Fast cardiac T1ρ,adiab mapping using slice-selective adiabatic spin-lock preparation pulses.

PURPOSE: Myocardial T1ρ mapping techniques commonly acquire multiple images in one breathhold to calculate a single-slice T1ρ map. Recently, non-selective adiabatic pulses have been used for robust spin-lock preparation (T1ρ,adiab). The objective of this study was to develop a fast multi-slice myocardial T1ρ,adiab mapping approach. METHODS: The proposed-sequence reduces the number of breathholds required for whole-heart 2D T1ρ,adiab mapping by acquiring multiple interleaved slices in each breathhold using slice-selective T1ρ,adiab preparation pulses. The proposed-sequence was implemented with two interleaved slices per breathhold scan and was quantitatively evaluated in phantom experiments and 10 healthy-volunteers against a single-slice T1ρ,adiab mapping sequence. The sequence was demonstrated in two patients with myocardial scar. RESULTS: The phantom experiments showed the proposed-sequence had slice-to-slice variation of 1.62% ± 1.05% and precision of 4.51 ± 0.68 ms. The healthy volunteer cohort subject-wise mean relaxation time was lower for the proposed-sequence than the single-slice sequence (137.7 ± 5.3 ms vs. 148.4 ± 8.3 ms, p < 0.001), and spatial-standard-deviation was better (18.7 ± 1.8 ms vs. 21.8 ± 3.4 ms, p < 0.018). The mean within-subject, coefficient of variation was 5.93% ± 1.57% for the proposed-sequence and 6.31% ± 1.92% for the single-slice sequence (p = 0.35) and the effect of slice variation (0.81 ± 4.87 ms) was not significantly different to zero (p = 0.61). In both patient examples increased T1ρ,adiab (maximum American Heart Association-segment mean = 174 and 197 ms) was measured within the myocardial scar. CONCLUSION: The proposed sequence provides a twofold acceleration for myocardial T1ρ,adiab mapping using a multi-slice approach. It has no significant difference in within-subject variability, and significantly better precision, compared to a 2D T1ρ,adiab mapping sequence based on non-selective adiabatic spin-lock preparations.

Fast reconstruction of SMS bSSFP myocardial perfusion images using noise map estimation network (NoiseMapNet): a head-to-head comparison with parallel imaging and iterative reconstruction.

Background: Simultaneous multi-slice (SMS) bSSFP imaging enables stress myocardial perfusion imaging with high spatial resolution and increased spatial coverage. Standard parallel imaging techniques (e.g., TGRAPPA) can be used for image reconstruction but result in high noise level. Alternatively, iterative reconstruction techniques based on temporal regularization (ITER) improve image quality but are associated with reduced temporal signal fidelity and long computation time limiting their online use. The aim is to develop an image reconstruction technique for SMS-bSSFP myocardial perfusion imaging combining parallel imaging and image-based denoising using a novel noise map estimation network (NoiseMapNet), which preserves both sharpness and temporal signal profiles and that has low computational cost. Methods: The proposed reconstruction of SMS images consists of a standard temporal parallel imaging reconstruction (TGRAPPA) with motion correction (MOCO) followed by image denoising using NoiseMapNet. NoiseMapNet is a deep learning network based on a 2D Unet architecture and aims to predict a noise map from an input noisy image, which is then subtracted from the noisy image to generate the denoised image. This approach was evaluated in 17 patients who underwent stress perfusion imaging using a SMS-bSSFP sequence. Images were reconstructed with (a) TGRAPPA with MOCO (thereafter referred to as TGRAPPA), (b) iterative reconstruction with integrated motion compensation (ITER), and (c) proposed NoiseMapNet-based reconstruction. Normalized mean squared error (NMSE) with respect to TGRAPPA, myocardial sharpness, image quality, perceived SNR (pSNR), and number of diagnostic segments were evaluated. Results: NMSE of NoiseMapNet was lower than using ITER for both myocardium (0.045 ± 0.021 vs. 0.172 ± 0.041, p < 0.001) and left ventricular blood pool (0.025 ± 0.014 vs. 0.069 ± 0.020, p < 0.001). There were no significant differences between all methods for myocardial sharpness (p = 0.77) and number of diagnostic segments (p = 0.36). ITER led to higher image quality than NoiseMapNet/TGRAPPA (2.7 ± 0.4 vs. 1.8 ± 0.4/1.3 ± 0.6, p < 0.001) and higher pSNR than NoiseMapNet/TGRAPPA (3.0 ± 0.0 vs. 2.0 ± 0.0/1.3 ± 0.6, p < 0.001). Importantly, NoiseMapNet yielded higher pSNR (p < 0.001) and image quality (p < 0.008) than TGRAPPA. Computation time of NoiseMapNet was only 20s for one entire dataset. Conclusion: NoiseMapNet-based reconstruction enables fast SMS image reconstruction for stress myocardial perfusion imaging while preserving sharpness and temporal signal profiles.

