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Purpose To develop a fast volumetric T1 mapping technique. Materials and methods A stack-of-stars (SOS) Look Locker technique based on the acquisition of undersampled radial data (>30× relative to Nyquist) and an efficient multi-slab excitation scheme is presented. A principal-component based reconstruction is used to reconstruct T1 maps. Computer simulations were performed to determine the best choice of partitions per slab and degree of undersampling. The technique was validated in phantoms against reference T1 values measured with a 2D Cartesian inversion-recovery spin-echo technique. The SOS Look Locker technique was tested in brain (n = 4) and prostate (n = 5). Brain T1 mapping was carried out with and without kz acceleration and results between the two approaches were compared. Prostate T1 mapping was compared to standard techniques. A reproducibility study was conducted in brain and prostate. Statistical analyses were performed using linear regression and Bland Altman analysis. Results Phantom T1 values showed excellent correlations between SOS Look Locker and the inversion-recovery spin-echo reference (r2 = 0.9965; p Conclusion A rapid volumetric T1 mapping technique was developed. The technique enables high-resolution T1 mapping with adequate anatomical coverage in a clinically acceptable time.

A deep learning MR parameter mapping framework which combines accelerated radial data acquisition with a multi-scale residual network (MS-ResNet) for image reconstruction is proposed. The proposed supervised learning strategy uses input image patches from multi-contrast images with radial undersampling artifacts and target image patches from artifact-free multi-contrast images. Subspace filtering is used during pre-processing to denoise input patches. For each anatomy and relaxation parameter, an individual network is trained. in vivo T(1) mapping results are obtained on brain and abdomen datasets and in vivo T(2) mapping results are obtained on brain and knee datasets. Quantitative results for the T(2) mapping of the knee show that MS-ResNet trained using either fully sampled or undersampled data outperforms conventional model-based compressed sensing methods. This is significant because obtaining fully sampled training data is not possible in many applications. in vivo brain and abdomen results for T(1) mapping and in vivo brain results for T(2) mapping demonstrate that MS-ResNet yields contrast-weighted images and parameter maps that are comparable to those achieved by model-based iterative methods while offering two orders of magnitude reduction in reconstruction times. The proposed approach enables recovery of high-quality contrast-weighted images and parameter maps from highly accelerated radial data acquisitions. The rapid image reconstructions enabled by the proposed approach makes it a good candidate for routine clinical use.

PURPOSE: To develop a fast volumetric T(1) mapping technique. MATERIALS AND METHODS: A stack-of-stars (SOS) Look Locker technique based on the acquisition of undersampled radial data (>30X relative to Nyquist) and an efficient multi-slab excitation scheme is presented. A principal-component based reconstruction is used to reconstruct T(1) maps. Computer simulations were performed to determine the best choice of partitions per slab and degree of undersampling. The technique was validated in phantoms against reference T(1) values measured with a 2D Cartesian inversion-recovery spin-echo technique. The SOS Look Locker technique was tested in brain (n=4) and prostate (n=5). Brain T(1) mapping was carried out with and without k(z) acceleration and results between the two approaches were compared. Prostate T(1) mapping was compared to standard techniques. A reproducibility study was conducted in brain and prostate. Statistical analyses were performed using linear regression and Bland Altman analysis. RESULTS: Phantom T(1) values showed excellent correlations between SOS Look Locker and the inversion-recovery spin-echo reference (r(2)=0.9965; p

Indirect echoes (such as stimulated echoes) are a source of signal contamination in a multi-echo spin-echo T2 quantification, and can lead to T2 overestimation if a conventional exponential T2 decay model is assumed. Recently, nonlinear least square fitting of a slice-resolve extended phase graph (SEPG) signal model has been shown to provide accurate T2 estimates with indirect echo compensation. However, the iterative nonlinear least square fitting is computationally expensive and the T2 map generation time is long. In this work, we present a pattern recognition T2 mapping technique based on the SEPG model that can be performed with a single pre-computed dictionary for any arbitrary echo spacing. Almost identical T2 and B1 maps were obtained from in vivo data using the proposed technique compared to conventional iterative nonlinear least square fitting, while the computation time was reduced by more than 14 fold.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly in use as an investigational biomarker of response in cancer clinical studies. Proper registration of images acquired at different time points is essential for deriving diagnostic information from quantitative pharmacokinetic analysis of these data. Motion artifacts in the presence of time-varying intensity due to contrast enhancement make this registration problem challenging. DCE-MRI of chest and abdominal lesions is typically performed during sequential breath-holds, which introduces misregistration due to inconsistent diaphragm positions and also places constraints on temporal resolution vis-a-vis free-breathing. In this work, we have employed a computer-generated DCE-MRI phantom to compare the performance of two published methods, Progressive Principal Component Registration and Pharmacokinetic Model-Driven Registration, with Sequential Elastic Registration (SER) to register adjacent time-sample images using a published general-purpose elastic registration algorithm. In all three methods, a 3D rigid-body registration scheme with a mutual information similarity measure was used as a preprocessing step. The DCE-MRI phantom images were mathematically deformed to simulate misregistration, which was corrected using the three schemes. All three schemes were comparably successful in registering large regions of interest (ROIs) such as muscle, liver, and spleen. SER was superior in retaining tumor volume and shape, and in registering smaller but important ROIs such as tumor core and tumor rim. The performance of SER on clinical DCE-MRI data sets is also presented.