scholarly journals Calibration-Less Multi-Coil Compressed Sensing Magnetic Resonance Image Reconstruction Based on OSCAR Regularization

2021 ◽  
Vol 7 (3) ◽  
pp. 58
Author(s):  
Loubna El Gueddari ◽  
Chaithya Giliyar Radhakrishna ◽  
Emilie Chouzenoux ◽  
Philippe Ciuciu
Keyword(s):  
Magnetic Resonance ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
7 Tesla ◽  
Reconstruction Method ◽  
Mr Images ◽  
Data Set ◽  
Algorithm Comparison ◽  
Primal Dual ◽  
Sensitivity Maps

Over the last decade, the combination of compressed sensing (CS) with acquisition over multiple receiver coils in magnetic resonance imaging (MRI) has allowed the emergence of faster scans while maintaining a good signal-to-noise ratio (SNR). Self-calibrating techniques, such as ESPiRIT, have become the standard approach to estimating the coil sensitivity maps prior to the reconstruction stage. In this work, we proceed differently and introduce a new calibration-less multi-coil CS reconstruction method. Calibration-less techniques no longer require the prior extraction of sensitivity maps to perform multi-coil image reconstruction but usually alternate estimation sensitivity map estimation and image reconstruction. Here, to get rid of the nonconvexity of the latter approach we reconstruct as many MR images as the number of coils. To compensate for the ill-posedness of this inverse problem, we leverage structured sparsity of the multi-coil images in a wavelet transform domain while adapting to variations in SNR across coils owing to the OSCAR (octagonal shrinkage and clustering algorithm for regression) regularization. Coil-specific complex-valued MR images are thus obtained by minimizing a convex but nonsmooth objective function using the proximal primal-dual Condat-Vù algorithm. Comparison and validation on retrospective Cartesian and non-Cartesian studies based on the Brain fastMRI data set demonstrate that the proposed reconstruction method outperforms the state-of-the-art (ℓ1-ESPIRiT, calibration-less AC-LORAKS and CaLM methods) significantly on magnitude images for the T1 and FLAIR contrasts. Additionally, further validation operated on 8 to 20-fold prospectively accelerated high-resolution ex vivo human brain MRI data collected at 7 Tesla confirms the retrospective results. Overall, OSCAR-based regularization preserves phase information more accurately (both visually and quantitatively) compared to other approaches, an asset that can only be assessed on real prospective experiments.

scholarly journals Median Filter Based Compressed Sensing Model with Application to MR Image Reconstruction

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yunyun Yang ◽  
Xuxu Qin ◽  
Boying Wu
Keyword(s):  
Magnetic Resonance ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Total Variation ◽  
Median Filter ◽  
Mr Images ◽  
Mr Image ◽  
Reconstruction Process ◽  
Sensing Model ◽  
Mr Image Reconstruction

Magnetic resonance imaging (MRI) has become a helpful technique and developed rapidly in clinical medicine and diagnosis. Magnetic resonance (MR) images can display more clearly soft tissue structures and are important for doctors to diagnose diseases. However, the long acquisition and transformation time of MR images may limit their application in clinical diagnosis. Compressed sensing methods have been widely used in faithfully reconstructing MR images and greatly shorten the scanning and transforming time. In this paper we present a compressed sensing model based on median filter for MR image reconstruction. By combining a total variation term, a median filter term, and a data fitting term together, we first propose a minimization problem for image reconstruction. The median filter term makes our method eliminate additional noise from the reconstruction process and obtain much clearer reconstruction results. One key point of the proposed method lies in the fact that both the total variation term and the median filter term are presented in the L1 norm formulation. We then apply the split Bregman technique for fast minimization and give an efficient algorithm. Finally, we apply our method to numbers of MR images and compare it with a related method. Reconstruction results and comparisons demonstrate the accuracy and efficiency of the proposed model.

scholarly journals Segmentation Method for Magnetic Resonance-Guided High-Intensity Focused Ultrasound Therapy Planning

