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Verschuur AS, Boswinkel V, Tax CM, van Osch JA, Nijholt IM, Slump CH, de Vries LS, van Wezel‐Meijler G, Leemans A, Boomsma MF. Improved neonatal brain MRI segmentation by interpolation of motion corrupted slices. J Neuroimaging 2022; 32:480-492. [PMID: 35253956 PMCID: PMC9314603 DOI: 10.1111/jon.12985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND PURPOSE To apply and evaluate an intensity-based interpolation technique, enabling segmentation of motion-affected neonatal brain MRI. METHODS Moderate-late preterm infants were enrolled in a prospective cohort study (Brain Imaging in Moderate-late Preterm infants "BIMP-study") between August 2017 and November 2019. T2-weighted MRI was performed around term equivalent age on a 3T MRI. Scans without motion (n = 27 [24%], control group) and with moderate-severe motion (n = 33 [29%]) were included. Motion-affected slices were re-estimated using intensity-based shape-preserving cubic spline interpolation, and automatically segmented in eight structures. Quality of interpolation and segmentation was visually assessed for errors after interpolation. Reliability was tested using interpolated control group scans (18/54 axial slices). Structural similarity index (SSIM) was used to compare T2-weighted scans, and Sørensen-Dice was used to compare segmentation before and after interpolation. Finally, volumes of brain structures of the control group were used assessing sensitivity (absolute mean fraction difference) and bias (confidence interval of mean difference). RESULTS Visually, segmentation of 25 scans (22%) with motion artifacts improved with interpolation, while segmentation of eight scans (7%) with adjacent motion-affected slices did not improve. Average SSIM was .895 and Sørensen-Dice coefficients ranged between .87 and .97. Absolute mean fraction difference was ≤0.17 for less than or equal to five interpolated slices. Confidence intervals revealed a small bias for cortical gray matter (0.14-3.07 cm3 ), cerebrospinal fluid (0.39-1.65 cm3 ), deep gray matter (0.74-1.01 cm3 ), and brainstem volumes (0.07-0.28 cm3 ) and a negative bias in white matter volumes (-4.47 to -1.65 cm3 ). CONCLUSION According to qualitative and quantitative assessment, intensity-based interpolation reduced the percentage of discarded scans from 29% to 7%.
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Affiliation(s)
- Anouk S. Verschuur
- Department of RadiologyIsalaZwolleThe Netherlands
- Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Vivian Boswinkel
- Women and Children's HospitalIsalaZwolleThe Netherlands
- UMC Utrecht Brain CenterUtrecht UniversityUtrechtThe Netherlands
| | - Chantal M.W. Tax
- Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
- Cardiff University Brain Research Imaging CentreCardiffUK
| | | | | | - Cornelis H. Slump
- Department of Robotics and MechatronicsUniversity of TwenteEnschedeThe Netherlands
| | - Linda S. de Vries
- Department of NeonatologyWilhelmina Children's HospitalUtrechtThe Netherlands
| | | | - Alexander Leemans
- Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
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152
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Plumley A, Watkins L, Treder M, Liebig P, Murphy K, Kopanoglu E. Rigid motion-resolved B1+ prediction using deep learning for real-time parallel-transmission pulse design. Magn Reson Med 2022; 87:2254-2270. [PMID: 34958134 PMCID: PMC7613077 DOI: 10.1002/mrm.29132] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 11/30/2021] [Accepted: 12/03/2021] [Indexed: 11/19/2022]
Abstract
PURPOSE Tailored parallel-transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real-time pulse redesign. A deep learning framework is proposed to estimate pTx B 1 + distributions following within-slice motion, which can then be used for tailored pTx pulse redesign. METHODS Using simulated data, conditional generative adversarial networks were trained to predict B 1 + distributions in the head following a displacement. Predictions were made for two virtual body models that were not included in training. Predicted maps were compared with ground-truth (simulated, following motion) B1 maps. Tailored pTx pulses were designed using B1 maps at the original position (simulated, no motion) and evaluated using simulated B1 maps at displaced position (ground-truth maps) to quantify motion-related excitation error. A second pulse was designed using predicted maps (also evaluated on ground-truth maps) to investigate improvement offered by the proposed method. RESULTS Predicted B 1 + maps corresponded well with ground-truth maps. Error in predicted maps was lower than motion-related error in 99% and 67% of magnitude and phase evaluations, respectively. Worst-case flip-angle normalized RMS error due to motion (76% of target flip angle) was reduced by 59% when pulses were redesigned using predicted maps. CONCLUSION We propose a framework for predicting B 1 + maps online with deep neural networks. Predicted maps can then be used for real-time tailored pulse redesign, helping to overcome head motion-related error in pTx.
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Affiliation(s)
- Alix Plumley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Luke Watkins
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- School of Physics & Astronomy, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom
| | - Matthias Treder
- School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | | | - Kevin Murphy
- School of Physics & Astronomy, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom
| | - Emre Kopanoglu
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
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153
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Koprivica D, Martinho RP, Novakovic M, Jaroszewicz MJ, Frydman L. A denoising method for multidimensional magnetic resonance spectroscopy and imaging based on compressed sensing. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 338:107187. [PMID: 35292421 DOI: 10.1016/j.jmr.2022.107187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 02/27/2022] [Accepted: 03/01/2022] [Indexed: 06/14/2023]
Abstract
Both in spectroscopy and imaging, t1-noise arising from instabilities such as temperature alterations, field-related frequency drifts, electronic and sample-spinning instabilities, or motions in in vivo experiments, affects many 2D Magnetic Resonance experiments. This work introduces a post-processing method that aims to attenuate t1-noise, by suitably averaging multiple signals/representations that have been reconstructed from the sampled data. The ensuing Compressed Sensing Multiplicative (CoSeM) denoising starts from a fully sampled 2D MR data set, discards random indirect-domain points, and makes up for these missing, masked data, by a compressed sensing reconstruction of the now incompletely sampled 2D data set. This procedure is repeated for multiple renditions of the masked data -some of which will have been more strongly affected by t1-noise than others. This leads to a large set of 2D NMR spectra/images compatible with the collected data; CoSeM chooses out of these those renditions that reduce the noise according to a suitable criterion, and then sums up their spectra/images leading to a reduction in t1-noise. The performance of the method was assessed in synthetic data, as well as in numerous different experiments: 2D solid and solution state NMR, 2D localized MRS of live brains, and 2D abdominal MRI. Throughout all these data, CoSeM processing evidenced 2-3 fold increases in SNR, without introducing biases, false peaks, or spectral/image blurring. CoSeM also retains a quantitative linearity in the information -allowing, for instance, reliable T1 inversion-recovery MRI mapping experiments.
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Affiliation(s)
- David Koprivica
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Ricardo P Martinho
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Mihajlo Novakovic
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Michael J Jaroszewicz
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Lucio Frydman
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel.
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154
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Laustsen M, Andersen M, Xue R, Madsen KH, Hanson LG. Tracking of rigid head motion during MRI using an EEG system. Magn Reson Med 2022; 88:986-1001. [PMID: 35468237 PMCID: PMC9325421 DOI: 10.1002/mrm.29251] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 02/26/2022] [Accepted: 03/08/2022] [Indexed: 11/21/2022]
Abstract
Purpose To demonstrate a novel method for tracking of head movements during MRI using electroencephalography (EEG) hardware for recording signals induced by native imaging gradients. Theory and Methods Gradient switching during simultaneous EEG–fMRI induces distortions in EEG signals, which depend on subject head position and orientation. When EEG electrodes are interconnected with high‐impedance carbon wire loops, the induced voltages are linear combinations of the temporal gradient waveform derivatives. We introduce head tracking based on these signals (CapTrack) involving 3 steps: (1) phantom scanning is used to characterize the target sequence and a fast calibration sequence; (2) a linear relation between changes of induced signals and head pose is established using the calibration sequence; and (3) induced signals recorded during target sequence scanning are used for tracking and retrospective correction of head movement without prolonging the scan time of the target sequence. Performance of CapTrack is compared directly to interleaved navigators. Results Head‐pose tracking at 27.5 Hz during echo planar imaging (EPI) was demonstrated with close resemblance to rigid body alignment (mean absolute difference: [0.14 0.38 0.15]‐mm translation, [0.30 0.27 0.22]‐degree rotation). Retrospective correction of 3D gradient‐echo imaging shows an increase of average edge strength of 12%/−0.39% for instructed/uninstructed motion with CapTrack pose estimates, with a tracking interval of 1561 ms and high similarity to interleaved navigator estimates (mean absolute difference: [0.13 0.33 0.12] mm, [0.28 0.15 0.22] degrees). Conclusion Motion can be estimated from recordings of gradient switching with little or no sequence modification, optionally in real time at low computational burden and synchronized to image acquisition, using EEG equipment already found at many research institutions.
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Affiliation(s)
- Malte Laustsen
- Section for Magnetic Resonance, DTU Health Tech, Technical University of Denmark, Kgs. Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark.,Sino-Danish Centre for Education and Research, Aarhus, Denmark.,University of Chinese Academic of Sciences, Beijing, China
| | - Mads Andersen
- Philips Healthcare, Copenhagen, Denmark.,Lund University Bioimaging Center, Lund University, Lund, Sweden
| | - Rong Xue
- University of Chinese Academic of Sciences, Beijing, China.,State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,Beijing Institute for Brain Disorders, Beijing, China
| | - Kristoffer H Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark.,DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Lars G Hanson
- Section for Magnetic Resonance, DTU Health Tech, Technical University of Denmark, Kgs. Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
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155
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Mitchell UH, Johnson AW, Adams L, Sonnefeld T, Owen PJ. Ultrasound imaging measures of vertebral bony landmark distances are weakly to moderately correlated with intervertebral disc height as assessed by MRI. BMJ Open Sport Exerc Med 2022; 8:e001292. [PMID: 35414957 PMCID: PMC8961152 DOI: 10.1136/bmjsem-2021-001292] [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] [Accepted: 03/18/2022] [Indexed: 11/17/2022] Open
Abstract
Objectives To assess the validity and reliability of ultrasound-derived interbony landmark distances as a proxy for MRI-derived intervertebral disc (IVD) height. Methods This is a cross-sectional criterion validity study. Twelve college-aged participants without current low back pain completed both MRI and ultrasound imaging of the lumbar spine in a prone position. Single-segment and multisegment distances between the spinous and mammillary processes at the lumbar segments (L2/L3, L3/L4, L4/L5) were measured twice using ultrasound and analysed digitally. Sagittal slices of the lumbar spine were taken via T1-weighted MRI and IVD height, and the overall distance between IVDs L2/L3 and L4/L5 was imaged once and measured twice. Results There was moderate correlation between multilevel-based measurements (overall distance between L2 and L5, r=0.677, p=0.016) and the average across three levels (r=0.596, p=0.041) when using the spinous processes as bony landmarks. Single-segment measures were not significantly correlated (all: p>0.092). Accuracy and precision were better for the overall MRI-derived distance between the three IVDs from L2 and L5 MRI and the distance measured between the spinous processes L2–L5. There was excellent reliability within multiple measurements at each location, with intraclass correlation coefficient, ICC(3,1), ranging from 0.93 to 0.99 (95% CI 0.82 to 0.99) for ultrasound and from 0.98 to 0.99 (95% CI 0.92 to 0.99) for MRI. Conclusion Findings do not support the use of ultrasound imaging for estimating single-segment IVD height, yet it may be used to measure the change in distance over time with a certain degree of precision based on its excellent reliability.
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Affiliation(s)
| | - A Wayne Johnson
- Exercises Sciences, Brigham Young University, Provo, Utah, USA
| | - Lauren Adams
- Exercises Sciences, Brigham Young University, Provo, Utah, USA
| | - Tayva Sonnefeld
- Exercises Sciences, Brigham Young University, Provo, Utah, USA
| | - Patrick J Owen
- Institute for Physical Activity and Nutrition, Deakin University, Burwood, Victoria, Australia
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156
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Vaculčiaková L, Podranski K, Edwards LJ, Ocal D, Veale T, Fox NC, Haak R, Ehses P, Callaghan MF, Pine KJ, Weiskopf N. Combining navigator and optical prospective motion correction for high-quality 500 μm resolution quantitative multi-parameter mapping at 7T. Magn Reson Med 2022; 88:787-801. [PMID: 35405027 DOI: 10.1002/mrm.29253] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 03/10/2022] [Accepted: 03/10/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE High-resolution quantitative multi-parameter mapping shows promise for non-invasively characterizing human brain microstructure but is limited by physiological artifacts. We implemented corrections for rigid head movement and respiration-related B0-fluctuations and evaluated them in healthy volunteers and dementia patients. METHODS Camera-based optical prospective motion correction (PMC) and FID navigator correction were implemented in a gradient and RF-spoiled multi-echo 3D gradient echo sequence for mapping proton density (PD), longitudinal relaxation rate (R1) and effective transverse relaxation rate (R2*). We studied their effectiveness separately and in concert in young volunteers and then evaluated the navigator correction (NAVcor) with PMC in a group of elderly volunteers and dementia patients. We used spatial homogeneity within white matter (WM) and gray matter (GM) and scan-rescan measures as quality metrics. RESULTS NAVcor and PMC reduced artifacts and improved the homogeneity and reproducibility of parameter maps. In elderly participants, NAVcor improved scan-rescan reproducibility of parameter maps (coefficient of variation decreased by 14.7% and 11.9% within WM and GM respectively). Spurious inhomogeneities within WM were reduced more in the elderly than in the young cohort (by 9% vs. 2%). PMC increased regional GM/WM contrast and was especially important in the elderly cohort, which moved twice as much as the young cohort. We did not find a significant interaction between the two corrections. CONCLUSION Navigator correction and PMC significantly improved the quality of PD, R1, and R2* maps, particularly in less compliant elderly volunteers and dementia patients.
