Nonrigid active shape model-based registration framework for motion correction of cardiac T1 mapping

Magnetic resonance myocardial T1ρ mapping : Technical overview, challenges, emerging developments, and clinical applications

The potential of cardiac magnetic resonance to improve cardiovascular care and patient management is considerable. Myocardial T1-rho (T1ρ) mapping, in particular, has emerged as a promising biomarker for quantifying myocardial injuries without exogenous contrast agents. Its potential as a contrast-agent-free ("needle-free") and cost-effective diagnostic marker promises high impact both in terms of clinical outcomes and patient comfort. However, myocardial T1ρ mapping is still at a nascent stage of development and the evidence supporting its diagnostic performance and clinical effectiveness is scant, though likely to change with technological improvements. The present review aims at providing a primer on the essentials of myocardial T1ρ mapping, and to describe the current range of clinical applications of the technique to detect and quantify myocardial injuries. We also delineate the important limitations and challenges for clinical deployment, including the urgent need for standardization, the evaluation of bias, and the critical importance of clinical testing. We conclude by outlining technical developments to be expected in the future. If needle-free myocardial T1ρ mapping is shown to improve patient diagnosis and prognosis, and can be effectively integrated in cardiovascular practice, it will fulfill its potential as an essential component of a cardiac magnetic resonance examination.

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).

The Road Toward Reproducibility of Parametric Mapping of the Heart: A Technical Review

Parametric mapping of the heart has become an essential part of many cardiovascular magnetic resonance imaging exams, and is used for tissue characterization and diagnosis in a broad range of cardiovascular diseases. These pulse sequences are used to quantify the myocardial T₁, T₂, T2*, and T_1ρ relaxation times, which are unique surrogate indices of fibrosis, edema and iron deposition that can be used to monitor a disease over time or to compare patients to one another. Parametric mapping is now well-accepted in the clinical setting, but its wider dissemination is hindered by limited inter-center reproducibility and relatively long acquisition times. Recently, several new parametric mapping techniques have appeared that address both of these problems, but substantial hurdles remain for widespread clinical adoption. This review serves both as a primer for newcomers to the field of parametric mapping and as a technical update for those already well at home in it. It aims to establish what is currently needed to improve the reproducibility of parametric mapping of the heart. To this end, we first give an overview of the metrics by which a mapping technique can be assessed, such as bias and variability, as well as the basic physics behind the relaxation times themselves and what their relevance is in the prospect of myocardial tissue characterization. This is followed by a summary of routine mapping techniques and their variations. The problems in reproducibility and the sources of bias and variability of these techniques are reviewed. Subsequently, novel fast, whole-heart, and multi-parametric techniques and their merits are treated in the light of their reproducibility. This includes state of the art segmentation techniques applied to parametric maps, and how artificial intelligence is being harnessed to solve this long-standing conundrum. We finish up by sketching an outlook on the road toward inter-center reproducibility, and what to expect in the future.

Non-rigid motion-corrected free-breathing 3D myocardial Dixon LGE imaging in a clinical setting

Objectives

To investigate the efficacy of an in-line non-rigid motion-compensated reconstruction (NRC) in an image-navigated high-resolution three-dimensional late gadolinium enhancement (LGE) sequence with Dixon water–fat separation, in a clinical setting.

Methods

Forty-seven consecutive patients were enrolled prospectively and examined with 1.5 T MRI. NRC reconstructions were compared to translational motion-compensated reconstructions (TC) of the same datasets in overall and different sub-category image quality scores, diagnostic confidence, contrast ratios, LGE pattern, and semiautomatic LGE quantification.

Results

NRC outperformed TC in all image quality scores (p < 0.001 to 0.016; e.g., overall image quality 5/5 points vs. 4/5). Overall image quality was downgraded in only 23% of NRC datasets vs. 53% of TC datasets due to residual respiratory motion. In both reconstructions, LGE was rated as ischemic in 11 patients and non-ischemic in 10 patients, while it was absent in 26 patients. NRC delivered significantly higher LGE-to-myocardium and blood-to-myocardium contrast ratios (median 6.33 vs. 5.96, p < 0.001 and 4.88 vs. 4.66, p < 0.001, respectively). Automatically detected LGE mass was significantly lower in the NRC reconstruction (p < 0.001). Diagnostic confidence was identical in all cases, with high confidence in 89% and probable in 11% datasets for both reconstructions. No case was rated as inconclusive.

