Dataset Information

Accelerating T1? cartilage imaging using compressed sensing with iterative locally adapted support detection and JSENSE.

ABSTRACT: To accelerate T1? quantification in cartilage imaging using combined compressed sensing with iterative locally adaptive support detection and JSENSE.To reconstruct T1? images from accelerated acquisition at different time of spin-lock (TSLs), we propose an approach to combine an advanced compressed sensing (CS) based reconstruction technique, LAISD (locally adaptive iterative support detection), and an advanced parallel imaging technique, JSENSE. Specifically, the reconstruction process alternates iteratively among local support detection in the domain of principal component analysis, compressed sensing reconstruction of the image sequence, and sensitivity estimation with JSENSE. T1? quantification results from accelerated scans using the proposed method are evaluated using in vivo knee cartilage data from bilateral scans of three healthy volunteers.T1? maps obtained from accelerated scans (acceleration factors of 3 and 3.5) using the proposed method showed results comparable to conventional full scans. The T1? errors in all compartments are below 1%, which is well below the in vivo reproducibility of cartilage T1? reported from previous studies.The proposed method can significantly accelerate the acquisition process of T1? quantification on human cartilage imaging without sacrificing accuracy, which will greatly facilitate the clinical translation of quantitative cartilage MRI.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC4834045 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Accelerating T1ρ cartilage imaging using compressed sensing with iterative locally adapted support detection and JSENSE.

Zhou Yihang Y Pandit Prachi P Pedoia Valentina V Rivoire Julien J Wang Yanhua Y Liang Dong D Li Xiaojuan X Ying Leslie L

Magnetic resonance in medicine 20150522 4

<h4>Purpose</h4>To accelerate T1ρ quantification in cartilage imaging using combined compressed sensing with iterative locally adaptive support detection and JSENSE.<h4>Methods</h4>To reconstruct T1ρ images from accelerated acquisition at different time of spin-lock (TSLs), we propose an approach to combine an advanced compressed sensing (CS) based reconstruction technique, LAISD (locally adaptive iterative support detection), and an advanced parallel imaging technique, JSENSE. Specifically, the ...[more]

PMID: 26010735

Similar Datasets

Project description:BackgroundEndogenous contrast T1? cardiovascular magnetic resonance (CMR) can detect scar or infiltrative fibrosis in patients with ischemic or non-ischemic cardiomyopathy. Existing 2D T1? techniques have limited spatial coverage or require multiple breath-holds. The purpose of this project was to develop an accelerated, free-breathing 3D T1? mapping sequence with whole left ventricle coverage using a multicoil, compressed sensing (CS) reconstruction technique for rapid reconstruction of undersampled k-space data.MethodsWe developed a cardiac- and respiratory-gated, free-breathing 3D T1? sequence and acquired data using a variable-density k-space sampling pattern (A?=?3). The effect of the transient magnetization trajectory, incomplete recovery of magnetization between T1?-preparations (heart rate dependence), and k-space sampling pattern on T1? relaxation time error and edge blurring was analyzed using Bloch simulations for normal and chronically infarcted myocardium. Sequence accuracy and repeatability was evaluated using MnCl2 phantoms with different T1? relaxation times and compared to 2D measurements. We further assessed accuracy and repeatability in healthy subjects and compared these results to 2D breath-held measurements.ResultsThe error in T1? due to incomplete recovery of magnetization between T1?-preparations was T1?healthy =?6.1% and T1?infarct =?10.8% at 60?bpm and T1?healthy =?13.2% and T1?infarct =?19.6% at 90?bpm. At a heart rate of 60?bpm, error from the combined effects of readout-dependent magnetization transients, k-space undersampling and reordering was T1?healthy =?12.6% and T1?infarct =?5.8%. CS reconstructions had improved edge sharpness (blur metric?=?0.15) compared to inverse Fourier transform reconstructions (blur metric?=?0.48). There was strong agreement between the mean T1? estimated from the 2D and accelerated 3D data (R2 =?0.99; P <?0.05) acquired on the MnCl2 phantoms. The mean R1? estimated from the accelerated 3D sequence was highly correlated with MnCl2 concentration (R2 =?0.99; P?<?0.05). 3D T1? acquisitions were successful in all human subjects. There was no significant bias between undersampled 3D T1? and breath-held 2D T1? (mean bias?=?0.87) and the measurements had good repeatability (COV2D =?6.4% and COV3D =?7.1%).ConclusionsThis is the first report of an accelerated, free-breathing 3D T1? mapping of the left ventricle. This technique may improve non-contrast myocardial tissue characterization in patients with heart disease in a scan time appropriate for patients.

Project description:PurposeTo develop a method that combines parallel imaging and compressed sensing to enable faster and/or higher spatial resolution magnetic resonance (MR) imaging and show its feasibility in a pediatric clinical setting.Materials and methodsInstitutional review board approval was obtained for this HIPAA-compliant study, and informed consent or assent was given by subjects. A pseudorandom k-space undersampling pattern was incorporated into a three-dimensional (3D) gradient-echo sequence; aliasing then has an incoherent noiselike pattern rather than the usual coherent fold-over wrapping pattern. This k-space-sampling pattern was combined with a compressed sensing nonlinear reconstruction method that exploits the assumption of sparsity of medical images to permit reconstruction from undersampled k-space data and remove the noiselike aliasing. Thirty-four patients (15 female and 19 male patients; mean age, 8.1 years; range, 0-17 years) referred for cardiovascular, abdominal, and knee MR imaging were scanned with this 3D gradient-echo sequence at high acceleration factors. Obtained k-space data were reconstructed with both a traditional parallel imaging algorithm and the nonlinear method. Both sets of images were rated for image quality, radiologist preference, and delineation of specific structures by two radiologists. Wilcoxon and symmetry tests were performed to test the hypothesis that there was no significant difference in ratings for image quality, preference, and delineation of specific structures.ResultsCompressed sensing images were preferred more often, had significantly higher image quality ratings, and greater delineation of anatomic structures (P < .001) than did images obtained with the traditional parallel reconstruction method.ConclusionA combination of parallel imaging and compressed sensing is feasible in a clinical setting and may provide higher resolution and/or faster imaging, addressing the challenge of delineating anatomic structures in pediatric MR imaging.

Dataset Information

Accelerating T1? cartilage imaging using compressed sensing with iterative locally adapted support detection and JSENSE.

Publications

Accelerating T1ρ cartilage imaging using compressed sensing with iterative locally adapted support detection and JSENSE.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets