Accelerated Cardiac MRI Cine with Use of Resolution Enhancement Generative Adversarial Inline Neural Network.

Background Cardiac cine can benefit from deep learning-based image reconstruction to reduce scan time and/or increase spatial and temporal resolution. Purpose To develop and evaluate a deep learning model that can be combined with parallel imaging or compressed sensing (CS). Materials and Methods The deep learning model was built on the enhanced super-resolution generative adversarial inline neural network, trained with use of retrospectively identified cine images and evaluated in participants prospectively enrolled from September 2021 to September 2022. The model was applied to breath-hold electrocardiography (ECG)-gated segmented and free-breathing real-time cine images collected with reduced spatial resolution with use of generalized autocalibrating partially parallel acquisitions (GRAPPA) or CS. The deep learning model subsequently restored spatial resolution. For comparison, GRAPPA-accelerated cine images were collected. Diagnostic quality and artifacts were evaluated by two readers with use of Likert scales and compared with use of Wilcoxon signed-rank tests. Agreement for left ventricle (LV) function, volume, and strain was assessed with Bland-Altman analysis. Results The deep learning model was trained on 1616 patients (mean age ± SD, 56 years ± 16; 920 men) and evaluated in 181 individuals, 126 patients (mean age, 57 years ± 16; 77 men) and 55 healthy subjects (mean age, 27 years ± 10; 15 men). In breath-hold ECG-gated segmented cine and free-breathing real-time cine, the deep learning model and GRAPPA showed similar diagnostic quality scores (2.9 vs 2.9, P = .41, deep learning vs GRAPPA) and artifact score (4.4 vs 4.3, P = .55, deep learning vs GRAPPA). Deep learning acquired more sections per breath-hold than GRAPPA (3.1 vs one section, P < .001). In free-breathing real-time cine, the deep learning showed a similar diagnostic quality score (2.9 vs 2.9, P = .21, deep learning vs GRAPPA) and lower artifact score (3.9 vs 4.3, P < .001, deep learning vs GRAPPA). For both sequences, the deep learning model showed excellent agreement for LV parameters, with near-zero mean differences and narrow limits of agreement compared with GRAPPA. Conclusion Deep learning-accelerated cardiac cine showed similarly accurate quantification of cardiac function, volume, and strain to a standardized parallel imaging method. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Vannier and Wang in this issue.

Improving accuracy of myocardial T1 estimation in MyoMapNet.

PURPOSE: To improve the accuracy and robustness of T1 estimation by MyoMapNet, a deep learning-based approach using 4 inversion-recovery T1 -weighted images for cardiac T1 mapping. METHODS: MyoMapNet is a fully connected neural network for T1 estimation of an accelerated cardiac T1 mapping sequence, which collects 4 T1 -weighted images by a single Look-Locker inversion-recovery experiment (LL4). MyoMapNet was originally trained using in vivo data from the modified Look-Locker inversion recovery sequence, which resulted in significant bias and sensitivity to various confounders. This study sought to train MyoMapNet using signals generated from numerical simulations and phantom MR data under multiple simulated confounders. The trained model was then evaluated by phantom data scanned using new phantom vials that differed from those used for training. The performance of the new model was compared with modified Look-Locker inversion recovery sequence and saturation-recovery single-shot acquisition for measuring native and postcontrast T1 in 25 subjects. RESULTS: In the phantom study, T1 values measured by LL4 with MyoMapNet were highly correlated with reference values from the spin-echo sequence. Furthermore, the estimated T1 had excellent robustness to changes in flip angle and off-resonance. Native and postcontrast myocardium T1 at 3 Tesla measured by saturation-recovery single-shot acquisition, modified Look-Locker inversion recovery sequence, and MyoMapNet were 1483 ± 46.6 ms and 791 ± 45.8 ms, 1169 ± 49.0 ms and 612 ± 36.0 ms, and 1443 ± 57.5 ms and 700 ± 57.5 ms, respectively. The corresponding extracellular volumes were 22.90% ± 3.20%, 28.88% ± 3.48%, and 30.65% ± 3.60%, respectively. CONCLUSION: Training MyoMapNet with numerical simulations and phantom data will improve the estimation of myocardial T1 values and increase its robustness to confounders while also reducing the overall T1 mapping estimation time to only 4 heartbeats.

