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Dubovan PI, Gilbert KM, Baron CA. A correction algorithm for improved magnetic field monitoring with distal field probes. Magn Reson Med 2023; 90:2242-2260. [PMID: 37598420 DOI: 10.1002/mrm.29781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 05/15/2023] [Accepted: 06/12/2023] [Indexed: 08/22/2023]
Abstract
PURPOSE A significant source of artifacts in MRI are field fluctuations. Field monitoring is a new technology that allows measurement of field dynamics during a scan via "field probes," which can be used to improve image reconstruction. Ideally, probes are located within the volume where gradients produce nominally linear field patterns. However, in some situations probes must be located far from isocenter where rapid field variation can arise, leading to erroneous field-monitoring characterizations and images. This work aimed to develop an algorithm that improves the robustness of field dynamics in these situations. METHODS The algorithm is split into three components. Component 1 calculates field dynamics one spatial order at a time, whereas the second implements a weighted least squares solution based on probe distance. Component 3 then calculates phase residuals and removes the residual phase for distant probes before recalculation. Two volunteers and a phantom were scanned on a 7T MRI using diffusion-weighted sequences, and field monitoring was performed. Image reconstructions were informed with field dynamics calculated conventionally, and with the correction algorithm, after which in vivo images were compared qualitatively and phantom image error was quantitatively assessed. RESULTS The algorithm was able to correct corrupted field dynamics, resulting in image-quality improvements. Significant artifact reduction was observed when correcting higher-order fits. Stepwise fitting provided the most correction benefit, which was marginally improved when adding the other correction strategies. CONCLUSION The proposed algorithm can mitigate effects of phase errors in field monitoring, providing improved characterization of field dynamics.
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Affiliation(s)
- Paul I Dubovan
- Department of Medical Biophysics, Western University, London, Ontario, Canada
- Center for Functional and Metabolic Mapping, Western University, London, Ontario, Canada
| | - Kyle M Gilbert
- Department of Medical Biophysics, Western University, London, Ontario, Canada
- Center for Functional and Metabolic Mapping, Western University, London, Ontario, Canada
| | - Corey A Baron
- Department of Medical Biophysics, Western University, London, Ontario, Canada
- Center for Functional and Metabolic Mapping, Western University, London, Ontario, Canada
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2
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Kanakaraj P, Rheault F, Cai LY, Newlin N, Yeh FC, Rogers BP, Schilling KG, Landman BA. Mapping the Impact of Approximate Gradient Nonlinearity Fields Correction on Tractography. Proc SPIE Int Soc Opt Eng 2023; 12464:1246427. [PMID: 37621418 PMCID: PMC10448744 DOI: 10.1117/12.2653884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
Nonlinear gradients impact diffusion weighted MRI by introducing spatial variation in estimated diffusion tensors. Recent studies have shown that increasing signal-to-noise ratios and the use of ultra-strong gradients may lead to clinically significant impacts on analyses due to these nonlinear gradients in microstructural measures. These effects can potentially bias tractography results and cause misinterpretation of data. Herein, we characterize the impact of an "approximate" gradient nonlinearity correction technique in tractography using empirically derived gradient nonlinear fields. This technique scales the diffusion signal by the change in magnitude due to the gradient nonlinearities, without concomitant correction of gradient direction errors. The impact of this correction on tractography is assessed through white matter bundle segmentation and connectomics via bundle-wise volume, fractional anisotropy, mean diffusivity, radial diffusivity, axial diffusivity, primary eigenvector, and length; as well as the modularity, global efficiency, and characteristic path length connectomics graph measures. We investigate the differences between (1) these measures directly and (2) the within session variability of these measures before and after approximate correction in 61 subjects from the MASiVar pediatric reproducibility dataset. We find approximate correction results is little to no differences on the population level, but large differences on the subject-specific level for both the measures directly and their within session variability. Thus, this study suggests though approximate correction of gradient nonlinearities may not change tractography findings on the population level, subject-specific interpretations may exhibit large fluctuations. A limitation is the lack of comparison with the empirical voxel-wise gradient table correction.
