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Kolokythas O, Yaman Akcicek E, Akcicek H, Briller N, Rajamohan N, Yokoo T, Peeters HM, Revels JW, Moura Cunha G, Sahani DV, Mileto A. T1-weighted Motion Mitigation in Abdominal MRI: Technical Principles, Clinical Applications, Current Limitations, and Future Prospects. Radiographics 2024; 44:e230173. [PMID: 38990776 DOI: 10.1148/rg.230173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
T1-weighted (T1W) pulse sequences are an indispensable component of clinical protocols in abdominal MRI but usually require multiple breath holds (BHs) during the examination, which not all patients can sustain. Patient motion can affect the quality of T1W imaging so that key diagnostic information, such as intrinsic signal intensity and contrast enhancement image patterns, cannot be determined. Patient motion also has a negative impact on examination efficiency, as multiple acquisition attempts prolong the duration of the examination and often remain noncontributory. Techniques for mitigation of motion-related artifacts at T1W imaging include multiple arterial acquisitions within one BH; free breathing with respiratory gating or respiratory triggering; and radial imaging acquisition techniques, such as golden-angle radial k-space acquisition (stack-of-stars). While each of these techniques has inherent strengths and limitations, the selection of a specific motion-mitigation technique is based on several factors, including the clinical task under investigation, downstream technical ramifications, patient condition, and user preference. The authors review the technical principles of free-breathing motion mitigation techniques in abdominal MRI with T1W sequences, offer an overview of the established clinical applications, and outline the existing limitations of these techniques. In addition, practical guidance for abdominal MRI protocol strategies commonly encountered in clinical scenarios involving patients with limited BH abilities is rendered. Future prospects of free-breathing T1W imaging in abdominal MRI are also discussed. ©RSNA, 2024 See the invited commentary by Fraum and An in this issue.
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Affiliation(s)
- Orpheus Kolokythas
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Ebru Yaman Akcicek
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Halit Akcicek
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Noah Briller
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Naveen Rajamohan
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Takeshi Yokoo
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Hans M Peeters
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Jonathan W Revels
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Guilherme Moura Cunha
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Dushyant V Sahani
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Achille Mileto
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
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Mio M, Tabata N, Toyofuku T, Nakamura H. [Reduction of Motion Artifacts in Liver MRI Using Deep Learning with High-pass Filtering]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2024; 80:510-518. [PMID: 38462509 DOI: 10.6009/jjrt.2024-1408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
PURPOSE To investigate whether deep learning with high-pass filtering can be used to effectively reduce motion artifacts in magnetic resonance (MR) images of the liver. METHODS The subjects were 69 patients who underwent liver MR examination at our hospital. Simulated motion artifact images (SMAIs) were created from non-artifact images (NAIs) and used for deep learning. Structural similarity index measure (SSIM) and contrast ratio (CR) were used to verify the effect of reducing motion artifacts in motion artifact reduction image (MARI) output from the obtained deep learning model. In the visual assessment, reduction of motion artifacts and image sharpness were evaluated between motion artifact images (MAIs) and MARIs. RESULTS The SSIM values were 0.882 on the MARIs and 0.869 on the SMAIs. There was no statistically significant difference in CR between NAIs and MARIs. The visual assessment showed that MARIs had reduced motion artifacts and improved sharpness compared to MAIs. CONCLUSION The learning model in this study is indicated to be reduced motion artifacts without decreasing the sharpness of liver MR images.
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Affiliation(s)
- Motohira Mio
- Department of Radiology, Fukuoka University Chikushi Hospital
| | - Nariaki Tabata
- Department of Radiology, Fukuoka University Chikushi Hospital
| | - Tatsuo Toyofuku
- Department of Radiology, Fukuoka University Chikushi Hospital
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Führes T, Saake M, Lorenz J, Seuss H, Bickelhaupt S, Uder M, Laun FB. Feature-guided deep learning reduces signal loss and increases lesion CNR in diffusion-weighted imaging of the liver. Z Med Phys 2024; 34:258-269. [PMID: 37543450 PMCID: PMC11156785 DOI: 10.1016/j.zemedi.2023.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/13/2023] [Accepted: 07/16/2023] [Indexed: 08/07/2023]
Abstract
PURPOSE This research aims to develop a feature-guided deep learning approach and compare it with an optimized conventional post-processing algorithm in order to enhance the image quality of diffusion-weighted liver images and, in particular, to reduce the pulsation-induced signal loss occurring predominantly in the left liver lobe. METHODS Data from 40 patients with liver lesions were used. For the conventional approach, the best-suited out of five examined algorithms was chosen. For the deep learning approach, a U-Net was trained. Instead of learning "gold-standard" target images, the network was trained to optimize four image features (lesion CNR, vessel darkness, data consistency, and pulsation artifact reduction), which could be assessed quantitatively using manually drawn ROIs. A quality score was calculated from these four features. As an additional quality assessment, three radiologists rated different features of the resulting images. RESULTS The conventional approach could substantially increase the lesion CNR and reduce the pulsation-induced signal loss. However, the vessel darkness was reduced. The deep learning approach increased the lesion CNR and reduced the signal loss to a slightly lower extent, but it could additionally increase the vessel darkness. According to the image quality score, the quality of the deep-learning images was higher than that of the images obtained using the conventional approach. The radiologist ratings were mostly consistent with the quantitative scores, but the overall quality ratings differed among the readers. CONCLUSION Unlike the conventional algorithm, the deep-learning algorithm increased the vessel darkness. Therefore, it may be a viable alternative to conventional algorithms.
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Affiliation(s)
- Tobit Führes
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
| | - Marc Saake
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jennifer Lorenz
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Hannes Seuss
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Department of Radiology, Klinikum Forchheim - Fränkische Schweiz, Forchheim, Germany
| | - Sebastian Bickelhaupt
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Frederik Bernd Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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Pan F, Fan Q, Xie H, Bai C, Zhang Z, Chen H, Yang L, Zhou X, Bao Q, Liu C. Correction of Arterial-Phase Motion Artifacts in Gadoxetic Acid-Enhanced Liver MRI Using an Innovative Unsupervised Network. Bioengineering (Basel) 2023; 10:1192. [PMID: 37892922 PMCID: PMC10604307 DOI: 10.3390/bioengineering10101192] [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: 08/16/2023] [Revised: 09/30/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
This study aims to propose and evaluate DR-CycleGAN, a disentangled unsupervised network by introducing a novel content-consistency loss, for removing arterial-phase motion artifacts in gadoxetic acid-enhanced liver MRI examinations. From June 2020 to July 2021, gadoxetic acid-enhanced liver MRI data were retrospectively collected in this center to establish training and testing datasets. Motion artifacts were semi-quantitatively assessed using a five-point Likert scale (1 = no artifact, 2 = mild, 3 = moderate, 4 = severe, and 5 = non-diagnostic) and quantitatively evaluated using the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The datasets comprised a training dataset (308 examinations, including 58 examinations with artifact grade = 1 and 250 examinations with artifact grade ≥ 2), a paired test dataset (320 examinations, including 160 examinations with artifact grade = 1 and paired 160 examinations with simulated motion artifacts of grade ≥ 2), and an unpaired test dataset (474 examinations with artifact grade ranging from 1 to 5). The performance of DR-CycleGAN was evaluated and compared with a state-of-the-art network, Cycle-MedGAN V2.0. As a result, in the paired test dataset, DR-CycleGAN demonstrated significantly higher SSIM and PSNR values and lower motion artifact grades compared to Cycle-MedGAN V2.0 (0.89 ± 0.07 vs. 0.84 ± 0.09, 32.88 ± 2.11 vs. 30.81 ± 2.64, and 2.7 ± 0.7 vs. 3.0 ± 0.9, respectively; p < 0.001 each). In the unpaired test dataset, DR-CycleGAN also exhibited a superior motion artifact correction performance, resulting in a significant decrease in motion artifact grades from 2.9 ± 1.3 to 2.0 ± 0.6 compared to Cycle-MedGAN V2.0 (to 2.4 ± 0.9, p < 0.001). In conclusion, DR-CycleGAN effectively reduces motion artifacts in the arterial phase images of gadoxetic acid-enhanced liver MRI examinations, offering the potential to enhance image quality.
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Affiliation(s)
- Feng Pan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (F.P.); (Q.F.); (H.C.); (L.Y.)
| | - Qianqian Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (F.P.); (Q.F.); (H.C.); (L.Y.)
| | - Han Xie
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (H.X.); (Z.Z.); (X.Z.)
| | - Chongxin Bai
- School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China;
| | - Zhi Zhang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (H.X.); (Z.Z.); (X.Z.)
| | - Hebing Chen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (F.P.); (Q.F.); (H.C.); (L.Y.)
| | - Lian Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (F.P.); (Q.F.); (H.C.); (L.Y.)
| | - Xin Zhou
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (H.X.); (Z.Z.); (X.Z.)
- University of Chinese Academy of Sciences, Beijing 100864, China
- Optics Valley Laboratory, Wuhan 430074, China
| | - Qingjia Bao
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (H.X.); (Z.Z.); (X.Z.)
| | - Chaoyang Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (H.X.); (Z.Z.); (X.Z.)