An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance.

BACKGROUND: Exercise cardiovascular magnetic resonance (Ex-CMR) is a promising stress imaging test for coronary artery disease (CAD). However, Ex-CMR requires accelerated imaging techniques that result in significant aliasing artifacts. Our goal was to develop and evaluate a free-breathing and electrocardiogram (ECG)-free real-time cine with deep learning (DL)-based radial acceleration for Ex-CMR. METHODS: A 3D (2D + time) convolutional neural network was implemented to suppress artifacts from aliased radial cine images. The network was trained using synthetic real-time radial cine images simulated using breath-hold, ECG-gated segmented Cartesian k-space data acquired at 3 T from 503 patients at rest. A prototype real-time radial sequence with acceleration rate = 12 was used to collect images with inline DL reconstruction. Performance was evaluated in 8 healthy subjects in whom only rest images were collected. Subsequently, 14 subjects (6 healthy and 8 patients with suspected CAD) were prospectively recruited for an Ex-CMR to evaluate image quality. At rest (n = 22), standard breath-hold ECG-gated Cartesian segmented cine and free-breathing ECG-free real-time radial cine images were acquired. During post-exercise stress (n = 14), only real-time radial cine images were acquired. Three readers evaluated residual artifact level in all collected images on a 4-point Likert scale (1-non-diagnostic, 2-severe, 3-moderate, 4-minimal). RESULTS: The DL model substantially suppressed artifacts in real-time radial cine images acquired at rest and during post-exercise stress. In real-time images at rest, 89.4% of scores were moderate to minimal. The mean score was 3.3 ± 0.7, representing increased (P < 0.001) artifacts compared to standard cine (3.9 ± 0.3). In real-time images during post-exercise stress, 84.6% of scores were moderate to minimal, and the mean artifact level score was 3.1 ± 0.6. Comparison of left-ventricular (LV) measures derived from standard and real-time cine at rest showed differences in LV end-diastolic volume (3.0 mL [- 11.7, 17.8], P = 0.320) that were not significantly different from zero. Differences in measures of LV end-systolic volume (7.0 mL [- 1.3, 15.3], P < 0.001) and LV ejection fraction (- 5.0% [- 11.1, 1.0], P < 0.001) were significant. Total inline reconstruction time of real-time radial images was 16.6 ms per frame. CONCLUSIONS: Our proof-of-concept study demonstrated the feasibility of inline real-time cine with DL-based radial acceleration for Ex-CMR.

Quantification of changes in myocardial T1 * values with exercise cardiac MRI using a free-breathing non-electrocardiograph radial imaging.

PURPOSE: To develop and evaluate a free breathing non-electrocardiograph (ECG) myocardial T1 * mapping sequence using radial imaging to quantify the changes in myocardial T1 * between rest and exercise (T1 *reactivity ) in exercise cardiac MRI (Ex-CMR). METHODS: A free-running T1 * sequence was developed using a saturation pulse followed by three Look-Locker inversion-recovery experiments. Each Look-Locker continuously acquired data as radial trajectory using a low flip-angle spoiled gradient-echo readout. Self-navigation was performed with a temporal resolution of ∼100 ms for retrospectively extracting respiratory motion. The mid-diastole phase for every cardiac cycle was retrospectively detected on the recorded electrocardiogram signal using an empirical model. Multiple measurements were performed to obtain mean value to reduce effects from the free-breathing acquisition. Finally, data acquired at both mid-diastole and end-expiration are picked and reconstructed by a low-rank plus sparsity constraint algorithm. The performance of this sequence was evaluated by simulations, phantoms, and in vivo studies at rest and after physiological exercise. RESULTS: Numerical simulation demonstrated that changes in T1 * are related to the changes in T1 ; however, other factors such as breathing motion could influence T1 * measurements. Phantom T1 * values measured using free-running T1 * mapping sequence had good correlation with spin-echo T1 values and was insensitive to heart rate. In the Ex-CMR study, the measured T1 * reactivity was 10% immediately after exercise and declined over time. CONCLUSION: The free-running T1 * mapping sequence allows free-breathing non-ECG quantification of changes in myocardial T1 * with physiological exercise. Although, absolute myocardial T1 * value is sensitive to various confounders such as B1 and B0 inhomogeneity, quantification of its change may be useful in revealing myocardial tissue properties with exercise.