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Affiliation(s)
| | - Francois Rheault
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Nancy Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburg, School of Medicine, Pittsburg, PA, USA
| | - Baxter P Rogers
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA
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Wang J, Ma C, Yang P, Wang Z, Chen Y, Bian Y, Shao C, Lu J. Diffusion-Weighted Imaging of the Abdomen: Correction for Gradient Nonlinearity Bias in Apparent Diffusion Coefficient. J Magn Reson Imaging 2022. [PMID: 36373955 DOI: 10.1002/jmri.28529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 10/31/2022] [Accepted: 11/01/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Gradient nonlinearity (GNL) introduces spatial nonuniformity bias in apparent diffusion coefficient (ADC) measurements, especially at large offsets from the magnet isocenter. PURPOSE To investigate the effects of GNL in abdominal ADC measurements and to develop an ADC bias correction procedure. STUDY TYPE Retrospective. PHANTOM/POPULATION Two homemade ultrapure water phantoms/25 patients with histologically confirmed pancreatic ductal adenocarcinoma (PDAC). FIELD STRENGTH/SEQUENCE A 3.0 T/diffusion-weighted imaging (DWI) with single-shot echo-planar imaging sequence. ASSESSMENT ADC bias was computed in the three orthogonal directions at different offset locations. The spatial-dependent correctors of ADC bias were generated from the ADCs of phantom 1. The ADCs were estimated before and after corrections for the phantom 1 with both the proposed approach and the theoretical GNL correction method. For the patients, ADCs were measured in abdominal tissues including left and right liver lobes, PDAC, spleen, bilateral kidneys, and bilateral paraspinal muscles. STATISTICAL TEST Friedman tests and Wilcoxon tests. RESULTS The ADC bias measured by phantom 1 was 9.7% and 12.6% higher in the right-left and anterior-posterior directions and 9.2% lower in the superior-inferior direction at the 150 mm offsets from the magnetic isocenter. The corrected vs. the uncorrected ADCs measurements (median: 2.20 × 10-3 mm2 /sec for both the proposed method and the theoretical GNL method vs. 2.31 × 10-3 mm2 /sec, respectively) and their relative ADC errors (0.014, 0.016, and 0.054, respectively) were lower in the phantom 1. The relative ADC errors substantially decreased after correction in the phantom 2 (median: 0.048 and -0.008, respectively). The ADCs of all the abdominal tissues were lower after correction except for the left liver lobes (P = 0.13). DATA CONCLUSION GNL bias in abdominal ADC can be measured by a DWI phantom. The proposed correction procedure was successfully applied for the bias correction in abdominal ADC. EVIDENCE LEVEL 3. TECHNICAL EFFICACY Stage 1.
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Affiliation(s)
- Jian Wang
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, China.,College of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Panpan Yang
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, China
| | - Zhen Wang
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, China
| | - Yufei Chen
- College of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Yun Bian
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, China
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Shan S, Li M, Li M, Tang F, Crozier S, Liu F. ReUINet: A fast GNL distortion correction approach on a 1.0 T MRI-Linac scanner. Med Phys 2021; 48:2991-3002. [PMID: 33763850 DOI: 10.1002/mp.14861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/17/2021] [Accepted: 03/19/2021] [Indexed: 11/12/2022] Open
Abstract
PURPOSE The hybrid system combining a magnetic resonance imaging (MRI) scanner with a linear accelerator (Linac) has become increasingly desirable for tumor treatment because of excellent soft tissue contrast and nonionizing radiation. However, image distortions caused by gradient nonlinearity (GNL) can have detrimental impacts on real-time radiotherapy using MRI-Linac systems, where accurate geometric information of tumors is essential. METHODS In this work, we proposed a deep convolutional neural network-based method to efficiently recover undistorted images (ReUINet) for real-time image guidance. The ReUINet, based on the encoder-decoder structure, was created to learn the relationship between the undistorted images and distorted images. The ReUINet was pretrained and tested on a publically available brain MR image dataset acquired from 23 volunteers. Then, transfer learning was adopted to implement the pretrained model (i.e., network with optimal weights) on the experimental three-dimensional (3D) grid phantom and in-vivo pelvis image datasets acquired from the 1.0 T Australian MRI-Linac system. RESULTS Evaluations on the phantom (768 slices) and pelvis data (88 slices) showed that the ReUINet achieved improvement over 15 times and 45 times on computational efficiency in comparison with standard interpolation and GNL-encoding methods, respectively. Moreover, qualitative and quantitative results demonstrated that the ReUINet provided better correction results than the standard interpolation method, and comparable performance compared to the GNL-encoding approach. CONCLUSIONS Validated by simulation and experimental results, the proposed ReUINet showed promise in obtaining accurate MR images for the implementation of real-time MRI-guided radiotherapy.
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Affiliation(s)
- Shanshan Shan
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia.,ACRF Image X Institute, School of Health Sciences, University of Sydney, Sydney, Australia
| | - Mao Li
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Mingyan Li
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Fangfang Tang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
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Tian Y, Lim Y, Zhao Z, Byrd D, Narayanan S, Nayak KS. Aliasing artifact reduction in spiral real-time MRI. Magn Reson Med 2021; 86:916-925. [PMID: 33728700 DOI: 10.1002/mrm.28746] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 01/09/2021] [Accepted: 02/02/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE To mitigate a common artifact in spiral real-time MRI, caused by aliasing of signal outside the desired FOV. This artifact frequently occurs in midsagittal speech real-time MRI. METHODS Simulations were performed to determine the likely origin of the artifact. Two methods to mitigate the artifact are proposed. The first approach, denoted as "large FOV" (LF), keeps an FOV that is large enough to include the artifact signal source during reconstruction. The second approach, denoted as "estimation-subtraction" (ES), estimates the artifact signal source before subtracting a synthetic signal representing that source in multicoil k-space raw data. Twenty-five midsagittal speech-production real-time MRI data sets were used to evaluate both of the proposed methods. Reconstructions without and with corrections were evaluated by two expert readers using a 5-level Likert scale assessing artifact severity. Reconstruction time was also compared. RESULTS The origin of the artifact was found to be a combination of gradient nonlinearity and imperfect anti-aliasing in spiral sampling. The LF and ES methods were both able to substantially reduce the artifact, with an averaged qualitative score improvement of 1.25 and 1.35 Likert levels for LF correction and ES correction, respectively. Average reconstruction time without correction, with LF correction, and with ES correction were 160.69 ± 1.56, 526.43 ± 5.17, and 171.47 ± 1.71 ms/frame. CONCLUSION Both proposed methods were able to reduce the spiral aliasing artifacts, with the ES-reduction method being more effective and more time efficient.