- University of Chinese Academy of Sciences, Beijing 100864, China
- Optics Valley Laboratory, Wuhan 430074, China
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Simkó A, Ruiter S, Löfstedt T, Garpebring A, Nyholm T, Bylund M, Jonsson J. Improving MR image quality with a multi-task model, using convolutional losses. BMC Med Imaging 2023; 23:148. [PMID: 37784039 PMCID: PMC10544274 DOI: 10.1186/s12880-023-01109-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: 05/09/2023] [Accepted: 09/25/2023] [Indexed: 10/04/2023] Open
Abstract
PURPOSE During the acquisition of MRI data, patient-, sequence-, or hardware-related factors can introduce artefacts that degrade image quality. Four of the most significant tasks for improving MRI image quality have been bias field correction, super-resolution, motion-, and noise correction. Machine learning has achieved outstanding results in improving MR image quality for these tasks individually, yet multi-task methods are rarely explored. METHODS In this study, we developed a model to simultaneously correct for all four aforementioned artefacts using multi-task learning. Two different datasets were collected, one consisting of brain scans while the other pelvic scans, which were used to train separate models, implementing their corresponding artefact augmentations. Additionally, we explored a novel loss function that does not only aim to reconstruct the individual pixel values, but also the image gradients, to produce sharper, more realistic results. The difference between the evaluated methods was tested for significance using a Friedman test of equivalence followed by a Nemenyi post-hoc test. RESULTS Our proposed model generally outperformed other commonly-used correction methods for individual artefacts, consistently achieving equal or superior results in at least one of the evaluation metrics. For images with multiple simultaneous artefacts, we show that the performance of using a combination of models, trained to correct individual artefacts depends heavily on the order that they were applied. This is not an issue for our proposed multi-task model. The model trained using our novel convolutional loss function always outperformed the model trained with a mean squared error loss, when evaluated using Visual Information Fidelity, a quality metric connected to perceptual quality. CONCLUSION We trained two models for multi-task MRI artefact correction of brain, and pelvic scans. We used a novel loss function that significantly improves the image quality of the outputs over using mean squared error. The approach performs well on real world data, and it provides insight into which artefacts it detects and corrects for. Our proposed model and source code were made publicly available.
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Affiliation(s)
- Attila Simkó
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.
| | - Simone Ruiter
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Tommy Löfstedt
- Department of Computing Science, Umeå University, Umeå, Sweden
| | | | - Tufve Nyholm
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Mikael Bylund
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Joakim Jonsson
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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Funayama S, Motosugi U, Ichikawa S, Morisaka H, Omiya Y, Onishi H. Model-based Deep Learning Reconstruction Using a Folded Image Training Strategy for Abdominal 3D T1-weighted Imaging. Magn Reson Med Sci 2023; 22:515-526. [PMID: 36351603 PMCID: PMC10552667 DOI: 10.2463/mrms.mp.2021-0103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 08/20/2022] [Indexed: 10/03/2023] Open
Abstract
PURPOSE To evaluate the feasibility of folded image training strategy (FITS) and the quality of images reconstructed using the improved model-based deep learning (iMoDL) network trained with FITS (FITS-iMoDL) for abdominal MR imaging. METHODS This retrospective study included abdominal 3D T1-weighted images of 122 patients. In the experimental analyses, peak SNR (PSNR) and structure similarity index (SSIM) of images reconstructed with FITS-iMoDL were compared with those with the following reconstruction methods: conventional model-based deep learning (conv-MoDL), MoDL trained with FITS (FITS-MoDL), total variation regularized compressed sensing (CS), and parallel imaging (CG-SENSE). In the clinical analysis, SNR and image contrast were measured on the reference, FITS-iMoDL, and CS images. Three radiologists evaluated the image quality using a 5-point scale to determine the mean opinion score (MOS). RESULTS The PSNR of FITS-iMoDL was significantly higher than that of FITS-MoDL, conv-MoDL, CS, and CG-SENSE (P < 0.001). The SSIM of FITS-iMoDL was significantly higher than those of the others (P < 0.001), except for FITS-MoDL (P = 0.056). In the clinical analysis, the SNR of FITS-iMoDL was significantly higher than that of the reference and CS (P < 0.0001). Image contrast was equivalent within an equivalence margin of 10% among these three image sets (P < 0.0001). MOS was significantly improved in FITS-iMoDL (P < 0.001) compared with CS images in terms of liver edge and vessels conspicuity, lesion depiction, artifacts, blurring, and overall image quality. CONCLUSION The proposed method, FITS-iMoDL, allowed a deeper MoDL reconstruction network without increasing memory consumption and improved image quality on abdominal 3D T1-weighted imaging compared with CS images.
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Affiliation(s)
- Satoshi Funayama
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Utaroh Motosugi
- Department of Radiology, Kofu-Kyoritsu Hospital, Kofu, Yamanashi, Japan
| | - Shintaro Ichikawa
- Department of Radiology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Hiroyuki Morisaka
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Yoshie Omiya
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Hiroshi Onishi
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
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Saleh M, Virarkar M, Javadi S, Mathew M, Vulasala SSR, Son JB, Sun J, Bayram E, Wang X, Ma J, Szklaruk J, Bhosale P. A Feasibility Study on Deep Learning Reconstruction to Improve Image Quality With PROPELLER Acquisition in the Setting of T2-Weighted Gynecologic Pelvic Magnetic Resonance Imaging. J Comput Assist Tomogr 2023; 47:721-728. [PMID: 37707401 DOI: 10.1097/rct.0000000000001491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Evaluate deep learning (DL) to improve the image quality of the PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction technique) for 3 T magnetic resonance imaging of the female pelvis. METHODS Three radiologists prospectively and independently compared non-DL and DL PROPELLER sequences from 20 patients with a history of gynecologic malignancy. Sequences with different noise reduction factors (DL 25%, DL 50%, and DL 75%) were blindly reviewed and scored based on artifacts, noise, relative sharpness, and overall image quality. The generalized estimating equation method was used to assess the effect of methods on the Likert scales. Quantitatively, the contrast-to-noise ratio and signal-to-noise ratio (SNR) of the iliac muscle were calculated, and pairwise comparisons were performed based on a linear mixed model. P values were adjusted using the Dunnett method. Interobserver agreement was assessed using the κ statistic. P value was considered statistically significant at less than 0.05. RESULTS Qualitatively, DL 50 and DL 75 were ranked as the best sequences in 86% of cases. Images generated by the DL method were significantly better than non-DL images ( P < 0.0001). Iliacus muscle SNR on DL 50 and DL 75 was significantly better than non-DL images ( P < 0.0001). There was no difference in contrast-to-noise ratio between the DL and non-DL techniques in the iliac muscle. There was a high percent agreement (97.1%) in terms of DL sequences' superior image quality (97.1%) and sharpness (100%) relative to non-DL images. CONCLUSION The utilization of DL reconstruction improves the image quality of PROPELLER sequences with improved SNR quantitatively.
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Affiliation(s)
- Mohammed Saleh
- From the Department of Internal Medicine, University of Texas health Science Center at Houston, Houston, TX
| | - Mayur Virarkar
- Department of Diagnostic Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Sanaz Javadi
- Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Manoj Mathew
- Department of Radiology, Stanford University, Stanford, CA
| | | | | | - Jia Sun
- Biostatistics, University of Texas MD Anderson Cancer Center
| | - Ersin Bayram
- Global MR Applications and Workflow, GE Healthcare, Houston, TX
| | - Xinzeng Wang
- Global MR Applications and Workflow, GE Healthcare, Houston, TX
| | | | - Janio Szklaruk
- Department of Diagnostic Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Priya Bhosale
- Department of Diagnostic Radiology, University of Florida College of Medicine, Jacksonville, FL
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9
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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10
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Hayashi N. [15. AI-assisted MRI Examination and Analysis]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2023; 79:187-192. [PMID: 36804809 DOI: 10.6009/jjrt.2023-2154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
Affiliation(s)
- Norio Hayashi
- School of Radiological Technology, Gunma Prefectural College of Health Sciences
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11
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Chen Z, Pawar K, Ekanayake M, Pain C, Zhong S, Egan GF. Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges. J Digit Imaging 2023; 36:204-230. [PMID: 36323914 PMCID: PMC9984670 DOI: 10.1007/s10278-022-00721-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 09/09/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022] Open
Abstract
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging.
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Affiliation(s)
- Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia.
- Department of Data Science and AI, Monash University, Melbourne, VIC, Australia.
| | - Kamlesh Pawar
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
| | - Mevan Ekanayake
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
- Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia
| | - Cameron Pain
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
- Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia
| | - Shenjun Zhong
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
- National Imaging Facility, Brisbane, QLD, Australia
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
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12
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Wu Y, Liu J, White GM, Deng J. Image-based motion artifact reduction on liver dynamic contrast enhanced MRI. Phys Med 2023; 105:102509. [PMID: 36565556 DOI: 10.1016/j.ejmp.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 10/13/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
Abstract
Liver MRI images often suffer from degraded quality due to ghosting or blurring artifacts caused by patient respiratory or bulk motion. In this study, we developed a two-stage deep learning model to reduce motion artifact on dynamic contrast enhanced (DCE) liver MRIs. The stage-I network utilized a deep residual network with a densely connected multi-resolution block (DRN-DCMB) network to remove most motion artifacts. The stage-II network applied the generative adversarial network (GAN) and perceptual loss compensation to preserve image structural features. The stage-I network served as the generator of GAN and its pretrained parameters in stage-I were further updated via backpropagation during stage-II training. The stage-I network was trained using small image patches with simulated motion artifacts including image-space rotational and translational motion, and K-space based centric and interleaved linear motion, sinusoidal, and rotational motion to mimic liver motion patterns. The stage-II network training used full-size images with the same types of simulated motion. The liver DCE-MRI image volumes without obvious motion artifacts in 10 patients were used for the training process, of which 1020 images of 8 patients were used for training and 240 images of 2 patients for validation. Finally, the whole two-stage deep learning model was tested with simulated motion images (312 clean images from 5 test patients) and patient images with real motion artifacts (28 motion images from 12 patients). The resulted images after two-stage processing demonstrated reduced motion artifacts while preserved anatomic details without image blurriness, with SSIM of 0.935 ± 0.092, MSE of 60.7 ± 9.0 × 10-3, and PSNR of 32.054 ± 2.219.
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Affiliation(s)
- Yunan Wu
- Department of Electrical Computer Engineering, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA; Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA.
| | - Junchi Liu
- Medical Imaging Research Center and Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA.
| | - Gregory M White
- Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA.
| | - Jie Deng
- Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA; Department of Radiation Oncology, UT Southwestern Medical Center, 2280 Inwood Rd, Dallas, TX 75235, USA.