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
A. Vargas-Olivares ◽  
O. Navarro-Hinojosa ◽  
M. Maqueo-Vicencio ◽  
L. Curiel ◽  
M. Alencastre-Miranda ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
High Intensity ◽  
Focused Ultrasound ◽  
High Intensity Focused Ultrasound ◽  
Segmentation Algorithm ◽  
Focal Region ◽  
Therapy Planning ◽  
Mr Images ◽  
Monitoring Method ◽  
Data Set

High-intensity focused ultrasound (HIFU) is a minimally invasive therapy modality in which ultrasound beams are concentrated at a focal region, producing a rise of temperature and selective ablation within the focal volume and leaving surrounding tissues intact. HIFU has been proposed for the safe ablation of both malignant and benign tissues and as an agent for drug delivery. Magnetic resonance imaging (MRI) has been proposed as guidance and monitoring method for the therapy. The identification of regions of interest is a crucial procedure in HIFU therapy planning. This procedure is performed in the MR images. The purpose of the present research work is to implement a time-efficient and functional segmentation scheme, based on the watershed segmentation algorithm, for the MR images used for the HIFU therapy planning. The achievement of a segmentation process with functional results is feasible, but preliminary image processing steps are required in order to define the markers for the segmentation algorithm. Moreover, the segmentation scheme is applied in parallel to an MR image data set through the use of a thread pool, achieving a near real-time execution and making a contribution to solve the time-consuming problem of the HIFU therapy planning.

Adaptive Dual Conjudate Gradient Projection Algorithm for Compressed Sensing Image Reconstruction

2013 ◽  
Vol 347-350 ◽  
pp. 2600-2604
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Reconstruction Accuracy ◽  
Reconstruction Quality ◽  
Dual Gradient

In order to improve the quality of noise signals reconstruction method, an algorithm of adaptive dual gradient projection for sparse reconstruction of compressed sensing theory is proposed. In ADGPSR algorithm, the pursuit direction is updated in two conjudate directions, the better original signals estimated value is computed by conjudate coefficient. Thus the reconstruction quality is improved. Experiment results show that, compared with the GPSR algorithm, the ADGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.

Validation of Cartilage Thickness Calculations Using Indentation Analysis

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Matthew F. Koff ◽  
Le Roy Chong ◽  
Patrick Virtue ◽  
Dan Chen ◽  
Xioanan Wang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Measurement Techniques ◽  
Magnetic Resonance Images ◽  
Cartilage Thickness ◽  
Mr Images ◽  
Data Set ◽  
Data Points ◽  
Indentation Analysis ◽  
Thickness Measurements

Different methods have been used to cross-validate cartilage thickness measurements from magnetic resonance images (MRIs); however, a majority of these methods rely on interpolated data points, regional mean and/or maximal thickness, or surface mean thickness for data analysis. Furthermore, the accuracy of MRI cartilage thickness measurements from commercially available software packages has not necessarily been validated and may lead to an under- or overestimation of cartilage thickness. The goal of this study was to perform a matching point-to-point validation of indirect cartilage thickness calculations using a magnetic resonance (MR) image data set with direct cartilage thickness measurements using biomechanical indentation testing at the same anatomical locations. Seven bovine distal femoral condyles were prepared and a novel phantom filled with dilute gadolinium solution was rigidly attached to each specimen. High resolution MR images were acquired, and thickness indentation analysis of the cartilage was performed immediately after scanning. Segmentation of the MR data and cartilage thickness calculation was performed using semi-automated software. Registration of MR and indentation data was performed using the fluid filled phantom. The inter- and intra-examiner differences of the measurements were also determined. A total of 105 paired MRI-indentation thickness data points were analyzed, and a significant correlation between them was found (r=0.88, p<0.0001). The mean difference (±std. dev.) between measurement techniques was 0.00±0.23 mm, with Bland–Altman limits of agreement of 0.45 mm and −0.46 mm. The intra- and inter-examiner measurement differences were 0.03±0.22 mm and 0.05±0.24 mm, respectively. This study validated cartilage thickness measurements from MR images with thickness measurements from indentation by using a novel phantom to register the image-based and laboratory-based data sets. The accuracy of the measurements was comparable to previous cartilage thickness validation studies in literature. The results of this study will aid in validating a tool for clinical evaluation of in-vivo cartilage thickness.

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