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Affiliation(s)
- Ye Tian
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Yongwan Lim
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Ziwei Zhao
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Dani Byrd
- Department of Linguistics, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, USA
| | - Shrikanth Narayanan
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.,Department of Linguistics, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, California, USA
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
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Guo F, de Luca A, Parker G, Jones DK, Viergever MA, Leemans A, Tax CMW. The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion magnetic resonance imaging data. Hum Brain Mapp 2021; 42:367-383. [PMID: 33035372 PMCID: PMC7776002 DOI: 10.1002/hbm.25228] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 09/22/2020] [Accepted: 09/24/2020] [Indexed: 12/12/2022] Open
Abstract
Gradient nonlinearities in magnetic resonance imaging (MRI) cause spatially varying mismatches between the imposed and the effective gradients and can cause significant biases in rotationally invariant diffusion MRI measures derived from, for example, diffusion tensor imaging. The estimation of the orientational organization of fibrous tissue, which is nowadays frequently performed with spherical deconvolution techniques ideally using higher diffusion weightings, can likewise be biased by gradient nonlinearities. We explore the sensitivity of two established spherical deconvolution approaches to gradient nonlinearities, namely constrained spherical deconvolution (CSD) and damped Richardson-Lucy (dRL). Additionally, we propose an extension of dRL to take into account gradient imperfections, without the need of data interpolation. Simulations show that using the effective b-matrix can improve dRL fiber orientation estimation and reduces angular deviations, while CSD can be more robust to gradient nonlinearity depending on the implementation. Angular errors depend on a complex interplay of many factors, including the direction and magnitude of gradient deviations, underlying microstructure, SNR, anisotropy of the effective response function, and diffusion weighting. Notably, angular deviations can also be observed at lower b-values in contrast to the perhaps common assumption that only high b-value data are affected. In in vivo Human Connectome Project data and acquisitions from an ultrastrong gradient (300 mT/m) scanner, angular differences are observed between applying and not applying the effective gradients in dRL estimation. As even small angular differences can lead to error propagation during tractography and as such impact connectivity analyses, incorporating gradient deviations into the estimation of fiber orientations should make such analyses more reliable.
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Affiliation(s)
- Fenghua Guo
- Image Sciences InstituteUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
| | - Alberto de Luca
- Image Sciences InstituteUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
| | - Greg Parker
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff UniversityCardiffUK
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff UniversityCardiffUK
| | - Max A. Viergever
- Image Sciences InstituteUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
| | - Alexander Leemans
- Image Sciences InstituteUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
| | - Chantal M. W. Tax
- Image Sciences InstituteUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff UniversityCardiffUK
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Yamamoto T, Fukunaga M, Sugawara SK, Hamano YH, Sadato N. Quantitative Evaluations of Geometrical Distortion Corrections in Cortical Surface-Based Analysis of High-Resolution Functional MRI Data at 7T. J Magn Reson Imaging 2020; 53:1220-1234. [PMID: 33151028 PMCID: PMC7984446 DOI: 10.1002/jmri.27420] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/16/2020] [Accepted: 10/16/2020] [Indexed: 11/20/2022] Open
Abstract
Background Although 7T functional MRI (fMRI) provides better signal‐to‐noise ratio and higher spatial resolution than 3T fMRI, geometric distortions become more challenging because fMRI is more susceptible to distortions than structural MRI. Accurate alignment of 7T fMRI to structural MRI data is critical for precise cortical surface‐based analysis. Purpose To quantify the effectiveness of distortion corrections of 7T fMRI data. Study Type Prospective. Subjects Fifteen healthy individuals aged 19–26 years (mean: 21.9 years). Field Strength/Sequence Multiband gradient‐echo echo‐planar imaging sequence at 7T; 3D T1/T2‐weighted sequences (magnetization prepared rapid acquisition with gradient echo [MPRAGE] and sampling perfection with application optimized contrast using different flip angle evolution [SPACE]) at 3T. Assessment fMRI data at 7T were registered to cortical surfaces reconstructed from 3T structural data acquired in the same subjects. Distortions induced by B0 inhomogeneity and gradient nonlinearity (B0 and gradient distortions) were evaluated as cortical fallout (misregistration of noncortical areas) and displacement (misregistration along gray matter). Statistical Tests Repeated measures analyses of variance with post‐hoc t‐tests with Bonferroni correction. Results The accuracy of fully corrected fMRI images based on the intensity distribution was 89.2%. Without any corrections, 9.7% of vertices in the whole surfaces were fallout and the average displacement was 0.96 mm for the rest of the vertices. B0 and gradient distortion corrections significantly reduced the fallout (to 2.1% and 8.7%) and displacement (to 0.29 mm and 0.86 mm). These corrections were effective even around regions with moderate distortions (the somatosensory and visual cortices for B0 distortion, and the anterior frontal, inferior temporal, and posterior occipital cortices for gradient distortion). Data Conclusion B0 distortion correction is crucial for surface‐based analysis of fine‐resolution fMRI at 7T. Gradient distortion correction should be considered when regions of interest include regions distant from the isocenter of scanners. Evidence Level 1 Technical Efficacy Stage 1