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13
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Cui L, Song Y, Wang Y, Wang R, Wu D, Xie H, Li J, Yang G. Motion artifact reduction for magnetic resonance imaging with deep learning and k-space analysis. PLoS One 2023; 18:e0278668. [PMID: 36603007 DOI: 10.1371/journal.pone.0278668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 11/22/2022] [Indexed: 01/06/2023] Open
Abstract
Motion artifacts deteriorate the quality of magnetic resonance (MR) images. This study proposes a new method to detect phase-encoding (PE) lines corrupted by motion and remove motion artifacts in MR images. 67 cases containing 8710 slices of axial T2-weighted images from the IXI public dataset were split into three datasets, i.e., training (50 cases/6500 slices), validation (5/650), and test (12/1560) sets. First, motion-corrupted k-spaces and images were simulated using a pseudo-random sampling order and random motion tracks. A convolutional neural network (CNN) model was trained to filter the motion-corrupted images. Then, the k-space of the filtered image was compared with the motion-corrupted k-space line-by-line, to detect the PE lines affected by motion. Finally, the unaffected PE lines were used to reconstruct the final image using compressed sensing (CS). For the simulated images with 35%, 40%, 45%, and 50% unaffected PE lines, the mean peak signal-to-noise ratio (PSNRs) of resulting images (mean±standard deviation) were 36.129±3.678, 38.646±3.526, 40.426±3.223, and 41.510±3.167, respectively, and the mean structural similarity (SSIMs) were 0.950±0.046, 0.964±0.035, 0.975±0.025, and 0.979±0.023, respectively. For images with more than 35% PE lines unaffected by motion, images reconstructed with proposed algorithm exhibited better quality than those images reconstructed with CS using 35% under-sampled data (PSNR 37.678±3.261, SSIM 0.964±0.028). It was proved that deep learning and k-space analysis can detect the k-space PE lines affected by motion and CS can be used to reconstruct images from unaffected data, effectively alleviating the motion artifacts.
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Affiliation(s)
- Long Cui
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yang Song
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yida Wang
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Rui Wang
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Dongmei Wu
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Haibin Xie
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Jianqi Li
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Guang Yang
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
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14
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Kojima S. [[MRI] 3. Current Status of AI Image Reconstruction in Clinical MRI Systems]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2023; 79:1200-1209. [PMID: 37866905 DOI: 10.6009/jjrt.2023-2260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Affiliation(s)
- Shinya Kojima
- Department of Medical Radiology, Faculty of Medical Technology, Teikyo University
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15
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Abstract
ABSTRACT Magnetic resonance neurography of the brachial plexus (BP) is challenging owing to its complex anatomy and technical obstacles around this anatomic region. Magnetic resonance techniques to improve image quality center around increasing nerve-to-background contrast ratio and mitigating imaging artifacts. General considerations include unilateral imaging of the BP at 3.0 T, appropriate selection and placement of surface coils, and optimization of pulse sequences. Technical considerations to improve nerve conspicuity include fat, vascular, and respiratory artifact suppression techniques; metal artifact reduction techniques; and 3-dimensional sequences. Specific optimization of these techniques for BP magnetic resonance neurography greatly improves image quality and diagnostic confidence to help guide nonoperative and operative management.
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16
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Nepal P, Bagga B, Feng L, Chandarana H. Respiratory Motion Management in Abdominal MRI: Radiology In Training. Radiology 2023; 306:47-53. [PMID: 35997609 PMCID: PMC9792710 DOI: 10.1148/radiol.220448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
A 96-year-old woman had a suboptimal evaluation of liver observations at abdominal MRI due to significant respiratory motion. State-of-the-art strategies to minimize respiratory motion during clinical abdominal MRI are discussed.
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Affiliation(s)
- Pankaj Nepal
- From the Department of Radiology, Massachusetts General Hospital, 55
Fruit St, Boston, MA 02114 (P.N.); Department of Radiology, New York University
School of Medicine, New York, NY (B.B., H.C.); and Biomedical Engineering and
Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount
Sinai, New York, NY (L.F.)
| | - Barun Bagga
- From the Department of Radiology, Massachusetts General Hospital, 55
Fruit St, Boston, MA 02114 (P.N.); Department of Radiology, New York University
School of Medicine, New York, NY (B.B., H.C.); and Biomedical Engineering and
Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount
Sinai, New York, NY (L.F.)
| | - Li Feng
- From the Department of Radiology, Massachusetts General Hospital, 55
Fruit St, Boston, MA 02114 (P.N.); Department of Radiology, New York University
School of Medicine, New York, NY (B.B., H.C.); and Biomedical Engineering and
Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount
Sinai, New York, NY (L.F.)
| | - Hersh Chandarana
- From the Department of Radiology, Massachusetts General Hospital, 55
Fruit St, Boston, MA 02114 (P.N.); Department of Radiology, New York University
School of Medicine, New York, NY (B.B., H.C.); and Biomedical Engineering and
Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount
Sinai, New York, NY (L.F.)
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17
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Wong PK, Chan IN, Yan HM, Gao S, Wong CH, Yan T, Yao L, Hu Y, Wang ZR, Yu HH. Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview. World J Gastroenterol 2022; 28:6363-6379. [PMID: 36533112 PMCID: PMC9753055 DOI: 10.3748/wjg.v28.i45.6363] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/25/2022] [Accepted: 11/17/2022] [Indexed: 12/02/2022] Open
Abstract
Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.
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Affiliation(s)
- Pak Kin Wong
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - In Neng Chan
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - Hao-Ming Yan
- School of Clinical Medicine, China Medical University, Shenyang 110013, Liaoning Province, China
| | - Shan Gao
- Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, Hubei Province, China
| | - Chi Hong Wong
- Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China
| | - Tao Yan
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Liang Yao
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Ying Hu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Zhong-Ren Wang
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Hon Ho Yu
- Department of Gastroenterology, Kiang Wu Hospital, Macau 999078, China
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18
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Penso M, Babbaro M, Moccia S, Guglielmo M, Carerj ML, Giacari CM, Chiesa M, Maragna R, Rabbat MG, Barison A, Martini N, Pepi M, Caiani EG, Pontone G. Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment. J Cardiovasc Magn Reson 2022; 24:62. [PMID: 36437452 PMCID: PMC9703740 DOI: 10.1186/s12968-022-00899-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 11/02/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED). METHODS In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi42(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability. RESULTS In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF - 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s). CONCLUSIONS Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.
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Affiliation(s)
- Marco Penso
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, Milan, Italy
| | - Mario Babbaro
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
| | - Sara Moccia
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Marco Guglielmo
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
| | - Maria Ludovica Carerj
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
- Department of Biomedical Sciences and Morphological and Functional Imaging, “G. Martino” University Hospital Messina, Messina, Italy
| | - Carlo Maria Giacari
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
| | - Mattia Chiesa
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, Milan, Italy
| | - Riccardo Maragna
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
| | - Mark G. Rabbat
- Loyola University of Chicago, Chicago, IL USA
- Edward Hines Jr. VA Hospital, Hines, IL USA
| | | | | | - Mauro Pepi
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
| | - Enrico G. Caiani
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, Milan, Italy
- Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, Milan, Italy
| | - Gianluca Pontone
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
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19
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Nárai Á, Hermann P, Auer T, Kemenczky P, Szalma J, Homolya I, Somogyi E, Vakli P, Weiss B, Vidnyánszky Z. Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans. Sci Data 2022; 9:630. [PMID: 36253426 PMCID: PMC9576686 DOI: 10.1038/s41597-022-01694-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 09/12/2022] [Indexed: 11/10/2022] Open
Abstract
Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion, however, often confounds the measurement. Approaches assessing, correcting, or preventing motion corruption of MRI measurements are under active development, and such efforts can greatly benefit from carefully controlled datasets. We present a unique dataset of structural brain MRI images collected from 148 healthy adults which includes both motion-free and motion-affected data acquired from the same participants. This matched dataset allows direct evaluation of motion artefacts, their impact on derived data, and testing approaches to correct for them. Our dataset further stands out by containing images with different levels of motion artefacts from the same participants, is enriched with expert scoring characterizing the image quality from a clinical point of view and is also complemented with standard image quality metrics obtained from MRIQC. The goal of the dataset is to raise awareness of the issue and provide a useful resource to assess and improve current motion correction approaches.
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Affiliation(s)
- Ádám Nárai
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.
| | - Petra Hermann
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Tibor Auer
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.,School of Psychology, University of Surrey, Guildford, United Kingdom
| | - Péter Kemenczky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - János Szalma
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - István Homolya
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Eszter Somogyi
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Pál Vakli
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Béla Weiss
- 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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20
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Li F, Xu W, Feng Y, Wang W, Tian H, He S, Li L, Xiang B, Wang Y. Preparation of ultrasound contrast agents: The exploration of the structure-echogenicity relationship of contrast agents based on neural network model. Front Oncol 2022; 12:964314. [PMID: 36276089 PMCID: PMC9581267 DOI: 10.3389/fonc.2022.964314] [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: 06/08/2022] [Accepted: 08/09/2022] [Indexed: 11/23/2022] Open
Abstract
There is a need to standardize the process of micro/nanobubble preparation to bring it closer to clinical translation. We explored a neural network-based model to predict the structure-echogenicity relationship for the preparation and fabrication of ultrasound-enhanced contrast agents. Seven formulations were screened, and 109 measurements were obtained. An artificial neural network-multilayer perceptron (ANN-MLP) model was used. The original data were divided into the training and testing groups, which included 73 and 36 groups of data, respectively. The hidden layer was selected from three hidden layers and included bias. The classification graph showed that the predicted values of the training and testing groups were 76.7% and 66.7%, respectively. According to the receiver operating characteristic curve, the accuracy of different imaging effects could achieve a prediction rate of 88.1–96.5%. The percentage graph showed that the data were gradually converging. The predictive analysis curves of different ultrasound effects gradually approached stable value of Gain. Normalized importance predicted contributions for the Pk1, poly-dispersity index (PDI), and intensity account were 100%, 98.5%, and 89.7%, respectively. The application of the ANN-MLP model is feasible and effective for the exploration of the synthesis process of ultrasound contrast agents. 1,2-Distearoyl-sn-glycero-3 phosphoethanolamine-N (methoxy[polyethylene glycol]-2000) (DSPE PEG-2000) correlated highly with the success rate of contrast agent synthesis.
