MyoFold: Joint myocardial tissue composition and wall motion quantification via a highly folded sequence.

PURPOSE: This study aims to develop and evaluate a novel cardiovascular MR sequence, MyoFold, designed for the simultaneous quantifications of myocardial tissue composition and wall motion. METHODS: MyoFold is designed as a 2D single breathing-holding sequence, integrating joint T1/T2 mapping and cine imaging. The sequence uses a 2-fold accelerated balanced SSFP (bSSFP) for data readout and incorporates electrocardiogram synchronization to align with the cardiac cycle. MyoFold initially acquires six single-shot inversion-recovery images, completed during the diastole of six successive heartbeats. T2 preparation (T2-prep) is applied to introduce T2 weightings for the last three images. Subsequently, over the following six heartbeats, segmented bSSFP is performed for the movie of the entire cardiac cycle, synchronized with an electrocardiogram. A neural network trained using numerical simulations of MyoFold is used for T1 and T2 calculations. MyoFold was validated through phantom and in vivo experiments, with comparisons made against MOLLI, SASHA, T2-prep bSSFP, and the conventional cine. RESULTS: In phantom studies, MyoFold exhibited a 10% overestimation in T1 measurements, whereas T2 measurements demonstrated high accuracy. In vivo experiments revealed that MyoFold T1 had comparable accuracy to SASHA and precision similar to MOLLI. MyoFold demonstrated good agreement with T2-prep bSSFP in myocardial T2 measurements. No significant differences were observed in the quantification of left-ventricle wall thickness and function between MyoFold and the conventional cine. CONCLUSION: MyoFold presents as a rapid, simple, and multitasking approach for quantitative cardiovascular MR examinations, offering simultaneous assessment of tissue composition and wall motion. The sequence's multitasking capabilities make it a promising tool for comprehensive cardiac evaluations in clinical settings.

DeepFittingNet: A deep neural network-based approach for simplifying cardiac T1 and T2 estimation with improved robustness.

PURPOSE: To develop and evaluate a deep neural network (DeepFittingNet) for T1 /T2 estimation of the most commonly used cardiovascular MR mapping sequences to simplify data processing and improve robustness. THEORY AND METHODS: DeepFittingNet is a 1D neural network composed of a recurrent neural network (RNN) and a fully connected (FCNN) neural network, in which RNN adapts to the different number of input signals from various sequences and FCNN subsequently predicts A, B, and Tx of a three-parameter model. DeepFittingNet was trained using Bloch-equation simulations of MOLLI and saturation-recovery single-shot acquisition (SASHA) T1 mapping sequences, and T2 -prepared balanced SSFP (T2 -prep bSSFP) T2 mapping sequence, with reference values from the curve-fitting method. Several imaging confounders were simulated to improve robustness. The trained DeepFittingNet was tested using phantom and in-vivo signals, and compared to the curve-fitting algorithm. RESULTS: In testing, DeepFittingNet performed T1 /T2 estimation of four sequences with improved robustness in inversion-recovery T1 estimation. The mean bias in phantom T1 and T2 between the curve-fitting and DeepFittingNet was smaller than 30 and 1 ms, respectively. Excellent agreements between both methods was found in the left ventricle and septum T1 /T2 with a mean bias <6 ms. There was no significant difference in the SD of both the left ventricle and septum T1 /T2 between the two methods. CONCLUSION: DeepFittingNet trained with simulations of MOLLI, SASHA, and T2 -prep bSSFP performed T1 /T2 estimation tasks for all these most used sequences. Compared with the curve-fitting algorithm, DeepFittingNet improved the robustness for inversion-recovery T1 estimation and had comparable performance in terms of accuracy and precision.

What Matters in Radiological Image Segmentation? Effect of Segmentation Errors on the Diagnostic Related Features.