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Affiliation(s)
- Tetsuya Yamamoto
- Department of System Neuroscience, Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Japan.,Department of Physiological Sciences, School of Life Science, The Graduate School for Advanced Studies (SOKENDAI), Hayama, Japan
| | - Masaki Fukunaga
- Department of System Neuroscience, Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Japan.,Department of Physiological Sciences, School of Life Science, The Graduate School for Advanced Studies (SOKENDAI), Hayama, Japan
| | - Sho K Sugawara
- Department of System Neuroscience, Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Japan.,Department of Physiological Sciences, School of Life Science, The Graduate School for Advanced Studies (SOKENDAI), Hayama, Japan.,Neural Prosthesis Project, Department of Dementia and Higher Brain Function, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Yuki H Hamano
- Department of System Neuroscience, Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Japan
| | - Norihiro Sadato
- Department of System Neuroscience, Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Japan.,Department of Physiological Sciences, School of Life Science, The Graduate School for Advanced Studies (SOKENDAI), Hayama, Japan
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Rudrapatna U, Parker GD, Roberts J, Jones DK. A comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra-strong gradient MRI scanners. Magn Reson Med 2020; 85:1104-1113. [PMID: 33009875 PMCID: PMC8103165 DOI: 10.1002/mrm.28464] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 06/27/2020] [Accepted: 07/13/2020] [Indexed: 11/15/2022]
Abstract
Purpose The analysis of diffusion data obtained under large gradient nonlinearities necessitates corrections during data reconstruction and analysis. While two such preprocessing pipelines have been proposed, no comparative studies assessing their performance exist. Furthermore, both pipelines neglect the impact of subject motion during acquisition, which, in the presence of gradient nonlinearities, induces spatio‐temporal B‐matrix variations. Here, spatio‐temporal B‐matrix tracking (STB) is proposed and its performance compared to established pipelines. Methods Diffusion tensor MRI (DT‐MRI) was performed using a 300 mT/m gradient system. Data were acquired with volunteers positioned in regions with pronounced gradient nonlinearities, and used to compare the performance of six different processing pipelines, including STB. Results Up to 30% errors were observed in DT‐MRI parameter estimates when neglecting gradient nonlinearities. Moreover, the order in which B0 inhomogeneity, eddy current and gradient nonlinearity corrections were performed was found to impact the consistency of parameter estimates significantly. Although, no pipeline emerged as a clear winner, the STB approach seemed to yield the most consistent parameter estimates under large gradient nonlinearities. Conclusions Under large gradient nonlinearities, the choice of preprocessing pipeline significantly impacts the estimated diffusion parameters. Motion‐induced spatio‐temporal B‐matrix variations can lead to systematic bias in the parameter estimates, that can be ameliorated using the proposed STB framework. Click here for author‐reader discussions
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Affiliation(s)
- Umesh Rudrapatna
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiffUnited Kingdom
| | - Greg D. Parker
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiffUnited Kingdom
| | - Jamie Roberts
- Royal United Hospitals BathNHS Foundation TrustBathUnited Kingdom
| | - Derek K. Jones
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiffUnited Kingdom
- Mary MacKillop Institute for Health Research, Faculty of Health SciencesAustralian Catholic UniversityMelbourneVictoriaAustralia
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Lee Y, Kettinger AO, Wilm BJ, Deichmann R, Weiskopf N, Lambert C, Pruessmann KP, Nagy Z. A comprehensive approach for correcting voxel-wise b-value errors in diffusion MRI. Magn Reson Med 2019; 83:2173-2184. [PMID: 31840300 PMCID: PMC7065087 DOI: 10.1002/mrm.28078] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 09/23/2019] [Accepted: 10/22/2019] [Indexed: 01/29/2023]
Abstract
PURPOSE In diffusion MRI, the actual b-value played out on the scanner may deviate from the nominal value due to magnetic field imperfections. A simple image-based correction method for this problem is presented. METHODS The apparent diffusion constant (ADC) of a water phantom was measured voxel-wise along 64 diffusion directions at b = 1000 s/mm2 . The true diffusion constant of water was estimated, considering the phantom temperature. A voxel-wise correction factor, providing an effective b-value including any magnetic field deviations, was determined for each diffusion direction by relating the measured ADC to the true diffusion constant. To test the method, the measured b-value map was used to calculate the corrected voxel-wise ADC for additionally acquired diffusion data sets on the same water phantom and data sets acquired on a small water phantom at three different positions. Diffusion tensor was estimated by applying the measured b-value map to phantom and in vivo data sets. RESULTS The b-value-corrected ADC maps of the phantom showed the expected spatial uniformity as well as a marked improvement in consistency across diffusion directions. The b-value correction for the brain data resulted in a 5.8% and 5.5% decrease in mean diffusivity and angular differences of the primary diffusion direction of 2.71° and 0.73° inside gray and white matter, respectively. CONCLUSION The actual b-value deviates significantly from its nominal setting, leading to a spatially variable error in the common diffusion outcome measures. The suggested method measures and corrects these artifacts.