Impact of deep learning architectures on accelerated cardiac T1 mapping using MyoMapNet.

The objective of the current study was to investigate the performance of various deep learning (DL) architectures for MyoMapNet, a DL model for T1 estimation using accelerated cardiac T1 mapping from four T1 -weighted images collected after a single inversion pulse (Look-Locker 4 [LL4]). We implemented and tested three DL architectures for MyoMapNet: (a) a fully connected neural network (FC), (b) convolutional neural networks (VGG19, ResNet50), and (c) encoder-decoder networks with skip connections (ResUNet, U-Net). Modified Look-Locker inversion recovery (MOLLI) images from 749 patients at 3 T were used for training, validation, and testing. The first four T1 -weighted images from MOLLI5(3)3 and/or MOLLI4(1)3(1)2 protocols were extracted to create accelerated cardiac T1 mapping data. We also prospectively collected data from 28 subjects using MOLLI and LL4 to further evaluate model performance. Despite rigorous training, conventional VGG19 and ResNet50 models failed to produce anatomically correct T1 maps, and T1 values had significant errors. While ResUNet yielded good quality maps, it significantly underestimated T1 . Both FC and U-Net, however, yielded excellent image quality with good T1 accuracy for both native (FC/U-Net/MOLLI = 1217 ± 64/1208 ± 61/1199 ± 61 ms, all p < 0.05) and postcontrast myocardial T1 (FC/U-Net/MOLLI = 578 ± 57/567 ± 54/574 ± 55 ms, all p < 0.05). In terms of precision, the U-Net model yielded better T1 precision compared with the FC architecture (standard deviation of 61 vs. 67 ms for the myocardium for native [p < 0.05], and 31 vs. 38 ms [p < 0.05], for postcontrast). Similar findings were observed in prospectively collected LL4 data. It was concluded that U-Net and FC DL models in MyoMapNet enable fast myocardial T1 mapping using only four T1 -weighted images collected from a single LL sequence with comparable accuracy. U-Net also provides a slight improvement in precision.

Accelerated cardiac T1 mapping in four heartbeats with inline MyoMapNet: a deep learning-based T1 estimation approach.

PURPOSE: To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses fully connected neural networks (FCNN) to estimate T1 values from four T1-weighted images collected after a single inversion pulse in four heartbeats (Look-Locker, LL4). METHOD: We implemented an FCNN for MyoMapNet to estimate T1 values from a reduced number of T1-weighted images and corresponding inversion-recovery times. We studied MyoMapNet performance when trained using native, post-contrast T1, or a combination of both. We also explored the effects of number of T1-weighted images (four and five) for native T1. After rigorous training using in-vivo modified Look-Locker inversion recovery (MOLLI) T1 mapping data of 607 patients, MyoMapNet performance was evaluated using MOLLI T1 data from 61 patients by discarding the additional T1-weighted images. Subsequently, we implemented a prototype MyoMapNet and LL4 on a 3 T scanner. LL4 was used to collect T1 mapping data in 27 subjects with inline T1 map reconstruction by MyoMapNet. The resulting T1 values were compared to MOLLI. RESULTS: MyoMapNet trained using a combination of native and post-contrast T1-weighted images had excellent native and post-contrast T1 accuracy compared to MOLLI. The FCNN model using four T1-weighted images yields similar performance compared to five T1-weighted images, suggesting that four T1 weighted images may be sufficient. The inline implementation of LL4 and MyoMapNet enables successful acquisition and reconstruction of T1 maps on the scanner. Native and post-contrast myocardium T1 by MOLLI and MyoMapNet was 1170 ± 55 ms vs. 1183 ± 57 ms (P = 0.03), and 645 ± 26 ms vs. 630 ± 30 ms (P = 0.60), and native and post-contrast blood T1 was 1820 ± 29 ms vs. 1854 ± 34 ms (P = 0.14), and 508 ± 9 ms vs. 514 ± 15 ms (P = 0.02), respectively. CONCLUSION: A FCNN, trained using MOLLI data, can estimate T1 values from only four T1-weighted images. MyoMapNet enables myocardial T1 mapping in four heartbeats with similar accuracy as MOLLI with inline map reconstruction.