Segmentation is a crucial step in extracting the medical image features for clinical diagnosis. Though multiple metrics have been proposed to evaluate the segmentation performance, there is no clear study on how or to what extent the segmentation errors will affect the diagnostic related features used in clinical practice. Therefore, we proposed a segmentation robustness plot (SRP) to build the link between segmentation errors and clinical acceptance, where relative area under the curve (R-AUC) was designed to help clinicians to identify the robust diagnostic related image features. In experiments, we first selected representative radiological series from time series (cardiac first-pass perfusion) and spatial series (T2 weighted images on brain tumors) of magnetic resonance images, respectively. Then, dice similarity coefficient (DSC) and Hausdorff distance (HD), as the widely used evaluation metrics, were used to systematically control the degree of the segmentation errors. Finally, the differences between diagnostic related image features extracted from the ground truth and the derived segmentation were analyzed, using the statistical method large sample size T-test to calculate the corresponding p values. The results are denoted in the SRP, where the x-axis indicates the segmentation performance using the aforementioned evaluation metric, and the y-axis shows the severity of the corresponding feature changes, which are expressed in either the p values for a single case or the proportion of patients without significant change. The experimental results in SRP show that when DSC is above 0.95 and HD is below 3 mm, the segmentation errors will not change the features significantly in most cases. However, when segmentation gets worse, additional metrics are required for further analysis. In this way, the proposed SRP indicates the impact of the segmentation errors on the severity of the corresponding feature changes. By using SRP, one could easily define the acceptable segmentation errors in a challenge. Additionally, the R-AUC calculated from SRP provides an objective reference to help the selection of reliable features in image analysis.

Three-dimensional High-Resolution Dark-Blood Late Gadolinium Enhancement Imaging for Improved Atrial Scar Evaluation.

Background Radiofrequency ablation (RFA) is a widely used treatment for atrial fibrillation, reducing the risk of cardiac arrhythmia. Detailed visualization and quantification of atrial scarring has the potential to improve preprocedural decision-making and postprocedural prognosis. Conventional bright-blood late gadolinium enhancement (LGE) MRI can help detect atrial scars; however, its suboptimal myocardium to blood contrast inhibits accurate scar estimation. Purpose To develop and test a free-breathing LGE cardiac MRI approach that simultaneously provides high-spatial-resolution dark-blood and bright-blood images for improved atrial scar detection and quantification. Materials and Methods A free-breathing, independent navigator-gated, dark-blood phase-sensitive inversion recovery (PSIR) sequence with whole-heart coverage was developed. Two coregistered high-spatial-resolution (1.25 × 1.25 × 3 mm3) three-dimensional (3D) volumes were acquired in an interleaved manner. The first volume combined inversion recovery and T2 preparation to achieve dark-blood imaging. The second volume functioned as the reference for phase-sensitive reconstruction with built-in T2 preparation for improved bright-blood contrast. The proposed sequence was tested in prospectively enrolled participants who had undergone RFA for atrial fibrillation (mean time since RFA, 89 days ± 26 [SD]) from October 2019 to October 2021. Image contrast was compared with conventional 3D bright-blood PSIR images using the relative signal intensity difference. Furthermore, native scar area quantification obtained from both imaging approaches was compared with measurements obtained with electroanatomic mapping (EAM) as the reference standard. Results A total of 20 participants (mean age, 62 years ± 9; 16 male) who underwent RFA for atrial fibrillation were included. The proposed PSIR sequence successfully acquired 3D high-spatial-resolution volumes in all participants, with a mean scan time of 8.3 minutes ± 2.4. The developed PSIR sequence improved scar to blood contrast compared with conventional PSIR sequence (mean contrast, 0.60 arbitrary units [au] ± 0.18 vs 0.20 au ± 0.19, respectively; P < .01) and correlated with EAM regarding scar area quantification (r = 0.66 [P < .01] vs r = 0.13 [P = .63]). Conclusion In participants who had undergone RFA for atrial fibrillation, an independent navigator-gated dark-blood PSIR sequence produced high-spatial-resolution dark-blood and bright-blood images with improved image contrast and native scar quantification compared with conventional bright-blood images. © RSNA, 2023 Supplemental material is available for this article.

Single breath-hold three-dimensional whole-heart T2 mapping with low-rank plus sparse reconstruction.

