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Owusu N, Magnotta VA. Factors influencing daily quality assurance measurements of magnetic resonance imaging scanners. Radiol Phys Technol 2021; 14:396-401. [PMID: 34623608 PMCID: PMC8497687 DOI: 10.1007/s12194-021-00638-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 09/23/2021] [Accepted: 09/27/2021] [Indexed: 11/25/2022]
Abstract
Magnetic resonance imaging is commonly used in hospitals and clinics to aid medical diagnoses. Scanner performance should be assessed regularly, including daily, weekly, and yearly evaluations to ensure high-quality and artifact-free images. Of these assessments, the daily quality assurance monitors the image quality of the scanner using a manufacturer-provided protocol. In this study, we sought to determine the factors that introduced variability in daily quality assurance data. A phantom was scanned using a head coil in two schemes: with varied phantom placement daily, and with a single phantom placement, and evaluated over approximately 1 month. Minor placement and localization changes accounted for approximately 50% of the variability in the signal-to-noise ratios observed in these measures, driven by changes in the measured signal, while the noise remained constant. The changes in the signal-to-noise ratios were small over the 2-month study period.
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Affiliation(s)
- Nana Owusu
- Department of Radiology, The University of Iowa, Iowa City, IA, 52240, USA.,Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, 52240, USA
| | - Vincent A Magnotta
- Department of Radiology, The University of Iowa, Iowa City, IA, 52240, USA. .,Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, 52240, USA. .,Department of Psychiatry, The University of Iowa, Iowa City, IA, 52240, USA. .,, 169 Newton Road, Iowa City, IA, 52242, USA.
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Pineda AR, Miedema H, Lingala SG, Nayak KS. Optimizing constrained reconstruction in magnetic resonance imaging for signal detection. Phys Med Biol 2021; 66:10.1088/1361-6560/ac1021. [PMID: 34192682 PMCID: PMC9169904 DOI: 10.1088/1361-6560/ac1021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 06/30/2021] [Indexed: 11/11/2022]
Abstract
Constrained reconstruction in magnetic resonance imaging (MRI) allows the use of prior information through constraints to improve reconstructed images. These constraints often take the form of regularization terms in the objective function used for reconstruction. Constrained reconstruction leads to images which appear to have fewer artifacts than reconstructions without constraints but because the methods are typically nonlinear, the reconstructed images have artifacts whose structure is hard to predict. In this work, we compared different methods of optimizing the regularization parameter using a total variation (TV) constraint in the spatial domain and sparsity in the wavelet domain for one-dimensional (2.56×) undersampling using variable density undersampling. We compared the mean squared error (MSE), structural similarity (SSIM), L-curve and the area under the receiver operating characteristic (AUC) using a linear discriminant for detecting a small and a large signal. We used a signal-known-exactly task with varying backgrounds in a simulation where the anatomical variation was the major source of clutter for the detection task. Our results show that the AUC dependence on regularization parameters varies with the imaging task (i.e. the signal being detected). The choice of regularization parameters for MSE, SSIM, L-curve and AUC were similar. We also found that a model-based reconstruction including TV and wavelet sparsity did slightly better in terms of AUC than just enforcing data consistency but using these constraints resulted in much better MSE and SSIM. These results suggest that the increased performance in MSE and SSIM over-estimate the improvement in detection performance for the tasks in this paper. The MSE and SSIM metrics show a big difference in performance where the difference in AUC is small. To our knowledge, this is the first time that signal detection with varying backgrounds has been used to optimize constrained reconstruction in MRI.