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Affiliation(s)
- Yoojin Lee
- Laboratory for Social and Neural Systems Research (SNS Lab), University of Zurich, Zurich, Switzerland.,Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Adam O Kettinger
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary.,Department of Nuclear Techniques, Budapest University of Technology and Economics, Budapest, Hungary
| | - Bertram Jakob Wilm
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Ralf Deichmann
- Brain Imaging Centre, Goethe University, Frankfurt, Germany.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Nikolaus Weiskopf
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Christian Lambert
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Klaas Paul Pruessmann
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Zoltan Nagy
- Laboratory for Social and Neural Systems Research (SNS Lab), University of Zurich, Zurich, Switzerland.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
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Papinutto N, Bakshi R, Bischof A, Calabresi PA, Caverzasi E, Constable RT, Datta E, Kirkish G, Nair G, Oh J, Pelletier D, Pham DL, Reich DS, Rooney W, Roy S, Schwartz D, Shinohara RT, Sicotte NL, Stern WA, Tagge I, Tauhid S, Tummala S, Henry RG. Gradient nonlinearity effects on upper cervical spinal cord area measurement from 3D T 1 -weighted brain MRI acquisitions. Magn Reson Med 2017; 79:1595-1601. [PMID: 28617996 DOI: 10.1002/mrm.26776] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Revised: 05/11/2017] [Accepted: 05/13/2017] [Indexed: 12/14/2022]
Abstract
PURPOSE To explore (i) the variability of upper cervical cord area (UCCA) measurements from volumetric brain 3D T1 -weighted scans related to gradient nonlinearity (GNL) and subject positioning; (ii) the effect of vendor-implemented GNL corrections; and (iii) easily applicable methods that can be used to retrospectively correct data. METHODS A multiple sclerosis patient was scanned at seven sites using 3T MRI scanners with the same 3D T1 -weighted protocol without GNL-distortion correction. Two healthy subjects and a phantom were additionally scanned at a single site with varying table positions. The 2D and 3D vendor-implemented GNL-correction algorithms and retrospective methods based on (i) phantom data fit, (ii) normalization with C2 vertebral body diameters, and (iii) the Jacobian determinant of nonlinear registrations to a template were tested. RESULTS Depending on the positioning of the subject, GNL introduced up to 15% variability in UCCA measurements from volumetric brain T1 -weighted scans when no distortion corrections were used. The 3D vendor-implemented correction methods and the three proposed methods reduced this variability to less than 3%. CONCLUSIONS Our results raise awareness of the significant impact that GNL can have on quantitative UCCA studies, and point the way to prospectively and retrospectively managing GNL distortions in a variety of settings, including clinical environments. Magn Reson Med 79:1595-1601, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Nico Papinutto
- Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Rohit Bakshi
- Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Antje Bischof
- Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Peter A Calabresi
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Eduardo Caverzasi
- Department of Neurology, University of California San Francisco, San Francisco, California, USA.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - R Todd Constable
- Yale University, School of Medicine, New Haven, Connecticut, USA
| | - Esha Datta
- Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Gina Kirkish
- Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Govind Nair
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA
| | - Jiwon Oh
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Neurology, University of Toronto, Toronto, Canada
| | - Daniel Pelletier
- Department of Neurology, University of Southern California, Los Angeles, California, USA
| | - Dzung L Pham
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, Maryland
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA
| | - William Rooney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Snehashis Roy
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, Maryland
| | - Daniel Schwartz
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nancy L Sicotte
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - William A Stern
- Department of Neurology, University of California San Francisco, San Francisco, California, USA
| | - Ian Tagge
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Shahamat Tauhid
- Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Subhash Tummala
- Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Roland G Henry
- Department of Neurology, University of California San Francisco, San Francisco, California, USA.,Department of Radiology, University of California San Francisco, San Francisco, California, USA
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- A complete list of the NAIMS participants is provided in the Acknowledgments section
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11
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Price RG, Knight RA, Hwang KP, Bayram E, Nejad-Davarani SP, Glide-Hurst CK. Optimization of a novel large field of view distortion phantom for MR-only treatment planning. J Appl Clin Med Phys 2017; 18:51-61. [PMID: 28497476 PMCID: PMC5539340 DOI: 10.1002/acm2.12090] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 03/13/2017] [Accepted: 03/16/2017] [Indexed: 11/09/2022] Open
Abstract
PURPOSE MR-only treatment planning requires images of high geometric fidelity, particularly for large fields of view (FOV). However, the availability of large FOV distortion phantoms with analysis software is currently limited. This work sought to optimize a modular distortion phantom to accommodate multiple bore configurations and implement distortion characterization in a widely implementable solution. METHOD AND MATERIALS To determine candidate materials, 1.0 T MR and CT images were acquired of twelve urethane foam samples of various densities and strengths. Samples were precision-machined to accommodate 6 mm diameter paintballs used as landmarks. Final material candidates were selected by balancing strength, machinability, weight, and cost. Bore sizes and minimum aperture width resulting from couch position were tabulated from the literature (14 systems, 5 vendors). Bore geometry and couch position were simulated using MATLAB to generate machine-specific models to optimize the phantom build. Previously developed software for distortion characterization was modified for several magnet geometries (1.0 T, 1.5 T, 3.0 T), compared against previously published 1.0 T results, and integrated into the 3D Slicer application platform. RESULTS All foam samples provided sufficient MR image contrast with paintball landmarks. Urethane foam (compressive strength ∼1000 psi, density ~20 lb/ft3 ) was selected for its accurate machinability and weight characteristics. For smaller bores, a phantom version with the following parameters was used: 15 foam plates, 55 × 55 × 37.5 cm3 (L×W×H), 5,082 landmarks, and weight ~30 kg. To accommodate > 70 cm wide bores, an extended build used 20 plates spanning 55 × 55 × 50 cm3 with 7,497 landmarks and weight ~44 kg. Distortion characterization software was implemented as an external module into 3D Slicer's plugin framework and results agreed with the literature. CONCLUSION The design and implementation of a modular, extendable distortion phantom was optimized for several bore configurations. The phantom and analysis software will be available for multi-institutional collaborations and cross-validation trials to support MR-only planning.