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Affiliation(s)
- Lenka Vaculčiaková
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Kornelius Podranski
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Luke J Edwards
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Dilek Ocal
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Thomas Veale
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK.,UK Dementia Research Institute at UCL, UCL, London, UK
| | - Nick C Fox
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK.,UK Dementia Research Institute at UCL, UCL, London, UK
| | - Rainer Haak
- Department of Cariology, Endodontology and Periodontology, University of Leipzig, Leipzig, Germany
| | - Philipp Ehses
- Department of MR Physics, German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Martina F Callaghan
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Kerrin J Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK.,Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
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157
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Feng L. Golden-Angle Radial MRI: Basics, Advances, and Applications. J Magn Reson Imaging 2022; 56:45-62. [PMID: 35396897 PMCID: PMC9189059 DOI: 10.1002/jmri.28187] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 12/21/2022] Open
Abstract
In recent years, golden‐angle radial sampling has received substantial attention and interest in the magnetic resonance imaging (MRI) community, and it has become a popular sampling trajectory for both research and clinical use. However, although the number of relevant techniques and publications has grown rapidly, there is still a lack of a review paper that provides a comprehensive overview and summary of the basics of golden‐angle rotation, the advantages and challenges/limitations of golden‐angle radial sampling, and recommendations in using different types of golden‐angle radial trajectories for MRI applications. Such a review paper is expected to be helpful both for clinicians who are interested in learning the potential benefits of golden‐angle radial sampling and for MRI physicists who are interested in exploring this research direction. The main purpose of this review paper is thus to present an overview and summary about golden‐angle radial MRI sampling. The review consists of three sections. The first section aims to answer basic questions such as: what is a golden angle; how is the golden angle calculated; why is golden‐angle radial sampling useful, and what are its limitations. The second section aims to review more advanced trajectories of golden‐angle radial sampling, including tiny golden‐angle rotation, stack‐of‐stars golden‐angle radial sampling, and three‐dimensional (3D) kooshball golden‐angle radial sampling. Their respective advantages and limitations and potential solutions to address these limitations are also discussed. Finally, the third section reviews MRI applications that can benefit from golden‐angle radial sampling and provides recommendations to readers who are interested in implementing golden‐angle radial trajectories in their MRI studies.
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Affiliation(s)
- Li Feng
- BioMedical Engineering and Imaging Institute (BMEII) and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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158
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Diffusion-Weighted MRI in the Genitourinary System. J Clin Med 2022; 11:jcm11071921. [PMID: 35407528 PMCID: PMC9000195 DOI: 10.3390/jcm11071921] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 03/25/2022] [Accepted: 03/28/2022] [Indexed: 12/12/2022] Open
Abstract
Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.
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159
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Generative Adversarial Networks in Brain Imaging: A Narrative Review. J Imaging 2022; 8:jimaging8040083. [PMID: 35448210 PMCID: PMC9028488 DOI: 10.3390/jimaging8040083] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 03/08/2022] [Accepted: 03/15/2022] [Indexed: 02/04/2023] Open
Abstract
Artificial intelligence (AI) is expected to have a major effect on radiology as it demonstrated remarkable progress in many clinical tasks, mostly regarding the detection, segmentation, classification, monitoring, and prediction of diseases. Generative Adversarial Networks have been proposed as one of the most exciting applications of deep learning in radiology. GANs are a new approach to deep learning that leverages adversarial learning to tackle a wide array of computer vision challenges. Brain radiology was one of the first fields where GANs found their application. In neuroradiology, indeed, GANs open unexplored scenarios, allowing new processes such as image-to-image and cross-modality synthesis, image reconstruction, image segmentation, image synthesis, data augmentation, disease progression models, and brain decoding. In this narrative review, we will provide an introduction to GANs in brain imaging, discussing the clinical potential of GANs, future clinical applications, as well as pitfalls that radiologists should be aware of.
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160
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Ismail TF, Strugnell W, Coletti C, Božić-Iven M, Weingärtner S, Hammernik K, Correia T, Küstner T. Cardiac MR: From Theory to Practice. Front Cardiovasc Med 2022; 9:826283. [PMID: 35310962 PMCID: PMC8927633 DOI: 10.3389/fcvm.2022.826283] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/17/2022] [Indexed: 01/10/2023] Open
Abstract
Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.
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Affiliation(s)
- Tevfik F. Ismail
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Cardiology Department, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Wendy Strugnell
- Queensland X-Ray, Mater Hospital Brisbane, Brisbane, QLD, Australia
| | - Chiara Coletti
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
| | - Maša Božić-Iven
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
- Computer Assisted Clinical Medicine, Heidelberg University, Mannheim, Germany
| | | | - Kerstin Hammernik
- Lab for AI in Medicine, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, United Kingdom
| | - Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Centre of Marine Sciences, Faro, Portugal
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany
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161
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Evaluation of axial gradient Echo spiral MRI of the spine at 1.5 T. Magn Reson Imaging 2022; 89:24-32. [DOI: 10.1016/j.mri.2022.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/28/2022] [Accepted: 02/28/2022] [Indexed: 11/18/2022]
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162
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McCowan CV, Salmon D, Hu J, Pudakalakatti S, Whiting N, Davis JS, Carson DD, Zacharias NM, Bhattacharya PK, Farach-Carson MC. Post-Acquisition Hyperpolarized 29Silicon Magnetic Resonance Image Processing for Visualization of Colorectal Lesions Using a User-Friendly Graphical Interface. Diagnostics (Basel) 2022; 12:diagnostics12030610. [PMID: 35328163 PMCID: PMC8947341 DOI: 10.3390/diagnostics12030610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/22/2022] [Accepted: 02/24/2022] [Indexed: 02/04/2023] Open
Abstract
Medical imaging devices often use automated processing that creates and displays a self-normalized image. When improperly executed, normalization can misrepresent information or result in an inaccurate analysis. In the case of diagnostic imaging, a false positive in the absence of disease, or a negative finding when disease is present, can produce a detrimental experience for the patient and diminish their health prospects and prognosis. In many clinical settings, a medical technical specialist is trained to operate an imaging device without sufficient background information or understanding of the fundamental theory and processes involved in image creation and signal processing. Here, we describe a user-friendly image processing algorithm that mitigates user bias and allows for true signal to be distinguished from background. For proof-of-principle, we used antibody-targeted molecular imaging of colorectal cancer (CRC) in a mouse model, expressing human MUC1 at tumor sites. Lesion detection was performed using targeted magnetic resonance imaging (MRI) of hyperpolarized silicon particles. Resulting images containing high background and artifacts were then subjected to individualized image post-processing and comparative analysis. Post-acquisition image processing allowed for co-registration of the targeted silicon signal with the anatomical proton magnetic resonance (MR) image. This new methodology allows users to calibrate a set of images, acquired with MRI, and reliably locate CRC tumors in the lower gastrointestinal tract of living mice. The method is expected to be generally useful for distinguishing true signal from background for other cancer types, improving the reliability of diagnostic MRI.
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Affiliation(s)
- Caitlin V. McCowan
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; (C.V.M.); (D.S.)
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center, Houston, TX 77054, USA
| | - Duncan Salmon
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA; (C.V.M.); (D.S.)
| | - Jingzhe Hu
- Department of Bioengineering, Rice University, Houston, TX 77005, USA;
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (S.P.); (N.W.); (P.K.B.)
| | - Shivanand Pudakalakatti
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (S.P.); (N.W.); (P.K.B.)
| | - Nicholas Whiting
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (S.P.); (N.W.); (P.K.B.)
| | - Jennifer S. Davis
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Daniel D. Carson
- Department of BioSciences, Rice University, Houston, TX 77005, USA;
| | - Niki M. Zacharias
- Department of Urology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Pratip K. Bhattacharya
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (S.P.); (N.W.); (P.K.B.)
| | - Mary C. Farach-Carson
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center, Houston, TX 77054, USA
- Department of Bioengineering, Rice University, Houston, TX 77005, USA;
- Department of BioSciences, Rice University, Houston, TX 77005, USA;
- Correspondence: ; Tel.: +1-713-486-4438
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Pirkl CM, Cencini M, Kurzawski JW, Waldmannstetter D, Li H, Sekuboyina A, Endt S, Peretti L, Donatelli G, Pasquariello R, Costagli M, Buonincontri G, Tosetti M, Menzel MI, Menze BH. Learning residual motion correction for fast and robust 3D multiparametric MRI. Med Image Anal 2022; 77:102387. [DOI: 10.1016/j.media.2022.102387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 11/25/2021] [Accepted: 02/01/2022] [Indexed: 11/28/2022]
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Sciarra A, Mattern H, Yakupov R, Chatterjee S, Stucht D, Oeltze-Jafra S, Godenschweger F, Speck O. Quantitative evaluation of prospective motion correction in healthy subjects at 7T MRI. Magn Reson Med 2022; 87:646-657. [PMID: 34463376 PMCID: PMC8663924 DOI: 10.1002/mrm.28998] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 07/28/2021] [Accepted: 08/16/2021] [Indexed: 02/03/2023]
Abstract
PURPOSE Quantitative assessment of prospective motion correction (PMC) capability at 7T MRI for compliant healthy subjects to improve high-resolution images in the absence of intentional motion. METHODS Twenty-one healthy subjects were imaged at 7 T. They were asked not to move, to consider only unintentional motion. An in-bore optical tracking system was used to monitor head motion and consequently update the imaging volume. For all subjects, high-resolution T1 (3D-MPRAGE), T2 (2D turbo spin echo), proton density (2D turbo spin echo), and T2∗ (2D gradient echo) weighted images were acquired with and without PMC. The images were evaluated through subjective and objective analysis. RESULTS Subjective evaluation overall has shown a statistically significant improvement (5.5%) in terms of image quality with PMC ON. In a separate evaluation of every contrast, three of the four contrasts (T1 , T2 , and proton density) have shown a statistically significant improvement (9.62%, 9.85%, and 9.26%), whereas the fourth one ( T2∗ ) has shown improvement, although not statistically significant. In the evaluation with objective metrics, average edge strength has shown an overall improvement of 6% with PMC ON, which was statistically significant; and gradient entropy has shown an overall improvement of 2%, which did not reach statistical significance. CONCLUSION Based on subjective assessment, PMC improved image quality in high-resolution images of healthy compliant subjects in the absence of intentional motion for all contrasts except T2∗ , in which no significant differences were observed. Quantitative metrics showed an overall trend for an improvement with PMC, but not all differences were significant.
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Affiliation(s)
- A. Sciarra
- Medicine and Digitalization - MedDigit, Medical Faculty, Univ. Dept. of Neurology, Otto von Guericke University, Magdeburg, 39120, Germany, Dept. of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg,39120, Germany, Institute for Physics, Otto von Guericke University, Magdeburg, 39106, Germany
| | - H. Mattern
- Dept. of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg,39120, Germany
| | - R. Yakupov
- German Centre for Neurodegenerative Diseases (DZNE), Magdeburg, 39120, Germany
| | - S. Chatterjee
- Dept. of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg,39120, Germany, Data and Knowledge Engineering Group, Faculty of Computer Science, Otto von Guericke University, Magdeburg
| | - D. Stucht
- Dept. of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg,39120, Germany
| | - S. Oeltze-Jafra
- Medicine and Digitalization - MedDigit, Medical Faculty, Univ. Dept. of Neurology, Otto von Guericke University, Magdeburg, 39120, Germany, German Centre for Neurodegenerative Diseases (DZNE), Magdeburg, 39120, Germany, Center for Behavioral Brain Sciences, Magdeburg, 39120, Germany
| | - F. Godenschweger
- Dept. of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg,39120, Germany
| | - O. Speck
- Dept. of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg,39120, Germany, Institute for Physics, Otto von Guericke University, Magdeburg, 39106, Germany, German Centre for Neurodegenerative Diseases (DZNE), Magdeburg, 39120, Germany, Leibniz Institute for Neurobiology, Magdeburg, 39120, Germany, Center for Behavioral Brain Sciences, Magdeburg, 39120, Germany
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165
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Kemenczky P, Vakli P, Somogyi E, Homolya I, Hermann P, Gál V, Vidnyánszky Z. Effect of head motion-induced artefacts on the reliability of deep learning-based whole-brain segmentation. Sci Rep 2022; 12:1618. [PMID: 35102199 PMCID: PMC8803940 DOI: 10.1038/s41598-022-05583-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 12/30/2021] [Indexed: 11/21/2022] Open
Abstract
Due to their robustness and speed, recently developed deep learning-based methods have the potential to provide a faster and hence more scalable alternative to more conventional neuroimaging analysis pipelines in terms of whole-brain segmentation based on magnetic resonance (MR) images. These methods were also shown to have higher test-retest reliability, raising the possibility that they could also exhibit superior head motion tolerance. We investigated this by comparing the effect of head motion-induced artifacts in structural MR images on the consistency of segmentation performed by FreeSurfer and recently developed deep learning-based methods to a similar extent. We used state-of-the art neural network models (FastSurferCNN and Kwyk) and developed a new whole-brain segmentation pipeline (ReSeg) to examine whether reliability depends on choice of deep learning method. Structural MRI scans were collected from 110 participants under rest and active head motion and were evaluated for image quality by radiologists. Compared to FreeSurfer, deep learning-based methods provided more consistent segmentations across different levels of image quality, suggesting that they also have the advantage of providing more reliable whole-brain segmentations of MR images corrupted by motion-induced artifacts, and provide evidence for their practical applicability in the study of brain structural alterations in health and disease.
