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Chen Z, Hua S, Gao J, Chen Y, Gong Y, Shen Y, Tang X, Emu Y, Jin W, Hu C. A dual-stage partially interpretable neural network for joint suppression of bSSFP banding and flow artifacts in non-phase-cycled cine imaging. J Cardiovasc Magn Reson 2023; 25:68. [PMID: 37993824 PMCID: PMC10666342 DOI: 10.1186/s12968-023-00988-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 11/12/2023] [Indexed: 11/24/2023] Open
Abstract
PURPOSE To develop a partially interpretable neural network for joint suppression of banding and flow artifacts in non-phase-cycled bSSFP cine imaging. METHODS A dual-stage neural network consisting of a voxel-identification (VI) sub-network and artifact-suppression (AS) sub-network is proposed. The VI sub-network provides identification of artifacts, which guides artifact suppression and improves interpretability. The AS sub-network reduces banding and flow artifacts. Short-axis cine images of 12 frequency offsets from 28 healthy subjects were used to train and test the dual-stage network. An additional 77 patients were retrospectively enrolled to evaluate its clinical generalizability. For healthy subjects, artifact suppression performance was analyzed by comparison with traditional phase cycling. The partial interpretability provided by the VI sub-network was analyzed via correlation analysis. Generalizability was evaluated for cine obtained with different sequence parameters and scanners. For patients, artifact suppression performance and partial interpretability of the network were qualitatively evaluated by 3 clinicians. Cardiac function before and after artifact suppression was assessed via left ventricular ejection fraction (LVEF). RESULTS For the healthy subjects, visual inspection and quantitative analysis found a considerable reduction of banding and flow artifacts by the proposed network. Compared with traditional phase cycling, the proposed network improved flow artifact scores (4.57 ± 0.23 vs 3.40 ± 0.38, P = 0.002) and overall image quality (4.33 ± 0.22 vs 3.60 ± 0.38, P = 0.002). The VI sub-network well identified the location of banding and flow artifacts in the original movie and significantly correlated with the change of signal intensities in these regions. Changes of imaging parameters or the scanner did not cause a significant change of overall image quality relative to the baseline dataset, suggesting a good generalizability. For the patients, qualitative analysis showed a significant improvement of banding artifacts (4.01 ± 0.50 vs 2.77 ± 0.40, P < 0.001), flow artifacts (4.22 ± 0.38 vs 2.97 ± 0.57, P < 0.001), and image quality (3.91 ± 0.45 vs 2.60 ± 0.43, P < 0.001) relative to the original cine. The artifact suppression slightly reduced the LVEF (mean bias = -1.25%, P = 0.01). CONCLUSIONS The dual-stage network simultaneously reduces banding and flow artifacts in bSSFP cine imaging with a partial interpretability, sparing the need for sequence modification. The method can be easily deployed in a clinical setting to identify artifacts and improve cine image quality.
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Affiliation(s)
- Zhuo Chen
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, 415 S Med-X Center, 1954 Huashan Road, Shanghai, 200030, China
| | - Sha Hua
- Department of Cardiovascular Medicine, Heart Failure Center, Ruijin Hospital Lu Wan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Juan Gao
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, 415 S Med-X Center, 1954 Huashan Road, Shanghai, 200030, China
| | - Yanjia Chen
- Department of Cardiovascular Medicine, Heart Failure Center, Ruijin Hospital Lu Wan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yiwen Gong
- Department of Cardiovascular Medicine, Heart Failure Center, Ruijin Hospital Lu Wan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yiwen Shen
- Department of Cardiovascular Medicine, Heart Failure Center, Ruijin Hospital Lu Wan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xin Tang
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, 415 S Med-X Center, 1954 Huashan Road, Shanghai, 200030, China
| | - Yixin Emu
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, 415 S Med-X Center, 1954 Huashan Road, Shanghai, 200030, China
| | - Wei Jin
- Department of Cardiovascular Medicine, Heart Failure Center, Ruijin Hospital Lu Wan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chenxi Hu
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, 415 S Med-X Center, 1954 Huashan Road, Shanghai, 200030, China.