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Affiliation(s)
- Ryan G Price
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA.,Department of Radiation Oncology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Robert A Knight
- Department of Neurology, NMR Laboratory, Henry Ford Health System, Detroit, MI, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ersin Bayram
- MR Applications & Workflow, GE Healthcare, Houston, TX, USA
| | | | - Carri K Glide-Hurst
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA.,Department of Radiation Oncology, Wayne State University School of Medicine, Detroit, MI, USA
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12
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Teh I, Maguire ML, Schneider JE. Efficient gradient calibration based on diffusion MRI. Magn Reson Med 2016; 77:170-179. [PMID: 26749277 PMCID: PMC5217059 DOI: 10.1002/mrm.26105] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 11/09/2015] [Accepted: 12/04/2015] [Indexed: 11/22/2022]
Abstract
Purpose To propose a method for calibrating gradient systems and correcting gradient nonlinearities based on diffusion MRI measurements. Methods The gradient scaling in x, y, and z were first offset by up to 5% from precalibrated values to simulate a poorly calibrated system. Diffusion MRI data were acquired in a phantom filled with cyclooctane, and corrections for gradient scaling errors and nonlinearity were determined. The calibration was assessed with diffusion tensor imaging and independently validated with high resolution anatomical MRI of a second structured phantom. Results The errors in apparent diffusion coefficients along orthogonal axes ranged from −9.2% ± 0.4% to + 8.8% ± 0.7% before calibration and −0.5% ± 0.4% to + 0.8% ± 0.3% after calibration. Concurrently, fractional anisotropy decreased from 0.14 ± 0.03 to 0.03 ± 0.01. Errors in geometric measurements in x, y and z ranged from −5.5% to + 4.5% precalibration and were likewise reduced to −0.97% to + 0.23% postcalibration. Image distortions from gradient nonlinearity were markedly reduced. Conclusion Periodic gradient calibration is an integral part of quality assurance in MRI. The proposed approach is both accurate and efficient, can be setup with readily available materials, and improves accuracy in both anatomical and diffusion MRI to within ±1%. Magn Reson Med 77:170–179, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Irvin Teh
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Mahon L Maguire
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.,British Heart Foundation (BHF) Centre of Regenerative Medicine, Oxford, United Kingdom
| | - Jürgen E Schneider
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.,British Heart Foundation (BHF) Centre of Regenerative Medicine, Oxford, United Kingdom
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13
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Tao S, Trzasko JD, Shu Y, Weavers PT, Huston J, Gray EM, Bernstein MA. Partial fourier and parallel MR image reconstruction with integrated gradient nonlinearity correction. Magn Reson Med 2015; 75:2534-44. [PMID: 26183425 DOI: 10.1002/mrm.25842] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Revised: 05/29/2015] [Accepted: 06/22/2015] [Indexed: 11/12/2022]
Abstract
PURPOSE To describe how integrated gradient nonlinearity (GNL) correction can be used within noniterative partial Fourier (homodyne) and parallel (SENSE and GRAPPA) MR image reconstruction strategies, and demonstrate that performing GNL correction during, rather than after, these routines mitigates the image blurring and resolution loss caused by postreconstruction image domain based GNL correction. METHODS Starting from partial Fourier and parallel magnetic resonance imaging signal models that explicitly account for GNL, noniterative image reconstruction strategies for each accelerated acquisition technique are derived under the same core mathematical assumptions as their standard counterparts. A series of phantom and in vivo experiments on retrospectively undersampled data were performed to investigate the spatial resolution benefit of integrated GNL correction over conventional postreconstruction correction. RESULTS Phantom and in vivo results demonstrate that the integrated GNL correction reduces the image blurring introduced by the conventional GNL correction, while still correcting GNL-induced coarse-scale geometrical distortion. Images generated from undersampled data using the proposed integrated GNL strategies offer superior depiction of fine image detail, for example, phantom resolution inserts and anatomical tissue boundaries. CONCLUSION Noniterative partial Fourier and parallel imaging reconstruction methods with integrated GNL correction reduce the resolution loss that occurs during conventional postreconstruction GNL correction while preserving the computational efficiency of standard reconstruction techniques. Magn Reson Med 75:2534-2544, 2016. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Shengzhen Tao
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.,Mayo Graduate School, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Yunhong Shu
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul T Weavers
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - John Huston
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Erin M Gray