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Affiliation(s)
- Péter Kemenczky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.
| | - Pál Vakli
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.
| | - Eszter Somogyi
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - István Homolya
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
- Institute of Nuclear Techniques, Budapest University of Technology and Economics, Budapest, Hungary
| | - Petra Hermann
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Viktor Gál
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
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166
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Fortea L, Albajes-Eizagirre A, Yao YW, Soler E, Verdolini N, Hauson AO, Fortea A, Madero S, Solanes A, Wollman SC, Serra-Blasco M, Wise T, Lukito S, Picó-Pérez M, Carlisi C, Zhang J, Pan P, Farré-Colomés Á, Arnone D, Kempton MJ, Soriano-Mas C, Rubia K, Norman L, Fusar-Poli P, Mataix-Cols D, Valentí M, Via E, Cardoner N, Solmi M, Shin JI, Vieta E, Radua J. Focusing on Comorbidity-A Novel Meta-Analytic Approach and Protocol to Disentangle the Specific Neuroanatomy of Co-occurring Mental Disorders. Front Psychiatry 2022; 12:807839. [PMID: 35115973 PMCID: PMC8805083 DOI: 10.3389/fpsyt.2021.807839] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 12/13/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND In mental health, comorbidities are the norm rather than the exception. However, current meta-analytic methods for summarizing the neural correlates of mental disorders do not consider comorbidities, reducing them to a source of noise and bias rather than benefitting from their valuable information. OBJECTIVES We describe and validate a novel neuroimaging meta-analytic approach that focuses on comorbidities. In addition, we present the protocol for a meta-analysis of all major mental disorders and their comorbidities. METHODS The novel approach consists of a modification of Seed-based d Mapping-with Permutation of Subject Images (SDM-PSI) in which the linear models have no intercept. As in previous SDM meta-analyses, the dependent variable is the brain anatomical difference between patients and controls in a voxel. However, there is no primary disorder, and the independent variables are the percentages of patients with each disorder and each pair of potentially comorbid disorders. We use simulations to validate and provide an example of this novel approach, which correctly disentangled the abnormalities associated with each disorder and comorbidity. We then describe a protocol for conducting the new meta-analysis of all major mental disorders and their comorbidities. Specifically, we will include all voxel-based morphometry (VBM) studies of mental disorders for which a meta-analysis has already been published, including at least 10 studies. We will use the novel approach to analyze all included studies in two separate single linear models, one for children/adolescents and one for adults. DISCUSSION The novel approach is a valid method to focus on comorbidities. The meta-analysis will yield a comprehensive atlas of the neuroanatomy of all major mental disorders and their comorbidities, which we hope might help develop potential diagnostic and therapeutic tools.
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Affiliation(s)
- Lydia Fortea
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
| | | | - Yuan-Wei Yao
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Edu Soler
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
| | - Norma Verdolini
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Barcelona, Spain
| | - Alexander O. Hauson
- Clinical Psychology PhD Program, California School of Professional Psychology, San Diego, CA, United States
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States
| | - Adriana Fortea
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
- Fundació Clínic per a la Recerca Biomèdica (FCRB), Barcelona, Spain
- Psychiatric and Psychology Service, Hospital Clinic, Barcelona, Spain
| | - Santiago Madero
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
- Schizophrenia Unit, Hospital Clinic, Barcelona, Spain
| | - Aleix Solanes
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
- Department of Psychiatry and Forensic Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Scott C. Wollman
- Clinical Psychology PhD Program, California School of Professional Psychology, San Diego, CA, United States
| | - Maria Serra-Blasco
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychology, Abat Oliba CEU (“Centro de Estudios Universitarios”) University, Barcelona, Spain
- Programa E-Health ICOnnecta't, Institut Català d'Oncologia, Barcelona, Spain
| | - Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - Steve Lukito
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - Maria Picó-Pérez
- Live and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
- ICVS/3B's, PT Government Associate Laboratory, Braga, Portugal
- Clinical Academic Center - Braga, Braga, Portugal
| | - Christina Carlisi
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- Division of Psychology and Language Sciences, University College London, London, United Kingdom
| | - JinTao Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - PingLei Pan
- Department of Neurology, Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Affiliated Yancheng Hospital of Southeast University, Yancheng, China
| | - Álvar Farré-Colomés
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
- Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Danilo Arnone
- Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- Department of Psychiatry and Behavioral Science, College of Medicine and Health Sciences, United Arab Emirates University (UAEU), Al Ain, United Arab Emirates
| | - Matthew J. Kempton
- Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Carles Soriano-Mas
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Psychiatry and Mental Health Group, Neuroscience Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Department of Psychobiology and Methodology of Health Sciences, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Katya Rubia
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - Luke Norman
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
- The Social and Behavioral Research Branch, National Human Genome Research Institute, National Institute of Health, Bethesda, MD, United States
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology, London, United Kingdom
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Outreach and Support in South London (OASIS) Service, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - David Mataix-Cols
- Department of Clinical Neuroscience, Center for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Marc Valentí
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Barcelona, Spain
- Psychiatric and Psychology Service, Hospital Clinic, Barcelona, Spain
| | - Esther Via
- Child and Adolescent Psychiatry and Psychology Department, Hospital Sant Joan de Déu, Barcelona, Spain
- Child and Adolescent Mental Health Research Group, Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Narcis Cardoner
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Autonomous University of Barcelona, Barcelona, Spain
- Mental Health Department, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí (I3PT), Sabadell, Spain
| | - Marco Solmi
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology, London, United Kingdom
- Department of Psychiatry, University of Ottawa, Ottawa, ON, Canada
- Department of Mental Health, The Ottawa Hospital, Ottawa, ON, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Jae I. Shin
- Department of Pediatrics, Yonsei University College of Medicine, Seoul, South Korea
| | - Eduard Vieta
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Barcelona, Spain
- Psychiatric and Psychology Service, Hospital Clinic, Barcelona, Spain
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
- Department of Clinical Neuroscience, Center for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
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Andre JB, Amthor T, Hall CS, Gunn ML, Hoff MN, Cohen W, Beauchamp NJ. Correlating the Radiological Assessment of Patient Motion with the Incidence of Repeat Sequences Documented by Log Files. Curr Probl Diagn Radiol 2022; 51:534-539. [DOI: 10.1067/j.cpradiol.2022.01.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 01/05/2022] [Indexed: 11/22/2022]
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168
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Nalawade SS, Yu FF, Bangalore Yogananda CG, Murugesan GK, Shah BR, Pinho MC, Wagner BC, Xi Y, Mickey B, Patel TR, Fei B, Madhuranthakam AJ, Maldjian JA. Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction. J Med Imaging (Bellingham) 2022; 9:016001. [PMID: 35118164 PMCID: PMC8794036 DOI: 10.1117/1.jmi.9.1.016001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 01/03/2022] [Indexed: 01/29/2023] Open
Abstract
Purpose: Deep learning has shown promise for predicting the molecular profiles of gliomas using MR images. Prior to clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. The purpose of this study is to perform a preliminary evaluation on the effects of simulated motion artifact on glioma marker classifier performance and determine if motion correction can restore classification accuracies. Approach: T2w images and molecular information were retrieved from the TCIA and TCGA databases. Simulated motion was added in the k-space domain along the phase encoding direction. Classifier performance for IDH mutation, 1p/19q co-deletion, and MGMT methylation was assessed over the range of 0% to 100% corrupted k-space lines. Rudimentary motion correction networks were trained on the motion-corrupted images. The performance of the three glioma marker classifiers was then evaluated on the motion-corrected images. Results: Glioma marker classifier performance decreased markedly with increasing motion corruption. Applying motion correction effectively restored classification accuracy for even the most motion-corrupted images. For isocitrate dehydrogenase (IDH) classification, 99% accuracy was achieved, exceeding the original performance of the network and representing a new benchmark in non-invasive MRI-based IDH classification. Conclusions: Robust motion correction can facilitate highly accurate deep learning MRI-based molecular marker classification, rivaling invasive tissue-based characterization methods. Motion correction may be able to increase classification accuracy even in the absence of a visible artifact, representing a new strategy for boosting classifier performance.
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Affiliation(s)
- Sahil S. Nalawade
- University of Texas Southwestern Medical Center, Advanced Neuroscience Imaging Research Lab, Department of Radiology, Dallas, Texas, United States
| | - Fang F. Yu
- University of Texas Southwestern Medical Center, Advanced Neuroscience Imaging Research Lab, Department of Radiology, Dallas, Texas, United States
| | - Chandan Ganesh Bangalore Yogananda
- University of Texas Southwestern Medical Center, Advanced Neuroscience Imaging Research Lab, Department of Radiology, Dallas, Texas, United States
| | - Gowtham K. Murugesan
- University of Texas Southwestern Medical Center, Advanced Neuroscience Imaging Research Lab, Department of Radiology, Dallas, Texas, United States
| | - Bhavya R. Shah
- University of Texas Southwestern Medical Center, Advanced Neuroscience Imaging Research Lab, Department of Radiology, Dallas, Texas, United States
| | - Marco C. Pinho
- University of Texas Southwestern Medical Center, Advanced Neuroscience Imaging Research Lab, Department of Radiology, Dallas, Texas, United States
| | - Benjamin C. Wagner
- University of Texas Southwestern Medical Center, Advanced Neuroscience Imaging Research Lab, Department of Radiology, Dallas, Texas, United States
| | - Yin Xi
- University of Texas Southwestern Medical Center, Advanced Neuroscience Imaging Research Lab, Department of Radiology, Dallas, Texas, United States
| | - Bruce Mickey
- University of Texas Southwestern Medical Center, Department of Neurological Surgery, Dallas, Texas, United States
| | - Toral R. Patel
- University of Texas Southwestern Medical Center, Department of Neurological Surgery, Dallas, Texas, United States
| | - Baowei Fei
- University of Texas at Dallas, Department of Bioengineering, Richardson, Texas, United States
| | - Ananth J. Madhuranthakam
- University of Texas Southwestern Medical Center, Advanced Neuroscience Imaging Research Lab, Department of Radiology, Dallas, Texas, United States
| | - Joseph A. Maldjian
- University of Texas Southwestern Medical Center, Advanced Neuroscience Imaging Research Lab, Department of Radiology, Dallas, Texas, United States,Address all correspondence to Joseph A. Maldjian,
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Ma S, Wang N, Xie Y, Fan Z, Li D, Christodoulou AG. Motion-robust quantitative multiparametric brain MRI with motion-resolved MR multitasking. Magn Reson Med 2022; 87:102-119. [PMID: 34398991 PMCID: PMC8616852 DOI: 10.1002/mrm.28959] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/30/2021] [Accepted: 07/20/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To address head motion in brain MRI with a novel motion-resolved imaging framework, with application to motion-robust quantitative multiparametric mapping. METHODS MR multitasking conceptualizes the underlying multiparametric image in the presence of motion as a multidimensional low-rank tensor. By incorporating a motion-state dimension into the parameter dimensions and introducing unsupervised motion-state binning and outlier motion reweighting mechanisms, the brain motion can be readily resolved for motion-robust quantitative imaging. A numerical motion phantom was used to simulate different discrete and periodic motion patterns under various translational and rotational scenarios, as well as investigate the sensitivity to exceptionally small and large displacements. In vivo brain MRI was performed to also evaluate different real discrete and periodic motion patterns. The effectiveness of motion-resolved imaging for simultaneous T1 /T2 /T1ρ mapping in the brain was investigated. RESULTS For all 14 simulation scenarios of small, intermediate, and large motion displacements, the motion-resolved approach produced T1 /T2 /T1ρ maps with less absolute difference errors against the ground truth, lower RMSE, and higher structural similarity index measure of T1 /T2 /T1ρ measurements compared to motion removal, and no motion handling. For in vivo experiments, the motion-resolved approach produced T1 /T2 /T1ρ maps with the best image quality free from motion artifacts under random discrete motion, tremor, periodic shaking, and nodding patterns compared to motion removal and no motion handling. The proposed method also yielded T1 /T2 /T1ρ measurement distributions closest to the motion-free reference, with minimal measurement bias and variance. CONCLUSION Motion-resolved quantitative brain imaging is achieved with multitasking, which is generalizable to various head motion patterns without explicit need for registration-based motion correction.