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Affiliation(s)
- Feng Li
- Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Wensheng Xu
- Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yujin Feng
- Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Wengang Wang
- Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Hui Tian
- Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Suhuan He
- The First Outpatient Department of Hebei Province, Shijiazhuang, Hebei, China
| | - Liang Li
- Department of Integrated Traditional Chinese and Western Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Bai Xiang
- Department of Pharmaceutics, School of Pharmaceutical Sciences, Hebei Medical University, Shijiazhuang, Hebei, China
- *Correspondence: Yueheng Wang, ; Bai Xiang,
| | - Yueheng Wang
- Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- *Correspondence: Yueheng Wang, ; Bai Xiang,
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21
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Zhou L, Liu H, Zou YX, Zhang G, Su B, Lu L, Chen YC, Yin X, Jiang HB. Clinical validation of an AI-based motion correction reconstruction algorithm in cerebral CT. Eur Radiol 2022; 32:8550-8559. [PMID: 35678857 DOI: 10.1007/s00330-022-08883-4] [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: 01/12/2022] [Revised: 04/25/2022] [Accepted: 05/13/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To evaluate the clinical performance of an artificial intelligence (AI)-based motion correction (MC) reconstruction algorithm for cerebral CT. METHODS A total of 53 cases, where motion artifacts were found in the first scan so that an immediate rescan was taken, were retrospectively enrolled. While the rescanned images were reconstructed with a hybrid iterative reconstruction (IR) algorithm (reference group), images of the first scan were reconstructed with both the hybrid IR (motion group) and the MC algorithm (MC group). Image quality was compared in terms of standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information (MI), as well as subjective scores. The diagnostic performance for each case was evaluated accordingly by lesion detectability or the Alberta Stroke Program Early CT Score (ASPECTS) assessment. RESULTS Compared with the motion group, the SNR and CNR of the MC group were significantly increased. The MSE, PSNR, SSIM, and MI with respect to the reference group were improved by 44.1%, 15.8%, 7.4%, and 18.3%, respectively (all p < 0.001). Subjective image quality indicators were scored higher for the MC than the motion group (p < 0.05). Improved lesion detectability and higher AUC (0.817 vs 0.614) in the ASPECTS assessment were found for the MC to the motion group. CONCLUSIONS The AI-based MC reconstruction algorithm has been clinically validated for reducing motion artifacts and improving diagnostic performance of cerebral CT. KEY POINTS • An artificial intelligence-based motion correction (MC) reconstruction algorithm has been clinically validated in both qualitative and quantitative manner. • The MC algorithm reduces motion artifacts in cerebral CT and increases the diagnostic confidence for brain lesions. • The MC algorithm can help avoiding rescans caused by motion and improving the efficiency of cerebral CT in the emergency department.
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Affiliation(s)
- Leilei Zhou
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Hao Liu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Yi-Xuan Zou
- United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Guozhi Zhang
- United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Bin Su
- United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Liyan Lu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China.
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Hong-Bing Jiang
- Department of Medical Equipment, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China. .,Nanjing Emergency Medical Center, No. 3 Zizhulin, Nanjing, 210003, China.
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22
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Ren Q, Zhu P, Li C, Yan M, Liu S, Zheng C, Xia X. Pretreatment Computed Tomography-Based Machine Learning Models to Predict Outcomes in Hepatocellular Carcinoma Patients who Received Combined Treatment of Trans-Arterial Chemoembolization and Tyrosine Kinase Inhibitor. Front Bioeng Biotechnol 2022; 10:872044. [PMID: 35677305 PMCID: PMC9168370 DOI: 10.3389/fbioe.2022.872044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/22/2022] [Indexed: 11/15/2022] Open
Abstract
Aim: Trans-arterial chemoembolization (TACE) in combination with tyrosine kinase inhibitor (TKI) has been evidenced to improve outcomes in a portion of patients with hepatocellular carcinoma (HCC). Developing biomarkers to identify patients who might benefit from the combined treatment is needed. This study aims to investigate the efficacy of radiomics/deep learning features-based models in predicting short-term disease control and overall survival (OS) in HCC patients who received the combined treatment. Materials and Methods: A total of 103 HCC patients who received the combined treatment from Sep. 2015 to Dec. 2019 were enrolled in the study. We exacted radiomics features and deep learning features of six pre-trained convolutional neural networks (CNNs) from pretreatment computed tomography (CT) images. The robustness of features was evaluated, and those with excellent stability were used to construct predictive models by combining each of the seven feature exactors, 13 feature selection methods and 12 classifiers. The models were evaluated for predicting short-term disease by using the area under the receiver operating characteristics curve (AUC) and relative standard deviation (RSD). The optimal models were further analyzed for predictive performance on overall survival. Results: A total of the 1,092 models (156 with radiomics features and 936 with deep learning features) were constructed. Radiomics_GINI_Nearest Neighbors (RGNN) and Resnet50_MIM_Nearest Neighbors (RMNN) were identified as optimal models, with the AUC of 0.87 and 0.94, accuracy of 0.89 and 0.92, sensitivity of 0.88 and 0.97, specificity of 0.90 and 0.90, precision of 0.87 and 0.83, F1 score of 0.89 and 0.92, and RSD of 1.30 and 0.26, respectively. Kaplan-Meier survival analysis showed that RGNN and RMNN were associated with better OS (p = 0.006 for RGNN and p = 0.033 for RMNN). Conclusion: Pretreatment CT-based radiomics/deep learning models could non-invasively and efficiently predict outcomes in HCC patients who received combined therapy of TACE and TKI.
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Affiliation(s)
- Qianqian Ren
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Peng Zhu
- Department of Hepatobiliary Surgery, Wuhan No.1 Hospital, Wuhan, China
| | - Changde Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Meijun Yan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Song Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xiangwen Xia
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- *Correspondence: Xiangwen Xia,
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23
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Feasibility of Deep Learning-Based Noise and Artifact Reduction in Coronal Reformation of Contrast-Enhanced Chest Computed Tomography. J Comput Assist Tomogr 2022; 46:593-603. [PMID: 35617647 DOI: 10.1097/rct.0000000000001326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE This study aimed to evaluate the feasibility of a deep learning method for imaging artifact and noise reduction in coronal reformation of contrast-enhanced chest computed tomography (CT). METHODS A total of 19,052 coronal reformatted chest CT images of 110 CT image sets (55 pairs of concordant 16- and 320-row CT image sets) were included and used to train a deep learning algorithm for artifact and noise correction. For internal validation, 4093 coronal reformatted CT images of 25 patients from 16-row CT images underwent correction processing. For external validation, chest CT images of 30 patients (1028 coronal reformatted CT images), acquired in other institutions using different scanners, were subjected to correction processing. For both validations, image quality was compared between original ("CTorigin") and deep learning-based corrected ("CTcorrect") CT images. Quantitative analysis for stair-step artifact (coefficient of variance of CT density on coronal reformation), image noise, signal-to-noise ratio, and contrast-to-noise ratio were evaluated. Subjective image quality scores were assigned for image contrast, artifact, and conspicuity of major structures. RESULTS CTcorrect showed significantly reduced stair-step artifact (mean coefficient of variance: CTorigin 7.35 ± 2.0 vs CTcorrect 5.17 ± 2.4, P < 0.001) and image noise and improved signal-to-noise ratio and contrast-to-noise ratio in the aorta, pulmonary artery, and liver, compared with those of CTorigin (P < 0.01). On subjective analysis, CTcorrect had higher image contrast, lower artifact, and better conspicuity than CTorigin. Most results of the external validation were consistent with those obtained from the internal validation, except for those concerning the pulmonary artery. CONCLUSIONS Deep learning-based artifact correction significantly improved the image quality of coronal reformation chest CT by reducing image noise and artifacts.
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Sombeck J, Heye J, Kumaravelu K, Goetz S, Peterchev AV, Grill WM, Bensmaia SJ, Miller LE. Characterizing the short-latency evoked response to intracortical microstimulation across a multi-electrode array. J Neural Eng 2022; 19. [PMID: 35378515 PMCID: PMC9142773 DOI: 10.1088/1741-2552/ac63e8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/04/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Persons with tetraplegia can use brain-machine interfaces to make visually guided reaches with robotic arms. Without somatosensory feedback, these movements will likely be slow and imprecise, like those of persons who retain movement but have lost proprioception. Intracortical microstimulation (ICMS) has promise for providing artificial somatosensory feedback. If ICMS can mimic naturally occurring neural activity, afferent interfaces may be more informative and easier to learn than interfaces that evoke unnaturalistic activity. To develop such biomimetic stimulation patterns, it is important to characterize the responses of neurons to ICMS. APPROACH Using a Utah multi-electrode array, we recorded activity evoked by single pulses and trains of ICMS at a wide range of amplitudes and frequencies in two rhesus macaques. As the electrical artifact caused by ICMS typically prevents recording for many milliseconds, we deployed a custom rapid-recovery amplifier with nonlinear gain to limit signal saturation on the stimulated electrode. Across all electrodes after stimulation, we removed the remaining slow return to baseline with acausal high-pass filtering of time-reversed recordings. MAIN RESULTS After single pulses of stimulation, we recorded what was likely transsynaptically-evoked activity even on the stimulated electrode as early as ~0.7 ms. This was immediately followed by suppressed neural activity lasting 10-150 ms. After trains, this long-lasting inhibition was replaced by increased firing rates for ~100 ms. During long trains, the evoked response on the stimulated electrode decayed rapidly while the response was maintained on non-stimulated channels. SIGNIFICANCE The detailed description of the spatial and temporal response to ICMS can be used to better interpret results from experiments that probe circuit connectivity or function of cortical areas. These results can also contribute to the design of stimulation patterns to improve afferent interfaces for artificial sensory feedback.