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Affiliation(s)
- Angel R Pineda
- Department of Mathematics, Manhattan College, Riverdale, NY 10471, United States of America
| | - Hope Miedema
- Department of Mathematics, Manhattan College, Riverdale, NY 10471, United States of America
| | - Sajan Goud Lingala
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, United States of America
| | - Krishna S Nayak
- Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, United States of America
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Naeyaert M, Aelterman J, Van Audekerke J, Golkov V, Cremers D, Pižurica A, Sijbers J, Verhoye M. Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing. Magn Reson Med 2020; 85:1397-1413. [PMID: 33009866 DOI: 10.1002/mrm.28520] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 08/22/2020] [Accepted: 08/24/2020] [Indexed: 11/11/2022]
Abstract
PURPOSE Echo planar imaging (EPI) is commonly used to acquire the many volumes needed for high angular resolution diffusion Imaging (HARDI), posing a higher risk for artifacts, such as distortion and deformation. An alternative to EPI is fast spin echo (FSE) imaging, which has fewer artifacts but is inherently slower. The aim is to accelerate FSE such that a HARDI data set can be acquired in a time comparable to EPI using compressed sensing. METHODS Compressed sensing was applied in either q-space or simultaneously in k-space and q-space, by undersampling the k-space in the phase-encoding direction or retrospectively eliminating diffusion directions for different degrees of undersampling. To test the replicability of the acquisition and reconstruction, brain data were acquired from six mice, and a numerical phantom experiment was performed. All HARDI data were analyzed individually using constrained spherical deconvolution, and the apparent fiber density and complexity metric were evaluated, together with whole-brain tractography. RESULTS The apparent fiber density and complexity metric showed relatively minor differences when only q-space undersampling was used, but deteriorate when k-space undersampling was applied. Likewise, the tract density weighted image showed good results when only q-space undersampling was applied using 15 directions or more, but information was lost when fewer volumes or k-space undersampling were used. CONCLUSION It was found that acquiring 15 to 20 diffusion directions with a full k-space and reconstructed using compressed sensing could suffice for a replicable measurement of quantitative measures in mice, where areas near the sinuses and ear cavities are untainted by signal loss.
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Affiliation(s)
| | - Jan Aelterman
- Imec-IPI, Department of Telecommunications and Information Processing, Ghent University, Ghent, Belgium
| | | | - Vladimir Golkov
- Department of Computer Science, Technical University of Munich, Garching, Germany
| | - Daniel Cremers
- Department of Computer Science, Technical University of Munich, Garching, Germany
| | - Aleksandra Pižurica
- Imec-IPI, Department of Telecommunications and Information Processing, Ghent University, Ghent, Belgium
| | - Jan Sijbers
- Imec-Vision Lab, University of Antwerp, Antwerp, Belgium
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Ye JC. Compressed sensing MRI: a review from signal processing perspective. BMC Biomed Eng 2019; 1:8. [PMID: 32903346 PMCID: PMC7412677 DOI: 10.1186/s42490-019-0006-z] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Accepted: 02/04/2019] [Indexed: 11/27/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires multi-dimensional k-space data through 1-D free induction decay or echo signals. This often limits the use of MRI, especially for high resolution or dynamic imaging. Accordingly, many investigators has developed various acceleration techniques to allow fast MR imaging. For the last two decades, one of the most important breakthroughs in this direction is the introduction of compressed sensing (CS) that allows accurate reconstruction from sparsely sampled k-space data. The recent FDA approval of compressed sensing products for clinical scans clearly reflect the maturity of this technology. Therefore, this paper reviews the basic idea of CS and how this technology have been evolved for various MR imaging problems.
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Affiliation(s)
- Jong Chul Ye
- Department of Bio and Brain Engineering, Korea Adv. Inst. of Science & Technology (KAIST), 291 Daehak-ro, Daejeon, Korea
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Ding Y, Ying L, Zhang N, Liang D. Noise behavior of MR brain reconstructions using compressed sensing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:5155-8. [PMID: 24110896 DOI: 10.1109/embc.2013.6610709] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Compressed sensing (CS) has demonstrated great potential to reconstruct high quality MR images from undersampled k-space data. However, successful application of CS in clinic is still limited by many factors. One of the key factors is that the noise behavior in CS reconstructions remains largely unexplored. The main objective of this work is to analyze the noise behavior of MR reconstructions using CS method with different reduction factors. Our work focuses on brain CS-MRI reconstructions using non-linear conjugate gradient (NLCG) solvers. After reconstruction, the noise behavior is characterized using the MP-Law method. The results show that the spatial noise distributed non-uniformly, and the noise variance from CS reconstruction increases with reduction factors. A kind of fitting model is given, which can be used to predict the noise behavior parameter for different reduction factors, and the noise amplification factor maps are shown to prove the denoising capability of CS reconstruction. The results provide a qualitative and quantitative understanding of the noise behavior in CS-MRI with different reduction factors.