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Affiliation(s)
- Sen Ma
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Zhaoyang Fan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA,Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA,Corresponding author: Anthony G. Christodoulou, 8700 Beverly Blvd, PACT 400, Los Angeles, CA 90048, , phone: 3104236754
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170
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Kim DW, Choi SH, Park T, Kim SY, Lee SS, Byun JH. Transient Severe Motion Artifact on Arterial Phase in Gadoxetic Acid-Enhanced Liver Magnetic Resonance Imaging: A Systematic Review and Meta-analysis. Invest Radiol 2022; 57:62-70. [PMID: 34224484 DOI: 10.1097/rli.0000000000000806] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES The aims of this study were to determine the incidence of transient severe motion artifact (TSM) on arterial phase gadoxetic acid-enhanced magnetic resonance imaging of the liver and to investigate the causes of heterogeneity in the published literature. MATERIALS AND METHODS Original studies reporting the incidence of TSM were identified in searches of PubMed, Embase, and Cochrane Library databases. The pooled incidence of TSM was calculated using random-effects meta-analysis of single proportions. Subgroup analyses were conducted to explore causes of heterogeneity. RESULTS A total of 24 studies were finally included (single arterial phase, 19 studies with 3065 subjects; multiple arterial phases, 8 studies with 2274 subjects). Studies using single arterial phase imaging reported individual TSM rates varying from 4.8% to 26.7% and a pooled incidence of TSM of 13.0% (95% confidence interval, 10.3%-16.2%), which showed substantial study heterogeneity. The pooled incidence of TSM in the studies using multiple arterial phase imaging was 3.2% (95% confidence interval, 1.9%-5.2%), which was significantly less than in those studies using single arterial phase imaging (P < 0.001). In the subgroup analysis, the geographical region of studies and the definition of TSM were found to be causes of heterogeneity. The incidence of TSM was higher in studies with Western populations from Europe or North America than in those with Eastern (Asia/Pacific) populations (16.0% vs 8.8%, P = 0.005). Regarding the definition of TSM, the incidence of TSM was higher when a 4-point scale was used for its categorization than when a 5-point scale was used (20.0% vs 11.0%, P = 0.008), and a definition considering motion artifact on phases other than arterial phase imaging lowered the incidence of TSM compared with it being defined only on arterial phase imaging (11.3% vs 20.3%, P = 0.018). CONCLUSIONS The incidence of TSM on arterial phase images varied across studies and was associated with the geographical region of studies and the definition of TSM. Careful interpretation of results reporting TSM might therefore be needed.
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Affiliation(s)
- Dong Wook Kim
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
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171
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Henry D, Fulton R, Maclaren J, Aksoy M, Bammer R, Kyme A. Optimizing a Feature-Based Motion Tracking System for Prospective Head Motion Estimation in MRI and PET/MRI. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3063260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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172
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Polak D, Splitthoff DN, Clifford B, Lo WC, Huang SY, Conklin J, Wald LL, Setsompop K, Cauley S. Scout accelerated motion estimation and reduction (SAMER). Magn Reson Med 2022; 87:163-178. [PMID: 34390505 PMCID: PMC8616778 DOI: 10.1002/mrm.28971] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 06/29/2021] [Accepted: 07/26/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To demonstrate a navigator/tracking-free retrospective motion estimation technique that facilitates clinically acceptable reconstruction time. METHODS Scout accelerated motion estimation and reduction (SAMER) uses a single 3-5 s, low-resolution scout scan and a novel sequence reordering to independently determine motion states by minimizing the data-consistency error in a SENSE plus motion forward model. This eliminates time-consuming alternating optimization as no updates to the imaging volume are required during the motion estimation. The SAMER approach was assessed quantitatively through extensive simulation and was evaluated in vivo across multiple motion scenarios and clinical imaging contrasts. Finally, SAMER was synergistically combined with advanced encoding (Wave-CAIPI) to facilitate rapid motion-free imaging. RESULTS The highly accelerated scout provided sufficient information to achieve accurate motion trajectory estimation (accuracy ~0.2 mm or degrees). The novel sequence reordering improved the stability of the motion parameter estimation and image reconstruction while preserving the clinical imaging contrast. Clinically acceptable computation times for the motion estimation (~4 s/shot) are demonstrated through a fully separable (non-alternating) motion search across the shots. Substantial artifact reduction was demonstrated in vivo as well as corresponding improvement in the quantitative error metric. Finally, the extension of SAMER to Wave-encoding enabled rapid high-quality imaging at up to R = 9-fold acceleration. CONCLUSION SAMER significantly improved the computational scalability for retrospective motion estimation and correction.
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Affiliation(s)
- Daniel Polak
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Siemens Healthcare GmbH, Erlangen, Germany
| | | | | | | | - Susie Y. Huang
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - John Conklin
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Lawrence L. Wald
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford School of Medicine, Stanford, California, USA
| | - Stephen Cauley
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
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173
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Sagawa H, Itagaki K, Matsushita T, Miyati T. Evaluation of motion artifacts in brain magnetic resonance images using convolutional neural network-based prediction of full-reference image quality assessment metrics. J Med Imaging (Bellingham) 2022; 9:015502. [PMID: 35106324 PMCID: PMC8782596 DOI: 10.1117/1.jmi.9.1.015502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 01/03/2022] [Indexed: 01/23/2023] Open
Abstract
Purpose: Motion artifacts in magnetic resonance (MR) images mostly undergo subjective evaluation, which is poorly reproducible, time consuming, and costly. Recently, full-reference image quality assessment (FR-IQA) metrics, such as structural similarity (SSIM), have been used, but they require a reference image and hence cannot be used to evaluate clinical images. We developed a convolutional neural network (CNN) model to quantify motion artifacts without using reference images. Approach: The brain MR images were obtained from an open dataset. The motion-corrupted images were generated retrospectively, and the peak signal-to-noise ratio, cross-correlation coefficient, and SSIM were calculated. The CNN was trained using these images and their FR-IQA metrics to predict the FR-IQA metrics without reference images. Receiver operating characteristic (ROC) curves were created for binary classification, with artifact scores < 4 indicating the need for rescanning. ROC curve analysis was performed on the binary classification of the real motion images. Results: The predicted FR-IQA metric having the highest correlation with the subjective evaluation was SSIM, which was able to classify images requiring rescanning with a sensitivity of 89.5%, specificity of 78.2%, and area under the ROC curve (AUC) of 0.930. The real motion artifacts were classified with the AUC of 0.928. Conclusions: Our CNN model predicts FR-IQA metrics with high accuracy, which enables quantitative assessment of motion artifacts in MR images without reference images. It enables classification of images requiring rescanning with a high AUC, which can improve the workflow of MR imaging examinations.
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Affiliation(s)
- Hajime Sagawa
- Kyoto University Hospital, Division of Clinical Radiology Service, Kyoto, Japan,Kanazawa University, Graduate School of Medical Sciences, Division of Health Sciences, Kanazawa, Japan,Address all correspondence to Hajime Sagawa,
| | - Koji Itagaki
- Kyoto University Hospital, Division of Clinical Radiology Service, Kyoto, Japan
| | - Tatsuhiko Matsushita
- Kyoto University Hospital, Division of Clinical Radiology Service, Kyoto, Japan,Kanazawa University, Pharmaceutical and Health Sciences, Institute of Medical, Faculty of Health Sciences, Kanazawa, Japan
| | - Tosiaki Miyati
- Kanazawa University, Graduate School of Medical Sciences, Division of Health Sciences, Kanazawa, Japan,Kanazawa University, Pharmaceutical and Health Sciences, Institute of Medical, Faculty of Health Sciences, Kanazawa, Japan
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174
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Gilbert KM, Dubovan PI, Gati JS, Menon RS, Baron CA. Integration of an RF coil and commercial field camera for ultrahigh-field MRI. Magn Reson Med 2021; 87:2551-2565. [PMID: 34932225 DOI: 10.1002/mrm.29130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/16/2021] [Accepted: 12/03/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE To develop an RF coil with an integrated commercial field camera for ultrahigh field (7T) neuroimaging. The RF coil would operate within a head-only gradient coil and be subject to the corresponding design constraints. The RF coil can thereafter be used for subject-specific correction of k-space trajectories-notably in gradient-sensitive sequences such as single-shot spiral imaging. METHODS The transmit and receive performance was evaluated before and after the integration of field probes, whereas field probes were evaluated when in an optimal configuration external to the coil and after their integration. Diffusion-weighted EPI and single-shot spiral acquisitions were employed to evaluate the efficacy of correcting higher order field perturbations and the consequent effect on image quality. RESULTS Field probes had a negligible effect on RF-coil performance, including the transmit efficiency, transmit uniformity, and mean SNR over the brain. Modest reductions in field-probe signal lifetimes were observed, caused primarily by nonidealities in the gradient and shim fields of the head-only gradient coil at the probe positions. The field-monitoring system could correct up to second-order field perturbations in single-shot spiral imaging. CONCLUSION The integrated RF coil and field camera was capable of concurrent-field monitoring within a 7T head-only scanner and facilitated the subsequent correction of k-space trajectories during spiral imaging.
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Affiliation(s)
- Kyle M Gilbert
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, Ontario, Canada.,Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
| | - Paul I Dubovan
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, Ontario, Canada.,Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
| | - Joseph S Gati
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, Ontario, Canada.,Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
| | - Ravi S Menon
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, Ontario, Canada.,Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
| | - Corey A Baron
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, Ontario, Canada.,Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada
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175
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Kustner T, Pan J, Qi H, Cruz G, Gilliam C, Blu T, Yang B, Gatidis S, Botnar R, Prieto C. LAPNet: Non-Rigid Registration Derived in k-Space for Magnetic Resonance Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3686-3697. [PMID: 34242163 DOI: 10.1109/tmi.2021.3096131] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts. Motion correction techniques have been proposed to compensate for these types of motion during thoracic scans, relying on accurate motion estimation from undersampled motion-resolved reconstruction. A particular interest and challenge lie in the derivation of reliable non-rigid motion fields from the undersampled motion-resolved data. Motion estimation is usually formulated in image space via diffusion, parametric-spline, or optical flow methods. However, image-based registration can be impaired by remaining aliasing artifacts due to the undersampled motion-resolved reconstruction. In this work, we describe a formalism to perform non-rigid registration directly in the sampled Fourier space, i.e. k-space. We propose a deep-learning based approach to perform fast and accurate non-rigid registration from the undersampled k-space data. The basic working principle originates from the Local All-Pass (LAP) technique, a recently introduced optical flow-based registration. The proposed LAPNet is compared against traditional and deep learning image-based registrations and tested on fully-sampled and highly-accelerated (with two undersampling strategies) 3D respiratory motion-resolved MR images in a cohort of 40 patients with suspected liver or lung metastases and 25 healthy subjects. The proposed LAPNet provided consistent and superior performance to image-based approaches throughout different sampling trajectories and acceleration factors.
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176
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Deep Learning Image Processing Enables 40% Faster Spinal MR Scans Which Match or Exceed Quality of Standard of Care : A Prospective Multicenter Multireader Study. Clin Neuroradiol 2021; 32:197-203. [PMID: 34846555 PMCID: PMC8894206 DOI: 10.1007/s00062-021-01121-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 11/03/2021] [Indexed: 01/12/2023]
Abstract
OBJECTIVE This prospective multicenter multireader study evaluated the performance of 40% scan-time reduced spinal magnetic resonance imaging (MRI) reconstructed with deep learning (DL). METHODS A total of 61 patients underwent standard of care (SOC) and accelerated (FAST) spine MRI. DL was used to enhance the accelerated set (FAST-DL). Three neuroradiologists were presented with paired side-by-side datasets (666 series). Datasets were blinded and randomized in sequence and left-right display order. Image features were preference rated. Structural similarity index (SSIM) and per pixel L1 was assessed for the image sets pre and post DL-enhancement as a quantitative assessment of image integrity impact. RESULTS FAST-DL was qualitatively better than SOC for perceived signal-to-noise ratio (SNR) and artifacts and equivalent for other features. Quantitative SSIM was high, supporting the absence of image corruption by DL processing. CONCLUSION DL enables 40% spine MRI scan time reduction while maintaining diagnostic integrity and image quality with perceived benefits in SNR and artifact reduction, suggesting potential for clinical practice utility.
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177
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Bash S, Wang L, Airriess C, Zaharchuk G, Gong E, Shankaranarayanan A, Tanenbaum LN. Deep Learning Enables 60% Accelerated Volumetric Brain MRI While Preserving Quantitative Performance: A Prospective, Multicenter, Multireader Trial. AJNR Am J Neuroradiol 2021; 42:2130-2137. [PMID: 34824098 DOI: 10.3174/ajnr.a7358] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 08/17/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE In this prospective, multicenter, multireader study, we evaluated the impact on both image quality and quantitative image-analysis consistency of 60% accelerated volumetric MR imaging sequences processed with a commercially available, vendor-agnostic, DICOM-based, deep learning tool (SubtleMR) compared with that of standard of care. MATERIALS AND METHODS Forty subjects underwent brain MR imaging examinations on 6 scanners from 5 institutions. Standard of care and accelerated datasets were acquired for each subject, and the accelerated scans were enhanced with deep learning processing. Standard of care, accelerated scans, and accelerated-deep learning were subjected to NeuroQuant quantitative analysis and classified by a neuroradiologist into clinical disease categories. Concordance of standard of care and accelerated-deep learning biomarker measurements were assessed. Randomized, side-by-side, multiplanar datasets (360 series) were presented blinded to 2 neuroradiologists and rated for apparent SNR, image sharpness, artifacts, anatomic/lesion conspicuity, image contrast, and gray-white differentiation to evaluate image quality. RESULTS Accelerated-deep learning was statistically superior to standard of care for perceived quality across imaging features despite a 60% sequence scan-time reduction. Both accelerated-deep learning and standard of care were superior to accelerated scans for all features. There was no difference in quantitative volumetric biomarkers or clinical classification for standard of care and accelerated-deep learning datasets. CONCLUSIONS Deep learning reconstruction allows 60% sequence scan-time reduction while maintaining high volumetric quantification accuracy, consistent clinical classification, and what radiologists perceive as superior image quality compared with standard of care. This trial supports the reliability, efficiency, and utility of deep learning-based enhancement for quantitative imaging. Shorter scan times may heighten the use of volumetric quantitative MR imaging in routine clinical settings.