Artifact reduction in free-breathing, free-running myocardial perfusion imaging with interleaved non-selective RF excitations.

PURPOSE: To reduce inflow and motion artifacts in free-breathing, free-running, steady-state spoiled gradient echo T1 -weighted (SPGR) myocardial perfusion imaging. METHOD: Unsaturated spins from inflowing blood or out-of-plane motion cause flashing artifacts in free-running SPGR myocardial perfusion. During free-running SPGR, 1 non-selective RF excitation was added after every 3 slice-selective RF excitations to suppress inflow artifacts by forcing magnetization in neighboring regions to steady-state. Bloch simulations and phantom experiments were performed to evaluate the impact of the flip angle and non-selective RF frequency on inflowing spins and tissue contrast. Free-running perfusion with (n = 11) interleaved non-selective RF or without (n = 11) were studied in 22 subjects (age = 60.2 ± 14.3 years, 11 male). Perfusion images were graded on a 5-point Likert scale for conspicuity of wall enhancement, inflow/motion artifact, and streaking artifact and compared using Wilcoxon sum-rank testing. RESULT: Numeric simulation showed that 1 non-selective RF excitation applied after every 3 slice-selective RF excitations produced superior out-of-plane signal suppression compared to 1 non-selective RF excitation applied after every 6 or 9 slice-selective RF excitations. In vitro experiments showed that a 30° flip angle produced near-optimal myocardial contrast. In vivo experiments demonstrated that the addition of interleaved non-selective RF significantly (P < .01) improved conspicuity of wall enhancement (mean score = 4.4 vs. 3.2) and reduced inflow/motion (mean score = 4.5 vs. 2.5) and streaking (mean score = 3.9 vs. 2.4) artifacts. CONCLUSION: Non-selective RF excitations interleaved between slice-selective excitations can reduce image artifacts in free-breathing, ungated perfusion images. Further studies are warranted to assess the diagnostic accuracy of the proposed solution for evaluating myocardial ischemia.

Highly accelerated free-breathing real-time phase contrast cardiovascular MRI via complex-difference deep learning.

PURPOSE: To develop and evaluate a real-time phase contrast (PC) MRI protocol via complex-difference deep learning (DL) framework. METHODS: DL used two 3D U-nets to separately filter aliasing artifact from radial real-time velocity-compensated and complex-difference images. U-nets were trained with synthetic real-time PC generated from electrocardiograph (ECG) -gated, breath-hold, segmented PC (ECG-gated segmented PC) acquired at the ascending aorta of 510 patients. In 21 patients, free-breathing, ungated real-time (acceleration rate = 28.8) and ECG-gated segmented (acceleration rate = 2) PC were prospectively acquired at the ascending aorta. Hemodynamic parameters (cardiac output [CO], stroke volume [SV], and mean velocity at peak systole [peak mean velocity]) were measured for ECG-gated segmented and DL-filtered synthetic real-time PC and compared using Bland-Altman and linear regression analyses. Additionally, hemodynamic parameters were quantified from DL-filtered, compressed-sensing (CS) -reconstructed, and gridding reconstructed prospective real-time PC and compared to ECG-gated segmented PC. RESULTS: Synthetic real-time PC with DL showed strong correlation (R > 0.98) and good agreement with ECG-gated segmented PC for quantified hemodynamic parameters (mean-difference: CO = -0.3 L/min, SV = -4.3 mL, peak mean velocity = -2.3 cm/s). On average, DL required 0.39 s/frame to filter prospective real-time PC, which was 4.6-fold faster than CS. Compared to CS, DL showed superior correlation, tighter limits of agreement (LOAs), better bias for peak mean velocity, and worse bias for CO and SV. Compared to gridding, DL showed similar correlation, tighter LOAs for CO and SV, similar bias for CO, and worse bias for SV and peak mean velocity. CONCLUSION: The complex-difference DL framework accelerated real-time PC-MRI by nearly 28-fold, enabling rapid free-running real-time assessment of flow hemodynamics.

Sensitivity of Myocardial Radiomic Features to Imaging Parameters in Cardiac MR Imaging.

