The proposed sequences are insensitive to heart rate variability, yield improved LGE images in the presence of arrhythmias, as well as T1 mapping with shorter scan times.

An improved image reconstruction method from undersampled k-space data, “LOw-dimensional-structure Self-learning and Thresholding (LOST),” which utilizes the structure from the underlying image is presented. A low resolution image from the fully-sampled k-space center is reconstructed to learn image patches of similar anatomical characteristics. These patches are arranged into “similarity clusters,” which are subsequently processed for de-aliasing and artifact removal, using underlying low-dimensional properties. The efficacy of the proposed method in scan time reduction was assessed in a pilot coronary MRI study. Initially, in a retrospective study on 10 healthy adult subjects, we evaluated retrospective undersampling and reconstruction using LOST, wavelet-based l1-norm minimization and total variation compressed-sensing (CS). Quantitative measures of vessel sharpness and mean square error, and qualitative image scores were used to compare reconstruction for rates of 2, 3 and 4. Subsequently, in a prospective study, coronary MRI data were acquired using these rates, and LOST-reconstructed images were compared with an accelerated data acquisition using uniform undersampling and sensitivity-encoding (SENSE) reconstruction. Subjective image quality and sharpness data indicate that LOST outperforms the alternative techniques for all rates. The prospective LOST yields images with superior quality compared to SENSE or l1-minimization CS. The proposed LOST technique greatly improves image reconstruction for accelerated coronary MRI acquisitions.

The STONE sequence allows accurate and precise quantification of native myocardial T times with the additional benefit of covering the entire ventricle. Magn Reson Med 74:115-124, 2015. © 2014 Wiley Periodicals, Inc.

Purpose To develop an improved T2 prepared (T2prep) balanced steady-state free-precession (bSSFP) sequence and signal relaxation curve fitting method for myocardial T2 mapping. Methods Myocardial T2 mapping is commonly performed by acquisition of multiple T2prep bSSFP images and estimating the voxel-wise T2 values using a 2-parameter fit for relaxation. However, a 2-parameter fit model does not take into account the effect of imaging pulses in a bSSFP sequence or other imperfections in T2prep RF pulses, which may decrease the robustness of T2 mapping. Therefore, we propose a novel T2 mapping sequence that incorporates an additional image acquired with saturation preparation, simulating a very long T2prep echo time. This enables the robust estimation of T2 maps using a 3-parameter fit model, which captures the effect of imaging pulses and other imperfections. Phantom imaging is performed to compare the T2 maps generated using the proposed 3-parameter model to the conventional 2-parameter model, as well as a spin echo reference. In-vivo imaging is performed on eight healthy subjects to compare the different fitting models. Results Phantom and in-vivo data show that the T2 values generated by the proposed 3-parameter model fitting do not change with different choices of the T2prep echo times, and are not statistically different than the reference values for the phantom (P = 0.10 with three T2prep echoes). The 2-parameter model exhibits dependence on the choice of T2prep echo times and are significantly different than the reference values (P = 0.01 with three T2prep echoes). Conclusion The proposed imaging sequence in combination with a 3-parameter model allows accurate measurement of myocardial T2 values, which is independent of number and duration of T2prep echo times.

Purpose To develop a heart-rate independent breath-held joint T1-T2 mapping sequence for accurate simultaneous estimation of co-registered 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 co-registered T1 and T2 maps are calculated from these images. Phantom imaging is performed to compare the proposed maps to 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: 260ms to 1460ms, T2: 40ms to 200ms). 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 co-registered quantitative myocardial T1 and T2 maps in a single breath-hold.

