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Spatially regularized T(1) estimation from variable flip angles MRI.

ABSTRACT: To develop efficient algorithms for fast voxel-by-voxel quantification of tissue longitudinal relaxation time (T(1)) from variable flip angles magnetic resonance images (MRI) to reduce voxel-level noise without blurring tissue edges.T(1) estimations regularized by total variation (TV) and quadratic penalty are developed to measure T(1) from fast variable flip angles MRI and to reduce voxel-level noise without decreasing the accuracy of the estimates. First, a quadratic surrogate for a log likelihood cost function of T(1) estimation is derived based upon the majorization principle, and then the TV-regularized surrogate function is optimized by the fast iterative shrinkage thresholding algorithm. A fast optimization algorithm for the quadratically regularized T(1) estimation is also presented. The proposed methods are evaluated by the simulated and experimental MR data.The means of the T(1) values in the simulated brain data estimated by the conventional, TV-regularized, and quadratically regularized methods have less than 3% error from the true T(1) in both GM and WM tissues with image noise up to 9%. The relative standard deviations (SDs) of the T(1) values estimated by the conventional method are more than 12% and 15% when the images have 7% and 9% noise, respectively. In comparison, the TV-regularized and quadratically regularized methods are able to suppress the relative SDs of the estimated T(1) to be less than 2% and 3%, respectively, regardless of the image noise level. However, the quadratically regularized method tends to overblur the edges compared to the TV-regularized method.The spatially regularized methods improve quality of T(1) estimation from multiflip angles MRI. Quantification of dynamic contrast-enhanced MRI can benefit from the high quality measurement of native T(1).

SUBMITTER: Wang H

PROVIDER: S-EPMC3390049 | biostudies-other | 2012 Jul

REPOSITORIES: biostudies-other

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Project description:ObjectivesThe lenticulostriate arteries (LSAs) with small diameters of a few hundred microns take origin directly from the high flow middle cerebral artery (MCA), making them especially susceptible to damage (e.g. by hypertension). This study aims to present high resolution (isotropic ∼0.5 mm), black blood MRI for the visualization and characterization of LSAs at both 3 T and 7 T.Materials and methodsT1-weighted 3D turbo spin-echo with variable flip angles (T1w TSE-VFA) sequences were optimized for the visualization of LSAs by performing extended phase graph (EPG) simulations. Twenty healthy volunteers (15 under 35 years old, 5 over 60 years old) were imaged with the T1w TSE-VFA sequences at both 3 T and 7 T. Contrast-to-noise ratio (CNR) was quantified, and LSAs were manually segmented using ITK-SNAP. Automated Reeb graph shape analysis was performed to extract features including vessel length and tortuosity. All quantitative metrics were compared between the two field strengths and two age groups using ANOVA.ResultsLSAs can be clearly delineated using optimized 3D T1w TSE-VFA at 3 T and 7 T, and a greater number of LSA branches can be detected compared to those by time-of-flight MR angiography (TOF MRA) at 7 T. The CNR of LSAs was comparable between 7 T and 3 T. T1w TSE-VFA showed significantly higher CNR than TOF MRA at the stem portion of the LSAs branching off the medial middle cerebral artery. The mean vessel length and tortuosity were greater on TOF MRA compared to TSE-VFA. The number of detected LSAs by both TSE-VFA and TOF MRA was significantly reduced in aged subjects, while the mean vessel length measured on 7 T TSE-VFA showed significant difference between the two age groups.ConclusionThe high-resolution black-blood 3D T1w TSE-VFA sequence offers a new method for the visualization and quantification of LSAs at both 3 T and 7 T, which may be applied for a number of pathological conditions related to the damage of LSAs.

Project description:Purpose To assess image quality and speed improvements for single-shot fast spin-echo (SSFSE) with variable refocusing flip angles and full-Fourier acquisition (vrfSSFSE) pelvic imaging via a prospective trial performed in the context of uterine leiomyoma evaluation. Materials and Methods Institutional review board approval and informed consent were obtained. vrfSSFSE and conventional SSFSE sagittal and coronal oblique acquisitions were performed in 54 consecutive female patients referred for 3-T magnetic resonance (MR) evaluation of known or suspected uterine leiomyomas. Two radiologists who were blinded to the image acquisition technique semiquantitatively scored images on a scale from -2 to 2 for noise, image contrast, sharpness, artifacts, and perceived ability to evaluate uterine, ovarian, and musculoskeletal structures. The null hypothesis of no significant difference between pulse sequences was assessed with a Wilcoxon signed rank test by using a Holm-Bonferroni correction for multiple comparisons. Results Because of reductions in specific absorption rate, vrfSSFSE imaging demonstrated significantly increased speed (more than twofold, P < .0001), with mean repetition times compared with conventional SSFSE imaging decreasing from 1358 to 613 msec for sagittal acquisitions and from 1494 to 621 msec for coronal oblique acquisitions. Almost all assessed image quality and perceived diagnostic capability parameters were significantly improved with vrfSSFSE imaging. These improvements included noise, sharpness, and ability to evaluate the junctional zone, myometrium, and musculoskeletal structures for both sagittal acquisitions (mean values of 0.56, 0.63, 0.42, 0.56, and 0.80, respectively; all P values < .0001) and coronal oblique acquisitions (mean values of 0.81, 1.09, 0.65, 0.93, and 1.12, respectively; all P values < .0001). For evaluation of artifacts, there was an insufficient number of cases with differences to allow statistical testing. Conclusion Compared with conventional SSFSE acquisition, vrfSSFSE acquisition increases 3-T imaging speed via reduced specific absorption rate and leads to significant improvements in perceived image quality and perceived diagnostic capability when evaluating pelvic structures. © RSNA, 2016 Online supplemental material is available for this article.

Project description:High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney.

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Spatially regularized T(1) estimation from variable flip angles MRI.

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