BACKGROUND: Cardiac magnetic resonance (MR) images are often collected with different imaging parameters, which may impact the calculated values of myocardial radiomic features. PURPOSE: To investigate the sensitivity of myocardial radiomic features to changes in imaging parameters in cardiac MR images. STUDY TYPE: Prospective. POPULATION: A total of 11 healthy participants/five patients. FIELD STRENGTH/ SEQUENCE: A 3 T/cine balanced steady-state free-precession, T1 -weighted spoiled gradient-echo, T2 -weighted turbo spin-echo, and quantitative T1 and T2 mapping. For each sequence, the flip angle, in-plane resolution, slice thickness, and parallel imaging technique were varied to study the sensitivity of radiomic features to alterations in imaging parameters. ASSESSMENT: Myocardial contours were manually delineated by experienced readers, and a total of 1023 radiomic features were extracted using PyRadiomics with 11 image filters and six feature families. STATISTICAL TESTS: Sensitivity was defined as the standardized mean difference (D effect size), and the robust features were defined at sensitivity < 0.2. Sensitivity analysis was performed on predefined sets of reproducible features. The analysis was performed using the entire cohort of 16 subejcts. RESULTS: 64% of radiomic features were robust (sensitivity < 0.2) to changes in any imaging parameter. In qualitative sequences, radiomic features were most sensitive to changes in in-plane spatial resolution (spatial resolution: 0.6 vs. flip angle: 0.19, parallel imaging: 0.18, slice thickness: 0.07; P < 0.01 for all); in quantitative sequences, radiomic features were least sensitive to changes in spatial resolution (spatial resolution: 0.07 vs. slice thickness: 0.16, flip angle: 0.24; P < 0.01 for all). In an individual feature level, no singular feature family/image filter was identified as robust (sensitivity < 0.2) across sequences; however, highly sensitive features were predominantly associated with high-frequency wavelet filters across all sequences (32/50 features). DATA CONCLUSION: In cardiac MR, a considerable number of radiomic features are sensitive to changes in sequence parameters. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Free-breathing whole-heart multi-slice myocardial T1 mapping in 2 minutes.

PURPOSE: To develop and validate a saturation-delay-inversion recovery preparation, slice tracking and multi-slice based sequence for measuring whole-heart native T1 . METHOD: The proposed free-breathing sequence performs T1 mapping of multiple left-ventricular slices by slice-interleaved acquisition to collect 10 electrocardiogram-triggered single-shot slice-selective images for each slice. A saturation-delay-inversion recovery pulse is used for T1 preparation. Prospective slice tracking by the diaphragm navigator and retrospective registration are used to reduce through-plane and in-plane motion, respectively. The proposed sequence was validated in both phantom and human subjects (12 healthy subjects and 15 patients who were referred for a clinical cardiac MR exam) and compared with saturation recovery single-shot acquisition (SASHA) and modified Look-Locker inversion recovery (MOLLI). RESULTS: Phantom T1 measured by the proposed sequence had excellent agreement (R2  = 0.99) with the ground-truth T1 and was insensitive to heart rate. In both healthy subjects and patients, the proposed sequence yielded nine left-ventricular T1 maps per volume in less than 2 minutes (healthy volunteers: 1.8 ± 0.4 minutes; patients: 1.9 ± 0.2 minutes). The average T1 of whole left ventricle for all healthy subjects and patients were 1560 ± 61 and 1535 ± 49 ms by SASHA, 1208 ± 42 and 1233 ± 56 ms by MOLLI5(3)3, and 1397 ± 34 and 1433 ± 56 ms by the proposed sequence, respectively. The corresponding coefficient of variation of T1 were 6.2 ± 1.4% and 5.8 ± 1.6%, 5.3 ± 1.1% and 5.1 ± 0.8%, and 4.9 ± 0.8% and 4.5 ± 0.8%, respectively. CONCLUSION: The proposed sequence enables quantification of whole heart T1 with good accuracy and precision in less than 2 minutes during free breathing.

Free-breathing simultaneous myocardial T1 and T2 mapping with whole left ventricle coverage.