The purpose of the current study was to develop and evaluate a three-dimensional single Breath-hOLd cardiac T2 mapping sequence (3D BOLT) with low-rank plus sparse (L + S) reconstruction for rapid whole-heart T2 measurement. 3D BOLT collects three highly accelerated electrocardiogram-triggered volumes with whole-heart coverage, all within a single 12-heartbeat breath-hold. Saturation pulses are performed every heartbeat to prepare longitudinal magnetization before T2 preparation (T2 -prep) or readout, and the echo time of T2 -prep is varied per volume for variable T2 weighting. Accelerated volumes are reconstructed jointly by an L + S algorithm. 3D BOLT was optimized and validated against gradient spin echo (GraSE) and a previously published approach (three-dimensional free-breathing cardiac T2 mapping [3DFBT2]) in both phantoms and human subjects (11 healthy subjects and 10 patients). The repeatability of 3D BOLT was validated on healthy subjects. Retrospective experiments indicated that 3D BOLT with 4.2-fold acceleration achieved T2 measurements comparable with those obtained with fully sampled data. T2 measured in phantoms using 3D BOLT demonstrated good accuracy and precision compared with the reference (R2 > 0.99). All in vivo imaging was successful and the average left ventricle T2 s measured by GraSE, 3DFBT2, and 3D BOLT were comparable and consistent for all healthy subjects (47.0 ± 2.3 vs. 47.7 ± 2.7 vs. 48.4 ± 1.8 ms) and patients (50.8 ± 3.0 vs. 48.6 ± 3.9 vs. 49.1 ± 3.7 ms), respectively. Myocardial T2 measured by 3D BOLT had excellent agreement with 3DFBT2 and there was no significant difference in mean, standard deviation, and coefficient of variation. 3D BOLT showed excellent repeatability (intraclass correlation coefficient: 0.938). The proposed 3D BOLT achieved whole-heart T2 mapping in a single breath-hold with good accuracy, precision, and repeatability on T2 measurements.

Simultaneous T1, T2, and T2* Mapping of Carotid Plaque: The SIMPLE* Technique.

Background Quantitative T1, T2, and T2* measurements of carotid atherosclerotic plaque are important in evaluating plaque vulnerability and monitoring its progression. Purpose To develop a sequence to simultaneously quantify T1, T2, and T2* of carotid plaque. Materials and Methods The simultaneous T1, T2, and T2* mapping of carotid plaque (SIMPLE*) sequence is composed of three modules with different T2 preparation pulses, inversion-recovery pulses, and acquisition schemas. Single-echo data were used for T1 and T2 quantification, while the multiecho (ME) data were used for T2* quantification. The quantitative accuracy of SIMPLE* was tested in a phantom study by comparing its measurements with those of reference standard sequences. In vivo feasibility of the technique was prospectively evaluated between November 2020 and February 2022 in healthy volunteers and participants with carotid atherosclerotic plaque. The Pearson or Spearman correlation test, Student t test, and Wilcoxon rank-sum test were used. Results T1, T2, and T2* estimated with SIMPLE* strongly correlated with inversion-recovery spin-echo (SE) (correlation coefficient [r] = 0.99), ME-SE (r = 0.99), and ME gradient-echo (r = 0.99) sequences in the phantom study. In five healthy volunteers (mean age, 25 years ± 3 [SD]; three women), measurements were similar between SIMPLE* and modified Look-Locker inversion recovery, or MOLLI (1151 msec ± 71 vs 1098 msec ± 64; P = .14), ME turbo SE (31 msec ± 1 vs 31 msec ± 1; P = .32), and ME turbo field echo (24 msec ± 2 vs 25 msec ± 2; P = .19). In 18 participants with carotid plaque (mean age, 65 years ± 9; 16 men), quantitative T1, T2, and T2* of plaque components were consistent with their signal characteristics on multicontrast images. Conclusion A quantitative technique for simultaneous T1, T2, and T2* mapping of carotid plaque with 100-mm3 coverage and 0.8-mm3 resolution was developed using the proposed SIMPLE* sequence and demonstrated high accuracy and in vivo feasibility. © RSNA, 2023 Supplemental material is available for this article.

Motion correction for native myocardial T1 mapping using self-supervised deep learning registration with contrast separation.