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Affiliation(s)
- Joseph Sombeck
- Department of Biomedical Engineering, Northwestern University, 2145 Sheridan Road, Evanston, Illinois, 60208, UNITED STATES
| | - Juliet Heye
- Department of Neuroscience, Northwestern University, 310 E. Superior St, Chicago, Illinois, 60202, UNITED STATES
| | - Karthik Kumaravelu
- Biomedical Engineering, Duke University, 2080 Duke University Road, Durham, North Carolina, 27708-0187, UNITED STATES
| | - Stefan Goetz
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, 2080 Duke University Road, Durham, North Carolina, 27708, UNITED STATES
| | - Angel V Peterchev
- Psychiatry & Behavioral Sciences, Duke University School of Medicine, 40 Duke Medicine Circle, Durham, North Carolina, 27710, UNITED STATES
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Hudson Hall 136, Box 90281, Durham, North Carolina, 27708-0281, UNITED STATES
| | - Sliman J Bensmaia
- Organismal Biology and Anatomy, University of Chicago, 1027 E 57th St, Chicago, IL 60637, USA, Chicago, Illinois, 60637, UNITED STATES
| | - Lee E Miller
- Neuroscience, Northwestern University Feinberg School of Medicine, 303 East Chicago Ave, Chicago, Illinois, 60611-3008, UNITED STATES
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25
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Artificial intelligence in gastrointestinal and hepatic imaging: past, present and future scopes. Clin Imaging 2022; 87:43-53. [DOI: 10.1016/j.clinimag.2022.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 03/09/2022] [Accepted: 04/11/2022] [Indexed: 11/19/2022]
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26
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Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes. Eur Radiol 2022; 32:6118-6125. [DOI: 10.1007/s00330-022-08729-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/23/2022] [Accepted: 03/07/2022] [Indexed: 12/22/2022]
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27
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Omari EA, Zhang Y, Ahunbay E, Paulson E, Amjad A, Chen X, Liang Y, Li XA. Multi parametric magnetic resonance imaging for radiation treatment planning. Med Phys 2022; 49:2836-2845. [PMID: 35170769 DOI: 10.1002/mp.15534] [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: 05/21/2021] [Revised: 10/05/2021] [Accepted: 01/03/2022] [Indexed: 11/09/2022] Open
Abstract
In recent years, multi-parametric magnetic resonance imaging (MpMRI) has played a major role in radiation therapy treatment planning. The superior soft tissue contrast, functional or physiological imaging capabilities and the flexibility of site-specific image sequence development has placed MpMRI at the forefront. In this article, the present status of MpMRI for external beam radiation therapy planning is reviewed. Common MpMRI sequences, preprocessing and QA strategies are briefly discussed, and various image registration techniques and strategies are addressed. Image segmentation methods including automatic segmentation and deep learning techniques for organs at risk and target delineation are reviewed. Due to the advancement in MRI guided online adaptive radiotherapy, treatment planning considerations addressing MRI only planning are also discussed. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Eenas A Omari
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ying Zhang
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ergun Ahunbay
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Asma Amjad
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Xinfeng Chen
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ying Liang
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
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Manso Jimeno M, Ravi KS, Jin Z, Oyekunle D, Ogbole G, Geethanath S. ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning. Magn Reson Imaging 2022; 89:42-48. [PMID: 35176447 DOI: 10.1016/j.mri.2022.02.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 01/14/2023]
Abstract
Low-field MR scanners are more accessible in resource-constrained settings where skilled personnel are scarce. Images acquired in such scenarios are prone to artifacts such as wrap-around and Gibbs ringing. Such artifacts negatively affect the diagnostic quality and may be confused with pathology or reduce the region of interest visibility. As a first step solution, ArtifactID identifies wrap-around and Gibbs ringing in low-field brain MRI. We utilized two datasets: 179 T1-weighted pathological brain images from a 0.36 T scanner and 581 publicly available T1-weighted brain images. Individual binary classification models were trained to identify through-plane wrap-around, in-plane wrap-around, and Gibbs ringing. Visual explanations obtained via the GradCAM method helped develop trust in the wrap-around model. The mean precision and recall metrics across the four implemented models were 97.6% and 92.83% respectively. Agreement analysis of the models and the radiologists' labels returned Cohen's kappa values of 0.768 ± 0.062, 1.00 ± 0.000, 0.89 ± 0.085, and 0.878 ± 0.103 for the through-plane wrap-around, in-plane wrap-around, and Gibbs ringing models, respectively.
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Affiliation(s)
- Marina Manso Jimeno
- Department of Biomedical Engineering, Columbia University in the City of New York, New York, NY 10027, USA; Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY 10027, USA
| | - Keerthi Sravan Ravi
- Department of Biomedical Engineering, Columbia University in the City of New York, New York, NY 10027, USA; Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY 10027, USA
| | - Zhezhen Jin
- Mailman School of Public Health, Columbia University in the City of New York, New York, NY 10027, USA
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital, Ibadan 200285, Nigeria
| | - Godwin Ogbole
- Department of Radiology, University College Hospital, Ibadan 200285, Nigeria
| | - Sairam Geethanath
- Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY 10027, USA.
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30
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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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31
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Sagawa H. [11. Deep Learning in Magnetic Resonance Imaging: An Overview and Applications]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:876-881. [PMID: 35989257 DOI: 10.6009/jjrt.2022-2069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Affiliation(s)
- Hajime Sagawa
- Clinical Radiology Service, Kyoto University Hospital
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32
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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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Moummad I, Jaudet C, Lechervy A, Valable S, Raboutet C, Soilihi Z, Thariat J, Falzone N, Lacroix J, Batalla A, Corroyer-Dulmont A. The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI. Cancers (Basel) 2021; 14:cancers14010036. [PMID: 35008198 PMCID: PMC8750741 DOI: 10.3390/cancers14010036] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/14/2021] [Accepted: 12/18/2021] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Due to the central role of magnetic resonance Imaging (MRI) in the management of patients with cancer, waiting lists exceed clinically relevant delays. For this reason, many research groups and MRI manufacturers develop algorithms as resampling and denoising models to allow faster acquisition time without deterioration in image quality. Whereas these algorithms are available in all new MRI, it is not clear how they will impact image features as well as the validity of statistical model of radiomics which use deep images characteristics to predict treatment outcome. The aim of this study was to develop resampling and denoising deep learning (DL) models and evaluate their impact on radiomics from post-Gd-T1w-MRI brain images with brain metastases. We show that resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast acquisition loses most of the radiomic-features and invalidates predictive radiomic models, DL models restore these parameters. Abstract Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied. The aim of this study was to evaluate the impact of resampling and denoising DL models on radiomics. Methods: Resampling and denoising DL model was developed on 14,243 T1 brain images from 1.5T-MRI. Radiomics were extracted from 40 brain metastases from 11 patients (2049 images). A total of 104 texture features of DL images were compared to original images with paired t-test, Pearson correlation and concordance-correlation-coefficient (CCC). Results: When two times shorter image acquisition shows strong disparities with the originals concerning the radiomics, with significant differences and loss of correlation of 79.81% and 48.08%, respectively. Interestingly, DL models restore textures with 46.15% of unstable parameters and 25.96% of low CCC and without difference for the first-order intensity parameters. Conclusions: Resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast MRI acquisition loses most of the radiomic features, DL models restore these parameters.
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Affiliation(s)
- Ilyass Moummad
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
| | - Cyril Jaudet
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
| | - Alexis Lechervy
- UMR GREYC, Normandie University, UNICAEN, ENSICAEN, CNRS, 14000 Caen, France;
| | - Samuel Valable
- ISTCT/CERVOxy Group, Normandie University, UNICAEN, CEA, CNRS, 14000 Caen, France;
| | - Charlotte Raboutet
- Radiology Department, CLCC François Baclesse, 14000 Caen, France; (C.R.); (J.L.)
| | - Zamila Soilihi
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
| | - Juliette Thariat
- Radiotherapy Department, CLCC François Baclesse, 14000 Caen, France;
| | - Nadia Falzone
- GenesisCare Theranostics, Building 1 & 11, The Mill, 41-43 Bourke Road, Alexandria, NSW 2015, Australia;
| | - Joëlle Lacroix
- Radiology Department, CLCC François Baclesse, 14000 Caen, France; (C.R.); (J.L.)