PURPOSE: To develop a free-breathing sequence, that is, Multislice Joint T1 -T2 , for simultaneous measurement of myocardial T1 and T2 for multiple slices to achieve whole left-ventricular coverage. METHODS: Multislice Joint T1 -T2 adopts slice-interleaved acquisition to collect 10 single-shot electrocardiogram-triggered images for each slice prepared by saturation and T2 preparation to simultaneously estimate myocardial T1 and T2 and achieve whole left-ventricular coverage. Prospective slice-tracking using a respiratory navigator and retrospective image registration are used to reduce through-plane and in-plane motion, respectively. Multislice Joint T1 -T2 was validated through numerical simulations and phantom and in vivo experiments, and compared with saturation-recovery single-shot acquisition and T2 -prepared balanced Steady-State Free Precession (T2 -prep SSFP) sequences. RESULTS: Phantom T1 and T2 from Multislice Joint T1 -T2 had good accuracy and precision, and were insensitive to heart rate. Multislice Joint T1 -T2 yielded T1 and T2 maps of nine left-ventricular slices in 1.4 minutes. The mean left-ventricular T1 difference between saturation-recovery single-shot acquisition and Multislice Joint T1 -T2 across healthy subjects and patients was 191 ms (1564 ± 60 ms versus 1373 ± 50 ms; P < .05) and 111 ms (1535 ± 49 ms vs 1423 ± 49 ms; P < .05), respectively. The mean difference in left-ventricular T2 between T2 -prep SSFP and Multislice Joint T1 -T2 across healthy subjects and patients was -6.3 ms (42.4 ± 1.4 ms vs 48.7 ± 2.5; P < .05) and -5.7 ms (41.6 ± 2.5 ms vs 47.3 ± 2.7; P < .05), respectively. CONCLUSION: Multislice Joint T1 -T2 enables quantification of whole left-ventricular T1 and T2 during free breathing within a clinically feasible scan time of less than 2 minutes.

Reproducibility of Segmentation-based Myocardial Radiomic Features with Cardiac MRI.

PURPOSE: To investigate reproducibility of myocardial radiomic features with cardiac MRI. MATERIALS AND METHODS: Test-retest studies were performed with a 3-T MRI system using commonly used cardiac MRI sequences of cine balanced steady-state free precession (cine bSSFP), T1-weighted and T2-weighted imaging, and quantitative T1 and T2 mapping in phantom experiments and 10 healthy participants (mean ± standard deviation age, 29 years ± 13). In addition, this study assessed repeatability in 51 patients (56 years ± 14) who underwent imaging twice during the same session. Three readers independently delineated the myocardium to investigate inter- and intraobserver reproducibility of radiomic features. A total of 1023 radiomic features were extracted by using PyRadiomics (https://pyradiomics.readthedocs.io/) with 11 image filters and six feature families. The intraclass correlation coefficient (ICC) was estimated to assess reproducibility and repeatability, and features with ICCs greater than or equal to 0.8 were considered reproducible. RESULTS: Different reproducibility patterns were observed among sequences in in vivo test-retest studies. In cine bSSFP, the gray-level run-length matrix was the most reproducible feature family, and the wavelet low-pass filter applied horizontally and vertically was the most reproducible image filter. In T1 and T2 maps, intensity-based statistics (first-order) and gray-level co-occurrence matrix features were the most reproducible feature families, without a dominant reproducible image filter. Across all sequences, gray-level nonuniformity was the most frequently identified reproducible feature name. In inter- and intraobserver reproducibility studies, respectively, only 32%-47% and 61%-73% of features were identified as reproducible. CONCLUSION: Only a small subset of myocardial radiomic features was reproducible, and these reproducible radiomic features varied among different sequences. Supplemental material is available for this article. © RSNA, 2020See also the commentary by Leiner in this issue.

Changes in Myocardial Native T1 and T2 After Exercise Stress: A Noncontrast CMR Pilot Study.