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Gao Y, Liu W(V, Li L, Liu C, Zha Y. Usefulness of T2-Weighted Images with Deep-Learning-Based Reconstruction in Nasal Cartilage. Diagnostics (Basel) 2023; 13:3044. [PMID: 37835786 PMCID: PMC10572289 DOI: 10.3390/diagnostics13193044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/22/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023] Open
Abstract
OBJECTIVE This study aims to evaluate the feasibility of visualizing nasal cartilage using deep-learning-based reconstruction (DLR) fast spin-echo (FSE) imaging in comparison to three-dimensional fast spoiled gradient-echo (3D FSPGR) images. MATERIALS AND METHODS This retrospective study included 190 set images of 38 participants, including axial T1- and T2-weighted FSE images using DLR (T1WIDL and T2WIDL, belong to FSEDL) and without using DLR (T1WIO and T2WIO, belong to FSEO) and 3D FSPGR images. Subjective evaluation (overall image quality, noise, contrast, artifacts, and identification of anatomical structures) was independently conducted by two radiologists. Objective evaluation including signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) was conducted using manual region-of-interest (ROI)-based analysis. Coefficient of variation (CV) and Bland-Altman plots were used to demonstrate the intra-rater repeatability of measurements for cartilage thickness on five different images. RESULTS Both qualitative and quantitative results confirmed superior FSEDL to 3D FSPGR images (both p < 0.05), improving the diagnosis confidence of the observers. Lower lateral cartilage (LLC), upper lateral cartilage (ULC), and septal cartilage (SP) were relatively well delineated on the T2WIDL, while 3D FSPGR showed poorly on the septal cartilage. For the repeatability of cartilage thickness measurements, T2WIDL showed the highest intra-observer (%CV = 8.7% for SP, 9.5% for ULC, and 9.7% for LLC) agreements. In addition, the acquisition time for T1WIDL and T2WIDL was respectively reduced by 14.2% to 29% compared to 3D FSPGR (both p < 0.05). CONCLUSIONS Two-dimensional equivalent-thin-slice T1- and T2-weighted images using DLR showed better image quality and shorter scan time than 3D FSPGR and conventional construction images in nasal cartilages. The anatomical details were preserved without losing clinical performance on diagnosis and prognosis, especially for pre-rhinoplasty planning.
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Affiliation(s)
- Yufan Gao
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | | | - Liang Li
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Changsheng Liu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
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Kim KH, Seo S, Do WJ, Luu HM, Park SH. Varying undersampling directions for accelerating multiple acquisition magnetic resonance imaging. NMR IN BIOMEDICINE 2022; 35:e4572. [PMID: 34114253 DOI: 10.1002/nbm.4572] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 05/27/2021] [Accepted: 05/27/2021] [Indexed: 06/12/2023]
Abstract
In this study, we propose a new sampling strategy for efficiently accelerating multiple acquisition MRI. The new sampling strategy is to obtain data along different phase-encoding directions across multiple acquisitions. The proposed sampling strategy was evaluated in multicontrast MR imaging (T1, T2, proton density) and multiple phase-cycled (PC) balanced steady-state free precession (bSSFP) imaging by using convolutional neural networks with central and random sampling patterns. In vivo MRI acquisitions as well as a public database were used to test the concept. Based on both visual inspection and quantitative analysis, the proposed sampling strategy showed better performance than sampling along the same phase-encoding direction in both multicontrast MR imaging and multiple PC-bSSFP imaging, regardless of sampling pattern (central, random) or datasets (public, retrospective and prospective in vivo). For the prospective in vivo applications, acceleration was performed by sampling along different phase-encoding directions at the time of acquisition with a conventional rectangular field of view, which demonstrated the advantage of the proposed sampling strategy in the real environment. Preliminary trials on compressed sensing (CS) also demonstrated improvement of CS with the proposed idea. Sampling along different phase-encoding directions across multiple acquisitions is advantageous for accelerating multiacquisition MRI, irrespective of sampling pattern or datasets, with further improvement through transfer learning.
