scholarly journals Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhanqi Hu ◽  
Cailei Zhao ◽  
Xia Zhao ◽  
Lingyu Kong ◽  
Jun Yang ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Parallel Imaging ◽  
Imaging Technique ◽  
Temporal Variations ◽  
Cine Mri ◽  
Reconstruction Process ◽  
Parallel Imaging Technique ◽  
In Vivo Experiments ◽  
Space Data

AbstractCompressed Sensing (CS) and parallel imaging are two promising techniques that accelerate the MRI acquisition process. Combining these two techniques is of great interest due to the complementary information used in each. In this study, we proposed a novel reconstruction framework that effectively combined compressed sensing and nonlinear parallel imaging technique for dynamic cardiac imaging. Specifically, the proposed method decouples the reconstruction process into two sequential steps: In the first step, a series of aliased dynamic images were reconstructed from the highly undersampled k-space data using compressed sensing; In the second step, nonlinear parallel imaging technique, i.e. nonlinear GRAPPA, was utilized to reconstruct the original dynamic images from the reconstructed k-space data obtained from the first step. In addition, we also proposed a tailored k-space down-sampling scheme that satisfies both the incoherent undersampling requirement for CS and the structured undersampling requirement for nonlinear parallel imaging. The proposed method was validated using four in vivo experiments of dynamic cardiac cine MRI with retrospective undersampling. Experimental results showed that the proposed method is superior at reducing aliasing artifacts and preserving the spatial details and temporal variations, compared with the competing k-t FOCUSS and k-t FOCUSS with sensitivity encoding methods, with the same numbers of measurements.

scholarly journals Joint Reconstruction Framework of Compressed Sensing and Nonlinear Parallel Imaging for Dynamic Cardiac Magnetic Resonance Imaging

2021 ◽  
Author(s):  
Zhanqi Hu ◽  
Cailei Zhao ◽  
Xia Zhao ◽  
Lingyu Kong ◽  
Jun Yang ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Parallel Imaging ◽  
Dynamic Imaging ◽  
Cine Mri ◽  
Reconstruction Process ◽  
Joint Reconstruction ◽  
Cardiac Cine ◽  
Nonlinear Technique ◽  
Space Data

Abstract Compressed Sensing (CS) and parallel imaging are two promising techniques that accelerate the MRI acquisition process. Combining these two techniques is of great interest due to the complementary information used in each. In this study, we propose a new reconstruction framework for dynamic cardiac imaging that takes advantage of both CS-based dynamic imaging and one nonlinear parallel imaging technique. The method decouples the reconstruction process into two sequential steps: use CS to reconstruct a series of aliased dynamic images from the highly undersampled k-space data; use nonlinear GRAPPA method, one nonlinear technique of parallel imaging, to reconstruct the original dynamic images from the k-space data that has been reconstructed by CS. The sampling scheme of the proposed method is designed to simultaneously satisfy the incoherent undersampling requirement for CS and the structured undersampling requirement for nonlinear parallel imaging. Four in vivo experiments of dynamic cardiac cine MRI were carried out with retrospective undersampling to evaluate the performance of the proposed method. Experiments show the proposed method of dynamic cardiac cine MRI is superior at reducing aliasing artifacts and preserving the spatial details and temporal variations, when compared with k-t FOCUSS and k-t FOCUSS with sensitivity encoding, using the same numbers of measurements. The proposed joint reconstruction framework effectively combines the CS method and one nonlinear technique of parallel imaging, and improves the image quality of dynamic cardiac cine MRI reconstruction when comparing to the state-of-the-art methods.

scholarly journals Highly Accelerated Vessel-Selective Arterial Spin Labelling Angiography using Sparsity and Smoothness Constraints

2019 ◽  
Author(s):  
Sophie Schauman ◽  
Mark Chiew ◽  
Thomas W. Okell
Keyword(s):  
Image Quality ◽  
Compressed Sensing ◽  
Arterial Spin Labeling ◽  
Parallel Imaging ◽  
Spin Labeling ◽  
Dominant Factor ◽  
Image Domain ◽  
In Vivo Experiments ◽  
Scan Time