OBJECTIVES: This study assessed changes in myocardial native T1 and T2 values after supine exercise stress in healthy subjects and in patients with suspected ischemia as potential imaging markers of ischemia. BACKGROUND: With emerging data on the long-term retention of gadolinium in the body and brain, there is a need for an alternative noncontrast cardiovascular magnetic resonance (CMR)-based myocardial ischemia assessment. METHODS: Twenty-eight healthy adult subjects and 14 patients with coronary artery disease (CAD) referred for exercise stress and/or rest single-photon emission computed tomography/myocardial perfusion imaging (SPECT/MPI) for evaluation of chest pain were prospectively enrolled. Free-breathing myocardial native T1 and T2 mapping were performed before and after supine bicycle exercise stress using a CMR-compatible supine ergometer positioned on the MR table. Differences in T1 rest, T2 rest and T1 post-exercise, T2 post-exercise values were calculated as T1 and T2 reactivity, respectively. RESULTS: The mean exercise intensity was 104 W, with exercise duration of 6 to 12 min. After exercise, native T1 was increased in healthy subjects (p < 0.001). T1 reactivity, but not T2 reactivity, correlated with the rate-pressure product as the index of myocardial blood flow during exercise (r = 0.62; p < 0.001). In patients with CAD, T1 reactivity was associated with the severity of myocardial perfusion abnormality on SPECT/MPI (normal: 4.9%; quartiles: 3.7% to 6.3%, mild defect: 1.2%, quartiles: 0.08% to 2.5%; moderate defect: 0.45%, quartiles: -0.35% to 1.4%; severe defect: 0.35%, quartiles: -0.44% to 0.8%) and had similar potential as SPECT/MPI to detect significant CAD (>50% diameter stenosis on coronary angiography). The area under the receiver-operating characteristic curve was 0.80 versus 0.72 (p = 0.40). The optimum cutoff value of T1 reactivity for predicting flow-limiting stenosis was 2.5%, with a sensitivity of 83% and a specificity of 92%, a negative predictive value of 96%, a positive predictive value of 71%, and an area under the curve of 0.86. CONCLUSIONS: Free-breathing stress/rest native T1 mapping, but not T2 mapping, can detect physiological changes in the myocardium during exercise. Our feasibility study in patients shows the potential of this technique as a method for detecting myocardial ischemia in patients with CAD without using a pharmacological stress agent.

Black blood myocardial T2 mapping.

PURPOSE: To develop a black blood heart-rate adaptive T2 -prepared balanced steady-state free-precession (BEATS) sequence for myocardial T2 mapping. METHODS: In BEATS, blood suppression is achieved by using a combination of preexcitation and double inversion recovery pulses. The timing and flip angles of the preexcitation pulse are auto-calculated in each patient based on heart rate. Numerical simulations, phantom studies, and in vivo studies were conducted to evaluate the performance of BEATS. BEATS T2 maps were acquired in 36 patients referred for clinical cardiac MRI and in 1 swine with recent myocardial infarction. Two readers assessed all images acquired in patients to identify the presence of artifacts associated with slow blood flow. RESULTS: Phantom experiments showed that the BEATS sequence provided accurate T2 values over a wide range of simulated heart rates. Black blood myocardial T2 maps were successfully obtained in all subjects. No significant difference was found between the average T2 measurements obtained from the BEATS and conventional bright-blood T2 ; however, there was a decrease in precision using the BEATS sequence. A suppression of the blood pool resulted in sharper definition of the blood-myocardium border and reduced partial voluming effect. The subjective assessment showed that 16% (18 out of 108) of short-axis slices have residual blood artifacts (12 in the apical slice, 4 in the midventricular slice, and 2 in the basal slice). CONCLUSION: The BEATS sequence yields dark blood myocardial T2 maps with better definition of the blood-myocardium border. Further studies are warranted to evaluate diagnostic accuracy of black blood T2 mapping.

Effect of isolated left bundle-branch block on biventricular volumes and ejection fraction: a cardiovascular magnetic resonance assessment.

BACKGROUND: Left bundle branch block (LBBB) is associated with abnormal left ventricular (LV) contraction, and is frequently associated with co-morbid cardiovascular disease, but the effect of an isolated (i.e. in the absence of cardiovascular dissease) LBBB on biventricular volumes and ejection fraction (EF) is not well characterized. The objective of this study was to compare LV and right ventricular (RV) volumes and EF in adults with an isolated LBBB to matched healthy controls and to population-derived normative values, using cardiovascular magnetic resonance (CMR) imaging. METHODS: We reviewed our clinical echocardiography database and the Framingham Heart Study Offspring cohort CMR database to identify adults with an isolated LBBB. Age-, sex-, hypertension-status, and body-surface area (BSA)-matched controls were identified from the Offspring cohort. All study subjects were scanned using the same CMR hardware and imaging sequence. Isolated-LBBB cases were compared with matched controls using Wilcoxon paired signed-rank test, and to normative reference values via Z-score. RESULTS: Isolated-LBBB subjects (n = 18, 10F) ranged in age from 37 to 82 years. An isolated LBBB was associated with larger LV end-diastolic and end-systolic volumes (both p < 0.01) and lower LVEF (56+/- 7% vs. 68+/- 6%; p <0.001) with similar myocardial contraction fraction. LVEF in isolated LBBB was nearly two standard deviations (Z = - 1.95) below mean sex and age-matched group values. LV stroke volume, cardiac output, and mass, and all RV parameters were similar (p = NS) between the groups. CONCLUSIONS: Adults with an isolated LBBB have greater LV volumes and markedly reduced LVEF, despite the absence of overt cardiovascular disease. These data may be useful toward the clinical interpretation of imaging studies performed on patients with an isolated LBBB.