Purpose:To evaluate the use of low-dimensional-structure self-learning and thresholding (LOST) compressed sensing acquisition and reconstruction in the assessment of left atrial (LA) and left ventricular (LV) scar by using late gadolinium enhancement (LGE) magnetic resonance (MR) imaging with isotropic spatial resolution. Materials andMethods:The study was approved by the local institutional review board and was compliant with HIPAA. All subjects provided written informed consent. Twenty-eight patients (eight women; mean age, 58.0 years 6 10.1) with a history of atrial fibrillation were recruited for the LA LGE study, and 14 patients (five women; mean age, 54.2 years 6 18.6) were recruited for assessment of LV myocardial infarction. With use of a pseudorandom k-space undersampling pattern, threefold accelerated three-dimensional (3D) LGE data were acquired with isotropic spatial resolution and reconstructed off-line by using LOST. For comparison, subjects were also imaged by using standard 3D LGE protocols with nonisotropic spatial resolution. Images were compared qualitatively by three cardiologists with regard to diagnostic value, presence of enhancement, and image quality. The signed rank test and Wilcoxon unpaired two-sample test were used to test the hypothesis that there would be no significant difference in image quality ratings with different resolutions. Results:Interpretable images were obtained in 26 of the 28 patients (93%) in the LA LGE study.LGE was seen in 17 of 30 cases (57%) with nonisotropic resolution and in 18 cases (60%) with isotropic resolution. Diagnostic quality scores of isotropic images were significantly higher than those of nonisotropic images with coronal views (median, 3 vs 2, respectively [25th and 75th percentiles: 3, 3 vs 2, 3]; P , .001) and sagittal views (median, 3 vs 2 [25th and 75th percentiles: 3, 4 vs 2, 3]; P , .001) but lower with axial views (median, 4 vs 3 [25th and 75th percentiles: 3, 4 vs 3, 3]; P , .001). For the LV LGE study, all patients had interpretable images.LGE was seen in six of 14 patients (43%), with 100% agreement between both data sets. Diagnostic quality scores of high-isotropic-resolution LV images were higher than those of nonisotropic images with short-axis views (median, 4 vs 3 [25th and 75th percentiles: 3, 4 vs 2, 3]; P = .014) and two-chamber views (median, 4 vs 3 [25th and 75th percentiles: 3, 4 vs 2, 3]; P = .001). Conclusion:An accelerated LGE acquisition with LOST enables imaging with high isotropic spatial resolution for improved assessment of LV, LA, and pulmonary vein scar.q RSNA, 2012 Supplemental material: http://radiology.rsna.org/lookup /suppl

Purpose To enable accelerated isotropic sub-millimeter whole-heart coronary MRI within a six-minute acquisition, and to compare this with a current state-of-the-art accelerated imaging technique at acceleration rates beyond what is used clinically. Methods Coronary MRI still faces major challenges, including lengthy acquisition time, low signal-to-noise-ratio (SNR), and suboptimal spatial resolution. Higher spatial resolution in the sub-millimeter (sub-mm) range is desirable, but this results in increased acquisition time and lower SNR, hindering its clinical implementation. In this study, we sought to utilize an advanced B1-weighted compressed sensing (CS) technique for highly-accelerated sub-mm whole-heart coronary MRI, and to compare the results to parallel imaging, the current-state-of-the-art, where both techniques were used at acceleration rates beyond what is used clinically. Two whole-heart coronary MRI datasets were acquired in seven healthy adult subjects (30.3 ± 12.1 yrs; 3 men), using prospective 6-fold acceleration, with random undersampling for the proposed CS technique and with uniform undersampling for SENSE reconstruction. Reconstructed images were qualitatively compared in terms of image scores and perceived SNR on a 4-point scale (1 = poor, 4 = excellent) by an experienced blinded reader. Results The proposed technique resulted in images with clear visualization of all coronary branches. Overall image quality and perceived SNR of the CS images were significantly higher than those of parallel imaging (P=0.03 for both), which suffered from noise amplification artifacts due to the reduced SNR. Conclusion The proposed CS-based reconstruction and acquisition technique for sub-mm WH coronary MRI provides 6-fold acceleration, where it outperforms parallel imaging with uniform undersampling.

A disadvantage of 3D isotropic acquisition in whole-heart coronary MRI is the prolonged data acquisition time. Isotropic 3D radial trajectories allow undersampling of k-space data in all three spatial dimensions, enabling accelerated acquisition of the volumetric data. Compressed sensing (CS) reconstruction can provide further acceleration in the acquisition by removing the incoherent artifacts due to undersampling and improving the image quality. However, the heavy computational overhead of the CS reconstruction has been a limiting factor for its application. In this paper, a parallelized implementation of an iterative CS reconstruction method for 3D radial acquisitions using a commercial graphics processing unit (GPU) is presented. The execution time of the GPU-implemented CS reconstruction was compared with that of the C++ implementation and the efficacy of the undersampled 3D radial acquisition with CS reconstruction was investigated in both phantom and whole-heart coronary data sets. Subsequently, the efficacy of CS in suppressing streaking artifacts in 3D whole-heart coronary MRI with 3D radial imaging and its convergence properties were studied. The CS reconstruction provides improved image quality (in terms of vessel sharpness and suppression of noise-like artifacts) compared with the conventional 3D gridding algorithm and the GPU implementation greatly reduces the execution time of CS reconstruction yielding 34–54 times speed-up compared with C++ implementation.