AbstractPurposeTo demonstrate that vessel-selectivity in arterial spin labeling angiography can be achieved without any scan time penalty or noticeable loss of image quality compared to conventional arterial spin labeling angiography.MethodsSimulations on a numerical phantom were used to assess whether the increased sparsity of vessel-encoded angiograms compared to non-vessel-encoded angiograms alone can improve reconstruction results in a compressed sensing framework. Further simulations were performed to study whether the difference in relative sparsity between non-selective and vessel-selective dynamic angiograms were sufficient to achieve similar image quality at matched scan times in the presence of noise. Finally, data were acquired from 5 healthy volunteers to validate the technique in vivo. All data, both simulated and in vivo, were sampled in 2D using a golden angle radial trajectory and reconstructed by enforcing both image domain sparsity and temporal smoothness on the angiograms in a parallel imaging and compressed sensing framework.ResultsRelative sparsity was established as a primary factor governing the reconstruction fidelity. Using the proposed reconstruction scheme, differences between vessel-selective and non-selective angiography were negligible compared to the dominant factor of total scan time in both simulations and in vivo experiments at acceleration factors up to R = 34. The reconstruction quality was not heavily dependent on hand-tuning the parameters of the reconstruction.ConclusionThe increase in relative sparsity of vessel-selective angiograms compared to non-selective angiograms can be leveraged to achieve higher acceleration without loss of image quality, resulting in the acquisition of vessel-selective information at no scan time cost.

scholarly journals Computational modeling of RBC and neutrophil transit through the pulmonary capillaries

2001 ◽  
Vol 90 (2) ◽  
pp. 545-564 ◽  
Author(s):  
Yaqi Huang ◽  
Claire M. Doerschuk ◽  
Roger D. Kamm
Keyword(s):  
Transpulmonary Pressure ◽  
Temporal Variations ◽  
Local Pressure ◽  
In Vivo Experiments ◽  
Pulmonary Capillaries ◽  
Transit Times ◽  
Capillary Bed ◽  
Pulmonary Microcirculation ◽  
Preferential Pathways

A computational model of the pulmonary microcirculation is developed and used to examine blood flow from arteriole to venule through a realistically complex alveolar capillary bed. Distributions of flow, hematocrit, and pressure are presented, showing the existence of preferential pathways through the system and of large segment-to-segment differences in all parameters, confirming and extending previous work. Red blood cell (RBC) and neutrophil transit are also analyzed, the latter drawing from previous studies of leukocyte aspiration into micropipettes. Transit time distributions are in good agreement with in vivo experiments, in particular showing that neutrophils are dramatically slowed relative to the flow of RBCs because of the need to contract and elongate to fit through narrower capillaries. Predicted neutrophil transit times depend on how the effective capillary diameter is defined. Transient blockage by a neutrophil can increase the local pressure drop across a segment by 100–300%, leading to temporal variations in flow and pressure as seen by videomicroscopy. All of these effects are modulated by changes in transpulmonary pressure and arteriolar pressure, although RBCs, neutrophils, and rigid microspheres all behave differently.

scholarly journals Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Dong Wang ◽  
Lori R. Arlinghaus ◽  
Thomas E. Yankeelov ◽  
Xiaoping Yang ◽  
David S. Smith
Keyword(s):  
Compressed Sensing ◽  
Volume Fraction ◽  
Extracellular Volume ◽  
Pharmacokinetic Parameters ◽  
Transfer Constant ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Space Data

Purpose. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled k-space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast. Methods. We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second-order total generalized variation (TGVα2), and nuclear norm (NN). We measured the signal-to-error ratio (SER) of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs) of the derived pharmacokinetic parameters Ktrans (volume transfer constant) and ve (extravascular-extracellular volume fraction) across a population of random sampling schemes. Results. NN produced the lowest image error (SER: 29.1), while TV/TGVα2 produced the most accurate Ktrans (CCC: 0.974/0.974) and ve (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate Ktrans (CCC: 0.842) and ve (CCC: 0.799). Conclusion. TV/TGVα2 should be used as temporal constraints for CS DCE-MRI of the breast.

