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Choi KH, Heo YJ, Baek HJ, Kim JH, Jang JY. Comparison of Inter-Method Agreement and Reliability for Automatic Brain Volumetry Using Three Different Clinically Available Software Packages. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:727. [PMID: 38792912 PMCID: PMC11122718 DOI: 10.3390/medicina60050727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/21/2024] [Accepted: 04/25/2024] [Indexed: 05/26/2024]
Abstract
Background and Objectives: No comparative study has evaluated the inter-method agreement and reliability between Heuron AD and other clinically available brain volumetric software packages. Hence, we aimed to investigate the inter-method agreement and reliability of three clinically available brain volumetric software packages: FreeSurfer (FS), NeuroQuant® (NQ), and Heuron AD (HAD). Materials and Methods: In this study, we retrospectively included 78 patients who underwent conventional three-dimensional (3D) T1-weighed imaging (T1WI) to evaluate their memory impairment, including 21 with normal objective cognitive function, 24 with mild cognitive impairment, and 33 with Alzheimer's disease (AD). All 3D T1WI scans were analyzed using three different volumetric software packages. Repeated-measures analysis of variance, intraclass correlation coefficient, effect size measurements, and Bland-Altman analysis were used to evaluate the inter-method agreement and reliability. Results: The measured volumes demonstrated substantial to almost perfect agreement for most brain regions bilaterally, except for the bilateral globi pallidi. However, the volumes measured using the three software packages showed significant mean differences for most brain regions, with consistent systematic biases and wide limits of agreement in the Bland-Altman analyses. The pallidum showed the largest effect size in the comparisons between NQ and FS (5.20-6.93) and between NQ and HAD (2.01-6.17), while the cortical gray matter showed the largest effect size in the comparisons between FS and HAD (0.79-1.91). These differences and variations between the software packages were also observed in the subset analyses of 45 patients without AD and 33 patients with AD. Conclusions: Despite their favorable reliability, the software-based brain volume measurements showed significant differences and systematic biases in most regions. Thus, these volumetric measurements should be interpreted based on the type of volumetric software used, particularly for smaller structures. Moreover, users should consider the replaceability-related limitations when using these packages in real-world practice.
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Affiliation(s)
- Kwang Ho Choi
- Department of Thoracic and Cardiovascular Surgery, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, 20 Geumo-ro, Mulgeum-eup, Yangsan-si 50612, Republic of Korea
| | - Young Jin Heo
- Department of Radiology, Busan Paik Hospital, Inje University College of Medicine, 75, Bokji-ro, Busanjin-gu, Busan 47392, Republic of Korea
| | - Hye Jin Baek
- Department of Radiology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, 11 Samjeongja-ro, Seongsan-gu, Changwon 51472, Republic of Korea
- Miracle Radiology Clinic, 201 Songpa-daero, Songpa-gu, Seoul 05854, Republic of Korea
| | - Jun-Ho Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Jeong Yoon Jang
- Division of Cardiology, Department of Internal Medicine, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, 11 Samjeongja-ro, Seongsan-gu, Changwon 51472, Republic of Korea
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Bottani S, Thibeau-Sutre E, Maire A, Ströer S, Dormont D, Colliot O, Burgos N. Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI. BMC Med Imaging 2024; 24:67. [PMID: 38504179 PMCID: PMC10953143 DOI: 10.1186/s12880-024-01242-3] [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: 05/22/2023] [Accepted: 03/07/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Clinical data warehouses provide access to massive amounts of medical images, but these images are often heterogeneous. They can for instance include images acquired both with or without the injection of a gadolinium-based contrast agent. Harmonizing such data sets is thus fundamental to guarantee unbiased results, for example when performing differential diagnosis. Furthermore, classical neuroimaging software tools for feature extraction are typically applied only to images without gadolinium. The objective of this work is to evaluate how image translation can be useful to exploit a highly heterogeneous data set containing both contrast-enhanced and non-contrast-enhanced images from a clinical data warehouse. METHODS We propose and compare different 3D U-Net and conditional GAN models to convert contrast-enhanced T1-weighted (T1ce) into non-contrast-enhanced (T1nce) brain MRI. These models were trained using 230 image pairs and tested on 77 image pairs from the clinical data warehouse of the Greater Paris area. RESULTS Validation using standard image similarity measures demonstrated that the similarity between real and synthetic T1nce images was higher than between real T1nce and T1ce images for all the models compared. The best performing models were further validated on a segmentation task. We showed that tissue volumes extracted from synthetic T1nce images were closer to those of real T1nce images than volumes extracted from T1ce images. CONCLUSION We showed that deep learning models initially developed with research quality data could synthesize T1nce from T1ce images of clinical quality and that reliable features could be extracted from the synthetic images, thus demonstrating the ability of such methods to help exploit a data set coming from a clinical data warehouse.