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Affiliation(s)
- Ki Hwan Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Sunghun Seo
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Won-Joon Do
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Huan Minh Luu
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Sung-Hong Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
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Bone and Soft Tissue Tumors. Radiol Clin North Am 2022; 60:339-358. [DOI: 10.1016/j.rcl.2021.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Keskin K, Yilmaz U, Cukur T. Constrained Ellipse Fitting for Efficient Parameter Mapping With Phase-Cycled bSSFP MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:14-26. [PMID: 34351856 DOI: 10.1109/tmi.2021.3102852] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Balanced steady-state free precession (bSSFP) imaging enables high scan efficiency in MRI, but differs from conventional sequences in terms of elevated sensitivity to main field inhomogeneity and nonstandard [Formula: see text]-weighted tissue contrast. To address these limitations, multiple bSSFP images of the same anatomy are commonly acquired with a set of different RF phase-cycling increments. Joint processing of phase-cycled acquisitions serves to mitigate sensitivity to field inhomogeneity. Recently phase-cycled bSSFP acquisitions were also leveraged to estimate relaxation parameters based on explicit signal models. While effective, these model-based methods often involve a large number of acquisitions (N ≈ 10-16), degrading scan efficiency. Here, we propose a new constrained ellipse fitting method (CELF) for parameter estimation with improved efficiency and accuracy in phase-cycled bSSFP MRI. CELF is based on the elliptical signal model framework for complex bSSFP signals; and it introduces geometrical constraints on ellipse properties to improve estimation efficiency, and dictionary-based identification to improve estimation accuracy. CELF generates maps of [Formula: see text], [Formula: see text], off-resonance and on-resonant bSSFP signal by employing a separate [Formula: see text] map to mitigate sensitivity to flip angle variations. Our results indicate that CELF can produce accurate off-resonance and banding-free bSSFP maps with as few as N = 4 acquisitions, while estimation accuracy for relaxation parameters is notably limited by biases from microstructural sensitivity of bSSFP imaging.
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What's New and What's Next in Diffusion MRI Preprocessing. Neuroimage 2021; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing.
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Richardson ML, Garwood ER, Lee Y, Li MD, Lo HS, Nagaraju A, Nguyen XV, Probyn L, Rajiah P, Sin J, Wasnik AP, Xu K. Noninterpretive Uses of Artificial Intelligence in Radiology. Acad Radiol 2021; 28:1225-1235. [PMID: 32059956 DOI: 10.1016/j.acra.2020.01.012] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/08/2020] [Accepted: 01/09/2020] [Indexed: 12/12/2022]
Abstract
We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would normally require intelligent action by a human. Much of the recent excitement about AI in the medical literature has revolved around the ability of AI models to recognize anatomy and detect pathology on medical images, sometimes at the level of expert physicians. However, AI can also be used to solve a wide range of noninterpretive problems that are relevant to radiologists and their patients. This review summarizes some of the newer noninterpretive uses of AI in radiology.