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Tao S, Trzasko JD, Shu Y, Huston J, Bernstein MA. Integrated image reconstruction and gradient nonlinearity correction. Magn Reson Med 2014; 74:1019-31. [PMID: 25298258 DOI: 10.1002/mrm.25487] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Revised: 08/28/2014] [Accepted: 09/16/2014] [Indexed: 11/05/2022]
Abstract
PURPOSE To describe a model-based reconstruction strategy for routine magnetic resonance imaging that accounts for gradient nonlinearity (GNL) during rather than after transformation to the image domain, and demonstrate that this approach reduces the spatial resolution loss that occurs during strictly image-domain GNL-correction. METHODS After reviewing conventional GNL-correction methods, we propose a generic signal model for GNL-affected magnetic resonance imaging acquisitions, discuss how it incorporates into contemporary image reconstruction platforms, and describe efficient nonuniform fast Fourier transform-based computational routines for these. The impact of GNL-correction on spatial resolution by the conventional and proposed approaches is investigated on phantom data acquired at varying offsets from gradient isocenter, as well as on fully sampled and (retrospectively) undersampled in vivo acquisitions. RESULTS Phantom results demonstrate that resolution loss that occurs during GNL-correction is significantly less for the proposed strategy than for the standard approach at distances >10 cm from isocenter with a 35 cm field-of-view gradient coil. The in vivo results suggest that the proposed strategy better preserves fine anatomical detail than retrospective GNL-correction while offering comparable geometric correction. CONCLUSION Accounting for GNL during image reconstruction allows geometric distortion to be corrected with less spatial resolution loss than is typically observed with the conventional image domain correction strategy.
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Affiliation(s)
- Shengzhen Tao
- Mayo Graduate School, Mayo Clinic, Rochester, Minnesota, USA.,Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Yunhong Shu
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - John Huston
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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Smith DS, Li X, Abramson RG, Chad Quarles C, Yankeelov TE, Brian Welch E. Potential of compressed sensing in quantitative MR imaging of cancer. Cancer Imaging 2013; 13:633-44. [PMID: 24434808 PMCID: PMC3893904 DOI: 10.1102/1470-7330.2013.0041] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/29/2013] [Indexed: 12/22/2022] Open
Abstract
Classic signal processing theory dictates that, in order to faithfully reconstruct a band-limited signal (e.g., an image), the sampling rate must be at least twice the maximum frequency contained within the signal, i.e., the Nyquist frequency. Recent developments in applied mathematics, however, have shown that it is often possible to reconstruct signals sampled below the Nyquist rate. This new method of compressed sensing (CS) requires that the signal have a concise and extremely dense representation in some mathematical basis. Magnetic resonance imaging (MRI) is particularly well suited for CS approaches, owing to the flexibility of data collection in the spatial frequency (Fourier) domain available in most MRI protocols. With custom CS acquisition and reconstruction strategies, one can quickly obtain a small subset of the full data and then iteratively reconstruct images that are consistent with the acquired data and sparse by some measure. Successful use of CS results in a substantial decrease in the time required to collect an individual image. This extra time can then be harnessed to increase spatial resolution, temporal resolution, signal-to-noise, or any combination of the three. In this article, we first review the salient features of CS theory and then discuss the specific barriers confronting CS before it can be readily incorporated into clinical quantitative MRI studies of cancer. We finally illustrate applications of the technique by describing examples of CS in dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI.
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Affiliation(s)
- David S. Smith
- Institute of Imaging Science, Departments of Radiology and Radiological Sciences, Biomedical Engineering, Physics and Astronomy, and Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - Xia Li
- Institute of Imaging Science, Departments of Radiology and Radiological Sciences, Biomedical Engineering, Physics and Astronomy, and Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - Richard G. Abramson
- Institute of Imaging Science, Departments of Radiology and Radiological Sciences, Biomedical Engineering, Physics and Astronomy, and Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - C. Chad Quarles
- Institute of Imaging Science, Departments of Radiology and Radiological Sciences, Biomedical Engineering, Physics and Astronomy, and Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - Thomas E. Yankeelov
- Institute of Imaging Science, Departments of Radiology and Radiological Sciences, Biomedical Engineering, Physics and Astronomy, and Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - E. Brian Welch
- Institute of Imaging Science, Departments of Radiology and Radiological Sciences, Biomedical Engineering, Physics and Astronomy, and Cancer Biology, Vanderbilt University, Nashville, TN, USA
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