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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14
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Malyarenko DI, Newitt D, J Wilmes L, Tudorica A, Helmer KG, Arlinghaus LR, Jacobs MA, Jajamovich G, Taouli B, Yankeelov TE, Huang W, Chenevert TL. Demonstration of nonlinearity bias in the measurement of the apparent diffusion coefficient in multicenter trials. Magn Reson Med 2015; 75:1312-23. [PMID: 25940607 DOI: 10.1002/mrm.25754] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Revised: 03/31/2015] [Accepted: 04/08/2015] [Indexed: 12/13/2022]
Abstract
PURPOSE Characterize system-specific bias across common magnetic resonance imaging (MRI) platforms for quantitative diffusion measurements in multicenter trials. METHODS Diffusion weighted imaging (DWI) was performed on an ice-water phantom along the superior-inferior (SI) and right-left (RL) orientations spanning ± 150 mm. The same scanning protocol was implemented on 14 MRI systems at seven imaging centers. The bias was estimated as a deviation of measured from known apparent diffusion coefficient (ADC) along individual DWI directions. The relative contributions of gradient nonlinearity, shim errors, imaging gradients, and eddy currents were assessed independently. The observed bias errors were compared with numerical models. RESULTS The measured systematic ADC errors scaled quadratically with offset from isocenter, and ranged between -55% (SI) and 25% (RL). Nonlinearity bias was dependent on system design and diffusion gradient direction. Consistent with numerical models, minor ADC errors (± 5%) due to shim, imaging and eddy currents were mitigated by double echo DWI and image coregistration of individual gradient directions. CONCLUSION The analysis confirms gradient nonlinearity as a major source of spatial DW bias and variability in off-center ADC measurements across MRI platforms, with minor contributions from shim, imaging gradients and eddy currents. The developed protocol enables empiric description of systematic bias in multicenter quantitative DWI studies.
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Affiliation(s)
| | - David Newitt
- Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Lisa J Wilmes
- Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Alina Tudorica
- Diagnostic Radiology, Oregon Health and Science University, Portland, Oregon, USA
| | - Karl G Helmer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Lori R Arlinghaus
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Michael A Jacobs
- John Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Guido Jajamovich
- Translational and Molecular Imaging Institute Icahn School of Medicine at Mount Sinai, New York, USA
| | - Bachir Taouli
- Translational and Molecular Imaging Institute Icahn School of Medicine at Mount Sinai, New York, USA
| | - Thomas E Yankeelov
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Departments of Radiology, Physics and Cancer Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health and Science University, Portland, Oregon, USA
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15
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Newitt DC, Tan ET, Wilmes LJ, Chenevert TL, Kornak J, Marinelli L, Hylton N. Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the american college of radiology imaging network 6698 breast cancer trial. J Magn Reson Imaging 2015; 42:908-19. [PMID: 25758543 DOI: 10.1002/jmri.24883] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 02/20/2015] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To evaluate a gradient nonlinearity correction (GNC) program for quantitative apparent diffusion coefficient (ADC) measurements on phantom and human subject diffusion-weighted (DW) magnetic resonance imaging (MRI) scans in a multicenter breast cancer treatment response study MATERIALS AND METHODS A GNC program using fifth-order spherical harmonics for gradient modeling was applied retrospectively to qualification phantom and human subject scans. Ice-water phantoms of known diffusion coefficient were scanned at five different study centers with different scanners and receiver coils. Human in vivo data consisted of baseline and early-treatment exams on 54 patients from four sites. ADC maps were generated with and without GNC. Regions of interest were defined to quantify absolute errors and changes with GNC over breast imaging positions. RESULTS Phantom ADC errors varied with region of interest (ROI) position and scanner configuration; the mean error by configuration ranged from 1.4% to 19.9%. GNC significantly reduced the overall mean error for all sites from 9.9% to 0.6% (P = 0.016). Spatial dependence of GNC was highest in the right-left (RL) and anterior-posterior (AP) directions. Human subject mean tumor ADC was reduced 0.2 to 12% by GNC at different sites. By regression, every 1-cm change in tumor ROI position between baseline and follow-up visits resulted in an estimated change of 2.4% in the ADC early-treatment response measurement. CONCLUSION GNC is effective for removing large, system-dependent errors in quantitative breast DWI. GNC may be important in ensuring reproducibility in multicenter studies and in reducing errors in longitudinal treatment response measures arising from spatial variations in tumor position between visits.