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Affiliation(s)
- S Bash
- From the RadNet Inc (S.B., L.N.T.), Los Angeles, California
| | - L Wang
- Subtle Medical (L.W., E.G., A.S.), Menlo Park, California
| | - C Airriess
- Cortechs.ai. (C.A.), San Diego, California
| | - G Zaharchuk
- Stanford University Medical Center (G.Z.), Stanford, California
| | - E Gong
- Subtle Medical (L.W., E.G., A.S.), Menlo Park, California
| | | | - L N Tanenbaum
- From the RadNet Inc (S.B., L.N.T.), Los Angeles, California.,Lenox Hill Radiolog (L.N.T.), New York, New York
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178
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Heldring N, Larsson A, Rezaie AR, Råsten-Almqvist P, Zilg B. A probability model for assessing age relative to the 18-year old threshold based on magnetic resonance imaging of the knee combined with radiography of third molars in the lower jaw. Forensic Sci Int 2021; 330:111108. [PMID: 34826761 DOI: 10.1016/j.forsciint.2021.111108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 11/11/2021] [Accepted: 11/15/2021] [Indexed: 11/16/2022]
Abstract
OBJECTIVE This study aims to generate a statistical model based on magnetic resonance imaging of the knee and radiography of third molars in the lower jaw, for assessing age relative to the 18-year old threshold. METHODS In total, 58 studies correlating knee or tooth development to age were assessed, 5 studies for knee and 7 studies for tooth were included in the statistical model. The relation between the development of the anatomical site, based on a binary system, and age were estimated using logistic regression. Separate meta-populations for knee and tooth were generated from the individual based data for men and women. A weighted estimate of probabilities was made by combining the probability densities for knee and tooth. Margin of errors for males and females in different age groups and knee and tooth maturity were calculated within the larger framework of transition analysis using a logit model as a base. Evidentiary values for combinations of knee and tooth maturity were evaluated with likelihood ratios. RESULTS For males, the sensitivity for the method was calculated to 0.78 (probability of correctly classifying adults), the specificity 0.90 (probability of correctly classifying minors), the negative predictive value 0.80 (proportion identified minors are minors) and the positive predictive value 0.89 (proportion identified adults are adults) indicating a model better at identifying minors than adults. The point at which half the female population has reached closed knee lies before the 18-year threshold, adding the knee as an indicator lowers specificity and increases sensitivity. The sensitivity when using tooth as an indicator for females is 0.24 and specificity 0.97, signifying few minors misclassified as adults but also a low probability of identifying adults. The negative predictive value for women when using tooth as the sole indicator is 0.56 and positive predictive value 0.88. Probabilities were calculated for males and females assuming a uniform age distribution between 15 and 21years. The calculated margin of error of minors classified as adults in a population between 15 and 21 years with the model was 11% for males and 12% for females. Further, the evidentiary value as well as margin of error vary for different combinations of knee and tooth maturity. CONCLUSION The statistical model based on the combination of MRI knee and radiography of mandibular third molars is a valid method to assess age relative to the 18-year old threshold when applied on males and of limited value in females.
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Affiliation(s)
- Nina Heldring
- Department of Forensic Medicine, Swedish National Board of Forensic Medicine, Retzius väg 5, SE-171 65 Stockholm, Sweden
| | - André Larsson
- Department of Forensic Medicine, Swedish National Board of Forensic Medicine, Retzius väg 5, SE-171 65 Stockholm, Sweden
| | - Ali-Reza Rezaie
- Department of Forensic Medicine, Swedish National Board of Forensic Medicine, Retzius väg 5, SE-171 65 Stockholm, Sweden
| | - Petra Råsten-Almqvist
- Department of Forensic Medicine, Swedish National Board of Forensic Medicine, Retzius väg 5, SE-171 65 Stockholm, Sweden
| | - Brita Zilg
- Department of Forensic Medicine, Swedish National Board of Forensic Medicine, Retzius väg 5, SE-171 65 Stockholm, Sweden
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Weingärtner S, Desmond KL, Obuchowski NA, Baessler B, Zhang Y, Biondetti E, Ma D, Golay X, Boss MA, Gunter JL, Keenan KE, Hernando D. Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group. Magn Reson Med 2021; 87:1184-1206. [PMID: 34825741 DOI: 10.1002/mrm.29084] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/20/2021] [Accepted: 10/27/2021] [Indexed: 12/26/2022]
Abstract
On behalf of the International Society for Magnetic Resonance in Medicine (ISMRM) Quantitative MR Study Group, this article provides an overview of considerations for the development, validation, qualification, and dissemination of quantitative MR (qMR) methods. This process is framed in terms of two central technical performance properties, i.e., bias and precision. Although qMR is confounded by undesired effects, methods with low bias and high precision can be iteratively developed and validated. For illustration, two distinct qMR methods are discussed throughout the manuscript: quantification of liver proton-density fat fraction, and cardiac T1 . These examples demonstrate the expansion of qMR methods from research centers toward widespread clinical dissemination. The overall goal of this article is to provide trainees, researchers, and clinicians with essential guidelines for the development and validation of qMR methods, as well as an understanding of necessary steps and potential pitfalls for the dissemination of quantitative MR in research and in the clinic.
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Affiliation(s)
- Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Kimberly L Desmond
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Nancy A Obuchowski
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio, USA
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Yuxin Zhang
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Emma Biondetti
- Department of Neuroscience, Imaging and Clinical Sciences, D'Annunzio University of Chieti and Pescara, Chieti, Italy
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xavier Golay
- Brain Repair & Rehabilitation, Institute of Neurology, University College London, United Kingdom.,Gold Standard Phantoms Limited, Rochester, United Kingdom
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, Pennsylvania, USA
| | | | - Kathryn E Keenan
- National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Diego Hernando
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
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He M, Xu J, Wu Q, Wang X, Ren J, Wang X, Xue H, Jin Z. Application of Compressed Sensing 3D MR cholangiopancreatography (CS-MRCP) with Contact-Free Physiological Monitoring (CFPM) for Pancreaticobiliary Disorders. Acad Radiol 2021; 28 Suppl 1:S148-S156. [PMID: 34756818 DOI: 10.1016/j.acra.2020.12.008] [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: 11/19/2020] [Revised: 12/08/2020] [Accepted: 12/14/2020] [Indexed: 10/19/2022]
Abstract
RATIONAL AND OBJECTIVES To prospectively evaluate the clinical feasibility of the magnetic resonance cholangiopancreatography (MRCP) protocol using both contact-free physiological monitoring (CFPM) and compressed sensing (CS) (CS-CFPM-MRCP) and to compare its performance with that of the standard navigator-triggered (NT) CS-NT-MRCP and NT-MRCP. MATERIALS AND METHODS A total of 63 patients (36 males, 27 females, age range: 18-83 years, mean age: 52.30 ± 15.70 years) suspected with duct-related pathologies were prospectively enrolled and performed the three MRCP protocols randomly. The acquisition time was compared. The pancreaticobiliary system was divided into 12 segments and evaluated based on a five-point Likert scale and compared by the Friedman test with a post hoc test. The diagnostic performance of the 3 MRCP was evaluated by the AUC value and compared by Delong's test. The interobserver agreement was evaluated by Kendall's W test. RESULTS Compared to NT-MRCP, the acquisition time of CS-NT-MRCP and CS-CFPM-MRCP was significantly decreased (both p < 0.001). There is no significant difference in the overall imaging quality (p > 0.05) between the NT-MRCP and CS-CFPM-MRCP protocols. CS-CFPM-MRCP depicted pancreatic duct and intrahepatic ducts better than CS-NT-MRCP (all p < 0.05) and was comparable with that of the NT-MRCP (all p > 0.05). For identification of abnormalities and diseases associated with MPD anatomy, the mean AUC value for NT-MRCP and CS-CFPM-MRCP were 0.896 (95%CI: 0.834, 0.958) and 0.905 (95%CI: 0.846, 0.964), which were significantly higher when compared to that for CS-NT-MRCP (0.713 [95%CI:0.622, 0.805]) (p = 0.001 and < 0.001). All evaluations showed good to excellent agreement (0.619-0.897). CONCLUSION The combination of CS and CFPM is considered feasible for shortening the scan time of 3D free breath MRCP without impairing the imaging quality and CS-CFPM-MRCP is considered feasible for patients suspected with pancreaticobiliary diseases.
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Platt T, Ladd ME, Paech D. 7 Tesla and Beyond: Advanced Methods and Clinical Applications in Magnetic Resonance Imaging. Invest Radiol 2021; 56:705-725. [PMID: 34510098 PMCID: PMC8505159 DOI: 10.1097/rli.0000000000000820] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/07/2021] [Accepted: 08/07/2021] [Indexed: 12/15/2022]
Abstract
ABSTRACT Ultrahigh magnetic fields offer significantly higher signal-to-noise ratio, and several magnetic resonance applications additionally benefit from a higher contrast-to-noise ratio, with static magnetic field strengths of B0 ≥ 7 T currently being referred to as ultrahigh fields (UHFs). The advantages of UHF can be used to resolve structures more precisely or to visualize physiological/pathophysiological effects that would be difficult or even impossible to detect at lower field strengths. However, with these advantages also come challenges, such as inhomogeneities applying standard radiofrequency excitation techniques, higher energy deposition in the human body, and enhanced B0 field inhomogeneities. The advantages but also the challenges of UHF as well as promising advanced methodological developments and clinical applications that particularly benefit from UHF are discussed in this review article.
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Affiliation(s)
- Tanja Platt
- From the Medical Physics in Radiology, German Cancer Research Center (DKFZ)
| | - Mark E. Ladd
- From the Medical Physics in Radiology, German Cancer Research Center (DKFZ)
- Faculty of Physics and Astronomy
- Faculty of Medicine, University of Heidelberg, Heidelberg
- Erwin L. Hahn Institute for MRI, University of Duisburg-Essen, Essen
| | - Daniel Paech
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg
- Clinic for Neuroradiology, University of Bonn, Bonn, Germany
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182
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Nelson TD, Brock RL, Yokum S, Tomaso CC, Savage CR, Stice E. Much Ado About Missingness: A Demonstration of Full Information Maximum Likelihood Estimation to Address Missingness in Functional Magnetic Resonance Imaging Data. Front Neurosci 2021; 15:746424. [PMID: 34658780 PMCID: PMC8514662 DOI: 10.3389/fnins.2021.746424] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 08/31/2021] [Indexed: 11/23/2022] Open
Abstract
The current paper leveraged a large multi-study functional magnetic resonance imaging (fMRI) dataset (N = 363) and a generated missingness paradigm to demonstrate different approaches for handling missing fMRI data under a variety of conditions. The performance of full information maximum likelihood (FIML) estimation, both with and without auxiliary variables, and listwise deletion were compared under different conditions of generated missing data volumes (i.e., 20, 35, and 50%). FIML generally performed better than listwise deletion in replicating results from the full dataset, but differences were small in the absence of auxiliary variables that correlated strongly with fMRI task data. However, when an auxiliary variable created to correlate r = 0.5 with fMRI task data was included, the performance of the FIML model improved, suggesting the potential value of FIML-based approaches for missing fMRI data when a strong auxiliary variable is available. In addition to primary methodological insights, the current study also makes an important contribution to the literature on neural vulnerability factors for obesity. Specifically, results from the full data model show that greater activation in regions implicated in reward processing (caudate and putamen) in response to tastes of milkshake significantly predicted weight gain over the following year. Implications of both methodological and substantive findings are discussed.
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Affiliation(s)
- Timothy D Nelson
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Rebecca L Brock
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Sonja Yokum
- Oregon Research Institute, Eugene, OR, United States
| | - Cara C Tomaso
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Cary R Savage
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Eric Stice
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
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183
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Butskova A, Juhl R, Zukić D, Chaudhary A, Pohl KM, Zhao Q. Adversarial Bayesian Optimization for Quantifying Motion Artifact Within MRI. PREDICTIVE INTELLIGENCE IN MEDICINE. PRIME (WORKSHOP) 2021; 12928:83-92. [PMID: 35749100 PMCID: PMC9212065 DOI: 10.1007/978-3-030-87602-9_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Subject motion during an MRI sequence can cause ghosting effects or diffuse image noise in the phase-encoding direction and hence is likely to bias findings in neuroimaging studies. Detecting motion artifacts often relies on experts visually inspecting MRIs, which is subjective and expensive. To improve this detection, we develop a framework to automatically quantify the severity of motion artifact within a brain MRI. We formulate this task as a regression problem and train the regressor from a data set of MRIs with various amounts of motion artifacts. To resolve the issue of missing fine-grained ground-truth labels (level of artifacts), we propose Adversarial Bayesian Optimization (ABO) to infer the distribution of motion parameters (i.e., rotation and translation) underlying the acquired MRI data and then inject synthetic motion artifacts sampled from that estimated distribution into motion-free MRIs. After training the regressor on the synthetic data, we applied the model to quantify the motion level in 990 MRIs collected by the National Consortium on Alcohol and Neurodevelopment in Adolescence. Results show that the motion level derived by our approach is more reliable than the traditional metric based on Entropy Focus Criterion and manually defined binary labels.
