Highly accelerated dynamic contrast enhanced imaging

Jones, Jesse; Ferré, J.-C.; Law, Meng; Nayak, Krishna S.

doi:10.1002/mrm.24710

Cited by 39 publications

(48 citation statements)

References 19 publications

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“…For example, arterial spin labeling (ASL), a non-contrast method for measuring perfusion, can be made significantly faster and yet more accurate using sparse reconstructions 150 . Similarly, accelerated dynamic contrast-enhanced imaging has been achieved for accurate characterization of brain perfusion parameters 151 . Accelerated scans have been used for qualitative and quantitative analysis of pituitary adenomas 152 .…”

Section: Clinical Applications Of Sparse Reconstruction Techniquesmentioning

confidence: 99%

“…For instance, in compressed sensing reconstructions, a number of different sparsifying transforms beyond finite differences and wavelets have been proposed, including but not limited to singular value decomposition 181 , principal component analysis 53,151 , Haar 182 and temporal Fourier transforms 182 . To complicate matters, some proposed compressed sensing implementations use more than one sparsifying transform 151,182 . Once the number and type of sparsifying transforms have been selected, it is often necessary to determine an appropriate value for the regularization parameter associated with each transform.…”

Section: Technical Challengesmentioning

confidence: 99%

“…Reconstruction times on the order of hours or even days have been reported 14,42,72,151 . Such long reconstruction times make it difficult to test the relative performance of different methods or determine optimal parameters.…”

Section: Technical Challengesmentioning

confidence: 99%

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Sparse Reconstruction Techniques in Magnetic Resonance Imaging

Yang

Kretzler

Sudarski

et al. 2016

Investigative Radiology

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The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in Magnetic Resonance Imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be employed to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MR imaging, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold-standards, are discussed.

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Section: Clinical Applications Of Sparse Reconstruction Techniquesmentioning

confidence: 99%

Section: Technical Challengesmentioning

confidence: 99%

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Sparse Reconstruction Techniques in Magnetic Resonance Imaging

Yang

Kretzler

Sudarski

et al. 2016

Investigative Radiology

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“…As a natural extension, the 3D radial/Cartesian sampling strategy may further improve the acceleration factor with the k-space undersampling on 2 phase-encoding directions. 68,69 Since the second-order derivative calculation involved in TGV would become computation intensive, the adoption of Graphics Processing Unit (GPU)-based reconstruction implementation may become necessary for future DCE-MRI acceleration in clinical use. 70 As discussed previously, the TGV term can maintain edge information while enforcing the smoothness in low-contrast region.…”

Section: Discussionmentioning

confidence: 99%

Accelerated Brain DCE-MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study

Wang

Yin

Kirkpatrick

et al. 2016

Technol Cancer Res Treat

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Purpose: To investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magnetic resonance imaging. Methods: Eight brain dynamic contrast-enhanced magnetic resonance imaging scans were retrospectively studied. Two k-space sparse sampling strategies were designed to achieve a simulated image acquisition acceleration factor of 4. They are (1) a golden ratio-optimized 32-ray radial sampling profile and (2) a Cartesian-based random sampling profile with spatiotemporal-regularized sampling density constraints. The undersampled data were reconstructed to yield images using the investigated reconstruction technique. In quantitative pharmacokinetic analysis on a voxel-by-voxel basis, the rate constant K trans in the extended Tofts model and blood flow F B and blood volume V B from the 2-compartment exchange model were analyzed. Finally, the quantitative pharmacokinetic parameters calculated from the undersampled data were compared with the corresponding calculated values from the fully sampled data. To quantify each parameter's accuracy calculated using the undersampled data, error in volume mean, total relative error, and cross-correlation were calculated. Results: The pharmacokinetic parameter maps generated from the undersampled data appeared comparable to the ones generated from the original full sampling data. Within the region of interest, most derived error in volume mean values in the region of interest was about 5% or lower, and the average error in volume mean of all parameter maps generated through either sampling strategy was about 3.54%. The average total relative error value of all parameter maps in region of interest was about 0.115, and the average crosscorrelation of all parameter maps in region of interest was about 0.962. All investigated pharmacokinetic parameters had no significant differences between the result from original data and the reduced sampling data. Conclusion: With sparsely sampled k-space data in simulation of accelerated acquisition by a factor of 4, the investigated dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic parameters can accurately estimate the total generalized variation-based iterative image reconstruction method for reliable clinical application.

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“…SWIFT can provide full matrix isotropic resolution images at sub-mm resolution at 6 second temporal resolution, greatly reducing the need to compromise between temporal and spatial resolution [26, 27]. Further improvements in images may be obtainable by application of parallel imaging and compressed sensing methods [28]. …”

Section: Discussionmentioning

confidence: 99%

High-Spatial- and High-Temporal-Resolution Dynamic Contrast-enhanced MR Breast Imaging with Sweep Imaging with Fourier Transformation: A Pilot Study

Corum¹,

Benson²,

Idiyatullin³

et al. 2015

Radiology

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Purpose To report the results of SWeep Imaging with Fourier Transformation (SWIFT) MR imaging for diagnostic breast imaging. Materials and Methods Informed consent was obtained from all subjects under one of two IRB-approved, HIPAA-compliant protocols. Twelve female patients (age range 19–54 years; mean age 41.2) and eight normal controls (age range 22–56 years; mean age 43.2) enrolled and completed the study from 1/28/2011 to 3/5/2013. Patients had previous findings of BI-RADS 4 and 5 based on mammography and/or ultrasound. Contrast-enhanced SWIFT imaging was completed using a Siemens/Agilent 4 T research MRI system. Non-contrast studies were completed on the normal controls. One of two sized single breast SWIFT-compatible transceiver coils was used for nine patients and five controls. Three patients and five controls used a SWIFT-compatible dual breast coil. Temporal resolution was 5.9 to 7.5 seconds. Spatial resolution was 1.00 mm isotropic initially, with later scans at 0.67 mm isotropic, and dual breast at 1.00 mm or 0.75 mm isotropic resolution. Results Two unblinded breast radiologists reported SWIFT image findings of normal breast tissue, benign fibroadenomas (N=6/6), and malignant lesions (N=10/12) concordant with other imaging modalities and pathology reports. Two lesions (in two patients) were not visualized due to coil field of view. The images yielded by SWIFT showed the presence and extent of known breast lesions. Conclusions The SWIFT technique could become an important addition to the repertoire of breast imaging modalities due to its ability to provide high spatial resolution at all time points during the dynamic contrast exam.

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Highly accelerated dynamic contrast enhanced imaging

Abstract: Highly accelerated dynamic MRI using compressed sensing and parallel imaging provides accurate permeability modeling and enables full brain, high resolution acquisitions.

Cited by 39 publications

References 19 publications

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

Accelerated Brain DCE-MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study

High-Spatial- and High-Temporal-Resolution Dynamic Contrast-enhanced MR Breast Imaging with Sweep Imaging with Fourier Transformation: A Pilot Study

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