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Affiliation(s)
- Simona Bottani
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France
| | - Elina Thibeau-Sutre
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France
| | - Aurélien Maire
- Innovation & Données - Département des Services Numériques, AP-HP, Paris, 75013, France
| | - Sebastian Ströer
- Hôpital Pitié Salpêtrière, Department of Neuroradiology, AP-HP, Paris, 75012, France
| | - Didier Dormont
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, DMU DIAMENT, Paris, 75013, France
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France
| | - Ninon Burgos
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, 75013, France.
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Kim HW, Kim HJ, Lee H, Yang H, Rieu Z, Lee JH. Magnetic resonance image-based brain age as a discriminator of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment. Sci Rep 2023; 13:22467. [PMID: 38105274 PMCID: PMC10725862 DOI: 10.1038/s41598-023-49465-8] [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: 07/10/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023] Open
Abstract
Patients with amyloid-negative amnestic mild cognitive impairment (MCI) have a conversion rate of approximately 10% to dementia within 2 years. We aimed to investigate whether brain age is an important factor in predicting conversion to dementia in patients with amyloid-negative amnestic MCI. We conducted a retrospective cohort study of patients with amyloid-negative amnestic MCI. All participants underwent detailed neuropsychological evaluation, brain magnetic resonance imaging (MRI), and [18F]-florbetaben positron emission tomography. Brain age was determined by the volumetric assessment of 12 distinct brain regions using an automatic segmentation software. During the follow-up period, 38% of the patients converted from amnestic MCI to dementia. Further, 73% of patients had a brain age greater than their actual chronological age. When defining 'survival' as the non-conversion of MCI to dementia, these groups differed significantly in survival probability (p = 0.036). The low-educated female group with a brain age greater than their actual age had the lowest survival rate among all groups. Our findings suggest that the MRI-based brain age used in this study can contribute to predicting conversion to dementia in patients with amyloid-negative amnestic MCI.
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Affiliation(s)
- Hye Weon Kim
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - Hyung-Ji Kim
- Department of Neurology, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu, Korea
| | - Hyunji Lee
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - Hyeonsik Yang
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - ZunHyan Rieu
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea.
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Koussis P, Toulas P, Glotsos D, Lamprou E, Kehagias D, Lavdas E. Reliability of automated brain volumetric analysis: A test by comparing NeuroQuant and volBrain software. Brain Behav 2023; 13:e3320. [PMID: 37997504 PMCID: PMC10726865 DOI: 10.1002/brb3.3320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND AND PURPOSE Brain volume analysis from magnetic resonance imaging (MRI) is gaining an important role in neurological diagnosis. This study compares the volumes of brain segments measured by two automated brain analysis software, NeuroQuant (NQ), and volBrain (VB) in order to test their reliability in brain volumetry. METHODS Using NQ and VB software, the same brain segment volumes were calculated and compared, taken from 56 patients scanned under the same MRI sequence. These segments were intracranial cavity, putamen, thalamus, amygdala, whole brain, cerebellum, white matter, and hippocampus. The paired t-test method has been used to determine if there was a significant difference in these measurements. The interclass correlation (ICC) is used to test inter-method reliability between the two software. Finally, regression analysis was used to examine the possibility of linear correlation. RESULTS In all brain segments tested but hippocampus, significant differences were found. ICC presents satisfactory to excellent reliability in all brain segments except thalamus and amygdala for which reliability has been proven to be poor. In most cases, linear correlation was found. CONCLUSIONS The significant differences found in the majority of the tested brain segments are raising questions about the reliability of automated brain analysis as a quantitative tool. Strong linear correlation of the volumetric measurements and good reliability indicates that, each software provides good qualitative information of brain structures size.
