7.5 ± 1.4 ms) and improved subjective score of T2 map quality (3.43 ± 0.79 vs. 3.69 ± 0.55, p < 0.001) were obtained using ARCTIC. CONCLUSIONS: The ARCTIC technique substantially reduces spatial mis-alignment among T2-weighted images and improves the reproducibility and spatial variability of in-vivo T2 mapping.