| | - Alain Batalla
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
| | - Aurélien Corroyer-Dulmont
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
- ISTCT/CERVOxy Group, Normandie University, UNICAEN, CEA, CNRS, 14000 Caen, France;
- Correspondence:
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Eldeniz C, Gan W, Chen S, Fraum TJ, Ludwig DR, Yan Y, Liu J, Vahle T, Krishnamurthy U, Kamilov US, An H. Phase2Phase: Respiratory Motion-Resolved Reconstruction of Free-Breathing Magnetic Resonance Imaging Using Deep Learning Without a Ground Truth for Improved Liver Imaging. Invest Radiol 2021; 56:809-819. [PMID: 34038064 DOI: 10.1097/rli.0000000000000792] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Respiratory binning of free-breathing magnetic resonance imaging data reduces motion blurring; however, it exacerbates noise and introduces severe artifacts due to undersampling. Deep neural networks can remove artifacts and noise but usually require high-quality ground truth images for training. This study aimed to develop a network that can be trained without this requirement. MATERIALS AND METHODS This retrospective study was conducted on 33 participants enrolled between November 2016 and June 2019. Free-breathing magnetic resonance imaging was performed using a radial acquisition. Self-navigation was used to bin the k-space data into 10 respiratory phases. To simulate short acquisitions, subsets of radial spokes were used in reconstructing images with multicoil nonuniform fast Fourier transform (MCNUFFT), compressed sensing (CS), and 2 deep learning methods: UNet3DPhase and Phase2Phase (P2P). UNet3DPhase was trained using a high-quality ground truth, whereas P2P was trained using noisy images with streaking artifacts. Two radiologists blinded to the reconstruction methods independently reviewed the sharpness, contrast, and artifact-freeness of the end-expiration images reconstructed from data collected at 16% of the Nyquist sampling rate. The generalized estimating equation method was used for statistical comparison. Motion vector fields were derived to examine the respiratory motion range of 4-dimensional images reconstructed using different methods. RESULTS A total of 15 healthy participants and 18 patients with hepatic malignancy (50 ± 15 years, 6 women) were enrolled. Both reviewers found that the UNet3DPhase and P2P images had higher contrast (P < 0.01) and fewer artifacts (P < 0.01) than the CS images. The UNet3DPhase and P2P images were reported to be sharper than the CS images by 1 reviewer (P < 0.01) but not by the other reviewer (P = 0.22, P = 0.18). UNet3DPhase and P2P were similar in sharpness and contrast, whereas UNet3DPhase had fewer artifacts (P < 0.01). The motion vector lengths for the MCNUFFT800 and P2P800 images were comparable (10.5 ± 4.2 mm and 9.9 ± 4.0 mm, respectively), whereas both were significantly larger than CS2000 (7.0 ± 3.9 mm; P < 0.0001) and UNnet3DPhase800 (6.9 ± 3.2; P < 0.0001) images. CONCLUSIONS Without a ground truth, P2P can reconstruct sharp, artifact-free, and high-contrast respiratory motion-resolved images from highly undersampled data. Unlike the CS and UNet3DPhase methods, P2P did not artificially reduce the respiratory motion range.
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Affiliation(s)
| | - Weijie Gan
- Department of Computer Science & Engineering
| | | | | | | | | | - Jiaming Liu
- Department of Electrical and System Engineering, Washington University in St. Louis, Missouri
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Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: A state-of-the-art review. World J Gastrointest Oncol 2021; 13:1599-1615. [PMID: 34853638 PMCID: PMC8603458 DOI: 10.4251/wjgo.v13.i11.1599] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/02/2021] [Accepted: 08/20/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common cancer and the second major contributor to cancer-related mortality. Radiomics, a burgeoning technology that can provide invisible high-dimensional quantitative and mineable data derived from routine-acquired images, has enormous potential for HCC management from diagnosis to prognosis as well as providing contributions to the rapidly developing deep learning methodology. This article aims to review the radiomics approach and its current state-of-the-art clinical application scenario in HCC. The limitations, challenges, and thoughts on future directions are also summarized.
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Affiliation(s)
- Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Han-Yu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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Hu R, Yang R, Liu Y, Li X. Simulation and Mitigation of the Wrap-Around Artifact in the MRI Image. Front Comput Neurosci 2021; 15:746549. [PMID: 34744675 PMCID: PMC8566355 DOI: 10.3389/fncom.2021.746549] [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: 07/24/2021] [Accepted: 09/15/2021] [Indexed: 11/13/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an essential clinical imaging modality for diagnosis and medical research, while various artifacts occur during the acquisition of MRI image, resulting in severe degradation of the perceptual quality and diagnostic efficacy. To tackle such challenges, this study deals with one of the most frequent artifact sources, namely the wrap-around artifact. In particular, given that the MRI data are limited and difficult to access, we first propose a method to simulate the wrap-around artifact on the artifact-free MRI image to increase the quantity of MRI data. Then, an image restoration technique, based on the deep neural networks, is proposed for wrap-around artifact reduction and overall perceptual quality improvement. This study presents a comprehensive analysis regarding both the occurrence of and reduction in the wrap-around artifact, with the aim of facilitating the detection and mitigation of MRI artifacts in clinical situations.
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Affiliation(s)
- Runze Hu
- Department of Information Science and Technology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Rui Yang
- Department of Information Science and Technology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Yutao Liu
- School of Computer Science and Technology, Ocean University of China, Qingdao, China
| | - Xiu Li
- Department of Information Science and Technology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
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Oh G, Lee JE, Ye JC. Unpaired MR Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3125-3139. [PMID: 34133276 DOI: 10.1109/tmi.2021.3089708] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Recently, deep learning approaches for MR motion artifact correction have been extensively studied. Although these approaches have shown high performance and lower computational complexity compared to classical methods, most of them require supervised training using paired artifact-free and artifact-corrupted images, which may prohibit its use in many important clinical applications. For example, transient severe motion (TSM) due to acute transient dyspnea in Gd-EOB-DTPA-enhanced MR is difficult to control and model for paired data generation. To address this issue, here we propose a novel unpaired deep learning scheme that does not require matched motion-free and motion artifact images. Specifically, the first step of our method is k -space random subsampling along the phase encoding direction that can remove some outliers probabilistically. In the second step, the neural network reconstructs fully sampled resolution image from a downsampled k -space data, and motion artifacts can be reduced in this step. Last, the aggregation step through averaging can further improve the results from the reconstruction network. We verify that our method can be applied for artifact correction from simulated motion as well as real motion from TSM successfully from both single and multi-coil data with and without k -space raw data, outperforming existing state-of-the-art deep learning methods.
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Hill CE, Biasiolli L, Robson MD, Grau V, Pavlides M. Emerging artificial intelligence applications in liver magnetic resonance imaging. World J Gastroenterol 2021; 27:6825-6843. [PMID: 34790009 PMCID: PMC8567471 DOI: 10.3748/wjg.v27.i40.6825] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 04/16/2021] [Accepted: 09/30/2021] [Indexed: 02/06/2023] Open
Abstract
Chronic liver diseases (CLDs) are becoming increasingly more prevalent in modern society. The use of imaging techniques for early detection, such as magnetic resonance imaging (MRI), is crucial in reducing the impact of these diseases on healthcare systems. Artificial intelligence (AI) algorithms have been shown over the past decade to excel at image-based analysis tasks such as detection and segmentation. When applied to liver MRI, they have the potential to improve clinical decision making, and increase throughput by automating analyses. With Liver diseases becoming more prevalent in society, the need to implement these techniques to utilize liver MRI to its full potential, is paramount. In this review, we report on the current methods and applications of AI methods in liver MRI, with a focus on machine learning and deep learning methods. We assess four main themes of segmentation, classification, image synthesis and artefact detection, and their respective potential in liver MRI and the wider clinic. We provide a brief explanation of some of the algorithms used and explore the current challenges affecting the field. Though there are many hurdles to overcome in implementing AI methods in the clinic, we conclude that AI methods have the potential to positively aid healthcare professionals for years to come.
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Affiliation(s)
- Charles E Hill
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Luca Biasiolli
- Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
| | | | - Vicente Grau
- Department of Engineering, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Michael Pavlides
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
- Translational Gastroenterology Unit, University of Oxford, Oxford OX3 9DU, United Kingdom
- Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford OX3 9DU, United Kingdom
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Abdi M, Feng X, Sun C, Bilchick KC, Meyer CH, Epstein FH. Suppression of artifact-generating echoes in cine DENSE using deep learning. Magn Reson Med 2021; 86:2095-2104. [PMID: 34021628 PMCID: PMC8295221 DOI: 10.1002/mrm.28832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 03/21/2021] [Accepted: 04/17/2021] [Indexed: 11/11/2022]
Abstract
PURPOSE To use deep learning for suppression of the artifact-generating T1 -relaxation echo in cine displacement encoding with stimulated echoes (DENSE) for the purpose of reducing the scan time. METHODS A U-Net was trained to suppress the artifact-generating T1 -relaxation echo using complementary phase-cycled data as the ground truth. A data-augmentation method was developed that generates synthetic DENSE images with arbitrary displacement-encoding frequencies to suppress the T1 -relaxation echo modulated for a range of frequencies. The resulting U-Net (DAS-Net) was compared with k-space zero-filling as an alternative method. Non-phase-cycled DENSE images acquired in shorter breath-holds were processed by DAS-Net and compared with DENSE images acquired with phase cycling for the quantification of myocardial strain. RESULTS The DAS-Net method effectively suppressed the T1 -relaxation echo and its artifacts, and achieved root Mean Square(RMS) error = 5.5 ± 0.8 and structural similarity index = 0.85 ± 0.02 for DENSE images acquired with a displacement encoding frequency of 0.10 cycles/mm. The DAS-Net method outperformed zero-filling (root Mean Square error = 5.8 ± 1.5 vs 13.5 ± 1.5, DAS-Net vs zero-filling, P < .01; and structural similarity index = 0.83 ± 0.04 vs 0.66 ± 0.03, DAS-Net vs zero-filling, P < .01). Strain data for non-phase-cycled DENSE images with DAS-Net showed close agreement with strain from phase-cycled DENSE. CONCLUSION The DAS-Net method provides an effective alternative approach for suppression of the artifact-generating T1 -relaxation echo in DENSE MRI, enabling a 42% reduction in scan time compared to DENSE with phase-cycling.
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Affiliation(s)
- Mohamad Abdi
- Departments of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
| | - Xue Feng
- Departments of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
| | - Changyu Sun
- Departments of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
| | - Kenneth C. Bilchick
- Department of Medicine, University of Virginia Health System, Charlottesville, Virginia
| | - Craig H. Meyer
- Departments of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
- Departments of Radiology, University of Virginia Health System, Charlottesville, Virginia
| | - Frederick H. Epstein
- Departments of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
- Departments of Radiology, University of Virginia Health System, Charlottesville, Virginia
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Li GY, Wang CY, Lv J. Current status of deep learning in abdominal image reconstruction. Artif Intell Med Imaging 2021; 2:86-94. [DOI: 10.35711/aimi.v2.i4.86] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 06/24/2021] [Accepted: 08/17/2021] [Indexed: 02/06/2023] Open
Abstract
Abdominal magnetic resonance imaging (MRI) and computed tomography (CT) are commonly used for disease screening, diagnosis, and treatment guidance. However, abdominal MRI has disadvantages including slow speed and vulnerability to motions, while CT suffers from problems of radiation. It has been reported that deep learning reconstruction can solve such problems while maintaining good image quality. Recently, deep learning-based image reconstruction has become a hot topic in the field of medical imaging. This study reviews the latest research on deep learning reconstruction in abdominal imaging, including the widely used convolutional neural network, generative adversarial network, and recurrent neural network.