In myocardial T1 mapping, undesirable motion poses significant challenges because uncorrected motion can affect T1 estimation accuracy and cause incorrect diagnosis. In this study, we propose and evaluate a motion correction method for myocardial T1 mapping using self-supervised deep learning based registration with contrast separation (SDRAP). A sparse coding based method was first proposed to separate the contrast component from T1 -weighted (T1w) images. Then, a self-supervised deep neural network with cross-correlation (SDRAP-CC) or mutual information as the registration similarity measurement was developed to register contrast separated images, after which signal fitting was performed on the motion corrected T1w images to generate motion corrected T1 maps. The registration network was trained and tested in 80 healthy volunteers with images acquired using the modified Look-Locker inversion recovery (MOLLI) sequence. The proposed SDRAP was compared with the free form deformation (FFD) registration method regarding (1) Dice similarity coefficient (DSC) and mean boundary error (MBE) of myocardium contours, (2) T1 value and standard deviation (SD) of T1 fitting, (3) subjective evaluation score for overall image quality and motion artifact level, and (4) computation time. Results showed that SDRAP-CC achieved the highest DSC of 85.0 ± 3.9% and the lowest MBE of 0.92 ± 0.25 mm among the methods compared. Additionally, SDRAP-CC performed the best by resulting in lower SD value (28.1 ± 17.6 ms) and higher subjective image quality scores (3.30 ± 0.79 for overall quality and 3.53 ± 0.68 for motion artifact) evaluated by a cardiologist. The proposed SDRAP took only 0.52 s to register one slice of MOLLI images, achieving about sevenfold acceleration over FFD (3.7 s/slice).

SAturation-recovery and Variable-flip-Angle-based three-dimensional free-breathing cardiovascular magnetic resonance T1 mapping at 3 T.

The purpose of the current study was to develop and validate a three-dimensional (3D) free-breathing cardiac T1 -mapping sequence using SAturation-recovery and Variable-flip-Angle (SAVA). SAVA sequentially acquires multiple electrocardiogram-triggered volumes using a multishot spoiled gradient-echo sequence. The first volume samples the equilibrium signal of the longitudinal magnetization, where a flip angle of 2° is used to reduce the time for the magnetization to return to equilibrium. The succeeding three volumes are saturation prepared with variable delays, and are acquired using a 15° flip angle to maintain the signal-to-noise ratio. A diaphragmatic navigator is used to compensate the respiratory motion. T1 is calculated using a saturation-recovery model that accounts for the flip angle. We validated SAVA by simulations, phantom, and human subject experiments at 3 T. SAVA was compared with modified Look-Locker inversion recovery (MOLLI) and saturation-recovery single-shot acquisition (SASHA) in vivo. In phantoms, T1 by SAVA had good agreement with the reference (R2 = 0.99). In vivo 3D T1 mapping by SAVA could achieve an imaging resolution of 1.25 × 1.25 × 8 mm3 . Both global and septal T1 values by SAVA (1347 ± 37 and 1332 ± 42 ms) were in between those by SASHA (1612 ± 63 and 1618 ± 51 ms) and MOLLI (1143 ± 59 and 1188 ± 65 ms). According to the standard deviation (SD) and coefficient of variation (CV), T1 precision measured by SAVA (SD: 99 ± 14 and 60 ± 8 ms; CV: 7.4% ± 0.9% and 4.5% ± 0.6%) was comparable with MOLLI (SD: 99 ± 25 and 46 ± 12 ms; CV: 8.8% ± 2.5% and 3.9% ± 1.1%) and superior to SASHA (SD: 222 ± 89 and 132 ± 33 ms; CV: 13.8% ± 5.5% and 8.1% ± 2.0%). It was concluded that the proposed free-breathing SAVA sequence enables more efficient 3D whole-heart T1 estimation with good accuracy and precision.

[Advancement of imaging technology for coronary microcirculation dysfunction assessment].

Coronary microcirculation dysfunction (CMVD) is an important risk factor for the prognosis of re-perfused ischemic heart. Recent studies showed that the evaluation of CMVD has significant impact on both the early diagnosis of heart diseases relevant to blood supply and prognosis after myocardial reperfusion. In this review, the definition of CMVD from the perspective of pathophysiology was clarified, the principles and features of the state-of-the-art imaging technologies for CMVD assessment were reviewed from the perspective of engineering and the further research direction was promoted.