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Affiliation(s)
| | - Elisabeth R Garwood
- Department of Radiology, University of Massachusetts, Worcester, Massachusetts
| | - Yueh Lee
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina
| | - Matthew D Li
- Department of Radiology, Harvard Medical School/Massachusetts General Hospital, Boston, Massachusets
| | - Hao S Lo
- Department of Radiology, University of Washington, Seattle, Washington
| | - Arun Nagaraju
- Department of Radiology, University of Chicago, Chicago, Illinois
| | - Xuan V Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Linda Probyn
- Department of Radiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario
| | - Prabhakar Rajiah
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Jessica Sin
- Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Ashish P Wasnik
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Kali Xu
- Department of Medicine, Santa Clara Valley Medical Center, Santa Clara, California
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van der Velde N, Hassing HC, Bakker BJ, Wielopolski PA, Lebel RM, Janich MA, Kardys I, Budde RPJ, Hirsch A. Improvement of late gadolinium enhancement image quality using a deep learning-based reconstruction algorithm and its influence on myocardial scar quantification. Eur Radiol 2021; 31:3846-3855. [PMID: 33219845 PMCID: PMC8128730 DOI: 10.1007/s00330-020-07461-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 10/09/2020] [Accepted: 11/03/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVES The aim of this study was to assess the effect of a deep learning (DL)-based reconstruction algorithm on late gadolinium enhancement (LGE) image quality and to evaluate its influence on scar quantification. METHODS Sixty patients (46 ± 17 years, 50% male) with suspected or known cardiomyopathy underwent CMR. Short-axis LGE images were reconstructed using the conventional reconstruction and a DL network (DLRecon) with tunable noise reduction (NR) levels from 0 to 100%. Image quality of standard LGE images and DLRecon images with 75% NR was scored using a 5-point scale (poor to excellent). In 30 patients with LGE, scar size was quantified using thresholding techniques with different standard deviations (SD) above remote myocardium, and using full width at half maximum (FWHM) technique in images with varying NR levels. RESULTS DLRecon images were of higher quality than standard LGE images (subjective quality score 3.3 ± 0.5 vs. 3.6 ± 0.7, p < 0.001). Scar size increased with increasing NR levels using the SD methods. With 100% NR level, scar size increased 36%, 87%, and 138% using 2SD, 4SD, and 6SD quantification method, respectively, compared to standard LGE images (all p values < 0.001). However, with the FWHM method, no differences in scar size were found (p = 0.06). CONCLUSIONS LGE image quality improved significantly using a DL-based reconstruction algorithm. However, this algorithm has an important impact on scar quantification depending on which quantification technique is used. The FWHM method is preferred because of its independency of NR. Clinicians should be aware of this impact on scar quantification, as DL-based reconstruction algorithms are being used. KEY POINTS • The image quality based on (subjective) visual assessment and image sharpness of late gadolinium enhancement images improved significantly using a deep learning-based reconstruction algorithm that aims to reconstruct high signal-to-noise images using a denoising technique. • Special care should be taken when scar size is quantified using thresholding techniques with different standard deviations above remote myocardium because of the large impact of these advanced image enhancement algorithms. • The full width at half maximum method is recommended to quantify scar size when deep learning algorithms based on noise reduction are used, as this method is the least sensitive to the level of noise and showed the best agreement with visual late gadolinium enhancement assessment.
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Affiliation(s)
- Nikki van der Velde
- Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - H Carlijne Hassing
- Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Brendan J Bakker
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Piotr A Wielopolski
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | | | - Isabella Kardys
- Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Ricardo P J Budde
- Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Alexander Hirsch
- Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
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Kielbasa JE, Meeks SL, Kelly P, Willoughby TR, Zeidan O, Shah AP. Evaluation of cine imaging during multileaf collimator and gantry motion for real-time magnetic resonance guided radiation therapy. J Appl Clin Med Phys 2020; 21:178-187. [PMID: 33226709 PMCID: PMC7769407 DOI: 10.1002/acm2.13085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 08/11/2020] [Accepted: 08/17/2020] [Indexed: 11/22/2022] Open
Abstract
Purpose Real‐time magnetic resonance guided radiation therapy (MRgRT) uses 2D cine imaging for target tracking. This work evaluates the percent image uniformity (PIU) and spatial integrity of cine images in the presence of multileaf collimator (MLC) and gantry motion in order to simulate sliding window and volumetric modulated arc therapy (VMAT) conditions. Methods Percent image uniformity and spatial integrity of cine images were measured (1) during MLC motion, (2) as a function of static gantry position, and (3) during gantry rotation. PIU was calculated according to the ACR MRI Quality Control Manual. Spatial integrity was evaluated by measuring the geometric distortion of 16 measured marker positions (10 cm or 15.225 cm from isocenter). Results The PIU of cine images did not vary by more than 1% from static linac conditions during MLC motion and did not vary by more than 3% during gantry rotation. Banding artifacts were present during gantry rotation. The geometric distortion in the cine images was less than 0.88 mm for all points measured throughout MLC motion. For all static gantry positions, the geometric distortion was less than 0.88 mm at 10 cm from isocenter and less than 1.4 mm at 15.225 cm from isocenter. During gantry rotation, the geometric distortion remained less than 0.92 mm at 10 cm from isocenter and less than 1.60 mm at 15.225 cm from isocenter. Conclusion During MLC motion, cine images maintained adequate PIU, and the geometric distortion of points within 15.225 cm from isocenter was less than the 1 mm threshold necessary for real‐time target tracking and gating. During gantry rotation, PIU was negatively affected by banding artifacts, and spatial integrity was only maintained within 10 cm from isocenter. Future work should investigate the effects imaging artifacts have on real‐time target tracking during MRgRT.