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Affiliation(s)
| | - Rain Juhl
- Stanford University, Stanford, CA, USA
| | | | | | - Kilian M Pohl
- Stanford University, Stanford, CA, USA
- SRI International, Menlo Park, CA, USA
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184
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Fractionated deep-inspiration breath-hold ZTE Compared with Free-breathing four-dimensional ZTE for detecting pulmonary nodules in oncological patients underwent PET/MRI. Sci Rep 2021; 11:17636. [PMID: 34480038 PMCID: PMC8417270 DOI: 10.1038/s41598-021-94702-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 02/16/2021] [Indexed: 12/04/2022] Open
Abstract
The zero echo time (ZTE) technique has improved the detection of lung nodules in PET/MRI but respiratory motion remains a challenge in lung scan. We investigated the feasibility and performance of fractionated deep-inspiration breath-hold (FDIBH) three-dimensional (3D) ZTE FDG PET/MRI for assessing lung nodules in patients with proved malignancy. Sixty patients who had undergone ZTE FDG PET/MRI and chest CT within a three-day interval were retrospectively included. Lung nodules less than 2 mm were excluded for analysis. Two physicians checked the adequacy of FDIBH ZTE and compared the lung nodule detection rates of FDIBH 3D ZTE and free-breathing (FB) four-dimensional (4D) ZTE, with chest CT as the reference standard. FDIBH resolved the effect of respiratory motion in 49 patients. The mean number and size of the pulmonary nodules identified in CT were 15 ± 31.3 per patient and 5.9 ± 4.6 mm in diameter. The overall nodule detection rate was 71% for FDIBH 3D ZTE and 70% for FB 4D ZTE (p = 0.73). FDIBH 3D ZTE significantly outperformed FB 4DZTE in detecting lung base nodules (72% and 68%; p = 0.03), especially for detecting those less than 6 mm (61% and 55%; p = 0.03). High inter-rater reliability for FDIBH 3D ZTE and FB 4D ZTE (k = 0.9 and 0.92) was noted. In conclusion, the capability of FDIBH 3D ZTE in respiratory motion resolution was limited with a technical failure rate of 18%. However, it could provide full expansion of the lung in a shorter scan time which enabled better detection of nodules (< 6 mm) in basal lungs, compared to FB 4D ZTE.
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185
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Hanson HM, Eiben B, McClelland JR, van Herk M, Rowland BC. Technical Note: Four-dimensional deformable digital phantom for MRI sequence development. Med Phys 2021; 48:5406-5413. [PMID: 34101858 DOI: 10.1002/mp.15036] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 05/14/2021] [Accepted: 05/26/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE MR-guided radiotherapy has different requirements for the images than diagnostic radiology, thus requiring development of novel imaging sequences. MRI simulation is an excellent tool for optimizing these new sequences; however, currently available software does not provide all the necessary features. In this paper, we present a digital framework for testing MRI sequences that incorporates anatomical structure, respiratory motion, and realistic presentation of MR physics. METHODS The extended Cardiac-Torso (XCAT) software was used to create T1 , T2 , and proton density maps that formed the anatomical structure of the phantom. Respiratory motion model was based on the XCAT deformation vector fields, modified to create a motion model driven by a respiration signal. MRI simulation was carried out with JEMRIS, an open source Bloch simulator. We developed an extension for JEMRIS, which calculates the motion of each spin independently, allowing for deformable motion. RESULTS The performance of the framework was demonstrated through simulating the acquisition of a two-dimensional (2D) cine and demonstrating expected motion ghosts from T2 weighted spin echo acquisitions with different respiratory patterns. All simulations were consistent with behavior previously described in literature. Simulations with deformable motion were not more time consuming than with rigid motion. CONCLUSIONS We present a deformable four-dimensional (4D) digital phantom framework for MR sequence development. The framework incorporates anatomical structure, realistic breathing patterns, deformable motion, and Bloch simulation to achieve accurate simulation of MRI. This method is particularly relevant for testing novel imaging sequences for the purpose of MR-guided radiotherapy in lungs and abdomen.
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Affiliation(s)
- Hanna M Hanson
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, The Christie NHS Foundation Trust, Manchester, UK
| | - Björn Eiben
- Centre for Medical Image Computing, Radiotherapy Image Computing Group, Department of Medical Physics and Biomedical Engineering University College London, London, UK
| | - Jamie R McClelland
- Centre for Medical Image Computing, Radiotherapy Image Computing Group, Department of Medical Physics and Biomedical Engineering University College London, London, UK
| | - Marcel van Herk
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, The Christie NHS Foundation Trust, Manchester, UK
| | - Benjamin C Rowland
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, The Christie NHS Foundation Trust, Manchester, UK
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186
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Düzel E, Costagli M, Donatelli G, Speck O, Cosottini M. Studying Alzheimer disease, Parkinson disease, and amyotrophic lateral sclerosis with 7-T magnetic resonance. Eur Radiol Exp 2021; 5:36. [PMID: 34435242 PMCID: PMC8387546 DOI: 10.1186/s41747-021-00221-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 04/07/2021] [Indexed: 12/18/2022] Open
Abstract
Ultra-high-field (UHF) magnetic resonance (MR) scanners, that is, equipment operating at static magnetic field of 7 tesla (7 T) and above, enable the acquisition of data with greatly improved signal-to-noise ratio with respect to conventional MR systems (e.g., scanners operating at 1.5 T and 3 T). The change in tissue relaxation times at UHF offers the opportunity to improve tissue contrast and depict features that were previously inaccessible. These potential advantages come, however, at a cost: in the majority of UHF-MR clinical protocols, potential drawbacks may include signal inhomogeneity, geometrical distortions, artifacts introduced by patient respiration, cardiac cycle, and motion. This article reviews the 7 T MR literature reporting the recent studies on the most widespread neurodegenerative diseases: Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis.
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Affiliation(s)
- Emrah Düzel
- Otto-von-Guericke University Magdeburg, Magdeburg, Germany. .,German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany. .,University College London, London, UK.
| | - Mauro Costagli
- IRCCS Stella Maris, Pisa, Italy.,University of Genoa, Genova, Italy
| | - Graziella Donatelli
- Fondazione Imago 7, Pisa, Italy.,Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Oliver Speck
- Otto-von-Guericke University Magdeburg, Magdeburg, Germany.,German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Mirco Cosottini
- Azienda Ospedaliero Universitaria Pisana, Pisa, Italy.,University of Pisa, Pisa, Italy
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187
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Taffel MT, Rosenkrantz AB, Foster JA, Karajgikar JA, Smereka PN, Calasso F, Qian K, Chandarana H. Retrospective Assessment of the Impact of Primary Language Video Instructions on Image Quality of Abdominal MRI. J Am Coll Radiol 2021; 18:1635-1642. [PMID: 34419478 DOI: 10.1016/j.jacr.2021.07.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/23/2021] [Accepted: 07/28/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE To assess the impact of instructional videos in patients' primary language on abdominal MR image quality for whom English is a second language (ESL). METHODS Twenty-nine ESL patients viewed Spanish or Mandarin Chinese instructional videos (approximately 2.5 min in duration) in the preparation room before abdominal MRI (ESL-video group). Comparison groups included 50 ESL patients who underwent MRI before video implementation (ESL-no video group) and 81 English-speaking patients who were matched for age, sex, magnet strength, and history of prior MRI with patients in the first two groups. Three radiologists independently assessed respiratory motion and image quality on turbo spin-echo T2-weighted images (T2WI) and postcontrast T1-weighted images (T1WI) using 1 to 5 Likert scales. Groups were compared using Kruskal-Wallis tests as well as generalized estimating equations (GEEs) to adjust for possible confounders. RESULTS For T2WI respiratory motion and T2WI overall image quality, Likert scores of the ESL-no video group (mean score across readers of 2.6 ± 0.1 and 2.6 ± 0.1) were lower (all P < .001) compared with English-speaking (3.3 ± 0.2 and 3.3 ± 0.1) and ESL-video (3.2 ± 0.1 and 3.0 ± 0.2) groups. In the GEE model, mean T2WI respiratory motion (both adjusted P < .001) and T2WI overall quality (adjusted P = .03 and .11) were higher in English and ESL-video groups compared with ESL-no video group. For T1WI respiratory motion and T1WI overall image quality, Likert scores were not different between groups (P > .05), including in the GEE model (adjusted P > .05). CONCLUSION Providing ESL patients with an instructional video in their primary language before abdominal MRI is an effective intervention to improve imaging quality.
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Affiliation(s)
- Myles T Taffel
- Associate Section Head, Abdominal Imaging, Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, New York, New York.
| | - Andrew B Rosenkrantz
- Section Head, Abdominal Imaging, Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Jonathan A Foster
- Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Jay A Karajgikar
- Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Paul N Smereka
- Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Felicia Calasso
- Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Kun Qian
- Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, NYU Langone Health, New York, New York
| | - Hersh Chandarana
- Associate Chair, Clinical and Translational Research, Department of Radiology, NYU Grossman School of Medicine, NYU Langone Health, New York, New York
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188
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Czyzewska D, Sushentsev N, Latoch E, Slough RA, Barrett T. T2-PROPELLER Compared to T2-FRFSE for Image Quality and Lesion Detection at Prostate MRI. Can Assoc Radiol J 2021; 73:355-361. [PMID: 34423672 DOI: 10.1177/08465371211030206] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
PURPOSE The primary objective was to compare T2-FRFSE and T2-PROPELLER sequences for image quality. The secondary objective was to compare the ability to detect prostate lesions at MRI in the presence and absence of motion artefact using the 2 sequences. METHODS 99 patients underwent 3 T MRI examination of the prostate, including T2-FRFSE and T2-PROPELLER sequences. All patients underwent prostate biopsy. Two independent readers rated overall image quality, presence of motion artefact, and blurring for both sequences using a 5-point Likert scale. Scores were compared for the whole group and for subgroups with and without significant motion artefact. Outcome for lesion detection at an MRI threshold of PI-RADS score ≥3 was compared between T2-FRFSE and T2-PROPELLER. RESULTS The overall image quality was not significantly different between T2-FRFSE and T2-PROPELLER sequences (3.74 vs. 3.93, p = 0.275). T2-PROPELLER recorded a lesser degree of motion artefact (score 4.53 vs. 3.78, p <0.0001), but demonstrated greater image blurring (score 3.29 vs. 3.73, p <0.001). However, in a subgroup of patients with significant motion artefact on T2-FRFSE, the T2-PROPELLER sequence demonstrated significantly higher image quality (3.46 vs. 2.49, p <0.001). T2-FRFSE and T2-PROPELLER showed comparable positive predictive values for lesion detection at 93.2% and 97.7%, respectively. CONCLUSIONS T2-PROPELLER provides higher quality imaging in the presence of motion artefact, but T2-FRFSE is preferred in the absence of motion. T2-PROPELLER is therefore recommended as a secondary T2 sequence when imaging requires repeat acquisition due to motion artefact.
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Affiliation(s)
- Dorota Czyzewska
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, Copenhagen, Denmark
| | - Nikita Sushentsev
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK
| | - Eryk Latoch
- Department of Pediatric Oncology and Hematology, Medical University of Bialystok, Poland
| | - Rhys A Slough
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK
| | - Tristan Barrett
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK.,CamPARI Prostate Cancer Group, Addenbrooke's Hospital and University of Cambridge, Cambridge, United Kingdom
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189
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Zhao G, Lau WKW, Wang C, Yan H, Zhang C, Lin K, Qiu S, Huang R, Zhang R. A Comparative Multimodal Meta-analysis of Anisotropy and Volume Abnormalities in White Matter in People Suffering From Bipolar Disorder or Schizophrenia. Schizophr Bull 2021; 48:69-79. [PMID: 34374427 PMCID: PMC8781378 DOI: 10.1093/schbul/sbab093] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Schizophrenia (SZ) and bipolar disorder (BD) share some similarities in terms of genetic-risk genes and abnormalities of gray-matter structure in the brain, but white matter (WM) abnormalities have not been studied in depth. We undertook a comparative multimodal meta-analysis to identify common and disorder-specific abnormalities in WM structure between SZ and BD. Anisotropic effect size-signed differential mapping software was used to conduct a comparative meta-analysis of 68 diffusion tensor imaging (DTI) and 34 voxel-based morphometry (VBM) studies comparing fractional anisotropy (FA) and white matter volume (WMV), respectively, between patients with SZ (DTI: N = 1543; VBM: N = 1068) and BD (DTI: N = 983; VBM: N = 518) and healthy controls (HCs). The bilateral corpus callosum (extending to the anterior and superior corona radiata) showed shared decreased WMV and FA in SZ and BD. Compared with BD patients, SZ patients showed remarkable disorder-specific WM abnormalities: decreased FA and increased WMV in the left cingulum, and increased FA plus decreased WMV in the right anterior limb of the internal capsule. SZ patients showed more extensive alterations in WM than BD cases, which may be the pathophysiological basis for the clinical continuity of both disorders. The disorder-specific regions in the left cingulum and right anterior limb of the internal capsule provided novel insights into both disorders. Our study adds value to further understanding of the pathophysiology, classification, and differential diagnosis of SZ and BD.