scholarly journals Fast Imaging Technique for fMRI: Consecutive Multishot Echo Planar Imaging Accelerated with GRAPPA Technique

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Daehun Kang ◽  
Yul-Wan Sung ◽  
Chang-Ki Kang
Keyword(s):  
Functional Imaging ◽  
Statistical Power ◽  
Parallel Imaging ◽  
Imaging Technique ◽  
Imaging Study ◽  
Echo Planar Imaging ◽  
Planar Imaging ◽  
Parallel Imaging Technique ◽  
Functional Imaging Study ◽  
Echo Planar

This study was to evaluate the proposed consecutive multishot echo planar imaging (cmsEPI) combined with a parallel imaging technique in terms of signal-to-noise ratio (SNR) and acceleration for a functional imaging study. We developed cmsEPI sequence using both consecutively acquired multishot EPI segments and variable flip angles to minimize the delay between segments and to maximize the SNR, respectively. We also combined cmsEPI with the generalized autocalibrating partially parallel acquisitions (GRAPPA) method. Temporal SNRs were measured at different acceleration factors and number of segments for functional sensitivity evaluation. We also examined the geometric distortions, which inherently occurred in EPI sequence. The practical acceleration factors,R=2orR=3, of the proposed technique improved the temporal SNR by maximally 18% in phantom test and by averagely 8.2% in in vivo experiment, compared to cmsEPI without parallel imaging. The data collection time was decreased in inverse proportion to the acceleration factor as well. The improved temporal SNR resulted in better statistical power when evaluated on the functional response of the brain. In this study, we demonstrated that the combination of cmsEPI with the parallel imaging technique could provide the improved functional sensitivity for functional imaging study, compensating for the lower SNR by cmsEPI.

scholarly journals Multi T1-weighted contrast imaging and T1 mapping with Compressed sensing FLAWS at 3T

2021 ◽  
Author(s):  
Jeremy Beaumont ◽  
Jurgen Fripp ◽  
Parnesh Raniga ◽  
Oscar Acosta ◽  
Jean-Christophe Ferre ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
T1 Mapping ◽  
Spin Echo ◽  
Acquisition Time ◽  
Contrast Imaging ◽  
Parallel Acquisition ◽  
In Vivo Experiments ◽  
Sequence Parameters

The Fluid And White matter Suppression (FLAWS) MRI sequence allows for the acquisition of multiple T1-weighted contrasts in a single sequence acquisition. However, its acquisition time is prohibitive for use in clinical practice when the k-space is linearly downsampled and reconstructed using the Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) technique. This study proposes a FLAWS sequence optimization tailored to allow for the acquisition of FLAWS images with a Cartesian phyllotaxis k-space undersampling and compressed sensing (CS) reconstruction at 3T. The CS FLAWS sequence parameters were determined using a method previously employed to optimize FLAWS imaging at 1.5T and 7T. In-vivo experiments show that the proposed CS FLAWS optimization allows to reduce the FLAWS sequence acquisition time from 8 mins to 6 mins without decreasing the FLAWS image quality. In addition, this study demonstrates for the first time that T1-weighted imaging with low B1 sensitivity and T1 mapping can be performed with the FLAWS sequence at 3T for both GRAPPA and CS reconstructions. The FLAWS T1 mapping was validated using in-silico, in-vitro and in-vivo experiments with comparison against the inversion recovery turbo spin echo and MP2RAGE T1 mappings. These new results suggest that the recent advances in FLAWS imaging allow to combine the MP2RAGE imaging benefits (T1-weigthed imaging with low B1 sensitivity and T1 mapping) and with the previous version of FLAWS imaging benefits (multi T1-weighted contrast imaging) in a single 6 mins sequence acquisition.

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