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Affiliation(s)
- Jerrold E Kielbasa
- Department of Radiation Oncology, Orlando Health - UF Health Cancer Center, Orlando, FL, USA
| | - Sanford L Meeks
- Department of Radiation Oncology, Orlando Health - UF Health Cancer Center, Orlando, FL, USA
| | - Patrick Kelly
- Department of Radiation Oncology, Orlando Health - UF Health Cancer Center, Orlando, FL, USA
| | - Twyla R Willoughby
- Department of Radiation Oncology, Orlando Health - UF Health Cancer Center, Orlando, FL, USA
| | - Omar Zeidan
- Department of Radiation Oncology, Orlando Health - UF Health Cancer Center, Orlando, FL, USA
| | - Amish P Shah
- Department of Radiation Oncology, Orlando Health - UF Health Cancer Center, Orlando, FL, USA
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Nguyen XV, Oztek MA, Nelakurti DD, Brunnquell CL, Mossa-Basha M, Haynor DR, Prevedello LM. Applying Artificial Intelligence to Mitigate Effects of Patient Motion or Other Complicating Factors on Image Quality. Top Magn Reson Imaging 2020; 29:175-180. [PMID: 32511198 DOI: 10.1097/rmr.0000000000000249] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Artificial intelligence, particularly deep learning, offers several possibilities to improve the quality or speed of image acquisition in magnetic resonance imaging (MRI). In this article, we briefly review basic machine learning concepts and discuss commonly used neural network architectures for image-to-image translation. Recent examples in the literature describing application of machine learning techniques to clinical MR image acquisition or postprocessing are discussed. Machine learning can contribute to better image quality by improving spatial resolution, reducing image noise, and removing undesired motion or other artifacts. As patients occasionally are unable to tolerate lengthy acquisition times or gadolinium agents, machine learning can potentially assist MRI workflow and patient comfort by facilitating faster acquisitions or reducing exogenous contrast dosage. Although artificial intelligence approaches often have limitations, such as problems with generalizability or explainability, there is potential for these techniques to improve diagnostic utility, throughput, and patient experience in clinical MRI practice.
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Affiliation(s)
- Xuan V Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH
| | - Murat Alp Oztek
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
- Seattle Children's Hospital, Seattle, WA
| | - Devi D Nelakurti
- Metro Early College High School, The Ohio State University, Columbus, OH
| | | | - Mahmud Mossa-Basha
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - David R Haynor
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Luciano M Prevedello
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH
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Luu HM, Kim DH, Kim JW, Choi SH, Park SH. qMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks. Magn Reson Med 2020; 85:298-308. [PMID: 32643202 DOI: 10.1002/mrm.28411] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 06/10/2020] [Accepted: 06/11/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE To develop a set of artificial neural networks, collectively termed qMTNet, to accelerate data acquisition and fitting for quantitative magnetization transfer (qMT) imaging. METHODS Conventional and interslice qMT data were acquired with two flip angles at six offset frequencies from seven subjects for developing the networks and from four young and four older subjects for testing the generalizability. Two subnetworks, qMTNet-acq and qMTNet-fit, were developed and trained to accelerate data acquisition and fitting, respectively. qMTNet-2 is the sequential application of qMTNet-acq and qMTNet-fit to produce qMT parameters (exchange rate, pool fraction) from undersampled qMT data (two offset frequencies rather than six). qMTNet-1 is one single integrated network having the same functionality as qMTNet-2. qMTNet-fit was compared with a Gaussian kernel-based fitting. qMT parameters generated by the networks were compared with those from ground truth fitted with a dictionary-driven approach. RESULTS The proposed networks achieved high peak signal-to-noise ratio (>30) and structural similarity index (>97) in reference to the ground truth. qMTNet-fit produced qMT parameters in concordance with the ground truth with better performance than the Gaussian kernel-based fitting. qMTNet-2 and qMTNet-1 could accelerate data acquisition at threefold and accelerate fitting at 5800- and 4218-fold, respectively. qMTNet-1 showed slightly better performance than qMTNet-2, whereas qMTNet-2 was more flexible for applications. CONCLUSION The proposed single (qMTNet-1) and two joint neural networks (qMTNet-2) can accelerate qMT workflow for both data acquisition and fitting significantly. qMTNet has the potential to accelerate qMT imaging for clinical applications, which warrants further investigation.