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Affiliation(s)
- Guorui Zhao
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Way K W Lau
- Department of Special Education and Counselling, The Education University of Hong Kong, Hong Kong, China
| | - Chanyu Wang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Haifeng Yan
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Chichen Zhang
- School of Management, Southern Medical University, Guangzhou, China
| | - Kangguang Lin
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Chinese traditional Medical University, Guangzhou, China
| | - Ruiwang Huang
- School of Psychology, South China Normal University, Guangzhou, China
| | - Ruibin Zhang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China,To whom correspondence should be addressed; Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, tel/fax:020-62789234, e-mail:
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190
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Copeland A, Silver E, Korja R, Lehtola SJ, Merisaari H, Saukko E, Sinisalo S, Saunavaara J, Lähdesmäki T, Parkkola R, Nolvi S, Karlsson L, Karlsson H, Tuulari JJ. Infant and Child MRI: A Review of Scanning Procedures. Front Neurosci 2021; 15:666020. [PMID: 34321992 PMCID: PMC8311184 DOI: 10.3389/fnins.2021.666020] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 05/04/2021] [Indexed: 12/13/2022] Open
Abstract
Magnetic resonance imaging (MRI) is a safe method to examine human brain. However, a typical MR scan is very sensitive to motion, and it requires the subject to lie still during the acquisition, which is a major challenge for pediatric scans. Consequently, in a clinical setting, sedation or general anesthesia is often used. In the research setting including healthy subjects anesthetics are not recommended for ethical reasons and potential longer-term harm. Here we review the methods used to prepare a child for an MRI scan, but also on the techniques and tools used during the scanning to enable a successful scan. Additionally, we critically evaluate how studies have reported the scanning procedure and success of scanning. We searched articles based on special subject headings from PubMed and identified 86 studies using brain MRI in healthy subjects between 0 and 6 years of age. Scan preparations expectedly depended on subject's age; infants and young children were scanned asleep after feeding and swaddling and older children were scanned awake. Comparing the efficiency of different procedures was difficult because of the heterogeneous reporting of the used methods and the success rates. Based on this review, we recommend more detailed reporting of scanning procedure to help find out which are the factors affecting the success of scanning. In the long term, this could help the research field to get high quality data, but also the clinical field to reduce the use of anesthetics. Finally, we introduce the protocol used in scanning 2 to 5-week-old infants in the FinnBrain Birth Cohort Study, and tips for calming neonates during the scans.
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Affiliation(s)
- Anni Copeland
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Eero Silver
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Riikka Korja
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychology, University of Turku, Turku, Finland
| | - Satu J. Lehtola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Harri Merisaari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Radiology, Turku University Hospital, University of Turku, Turku, Finland
| | - Ekaterina Saukko
- Department of Radiology, Turku University Hospital, University of Turku, Turku, Finland
| | - Susanne Sinisalo
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Tuire Lähdesmäki
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Pediatric Neurology, Turku University Hospital, University of Turku, Turku, Finland
| | - Riitta Parkkola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Radiology, Turku University Hospital, University of Turku, Turku, Finland
| | - Saara Nolvi
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychology and Speech-Language Pathology, Turku Institute for Advanced Studies, University of Turku, Turku, Finland
| | - Linnea Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
| | - Hasse Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Jetro J. Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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191
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Harder FN, Budjan J, Nickel MD, Grimm R, Pietsch H, Schoenberg SO, Jost G, Attenberger UI. Intraindividual Comparison of Compressed Sensing-Accelerated Cartesian and Radial Arterial Phase Imaging of the Liver in an Experimental Tumor Model. Invest Radiol 2021; 56:433-441. [PMID: 33813577 DOI: 10.1097/rli.0000000000000767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES The aim of this study was to intraindividually compare the performance of 2 compressed sensing (CS)-accelerated magnetic resonance imaging (MRI) sequences, 1 featuring Cartesian (compressed sensing volumetric interpolated breath-hold examination [CS-VIBE]) and the other radial (golden-angle radial sparse parallel [GRASP]) k-space sampling in continuous dynamic imaging during hepatic vascular phases, using extracellular and hepatocyte-specific contrast agents. MATERIALS AND METHODS Seven New Zealand white rabbits, with induced VX2 liver tumors (median number of lesions, 2 ± 0.83; range, 1-3), received 2 continuously acquired T1-weighted prototype CS-accelerated MRI sequences (CS-VIBE and GRASP) with high spatial (0.8 × 0.8 × 1.5 mm) and temporal resolution (3.5 seconds) in randomized order on 2 separate days using a 1.5-T scanner. In all animals, imaging was performed using first gadobutrol at a dose of 0.1 mmol/kg and, then 45 minutes later, gadoxetic acid at a dose of 0.025 mmol/kg.The following qualitative parameters were assessed using 3- and 5-point Likert scales (3 and 5 being the highest scores respectively): image quality (IQ), arterial and venous vessel delineation, tumor enhancement, motion artifacts, and sequence-specific artifacts. Furthermore, the following quantitative parameters were obtained: relative peak signal enhancement, time to peak, mean transit time, and plasma flow ratios. Paired sampled t tests and Wilcoxon signed rank tests were used for intraindividual comparison. Image analysis was performed by 2 radiologists. RESULTS Six of 7 animals underwent the full imaging protocol and obtained data were analyzed statistically. Overall IQ was rated moderate to excellent, not differing significantly between the 2 sequences.Gadobutrol-enhanced CS-VIBE examinations revealed the highest mean Likert scale values in terms of vessel delineation and tumor enhancement (arterial 4.4 [4-5], venous 4.3 [3-5], and tumor 2.9 [2-3]). Significantly, more sequence-specific artifacts were seen in GRASP examinations (P = 0.008-0.031). However, these artifacts did not impair IQ. Excellent Likert scale ratings were found for motion artifacts in both sequences. In both sequences, a maximum of 4 hepatic arterial dominant phases were obtained. Regarding the relative peak signal enhancement, CS-VIBE and GRASP showed similar results. The relative peak signal enhancement values did not differ significantly between the 2 sequences in the aorta, the hepatic artery, or the inferior vena cava (P = 0.063-0.536). However, significantly higher values were noted for CS-VIBE in gadoxetic acid-enhanced examinations in the portal vein (P = 0.031) and regarding the tumor enhancement (P = 0.005). Time to peak and mean transit time or plasma flow ratios did not differ significantly between the sequences. CONCLUSIONS Both CS-VIBE and GRASP provide excellent results in dynamic liver MRI using extracellular and hepatocyte-specific contrast agents, in terms of IQ, peak signal intensity, and presence of artifacts.
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Affiliation(s)
- Felix N Harder
- From the Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich
| | | | | | | | | | - Stefan O Schoenberg
- Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim
| | - Gregor Jost
- MR and CT Contrast Media Research, Bayer AG, Berlin
| | - Ulrike I Attenberger
- Department of Diagnostic and Interventional Radiology, University of Bonn, Bonn, Germany
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192
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van Riel MHC, Yu Z, Hodono S, Xia D, Chandarana H, Fujimoto K, Cloos MA. Free-breathing abdominal T 1 mapping using an optimized MR fingerprinting sequence. NMR IN BIOMEDICINE 2021; 34:e4531. [PMID: 33902155 PMCID: PMC8218311 DOI: 10.1002/nbm.4531] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 04/03/2021] [Accepted: 04/05/2021] [Indexed: 05/31/2023]
Abstract
In this work, we propose a free-breathing magnetic resonance fingerprinting (MRF) method that can be used to obtain B1+ -robust quantitative T1 maps of the abdomen in a clinically acceptable time. A three-dimensional MRF sequence with a radial stack-of-stars trajectory was implemented, and its k-space acquisition ordering was adjusted to improve motion-robustness in the context of MRF. The flip angle pattern was optimized using the Cramér-Rao Lower Bound, and the encoding efficiency of sequences with 300, 600, 900 and 1800 flip angles was evaluated. To validate the sequence, a movable multicompartment phantom was developed. Reference multiparametric maps were acquired under stationary conditions using a previously validated MRF method. Periodic motion of the phantom was used to investigate the motion-robustness of the proposed sequence. The best performing sequence length (600 flip angles) was used to image the abdomen during a free-breathing volunteer scan. When using a series of 600 or more flip angles, the estimated T1 values in the stationary phantom showed good agreement with the reference scan. Phantom experiments revealed that motion-related artifacts can appear in the quantitative maps and confirmed that a motion-robust k-space ordering is essential. The in vivo scan demonstrated that the proposed sequence can produce clean parameter maps while the subject breathes freely. Using this sequence, it is possible to generate B1+ -robust quantitative maps of T1 and B1+ next to M0 -weighted images under free-breathing conditions at a clinically usable resolution within 5 min.
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Affiliation(s)
- Max H. C. van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Zidan Yu
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
| | - Shota Hodono
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
- Centre for Advanced Imaging, University of Queensland, St Lucia, Queensland, Australia
| | - Ding Xia
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Koji Fujimoto
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Real World Data Research and Development, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Martijn A. Cloos
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
- Centre for Advanced Imaging, University of Queensland, St Lucia, Queensland, Australia
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193
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Dell'Orso A, Positano V, Arisi G, d'Errico F, Taddei A, Banchi B, De Felice C. OPERA: a novel method to reduce ghost and aliasing artifacts. MAGMA (NEW YORK, N.Y.) 2021; 34:451-467. [PMID: 32785807 DOI: 10.1007/s10334-020-00881-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 06/29/2020] [Accepted: 08/03/2020] [Indexed: 06/11/2023]
Abstract
OBJECTIVE A method for Orthogonal Phase Encoding Reduction of Artifact (OPERA) was developed and tested. MATERIALS AND METHODS Because the position of ghosts and aliasing artifacts is predictable along columns or rows, OPERA combines the intensity values of two images acquired using the same parameters, but with swapped phase-encoding directions, to correct the artifacts. Simulations and phantom experiments were conducted to define the efficacy, robustness, and reproducibility. Clinical validation was performed on a total of 1003 images by comparing the OPERA-corrected images and the corresponding image standard in terms of Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR). The method efficacy was also rated using a Likert-type scale response by two experienced independent radiologists using a single-blinded procedure. RESULTS Simulations and phantom experiments demonstrated the robustness and effectiveness of OPERA in reducing artifacts strength. OPERA application did not significantly change the SNR [+ 4.16%; inter-quartile range (IQR): 2.72-5.01%] and CNR (+ 4.30%; IQR: 2.86-6.04%) values. The two radiologists observed a total of 893 original images with artifacts (89.03% of the total images), a reduction in the perceived artifacts of 82.0% and 83.9% (p < 0.0001), and an improvement in the perceived SNR (82.8% and 88.5%; K = 0.714) and perceived CNR (86.9-88.9%; K = 0.722). DISCUSSION The study demonstrated that OPERA reduces MR artifacts and improves the perceived image quality.
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Affiliation(s)
- Andrea Dell'Orso
- Department of Radiology, San Giuseppe Hospital, Empoli AO Toscana Centro, Viale Boccaccio 14, Florence, Italy.
| | | | | | - Francesco d'Errico
- Università di Pisa, Scuola di Ingegneria, Pisa, Italy
- School of Medicine, Yale University, New Haven, CT, USA
| | - Aldo Taddei
- Clinical Department of Radiology, AO Toscana SUD-EST, Poggibonsi General Hospital, Poggibonsi, Italy
| | | | - Claudio De Felice
- AOUS, Neonatal Intensive Care Unit, S.M. Alle Scotte General Hospital, Siena, Italy
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194
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Riedel Né Steinhoff M, Setsompop K, Mertins A, Börnert P. Segmented simultaneous multi-slice diffusion-weighted imaging with navigated 3D rigid motion correction. Magn Reson Med 2021; 86:1701-1717. [PMID: 33955588 DOI: 10.1002/mrm.28813] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 03/29/2021] [Accepted: 03/29/2021] [Indexed: 11/09/2022]
Abstract
PURPOSE To improve the robustness of diffusion-weighted imaging (DWI) data acquired with segmented simultaneous multi-slice (SMS) echo-planar imaging (EPI) against in-plane and through-plane rigid motion. THEORY AND METHODS The proposed algorithm incorporates a 3D rigid motion correction and wavelet denoising into the image reconstruction of segmented SMS-EPI diffusion data. Low-resolution navigators are used to estimate shot-specific diffusion phase corruptions and 3D rigid motion parameters through SMS-to-volume registration. The shot-wise rigid motion and phase parameters are integrated into a SENSE-based full-volume reconstruction for each diffusion direction. The algorithm is compared to a navigated SMS reconstruction without gross motion correction in simulations and in vivo studies with four-fold interleaved 3-SMS diffusion tensor acquisitions. RESULTS Simulations demonstrate high fidelity was achieved in the SMS-to-volume registration, with submillimeter registration errors and improved image reconstruction quality. In vivo experiments validate successful artifact reduction in 3D motion-compromised in vivo scans with a temporal motion resolution of approximately 0.3 s. CONCLUSION This work demonstrates the feasibility of retrospective 3D rigid motion correction from shot navigators for segmented SMS DWI.