Characterization of quantitative susceptibility mapping in the left ventricular myocardium.

BACKGROUND: Myocardial quantitative susceptibility mapping (QSM) may offer better specificity to iron than conventional T2* imaging in the assessment of cardiac diseases, including intra-myocardial hemorrhage. However, the precision and repeatability of cardiac QSM have not yet been characterized. The aim of this study is to characterize these key metrics in a healthy volunteer cohort and show the feasibility of the method in patients. METHODS: Free breathing respiratory-navigated multi-echo 3D gradient echo images were acquired, from which QSM maps were reconstructed using the Morphology Enhanced Dipole Inversion toolbox. This technique was first evaluated in a susceptibility phantom containing tubes with known concentrations of gadolinium. In vivo characterization of myocardial QSM was then performed in a cohort of 10 healthy volunteers where each subject was scanned twice. Mean segment susceptibility, precision (standard deviation of voxel magnetic susceptibilities within one segment), and repeatability (absolute difference in segment mean susceptibility between repeats) of QSM were calculated for each American Heart Association (AHA) myocardial segment. Finally, the feasibility of the method was shown in 10 patients, including four with hemorrhagic infarcts. RESULTS: The phantom experiment showed a strong linear relationship between measured and predicted susceptibility shifts (R2 > 0.99). For the healthy volunteer cohort, AHA segment analysis showed the mean segment susceptibility was 0.00 ± 0.02 ppm, the mean precision was 0.05 ± 0.04 ppm, and the mean repeatability was 0.02 ± 0.02 ppm. Cardiac QSM was successfully performed in all patients. Focal iron deposits were successfully visualized in the patients with hemorrhagic myocardial infarctions. CONCLUSION: The precision and repeatability of cardiac QSM were successfully characterized in phantom and in vivo experiments. The feasibility of the technique was also successfully demonstrated in patients. While challenges still remain, further clinical evaluation of the technique is now warranted. TRIAL REGISTRATION: This work does not report on a health care intervention.

Real-time automatic image-based slice tracking of gadolinium-filled balloon wedge catheter during MR-guided cardiac catheterization: A proof-of-concept study.

PURPOSE: MR-guided cardiac catheterization procedures currently use passive tracking approaches to follow a gadolinium-filled catheter balloon during catheter navigation. This requires frequent manual tracking and repositioning of the imaging slice during navigation. In this study, a novel framework for automatic real-time catheter tracking during MR-guided cardiac catheterization is presented. METHODS: The proposed framework includes two imaging modes (Calibration and Runtime). The sequence starts in Calibration mode, in which the 3D catheter coordinates are determined using a stack of 10-20 contiguous saturated slices combined with real-time image processing. The sequence then automatically switches to Runtime mode, where three contiguous slices (acquired with partial saturation), initially centered on the catheter balloon using the Calibration feedback, are acquired continuously. The 3D catheter balloon coordinates are estimated in real time from each Runtime slice stack using image processing. Each Runtime stack is repositioned to maintain the catheter balloon in the central slice based on the prior Runtime feedback. The sequence switches back to Calibration mode if the catheter is not detected. This framework was evaluated in a heart phantom and 3 patients undergoing MR-guided cardiac catheterization. Catheter detection accuracy and rate of catheter visibility were evaluated. RESULTS: The automatic detection accuracy for the catheter balloon during the Calibration/Runtime mode was 100%/95% in phantom and 100%/97 ± 3% in patients. During Runtime, the catheter was visible in 82% and 98 ± 2% of the real-time measurements in the phantom and patients, respectively. CONCLUSION: The proposed framework enabled real-time continuous automatic tracking of a gadolinium-filled catheter balloon during MR-guided cardiac catheterization.