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Affiliation(s)
- Guang-Yuan Li
- School of Computer and Control Engineering, Yantai University, Yantai 264000, Shandong Province, China
| | - Cheng-Yan Wang
- Human Phenome Institute, Fudan University, Shanghai 201203, China
| | - Jun Lv
- School of Computer and Control Engineering, Yantai University, Yantai 264000, Shandong Province, China
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41
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Lyu Q, Shan H, Xie Y, Kwan AC, Otaki Y, Kuronuma K, Li D, Wang G. Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2170-2181. [PMID: 33856986 PMCID: PMC8376223 DOI: 10.1109/tmi.2021.3073381] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multi-scale convolutions gather both local and global features. We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics. Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames, improving the temporal resolution of cine cardiac MRI sequences.
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Ghodrati V, Bydder M, Bedayat A, Prosper A, Yoshida T, Nguyen KL, Finn JP, Hu P. Temporally aware volumetric generative adversarial network-based MR image reconstruction with simultaneous respiratory motion compensation: Initial feasibility in 3D dynamic cine cardiac MRI. Magn Reson Med 2021; 86:2666-2683. [PMID: 34254363 PMCID: PMC10172149 DOI: 10.1002/mrm.28912] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 06/02/2021] [Accepted: 06/12/2021] [Indexed: 12/26/2022]
Abstract
PURPOSE Develop a novel three-dimensional (3D) generative adversarial network (GAN)-based technique for simultaneous image reconstruction and respiratory motion compensation of 4D MRI. Our goal was to enable high-acceleration factors 10.7X-15.8X, while maintaining robust and diagnostic image quality superior to state-of-the-art self-gating (SG) compressed sensing wavelet (CS-WV) reconstruction at lower acceleration factors 3.5X-7.9X. METHODS Our GAN was trained based on pixel-wise content loss functions, adversarial loss function, and a novel data-driven temporal aware loss function to maintain anatomical accuracy and temporal coherence. Besides image reconstruction, our network also performs respiratory motion compensation for free-breathing scans. A novel progressive growing-based strategy was adapted to make the training process possible for the proposed GAN-based structure. The proposed method was developed and thoroughly evaluated qualitatively and quantitatively based on 3D cardiac cine data from 42 patients. RESULTS Our proposed method achieved significantly better scores in general image quality and image artifacts at 10.7X-15.8X acceleration than the SG CS-WV approach at 3.5X-7.9X acceleration (4.53 ± 0.540 vs. 3.13 ± 0.681 for general image quality, 4.12 ± 0.429 vs. 2.97 ± 0.434 for image artifacts, P < .05 for both). No spurious anatomical structures were observed in our images. The proposed method enabled similar cardiac-function quantification as conventional SG CS-WV. The proposed method achieved faster central processing unit-based image reconstruction (6 s/cardiac phase) than the SG CS-WV (312 s/cardiac phase). CONCLUSION The proposed method showed promising potential for high-resolution (1 mm3 ) free-breathing 4D MR data acquisition with simultaneous respiratory motion compensation and fast reconstruction time.
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Affiliation(s)
- Vahid Ghodrati
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, California, USA
| | - Mark Bydder
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Arash Bedayat
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Ashley Prosper
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Takegawa Yoshida
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Kim-Lien Nguyen
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, California, USA.,Department of Medicine (Cardiology), David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - J Paul Finn
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Peng Hu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, California, USA
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Abstract
Clinical MRI systems have continually improved over the years since their introduction in the 1980s. In MRI technical development, the developments in each MRI system component, including data acquisition, image reconstruction, and hardware systems, have impacted the others. Progress in each component has induced new technology development opportunities in other components. New technologies outside of the MRI field, for example, computer science, data processing, and semiconductors, have been immediately incorporated into MRI development, which resulted in innovative applications. With high performance computing and MR technology innovations, MRI can now provide large volumes of functional and anatomical image datasets, which are important tools in various research fields. MRI systems are now combined with other modalities, such as positron emission tomography (PET) or therapeutic devices. These hybrid systems provide additional capabilities. In this review, MRI advances in the last two decades will be considered. We will discuss the progress of MRI systems, the enabling technology, established applications, current trends, and the future outlook.
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Affiliation(s)
- Hiroyuki Kabasawa
- Department of Radiological Sciences, School of Health Sciences at Narita, International University of Health and Welfare
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44
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Khalid WB, Farhat N, Lavery L, Jarnagin J, Delany JP, Kim K. Non-invasive Assessment of Liver Fat in ob/ob Mice Using Ultrasound-Induced Thermal Strain Imaging and Its Correlation with Hepatic Triglyceride Content. ULTRASOUND IN MEDICINE & BIOLOGY 2021; 47:1067-1076. [PMID: 33468357 PMCID: PMC7936391 DOI: 10.1016/j.ultrasmedbio.2020.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 11/21/2020] [Accepted: 12/12/2020] [Indexed: 06/12/2023]
Abstract
Non-alcoholic fatty liver disease is the accumulation of triglycerides in liver. In its malignant form, it can proceed to steatohepatitis, fibrosis, cirrhosis, cancer and ultimately liver impairment, leading to liver transplantation. In a previous study, ultrasound-induced thermal strain imaging (US-TSI) was used to distinguish between excised fatty livers from obese mice and non-fatty livers from control mice. In this study, US-TSI was used to quantify lipid composition of fatty livers in ob/ob mice (n = 28) at various steatosis stages. A strong correlation coefficient was observed (R2 = 0.85) between lipid composition measured with US-TSI and hepatic triglyceride content. Hepatic triglyceride content is used to quantify adipose tissue in liver. The ob/ob mice were divided into three groups based on the degree of steatosis that is used in clinics: none, mild and moderate. A non-parametric Kruskal-Wallis test was conducted to determine if US-TSI can potentially differentiate among the steatosis grades in non-alcoholic fatty liver disease.
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Affiliation(s)
- Waqas B Khalid
- Department of Bioengineering, University of Pittsburgh School of Engineering, Pittsburgh, Pennsylvania, USA
| | - Nadim Farhat
- Department of Bioengineering, University of Pittsburgh School of Engineering, Pittsburgh, Pennsylvania, USA
| | - Linda Lavery
- Center for Ultrasound Molecular Imaging and Therapeutics, Department of Medicine, University of Pittsburgh School of Medicine, Heart and Vascular Institute, University of Pittsburgh Medical Center
| | - Josh Jarnagin
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - James P Delany
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Kang Kim
- Department of Bioengineering, University of Pittsburgh School of Engineering, Pittsburgh, Pennsylvania, USA; Center for Ultrasound Molecular Imaging and Therapeutics, Department of Medicine, University of Pittsburgh School of Medicine, Heart and Vascular Institute, University of Pittsburgh Medical Center; Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA; Department of Mechanical Engineering and Materials Science, University of Pittsburgh School of Engineering, Pittsburgh, Pennsylvania, USA; McGowan Institute for Regenerative Medicine, University of Pittsburgh and University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.
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45
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Liu S, Thung KH, Qu L, Lin W, Shen D, Yap PT. Learning MRI artefact removal with unpaired data. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-020-00270-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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46
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Shimohira M, Kiyosue H, Osuga K, Gobara H, Kondo H, Nakazawa T, Matsui Y, Hamamoto K, Ishiguro T, Maruno M, Sugimoto K, Koganemaru M, Kitagawa A, Yamakado K. Location of embolization affects patency after coil embolization for pulmonary arteriovenous malformations: importance of time-resolved magnetic resonance angiography for diagnosis of patency. Eur Radiol 2021; 31:5409-5420. [PMID: 33449178 DOI: 10.1007/s00330-020-07669-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/13/2020] [Accepted: 12/23/2020] [Indexed: 11/26/2022]
Abstract
OBJECTIVES This study aimed to assess the diagnostic accuracy of computed tomography (CT) and time-resolved magnetic resonance angiography (TR-MRA) for patency after coil embolization of pulmonary arteriovenous malformations (PAVMs) and identify factors affecting patency. METHODS Data from the records of 205 patients with 378 untreated PAVMs were retrospectively analyzed. Differences in proportional reduction of the sac or draining vein on CT between occluded and patent PAVMs were examined, and receiver operating characteristic analysis was performed to assess the accuracy of CT using digital subtraction angiography (DSA) as the definitive diagnostic modality. The accuracy of TR-MRA was also assessed in comparison to DSA. Potential factors affecting patency, including sex, age, number of PAVMs, location of PAVMs, type of PAVM, and location of embolization, were evaluated. RESULTS The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of CT were 82%, 81%, 77%, 85%, and 82%, respectively, when the reduction rate threshold was set to 55%, which led to the highest diagnostic accuracy. The sensitivity, specificity, PPV, NPV, and accuracy of TR-MRA were 89%, 95%, 89%, 95%, and 93%, respectively. On both univariable and multivariable analyses, embolization of the distal position to the last normal branch of the pulmonary artery was a factor that significantly affected the prevention of patency. CONCLUSIONS TR-MRA appears to be an appropriate method for follow-up examinations due to its high accuracy for the diagnosis of patency after coil embolization of PAVMs. The location of embolization is a factor affecting patency. KEY POINTS • Diagnosis of patency after coil embolization for pulmonary arteriovenous malformations (PAVMs) is important because a patent PAVM can lead to neurologic complications. • The diagnostic accuracies of CT with a cutoff value of 55% and TR-MRA were 82% and 93%, respectively. • The positioning of the coils relative to the sac and the last normal branch of the artery was significant for preventing PAVM patency.