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Affiliation(s)
- David C Newitt
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Ek T Tan
- MRI Lab, GE Global Research, One Research Circle, Niskayuna, New York, USA
| | - Lisa J Wilmes
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Thomas L Chenevert
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
| | - Luca Marinelli
- MRI Lab, GE Global Research, One Research Circle, Niskayuna, New York, USA
| | - Nola Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
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16
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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: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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17
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Yarach U, Luengviriya C, Danishad A, Stucht D, Godenschweger F, Schulze P, Speck O. Correction of gradient nonlinearity artifacts in prospective motion correction for 7T MRI. Magn Reson Med 2014; 73:1562-9. [PMID: 24798889 DOI: 10.1002/mrm.25283] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Revised: 04/07/2014] [Accepted: 04/15/2014] [Indexed: 11/11/2022]
Abstract
PURPOSE To demonstrate the effect of gradient nonlinearity and develop a method for correction of gradient nonlinearity artifacts in prospective motion correction (Mo-Co). METHODS Nonlinear gradients can induce geometric distortions in magnetic resonance imaging, leading to pixel shifts with errors of up to several millimeters, thereby interfering with precise localization of anatomical structures. Prospective Mo-Co has been extended by conventional gradient warp correction applied to individual phase encoding steps/groups during the reconstruction. The gradient-related displacements are approximated using spherical harmonic functions. In addition, the combination of this method with a retrospective correction of the changes in the coil sensitivity profiles relative to the object (augmented sensitivity encoding (SENSE) reconstruction) was evaluated in simulation and experimental data. RESULTS Prospective Mo-Co under gradient fields and coils sensitivity inconsistencies results in residual blurring, spatial distortion, and coil sensitivity mismatch artifacts. These errors can be considerably mitigated by the proposed method. High image quality with very little remaining artifacts was achieved after a few iterations. The relative image errors decreased from 25.7% to below 17.3% after 10 iterations. CONCLUSION The combined correction of gradient nonlinearity and sensitivity map variation leads to a pronounced reduction of residual motion artifacts in prospectively motion-corrected data.
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Affiliation(s)
- Uten Yarach
- Department of Biomedical Magnetic Resonance, Otto-von-Guericke University Magdeburg, Magdeburg, Germany; Department of Radiological Technology, Chiangmai University, Chiang Mai, Thailand
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18
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Tan ET, Marinelli L, Slavens ZW, King KF, Hardy CJ. Improved correction for gradient nonlinearity effects in diffusion-weighted imaging. J Magn Reson Imaging 2012; 38:448-53. [PMID: 23172675 DOI: 10.1002/jmri.23942] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2012] [Accepted: 10/09/2012] [Indexed: 01/17/2023] Open
Abstract
PURPOSE To provide an improved correction for gradient nonlinearity (GN) effects in diffusion-weighted imaging (DWI). These effects produce spatially varying apparent diffusion coefficient (ADC), a result that will be significant in large field-of-view imaging, and may be confounded by distortion and concomitant fields related to the DWI acquisition. MATERIALS AND METHODS The effect of more accurate gradient field maps on GN correction (GNC) of ADC was evaluated. A simulation compared GN effects in commonly imaged anatomy. A temperature-controlled phantom was imaged at positions 0 cm and 11 cm from isocenter and in two whole-body MRI systems at 1.5T with different patient bore diameters (55 cm and 60 cm). Varying correction methods were applied to determine the errors from spatial variance and interscanner reproducibility. RESULTS As compared to conventional fifth-order spherical harmonics, a seventh-order GNC improved ADC accuracy by 1%. The combination of GNC with a dual-spin-echo pulse sequence and a retrospective concomitant field correction reduced ADC error due to spatial variance from 9.5% to 1.8% (55 cm bore) and from 4.2% to 1.8% (60 cm bore). The error in ADC attributed to interscanner reproducibility was reduced from 5.8% to 0.15% (at isocenter) and from 10% to 0.63% (11 cm from isocenter). CONCLUSION GNC in DWI improved spatial accuracy and interscanner reproducibility of ADC.
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Affiliation(s)
- Ek T Tan
- GE Global Research, Niskayuna, New York 12309, USA.
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19
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Aksel B, Marinelli L, Collick BD, Von Morze C, Bottomley PA, Hardy CJ. Local planar gradients with order-of-magnitude strength and speed advantage. Magn Reson Med 2007; 58:134-143. [PMID: 17659620 PMCID: PMC2588671 DOI: 10.1002/mrm.21263] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2007] [Accepted: 03/15/2007] [Indexed: 11/11/2022]
Abstract
A three-axis uniplanar gradient coil was designed and built to provide order-of-magnitude increases in gradient strength of up to 500 mT/m on the x- and y-axes, and 1000 mT/m for the z-axis at 640 A input over a limited FOV ( approximately 16 cm) for superficial regions, compared to conventional gradient coils, with significant gradient strengths extending deeper into the body. The gradient set is practically accommodated in the bore of a conventional whole-body, cylindrical-geometry MRI scanner, and operated using standard gradient supplies. The design was optimized for gradient linearity over a restricted volume while accounting for the practical problems of torque and heating. Tests at 320 A demonstrated up to 420-mT/m gradients near the surface at efficiencies of up to 1.4 mT/m/A. A new true 2D gradient-nonlinearity correction algorithm was developed to rectify gradient nonlinearities and considerably expand the imageable volumes. The gradient system and correction algorithm were implemented in a standard 1.5 T scanner and demonstrated by high-resolution imaging of phantoms and humans.
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Affiliation(s)
| | | | | | - Cornelius Von Morze
- UCSF/UCB Joint Graduate Group in Bioengineering, University of California-San Francisco, San Francisco, California, USA
| | - Paul A Bottomley
- Johns Hopkins University Department of Radiology, Baltimore, Maryland, USA
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