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Affiliation(s)
- Huan Minh Luu
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Dong-Hyun Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Jae-Woong Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Seung-Hong Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Sung-Hong Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
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Wagner MW, Bilbily A, Beheshti M, Shammas A, Vali R. Artificial intelligence and radiomics in pediatric molecular imaging. Methods 2020; 188:37-43. [PMID: 32544594 DOI: 10.1016/j.ymeth.2020.06.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 06/02/2020] [Accepted: 06/10/2020] [Indexed: 12/22/2022] Open
Abstract
In the past decade, a new approach for quantitative analysis of medical images and prognostic modelling has emerged. Defined as the extraction and analysis of a large number of quantitative parameters from medical images, radiomics is an evolving field in precision medicine with the ultimate goal of the discovery of new imaging biomarkers for disease. Radiomics has already shown promising results in extracting diagnostic, prognostic, and molecular information latent in medical images. After acquisition of the medical images as part of the standard of care, a region of interest is defined often via a manual or semi-automatic approach. An algorithm then extracts and computes quantitative radiomics parameters from the region of interest. Whereas radiomics captures quantitative values of shape and texture based on predefined mathematical terms, neural networks have recently been used to directly learn and identify predictive features from medical images. Thereby, neural networks largely forego the need for so called "hand-engineered" features, which appears to result in significantly improved performance and reliability. Opportunities for radiomics and neural networks in pediatric nuclear medicine/radiology/molecular imaging are broad and can be thought of in three categories: automating well-defined administrative or clinical tasks, augmenting broader administrative or clinical tasks, and unlocking new methods of generating value. Specific applications include intelligent order sets, automated protocoling, improved image acquisition, computer aided triage and detection of abnormalities, next generation voice dictation systems, biomarker development, and therapy planning.
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Affiliation(s)
- Matthias W Wagner
- Department of Diagnostic Imaging, Division of Neuroradiology, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Alexander Bilbily
- Department of Diagnostic Imaging, Division of Nuclear Medicine, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Mohsen Beheshti
- Department of Nuclear Medicine, University Hospital, RWTH University, Aachen, Germany; Department of Nuclear Medicine & Endocrinology, Paracelsus Medical University, Salzburg, Austria
| | - Amer Shammas
- Department of Diagnostic Imaging, Division of Nuclear Medicine, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Reza Vali
- Department of Diagnostic Imaging, Division of Nuclear Medicine, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada.
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Bıyık E, Keskin K, Uh Dar S, Koç A, Çukur T. Factorized sensitivity estimation for artifact suppression in phase-cycled bSSFP MRI. NMR IN BIOMEDICINE 2020; 33:e4228. [PMID: 31985879 DOI: 10.1002/nbm.4228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 10/08/2019] [Accepted: 10/25/2019] [Indexed: 06/10/2023]
Abstract
OBJECTIVE Balanced steady-state free precession (bSSFP) imaging suffers from banding artifacts in the presence of magnetic field inhomogeneity. The purpose of this study is to identify an efficient strategy to reconstruct banding-free bSSFP images from multi-coil multi-acquisition datasets. METHOD Previous techniques either assume that a naïve coil-combination is performed a priori resulting in suboptimal artifact suppression, or that artifact suppression is performed for each coil separately at the expense of significant computational burden. Here we propose a tailored method that factorizes the estimation of coil and bSSFP sensitivity profiles for improved accuracy and/or speed. RESULTS In vivo experiments show that the proposed method outperforms naïve coil-combination and coil-by-coil processing in terms of both reconstruction quality and time. CONCLUSION The proposed method enables computationally efficient artifact suppression for phase-cycled bSSFP imaging with modern coil arrays. Rapid imaging applications can efficiently benefit from the improved robustness of bSSFP imaging against field inhomogeneity.