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Affiliation(s)
- Masashi Shimohira
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, 467-8601, Japan.
| | - Hiro Kiyosue
- Department of Radiology, Oita University, Yufu, Japan
| | - Keigo Osuga
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Diagnostic Radiology, Osaka Medical College, Takatsuki, Japan
| | - Hideo Gobara
- Department of Radiology, Okayama University Medical School, Okayama, Japan
| | - Hiroshi Kondo
- Department of Radiology, Teikyo University School of Medicine, Itabashi, Tokyo, Japan
| | - Tetsuro Nakazawa
- Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Diagnostic Imaging, Osaka General Medical Center, Osaka, Japan
| | - Yusuke Matsui
- Department of Radiology, Okayama University Medical School, Okayama, Japan
| | - Kohei Hamamoto
- Department of Radiology, Jichi Medical University, Saitama Medical Center, Saitama, Japan
| | - Tomoya Ishiguro
- Department of Neuro-Intervention, Osaka City General Hospital, Osaka, Japan
| | - Miyuki Maruno
- Department of Radiology, Oita University, Yufu, Japan
| | - Koji Sugimoto
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | | | - Akira Kitagawa
- Department of Radiology, Aichi Medical University, Nagakute, Japan
| | - Koichiro Yamakado
- Department of Radiology, Hyogo College of Medicine, Nishinomiya, Japan
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Masoudi S, Harmon SA, Mehralivand S, Walker SM, Raviprakash H, Bagci U, Choyke PL, Turkbey B. Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research. J Med Imaging (Bellingham) 2021; 8:010901. [PMID: 33426151 PMCID: PMC7790158 DOI: 10.1117/1.jmi.8.1.010901] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 12/04/2020] [Indexed: 12/25/2022] Open
Abstract
Purpose: Deep learning has achieved major breakthroughs during the past decade in almost every field. There are plenty of publicly available algorithms, each designed to address a different task of computer vision in general. However, most of these algorithms cannot be directly applied to images in the medical domain. Herein, we are focused on the required preprocessing steps that should be applied to medical images prior to deep neural networks. Approach: To be able to employ the publicly available algorithms for clinical purposes, we must make a meaningful pixel/voxel representation from medical images which facilitates the learning process. Based on the ultimate goal expected from an algorithm (classification, detection, or segmentation), one may infer the required pre-processing steps that can ideally improve the performance of that algorithm. Required pre-processing steps for computed tomography (CT) and magnetic resonance (MR) images in their correct order are discussed in detail. We further supported our discussion by relevant experiments to investigate the efficiency of the listed preprocessing steps. Results: Our experiments confirmed how using appropriate image pre-processing in the right order can improve the performance of deep neural networks in terms of better classification and segmentation. Conclusions: This work investigates the appropriate pre-processing steps for CT and MR images of prostate cancer patients, supported by several experiments that can be useful for educating those new to the field (https://github.com/NIH-MIP/Radiology_Image_Preprocessing_for_Deep_Learning).
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Affiliation(s)
- Samira Masoudi
- National Cancer Institute, National Institutes of Health, Molecular Imaging Branch, Bethesda, Maryland, United States
| | - Stephanie A. Harmon
- National Cancer Institute, National Institutes of Health, Molecular Imaging Branch, Bethesda, Maryland, United States
| | - Sherif Mehralivand
- National Cancer Institute, National Institutes of Health, Molecular Imaging Branch, Bethesda, Maryland, United States
| | - Stephanie M. Walker
- National Cancer Institute, National Institutes of Health, Molecular Imaging Branch, Bethesda, Maryland, United States
| | - Harish Raviprakash
- National Institutes of Health, Department of Radiology and Imaging Sciences, Bethesda, Maryland, United States
| | - Ulas Bagci
- University of Central Florida, Orlando, Florida, United States
| | - Peter L. Choyke
- National Cancer Institute, National Institutes of Health, Molecular Imaging Branch, Bethesda, Maryland, United States
| | - Baris Turkbey
- National Cancer Institute, National Institutes of Health, Molecular Imaging Branch, Bethesda, Maryland, United States
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48
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van der Voort SR, Smits M, Klein S. DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data. Neuroinformatics 2021; 19:159-184. [PMID: 32627144 PMCID: PMC7782469 DOI: 10.1007/s12021-020-09475-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
With the increasing size of datasets used in medical imaging research, the need for automated data curation is arising. One important data curation task is the structured organization of a dataset for preserving integrity and ensuring reusability. Therefore, we investigated whether this data organization step can be automated. To this end, we designed a convolutional neural network (CNN) that automatically recognizes eight different brain magnetic resonance imaging (MRI) scan types based on visual appearance. Thus, our method is unaffected by inconsistent or missing scan metadata. It can recognize pre-contrast T1-weighted (T1w),post-contrast T1-weighted (T1wC), T2-weighted (T2w), proton density-weighted (PDw) and derived maps (e.g. apparent diffusion coefficient and cerebral blood flow). In a first experiment,we used scans of subjects with brain tumors: 11065 scans of 719 subjects for training, and 2369 scans of 192 subjects for testing. The CNN achieved an overall accuracy of 98.7%. In a second experiment, we trained the CNN on all 13434 scans from the first experiment and tested it on 7227 scans of 1318 Alzheimer's subjects. Here, the CNN achieved an overall accuracy of 98.5%. In conclusion, our method can accurately predict scan type, and can quickly and automatically sort a brain MRI dataset virtually without the need for manual verification. In this way, our method can assist with properly organizing a dataset, which maximizes the shareability and integrity of the data.
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Affiliation(s)
- Sebastian R van der Voort
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Nuclear Medicine and Medical Informatics, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands.
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Nuclear Medicine and Medical Informatics, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
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49
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Deep Convolutional Encoder-Decoder algorithm for MRI brain reconstruction. Med Biol Eng Comput 2020; 59:85-106. [PMID: 33231848 DOI: 10.1007/s11517-020-02285-8] [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: 04/17/2020] [Accepted: 10/31/2020] [Indexed: 10/22/2022]
Abstract
Compressed Sensing Magnetic Resonance Imaging (CS-MRI) could be considered a challenged task since it could be designed as an efficient technique for fast MRI acquisition which could be highly beneficial for several clinical routines. In fact, it could grant better scan quality by reducing motion artifacts amount as well as the contrast washout effect. It offers also the possibility to reduce the exploration cost and the patient's anxiety. Recently, Deep Learning Neuronal Network (DL) has been suggested in order to reconstruct MRI scans with conserving the structural details and improving parallel imaging-based fast MRI. In this paper, we propose Deep Convolutional Encoder-Decoder architecture for CS-MRI reconstruction. Such architecture bridges the gap between the non-learning techniques, using data from only one image, and approaches using large training data. The proposed approach is based on autoencoder architecture divided into two parts: an encoder and a decoder. The encoder as well as the decoder has essentially three convolutional blocks. The proposed architecture has been evaluated through two databases: Hammersmith dataset (for the normal scans) and MICCAI 2018 (for pathological MRI). Moreover, we extend our model to cope with noisy pathological MRI scans. The normalized mean square error (NMSE), the peak-to-noise ratio (PSNR), and the structural similarity index (SSIM) have been adopted as evaluation metrics in order to evaluate the proposed architecture performance and to make a comparative study with the state-of-the-art reconstruction algorithms. The higher PSNR and SSIM values as well as the lowest NMSE values could attest that the proposed architecture offers better reconstruction and preserves textural image details. Furthermore, the running time is about 0.8 s, which is suitable for real-time processing. Such results could encourage the neurologist to adopt it in their clinical routines. Graphical abstract.
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50
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An H, Shin HG, Ji S, Jung W, Oh S, Shin D, Park J, Lee J. DeepResp: Deep learning solution for respiration-induced B 0 fluctuation artifacts in multi-slice GRE. Neuroimage 2020; 224:117432. [PMID: 33038539 DOI: 10.1016/j.neuroimage.2020.117432] [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: 07/19/2020] [Revised: 09/23/2020] [Accepted: 09/30/2020] [Indexed: 11/25/2022] Open
Abstract
Respiration-induced B0 fluctuation corrupts MRI images by inducing phase errors in k-space. A few approaches such as navigator have been proposed to correct for the artifacts at the expense of sequence modification. In this study, a new deep learning method, which is referred to as DeepResp, is proposed for reducing the respiration-artifacts in multi-slice gradient echo (GRE) images. DeepResp is designed to extract the respiration-induced phase errors from a complex image using deep neural networks. Then, the network-generated phase errors are applied to the k-space data, creating an artifact-corrected image. For network training, the computer-simulated images were generated using artifact-free images and respiration data. When evaluated, both simulated images and in-vivo images of two different breathing conditions (deep breathing and natural breathing) show improvements (simulation: normalized root-mean-square error (NRMSE) from 7.8 ± 5.2% to 1.3 ± 0.6%; structural similarity (SSIM) from 0.88 ± 0.08 to 0.99 ± 0.01; ghost-to-signal-ratio (GSR) from 7.9 ± 7.2% to 0.6 ± 0.6%; deep breathing: NRMSE from 13.9 ± 4.6% to 5.8 ± 1.4%; SSIM from 0.86 ± 0.03 to 0.95 ± 0.01; GSR 20.2 ± 10.2% to 5.7 ± 2.3%; natural breathing: NRMSE from 5.2 ± 3.3% to 4.0 ± 2.5%; SSIM from 0.94 ± 0.04 to 0.97 ± 0.02; GSR 5.7 ± 5.0% to 2.8 ± 1.1%). Our approach does not require any modification of the sequence or additional hardware, and may therefore find useful applications. Furthermore, the deep neural networks extract respiration-induced phase errors, which is more interpretable and reliable than results of end-to-end trained networks.
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Affiliation(s)
- Hongjun An
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Hyeong-Geol Shin
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Sooyeon Ji
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Woojin Jung
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Sehong Oh
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, South Korea
| | - Dongmyung Shin
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Juhyung Park
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea.
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