Diffuse myocardial fibrosis in patients with mitral valve prolapse and ventricular arrhythmia.

OBJECTIVE: We aimed to investigate the association of diffuse myocardial fibrosis by cardiac magnetic resonance (CMR) T1 with complex ventricular arrhythmia (ComVA) in mitral valve prolapse (MVP). METHODS: A retrospective analysis was performed on 41 consecutive patients with MVP referred for CMR between 2006 and 2011, and 31 healthy controls. Arrhythmia analysis was available in 23 patients with MVP with Holter/event monitors. Left ventricular (LV) septal T1 times were derived from Look-Locker sequences after administration of 0.2 mmol/kg gadopentetate dimeglumine. Late gadolinium enhancement (LGE) CMR images were available for all subjects. RESULTS: Patients with MVP had significantly shorter postcontrast T1 times when compared with controls (334±52 vs 363±58 ms; p=0.03) despite similar LV ejection fraction (LVEF) (63±7 vs 60±6%, p=0.10). In a multivariable analysis, LV end-diastolic volume, LVEF and mitral regurgitation fraction were all correlates of T1 times, with LVEF and LV end-diastolic volume being the strongest (p=0.005, p=0.008 and p=0.045, respectively; model adjusted R2=0.30). Patients with MVP with ComVA had significantly shorter postcontrast T1 times when compared with patients with MVP without ComVA (324 (296, 348) vs 354 (327, 376) ms; p=0.03) and only 5/14 (36%) had evidence of papillary muscle LGE. CONCLUSIONS: MVP may be associated with diffuse LV myocardial fibrosis as suggested by reduced postcontrast T1 times. Diffuse interstitial derangement is linked to subclinical systolic dysfunction, and may contribute to ComVA in MVP-related mitral regurgitation, even in the absence of focal fibrosis.

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.

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.

Accelerated cardiac MR stress perfusion with radial sampling after physical exercise with an MR-compatible supine bicycle ergometer.

PURPOSE: To evaluate the feasibility of accelerated cardiac MR (CMR) perfusion with radial sampling using nonlinear image reconstruction after exercise on an MR-compatible supine bike ergometer. METHODS: Eight healthy subjects were scanned on two separate days using radial and Cartesian CMR perfusion sequences in rest and exercise stress perfusion. Four different methods (standard gridding, conjugate gradient SENSE [CG-SENSE], nonlinear inversion with joint estimation of coil-sensitivity profiles [NLINV] and compressed sensing with a total variation constraint [TV]) were compared for the reconstruction of radial data. Cartesian data were reconstructed using SENSE. All images were assessed by two blinded readers in terms of image quality and diagnostic value. RESULTS: CG-SENSE and NLINV were scored more favorably than TV (in both rest and stress perfusion cases, P < 0.05) and gridding (for rest perfusion cases, P < 0.05). TV images showed patchy artifacts, which negatively influenced image quality especially in the stress perfusion images acquired with a low number of radial spokes. Although CG-SENSE and NLINV received better scores than Cartesian sampling in both rest and exercise stress perfusion cases, these differences were not statistically significant (P > 0.05). CONCLUSION: We have demonstrated the feasibility of accelerated CMR perfusion using radial sampling after physical exercise using a supine bicycle ergometer in healthy subjects. For reconstruction of undersampled radial perfusion, CG-SENSE and NLINV resulted in better image quality than standard gridding or TV reconstruction. Further technical improvements and clinical assessment are needed before using this approach in patients with suspected coronary artery disease.