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Affiliation(s)
- Erdem Bıyık
- Department of Electrical Engineering, Stanford University, CA, USA
- Intelligent Data Analytics Research Program Department, Aselsan Research Center, Ankara, Turkey
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey
| | - Kübra Keskin
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey
- National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey
| | - Salman Uh Dar
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey
- National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey
| | - Aykut Koç
- Intelligent Data Analytics Research Program Department, Aselsan Research Center, Ankara, Turkey
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey
- National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey
| | - Tolga Çukur
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey
- National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey
- Neuroscience Program at Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey
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Do W, Seo S, Han Y, Ye JC, Choi SH, Park S. Reconstruction of multicontrast MR images through deep learning. Med Phys 2020; 47:983-997. [DOI: 10.1002/mp.14006] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Revised: 12/23/2019] [Accepted: 12/23/2019] [Indexed: 12/31/2022] Open
Affiliation(s)
- Won‐Joon Do
- Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Korea
| | - Sunghun Seo
- Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Korea
| | - Yoseob Han
- Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Korea
| | - Jong Chul Ye
- Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Korea
| | - Seung Hong Choi
- Department of Radiology Seoul National University College of Medicine Seoul Korea
| | - Sung‐Hong Park
- Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Korea
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15
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Seo S, Do W, Luu HM, Kim KH, Choi SH, Park S. Artificial neural network for Slice Encoding for Metal Artifact Correction (SEMAC) MRI. Magn Reson Med 2019; 84:263-276. [DOI: 10.1002/mrm.28126] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 11/21/2019] [Accepted: 11/21/2019] [Indexed: 01/09/2023]
Affiliation(s)
- Sunghun Seo
- Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Korea
| | - Won‐Joon Do
- Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Korea
| | - Huan Minh Luu
- Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Korea
| | - Ki Hwan Kim
- Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Korea
| | - Seung Hong Choi
- Department of Radiology Seoul National University College of Medicine Seoul Korea
| | - Sung‐Hong Park
- Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Korea
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16
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Improvement of image quality at CT and MRI using deep learning. Jpn J Radiol 2018; 37:73-80. [PMID: 30498876 DOI: 10.1007/s11604-018-0796-2] [Citation(s) in RCA: 111] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 11/18/2018] [Indexed: 12/22/2022]
Abstract
Deep learning has been developed by computer scientists. Here, we discuss techniques for improving the image quality of diagnostic computed tomography and magnetic resonance imaging with the aid of deep learning. We categorize the techniques for improving the image quality as "noise and artifact reduction", "super resolution" and "image acquisition and reconstruction". For each category, we present and outline the features of some studies.
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Chen J, Feng J, Lu S, Shen Z, Du Y, Peng L, Nian P, Yuan S, Zhang A. Non-thermal plasma and Fe2+ activated persulfate ignited degradation of aqueous crystal violet: Degradation mechanism and artificial neural network modeling. Sep Purif Technol 2018. [DOI: 10.1016/j.seppur.2017.09.016] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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18
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Kwon K, Kim D, Park H. A parallel MR imaging method using multilayer perceptron. Med Phys 2017; 44:6209-6224. [DOI: 10.1002/mp.12600] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Revised: 09/17/2017] [Accepted: 09/18/2017] [Indexed: 11/08/2022] Open
Affiliation(s)
- Kinam Kwon
- Department of Electrical Engineering; Korea Advanced Institute of Science and Technology (KAIST); Daejeon South Korea
| | - Dongchan Kim
- College of Medicine; Gachon University; Incheon South Korea
| | - HyunWook Park
- Department of Electrical Engineering; Korea Advanced Institute of Science and Technology (KAIST); Daejeon South Korea
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