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Affiliation(s)
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Alfred Mertins
- Institute for Signal Processing, University of Luebeck, Luebeck, Germany
| | - Peter Börnert
- Philips Research, Hamburg, Germany.,Radiology, C.J. Gorter Center for High-Field MRI, Leiden University Medical Center, Leiden, The Netherlands
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195
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Andronesi OC, Bhattacharyya PK, Bogner W, Choi IY, Hess AT, Lee P, Meintjes E, Tisdall MD, Zaitzev M, van der Kouwe A. Motion correction methods for MRS: experts' consensus recommendations. NMR IN BIOMEDICINE 2021; 34:e4364. [PMID: 33089547 PMCID: PMC7855523 DOI: 10.1002/nbm.4364] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 06/07/2020] [Accepted: 06/08/2020] [Indexed: 05/07/2023]
Abstract
Long acquisition times due to intrinsically low signal-to-noise ratio and the need for highly homogeneous B0 field make MRS particularly susceptible to motion or scanner instability compared with MRI. Motion-induced changes in both localization and shimming (ie B0 homogeneity) degrade MRS data quality. To mitigate the effects of motion three approaches can be employed: (1) subject immobilization, (2) retrospective correction, and (3) prospective real-time correction using internal and/or external tracking methods. Prospective real-time correction methods can simultaneously update localization and the B0 field to improve MRS data quality. While localization errors can be corrected with both internal (navigators) and external (optical camera, NMR probes) tracking methods, the B0 field correction requires internal navigator methods to measure the B0 field inside the imaged volume and the possibility to update the scanner shim hardware in real time. Internal and external tracking can rapidly update the MRS localization with submillimeter and subdegree precision, while scanner frequency and first-order shims of scanner hardware can be updated by internal methods every sequence repetition. These approaches are most well developed for neuroimaging, for which rigid transformation is primarily applicable. Real-time correction greatly improves the stability of MRS acquisition and quantification, as shown in clinical studies on subjects prone to motion, including children and patients with movement disorders, enabling robust measurement of metabolite signals including those with low concentrations, such as gamma-aminobutyric acid and glutathione. Thus, motion correction is recommended for MRS users and calls for tighter integration and wider availability of such methods by MR scanner manufacturers.
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Affiliation(s)
- Ovidiu C. Andronesi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Corresponding Author: Ovidiu C. Andronesi, MD, PhD, Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Thirteenth Street, Charlestown, MA 02129, USA;
| | | | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - In-Young Choi
- Department of Neurology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, USA
| | - Aaron T. Hess
- University of Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, University of Oxford
| | - Phil Lee
- Department of Radiology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, USA
| | - Ernesta Meintjes
- UCT Medical Imaging Research Unit, Division of Biomedical Engineering, Department of Human Biology, University of Cape Town
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania
| | - Maxim Zaitzev
- Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- High Field Magnetic Resonance Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - André van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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196
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Radial Undersampling-Based Interpolation Scheme for Multislice CSMRI Reconstruction Techniques. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6638588. [PMID: 33954189 PMCID: PMC8057880 DOI: 10.1155/2021/6638588] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 04/05/2021] [Indexed: 11/18/2022]
Abstract
Magnetic Resonance Imaging (MRI) is an important yet slow medical imaging modality. Compressed sensing (CS) theory has enabled to accelerate the MRI acquisition process using some nonlinear reconstruction techniques from even 10% of the Nyquist samples. In recent years, interpolated compressed sensing (iCS) has further reduced the scan time, as compared to CS, by exploiting the strong interslice correlation of multislice MRI. In this paper, an improved efficient interpolated compressed sensing (EiCS) technique is proposed using radial undersampling schemes. The proposed efficient interpolation technique uses three consecutive slices to estimate the missing samples of the central target slice from its two neighboring slices. Seven different evaluation metrics are used to analyze the performance of the proposed technique such as structural similarity index measurement (SSIM), feature similarity index measurement (FSIM), mean square error (MSE), peak signal to noise ratio (PSNR), correlation (CORR), sharpness index (SI), and perceptual image quality evaluator (PIQE) and compared with the latest interpolation techniques. The simulation results show that the proposed EiCS technique has improved image quality and performance using both golden angle and uniform angle radial sampling patterns, with an even lower sampling ratio and maximum information content and using a more practical sampling scheme.
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197
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Hucker P, Dacko M, Zaitsev M. Combining prospective and retrospective motion correction based on a model for fast continuous motion. Magn Reson Med 2021; 86:1284-1298. [PMID: 33829538 DOI: 10.1002/mrm.28783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 03/05/2021] [Accepted: 03/06/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE Prospective motion correction (PMC) and retrospective motion correction (RMC) have different advantages and limitations. The present work aims to combine the advantages of both for rigid body motion, aiming at correcting for faster motions than was previously achievable. Additionally, it provides insights into the effects of motion on pulse sequences and MR signals with a goal of further improving motion correction in the future. METHODS The effective encoding trajectory and a global phase offset in a moving object are calculated based on complete gradient waveforms of an arbitrary sequence and a continuous motion model. These data are used to feed the forward signal model, which is then used in iterative image reconstruction to suppress the artifacts still present after the PMC. RESULTS Verification experiments with a rotation phantom and in vivo were performed. Predictions of simulated motion artifacts for PMC based on sequence waveforms are very accurate. The performance at combined PMC+RMC is limited by Nyquist violations in the sampled k-space and can be compensated by oversampling. CONCLUSION The combined correction results in better images than pure PMC in the presence of fast motion. The predictions of artifacts are very accurate, allowing for comparing sequences or protocols in simulations. The observed artifacts due to Nyquist violations are expected to be corrected by utilizing parallel imaging.
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Affiliation(s)
- Patrick Hucker
- Center for Diagnostic and Therapeutic Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michael Dacko
- Center for Diagnostic and Therapeutic Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Maxim Zaitsev
- Center for Diagnostic and Therapeutic Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,High Field Magnetic Resonance Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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198
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Xiao Z, Du N, Liu J, Zhang W. SR-Net: A sequence offset fusion net and refine net for undersampled multislice MR image reconstruction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 202:105997. [PMID: 33621943 DOI: 10.1016/j.cmpb.2021.105997] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 02/06/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE The study of deep learning-based fast magnetic resonance imaging (MRI) reconstruction methods has become popular in recent years. However, there is still a challenge when MRI results undersample large acceleration factors. The objective of this study was to improve the reconstruction quality of undersampled MR images by exploring data redundancy among slices. METHODS There are two aspects of redundancy in multislice MR images including correlations inside a single slice and correlations among slices. Thus, we built two subnets for the two kinds of redundancy. For correlations among slices, we built a bidirectional recurrent convolutional neural network, named Sequence Offset Fusion Net (S-Net). In S-Net, we used a deformable convolution module to construct a neighbor slice feature extractor. For the correlation inside a single slice, we built a Refine Net (R-Net), which has 5 layers of 2D convolutions. In addition, we used a data consistency (DC) operation to maintain data fidelity in k-space. Finally, we treated the reconstruction task as a dealiasing problem in the image domain, and S-Net and R-Net are applied alternately and iteratively to generate the final reconstructions. RESULTS The proposed algorithm was evaluated using two online public MRI datasets. Compared with several state-of-the-art methods, the proposed method achieved better reconstruction results in terms of dealiasing and restoring tissue structure. Moreover, with over 14 slices per second reconstruction speed on 256x256 pixel images, the proposed method can meet the need for real-time processing. CONCLUSION With spatial correlation among slices as additional prior information, the proposed method dramatically improves the reconstruction quality of undersampled MR images.
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Affiliation(s)
- Zhiyong Xiao
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
| | - Nianmao Du
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
| | - Jianjun Liu
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
| | - Weidong Zhang
- Department of Automation, Shanghai JiaoTong University, Shanghai 200240, China.
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199
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Parker DB, Spincemaille P, Razlighi QR. Attenuation of motion artifacts in fMRI using discrete reconstruction of irregular fMRI trajectories (DRIFT). Magn Reson Med 2021; 86:1586-1599. [PMID: 33797118 DOI: 10.1002/mrm.28723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 01/16/2021] [Accepted: 01/19/2021] [Indexed: 11/10/2022]
Abstract
PURPOSE Numerous studies report motion as the most detrimental source of noise and artifacts in fMRI. Current motion correction methods fail to completely address the motion problem. Retrospective techniques such as spatial realignment can correct for between-volume misalignment but fail to address within volume contamination and spin-history artifacts. Prospective motion correction can prevent spin-history artifacts but currently cannot update the gradients fast enough to remove k-space filling artifacts, calling for a hybrid approach to fully address these problems. THEORY AND METHODS Motion can be mathematically formulated into the MR signal equation to describe the motion artifacts at their origin in k-space. From these equations, it is demonstrated that different motions have different effects on the signal. A novel motion correction algorithm is designed from these equations to remove motion-induced artifacts directly in k-space, discrete reconstruction of irregular fMRI trajectory (DRIFT). This method is evaluated rigorously using fMRI simulations and data from a rotating phantom inside the scanner. RESULTS The results indicate that although some motion types have negligible effects on the MR signal, others produce catastrophic and lasting artifacts even after motion cessation. In simulation, DRIFT is able to remove motion artifacts in the absence of spin history. In a phantom scan, DRIFT significantly attenuates the motion artifacts in the fMRI data. CONCLUSION Neither prospective nor retrospective motion correction methods could completely remove the motion artifacts from the fMRI data. However, DRIFT, as a retrospective technique, when combined with prospective motion correction, can eliminate a significant portion of motion artifacts.
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Affiliation(s)
- David B Parker
- Department of Biomedical Engineering, Columbia University, New York City, NY, USA
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200
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Fantini I, Yasuda C, Bento M, Rittner L, Cendes F, Lotufo R. Automatic MR image quality evaluation using a Deep CNN: A reference-free method to rate motion artifacts in neuroimaging. Comput Med Imaging Graph 2021; 90:101897. [PMID: 33770561 DOI: 10.1016/j.compmedimag.2021.101897] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2018] [Revised: 01/25/2019] [Accepted: 03/05/2021] [Indexed: 12/21/2022]
Abstract
Motion artifacts on magnetic resonance (MR) images degrade image quality and thus negatively affect clinical and research scanning. Considering the difficulty in preventing patient motion during MR examinations, the identification of motion artifact has attracted significant attention from researchers. We propose an automatic method for the evaluation of motion corrupted images using a deep convolutional neural network (CNN). Deep CNNs has been used widely in image classification tasks. While such methods require a significant amount of annotated training data, a scarce resource in medical imaging, the transfer learning and fine-tuning approaches allow us to use a smaller amount of data. Here we selected four renowned architectures, initially trained on Imagenet contest dataset, to fine-tune. The models were fine-tuned using patches from an annotated dataset composed of 68 T1-weighted volumetric acquisitions from healthy volunteers. For training and validation 48 images were used, while the remaining 20 images were used for testing. Each architecture was fine-tuned for each MR axis, detecting the motion artifact per patches from the three orthogonal MR acquisition axes. The overall average accuracy for the twelve models (three axes for each of four architecture) was 86.3%. As our goal was to detect fine-grained corruption in the image, we performed an extensive search on lower layers from each of the four architectures, since they filter small regions in the original input. Experiments showed that architectures with fewer layers than the original ones reported the better results for image patches with an overall average accuracy of 90.4%. The accuracies per architecture were similar so we decided to explore all four architectures performing a result consensus. Also, to determine the probability of motion artifacts presence on the whole acquisition a combination of the three axes were performed. The final architecture consists of an artificial neural network (ANN) classifier combining all models from the four shallower architectures, which overall acquisition-based accuracy was 100.0%. The proposed method generalization was tested using three different MR data: (1) MR image acquired in epilepsy patients (93 acquisitions); (2) MR image presenting susceptibility artifact (22 acquisitions); and (3) MR image acquired from different scanner vendor (20 acquisitions). The achieved acquisition-based accuracy on generalization tests (1) 90.3%, (2) 63.6%, and (3) 75.0%) suggests that domain adaptation is necessary. Our proposed method can be rapidly applied to large amounts of image data, providing a motion probability p∈[0,1] per acquisition. This method output can be used as a scale to identify the motion corrupted images from the dataset, thus minimizing the time spent on visual quality control.
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Affiliation(s)
- Irene Fantini
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Brazil.
| | - Clarissa Yasuda
- Neuroimaging Laboratory, Faculty of Medical Sciences, University of Campinas (UNICAMP), Brazil; Department of Neurology, Faculty of Medical Sciences, University of Campinas (UNICAMP), Brazil
| | - Mariana Bento
- Radiology and Clinical Neurosciences, Hothckiss Brain Institute, University of Calgary, Canada; Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Alberta Heath Services, Canada
| | - Leticia Rittner
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Brazil
| | - Fernando Cendes
- Neuroimaging Laboratory, Faculty of Medical Sciences, University of Campinas (UNICAMP), Brazil; Department of Neurology, Faculty of Medical Sciences, University of Campinas (UNICAMP), Brazil
| | - Roberto Lotufo
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Brazil
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