Conclusions

The in-line implementation of a non-rigid motion-compensated reconstruction framework improved image quality in image-navigated free-breathing, isotropic high-resolution 3D LGE imaging with undersampled spiral-like Cartesian sampling and Dixon water–fat separation compared to translational motion correction of the same datasets. The sharper depictions of LGE may lead to more accurate measures of LGE mass.

Key Points

• 3D LGE imaging provides high-resolution detection of myocardial scarring.

• Non-rigid motion correction provides better image quality in cardiac MRI.

• Non-rigid motion correction may lead to more accurate measures of LGE mass.

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.

Myocardial extracellular volume fraction quantification based on T1 mapping at 3 T: quality optimization by contour-based registration and segmental analysis

BACKGROUND

Myocardial extracellular volume fraction (ECV) assessment can be affected by various technical and subject-related factors.

PURPOSE

To evaluate the role of contour-based registration in quantification of ECV and investigate normal segment-based myocardial ECV values at 3T.

MATERIAL AND METHODS

Pre- and post-contrast T1 mapping images of the left ventricular basal, mid-cavity, and apical slices were obtained in 26 healthy volunteers. ECV maps were generated using motion correction with and without contour-based registration. The image quality of all ECV maps was evaluated by a 4-point scale. Slices were dichotomized according to the occurrence of misregistration in the source data. Contour-registered ECVs and standard ECVs were compared within each subgroup using analysis of variance for repeated measurements and generalized linear mixed models.

RESULTS

In all three slices, higher quality of ECV maps were found using contour-registered method than using standard method. Standard ECVs were statistically different from contour-registered ECVs in global (26.8% ± 2.8% vs. 25.8% ± 2.4%; P = 0.001), mid-cavity (25.4% ± 3.1% vs. 24.3% ± 2.5%; P = 0.016), and apical slices (28.7% ± 4.1% vs. 27.2% ± 3.4%; P = 0.010). In the misregistration subgroups, contour-registered ECVs were lower with smaller SDs (basal: 25.2% ± 1.8% vs. 26.7% ± 2.6%; P = 0.038; mid-cavity: 24.4% ± 2.3% vs. 26.8% ± 3.1%; P = 0.012; apical: 27.5% ± 3.6% vs. 29.7% ± 4.5%; P = 0.016). Apical (27.2% ± 3.4%) and basal-septal ECVs (25.6% ± 2.6%) were statistically higher than mid-cavity ECV (24.3% ± 2.5%; both P < 0.001).

CONCLUSION

Contour-based registration can optimize image quality and improve the precision of ECV quantification in cases demonstrating ventricular misregistration among source images.

Improving the robustness of MOLLI T1 maps with a dedicated motion correction algorithm

Myocardial tissue T1 constitutes a reliable indicator of several heart diseases related to extracellular changes (e.g. edema, fibrosis) as well as fat, iron and amyloid content. Magnetic resonance (MR) T1-mapping is typically achieved by pixel-wise exponential fitting of a series of inversion or saturation recovery measurements. Good anatomical alignment between these measurements is essential for accurate T1 estimation. Motion correction is recommended to improve alignment. However, in the case of inversion recovery sequences, this correction is compromised by the intrinsic contrast variation between frames. A model-based, non-rigid motion correction method for MOLLI series was implemented and validated on a large database of cardiac clinical cases (n = 186). The method relies on a dedicated similarity metric that accounts for the intensity changes caused by T1 magnetization relaxation. The results were compared to uncorrected series and to the standard motion correction included in the scanner. To automate the quantitative analysis of results, a custom data alignment metric was defined. Qualitative evaluation was performed on a subset of cases to confirm the validity of the new metric. Motion correction caused noticeable (i.e. > 5%) performance degradation in 12% of cases with the standard method, compared to 0.3% with the new dedicated method. The average alignment quality was 85% ± 9% with the default correction and 90% ± 7% with the new method. The results of the qualitative evaluation were found to correlate with the quantitative metric. In conclusion, a dedicated motion correction method for T1 mapping MOLLI series has been evaluated on a large database of clinical cardiac MR cases, confirming its increased robustness with respect to the standard method implemented in the scanner.

Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping

Background

The pattern of myocardial fibrosis differs significantly between different cardiomyopathies. Fibrosis in hypertrophic cardiomyopathy (HCM) is characteristically as patchy and regional but in dilated cardiomyopathy (DCM) as diffuse and global. We sought to investigate if texture analyses on myocardial native T₁ mapping can differentiate between fibrosis patterns in patients with HCM and DCM.

Methods

We prospectively acquired native myocardial T₁ mapping images for 321 subjects (55±15 years, 70% male): 65 control, 116 HCM, and 140 DCM patients. To quantify different fibrosis patterns, four sets of texture descriptors were used to extract 152 texture features from native T₁ maps. Seven features were sequentially selected to identify HCM- and DCM-specific patterns in 70% of data (training dataset). Pattern reproducibility and generalizability were tested on the rest of data (testing dataset) using support vector machines (SVM) and regression models.

Results

Pattern-derived texture features were capable to identify subjects in HCM, DCM, and controls cohorts with 202/237(85.2%) accuracy of all subjects in the training dataset using 10-fold cross-validation on SVM (AUC = 0.93, 0.93, and 0.93 for controls, HCM and DCM, respectively), while pattern-independent global native T₁ mapping was poorly capable to identify those subjects with 121/237(51.1%) accuracy (AUC = 0.78, 0.51, and 0.74) (P<0.001 for all). The pattern-derived features were reproducible with excellent intra- and inter-observer reliability and generalizable on the testing dataset with 75/84(89.3%) accuracy.

Conclusion

Texture analysis of myocardial native T₁ mapping can characterize fibrosis patterns in HCM and DCM patients and provides additional information beyond average native T₁ values.

Automated Myocardial T2 and Extracellular Volume Quantification in Cardiac MRI Using Transfer Learning-based Myocardium Segmentation

Purpose

To assess the performance of an automated myocardial T2 and extracellular volume (ECV) quantification method using transfer learning of a fully convolutional neural network (CNN) pretrained to segment the myocardium on T1 mapping images.

Materials and Methods

A single CNN previously trained and tested using 11 550 manually segmented native T1-weighted images was used to segment the myocardium for automated myocardial T2 and ECV quantification. Reference measurements from 1525 manually processed T2 maps and 1525 ECV maps (from 305 patients) were used to evaluate the performance of the pretrained network. Correlation coefficient (R) and Bland-Altman analysis were used to assess agreement between automated and reference values on per-patient, per-slice, and per-segment analyses. Furthermore, transfer learning effectiveness in the CNN was evaluated by comparing its performance to four CNNs trained using manually segmented T2-weighted and postcontrast T1-weighted images and initialized using random-weights or weights of the pretrained CNN.

Results

T2 and ECV measurements using the pretrained CNN strongly correlated with reference values in per-patient (T2: R = 0.88, 95% confidence interval [CI]: 0.85, 0.91; ECV: R = 0.91, 95% CI: 0.89, 0.93), per-slice (T2: R = 0.83, 95% CI: 0.81, 0.85; ECV: R = 0.84, 95% CI: 0.82, 0.86), and per-segment (T2: R = 0.75, 95% CI: 0.74, 0.77; ECV: R = 0.76, 95% CI: 0.75, 0.77) analyses. In Bland-Altman analysis, the automatic and reference values were in good agreement in per-patient (T2: 0.3 msec ± 2.9; ECV: -0.3% ± 1.7), per-slice (T2: 0.1 msec ± 4.6; ECV: -0.3% ± 2.5), and per-segment (T2: 0.0 msec ± 6.5; ECV: -0.4% ± 3.5) analyses. The performance of the pretrained network was comparable to networks refined or trained from scratch using additional manually segmented images.

Conclusion

Transfer learning extends the utility of pretrained CNN-based automated native T1 mapping analysis to T2 and ECV mapping without compromising performance. Supplemental material is available for this article. © RSNA, 2020.

Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks

BACKGROUND

Cardiovascular magnetic resonance (CMR) myocardial native T1 mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T1 are measured manually by drawing region of interest in motion-corrected T1 maps. The manual analysis contributes to an already lengthy CMR analysis workflow and impacts measurements reproducibility. In this study, we propose an automated method for combined myocardium segmentation, alignment, and T1 calculation for myocardial T1 mapping.

METHODS

A deep fully convolutional neural network (FCN) was used for myocardium segmentation in T1 weighted images. The segmented myocardium was then resampled on a polar grid, whose origin is located at the center-of-mass of the segmented myocardium. Myocardium T1 maps were reconstructed from the resampled T1 weighted images using curve fitting. The FCN was trained and tested using manually segmented images for 210 patients (5 slices, 11 inversion times per patient). An additional image dataset for 455 patients (5 slices and 11 inversion times per patient), analyzed by an expert reader using a semi-automatic tool, was used to validate the automatically calculated global and regional T1 values. Bland-Altman analysis, Pearson correlation coefficient, r, and the Dice similarity coefficient (DSC) were used to evaluate the performance of the FCN-based analysis on per-patient and per-slice basis. Inter-observer variability was assessed using intraclass correlation coefficient (ICC) of the T1 values calculated by the FCN-based automatic method and two readers.

RESULTS

The FCN achieved fast segmentation (< 0.3 s/image) with high DSC (0.85 ± 0.07). The automatically and manually calculated T1 values (1091 ± 59 ms and 1089 ± 59 ms, respectively) were highly correlated in per-patient (r = 0.82; slope = 1.01; p < 0.0001) and per-slice (r = 0.72; slope = 1.01; p < 0.0001) analyses. Bland-Altman analysis showed good agreement between the automated and manual measurements with 95% of measurements within the limits-of-agreement in both per-patient and per-slice analyses. The intraclass correllation of the T1 calculations by the automatic method vs reader 1 and reader 2 was respectively 0.86/0.56 and 0.74/0.49 in the per-patient/per-slice analyses, which were comparable to that between two expert readers (=0.72/0.58 in per-patient/per-slice analyses).

CONCLUSION

The proposed FCN-based image processing platform allows fast and automatic analysis of myocardial native T1 mapping images mitigating the burden and observer-related variability of manual analysis.

Robust motion correction for cardiac T1 and ECV mapping using a T1 relaxation model approach

T1 and ECV mapping are quantitative methods for myocardial tissue characterization using cardiac MRI, and are highly relevant for the diagnosis of diffuse myocardial diseases. Since the maps are calculated pixel-by-pixel from a set of MRI images with different T1-weighting, it is critical to assure exact spatial correspondence between these images. However, in practice, different sources of motion e.g. cardiac motion, respiratory motion or patient motion, hamper accurate T1 and ECV calculation such that retrospective motion correction is required. We propose a new robust non-rigid registration framework combining a data-driven initialization with a model-based registration approach, which uses a model for T1 relaxation to avoid direct registration of images with highly varying contrast. The registration between native T1 and enhanced T1 to obtain a motion free ECV map is also calculated using information from T1 model-fitting. The method was validated on three datasets recorded with two substantially different acquisition protocols (MOLLI (dataset 1 (n=15) and dataset 2 (n=29)) and STONE (dataset 3 (n = 210))), one in breath-hold condition and one free-breathing. The average Dice coefficient increased from 72.6 ± 12.1% to 82.3 ± 7.4% (P < 0.05) and mean boundary error decreased from 2.91 ± 1.51mm to 1.62 ± 0.80mm (P < 0.05) for motion correction in a single T1-weighted image sequence (3 datasets) while average Dice coefficient increased from 63.4 ± 22.5% to 79.2 ± 8.5% (P < 0.05) and mean boundary error decreased from 3.26 ± 2.64mm to 1.77 ± 0.86mm (P < 0.05) between native and enhanced sequences (dataset 1 and 2). Overall, the native T1 SD error decreased from 67.32 ± 32.57ms to 58.11 ± 21.59ms (P < 0.05), enhanced SD error from 30.15 ± 25ms to 22.74 ± 8.94ms (P < 0.05) and ECV SD error from 10.08 ± 9.59% to 5.42 ± 3.21% (P < 0.05) (dataset 1 and 2).