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Affiliation(s)
- Panagiotis Koussis
- Bioiatriki SA/MRI DepartmentKifissias ave and PapadaAthensGreece
- Department of Biomedical SciencesUniversity of West AttikaAthensGreece
| | | | - Demetrios Glotsos
- Department of Biomedical SciencesUniversity of West AttikaAthensGreece
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Davis MA, Ramakrishnan D, Sala M, Aboian M. Local Economic Considerations in Selecting Artificial Intelligence Tools for Implementation. J Am Coll Radiol 2023; 20:981-984. [PMID: 37604325 DOI: 10.1016/j.jacr.2023.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 08/23/2023]
Affiliation(s)
- Melissa A Davis
- Vice Chair of Medical Informatics and Medical Director of Informatics, Yale University, New Haven, Connecticut; Co-Chair of Patient and Family Centered Care - American College of Radiology; and President of SRRO Chapter, National Medical Association.
| | | | | | - Mariam Aboian
- Director, 3D Lab, Yale University, New Haven, Connecticut
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Suh PS, Jung W, Suh CH, Kim J, Oh J, Heo H, Shim WH, Lim JS, Lee JH, Kim HS, Kim SJ. Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg. Front Neurol 2023; 14:1221892. [PMID: 37719763 PMCID: PMC10503131 DOI: 10.3389/fneur.2023.1221892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 08/07/2023] [Indexed: 09/19/2023] Open
Abstract
Background and purpose To develop and validate a deep learning-based automatic segmentation model for assessing intracranial volume (ICV) and to compare the accuracy determined by NeuroQuant (NQ), FreeSurfer (FS), and SynthSeg. Materials and methods This retrospective study included 60 subjects [30 Alzheimer's disease (AD), 21 mild cognitive impairment (MCI), 9 cognitively normal (CN)] from a single tertiary hospital for the training and validation group (50:10). The test group included 40 subjects (20 AD, 10 MCI, 10 CN) from the ADNI dataset. We propose a robust ICV segmentation model based on the foundational 2D UNet architecture trained with four types of input images (both single and multimodality using scaled or unscaled T1-weighted and T2-FLAIR MR images). To compare with our model, NQ, FS, and SynthSeg were also utilized in the test group. We evaluated the model performance by measuring the Dice similarity coefficient (DSC) and average volume difference. Results The single-modality model trained with scaled T1-weighted images showed excellent performance with a DSC of 0.989 ± 0.002 and an average volume difference of 0.46% ± 0.38%. Our multimodality model trained with both unscaled T1-weighted and T2-FLAIR images showed similar performance with a DSC of 0.988 ± 0.002 and an average volume difference of 0.47% ± 0.35%. The overall average volume difference with our model showed relatively higher accuracy than NQ (2.15% ± 1.72%), FS (3.69% ± 2.93%), and SynthSeg (1.88% ± 1.18%). Furthermore, our model outperformed the three others in each subgroup of patients with AD, MCI, and CN subjects. Conclusion Our deep learning-based automatic ICV segmentation model showed excellent performance for the automatic evaluation of ICV.
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Affiliation(s)
- Pae Sun Suh
- Department of Radiology, Asan Medical Center, Seoul, Republic of Korea
| | | | - Chong Hyun Suh
- Department of Radiology, Asan Medical Center, Seoul, Republic of Korea
| | | | - Jio Oh
- R&D Center, VUNO, Seoul, Republic of Korea
| | - Hwon Heo
- Department of Radiology, Asan Medical Center, Seoul, Republic of Korea
| | - Woo Hyun Shim
- Department of Radiology, Asan Medical Center, Seoul, Republic of Korea
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, College of Medicine, University of Ulsan, Ulsan, Republic of Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, College of Medicine, University of Ulsan, Ulsan, Republic of Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, Republic of Korea
| | - Sang Joon Kim
- Department of Radiology, Asan Medical Center, Seoul, Republic of Korea
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Yang MH, Kim EH, Choi ES, Ko H. Comparison of Normative Percentiles of Brain Volume Obtained from NeuroQuant ® vs. DeepBrain ® in the Korean Population: Correlation with Cranial Shape. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:1080-1090. [PMID: 37869130 PMCID: PMC10585089 DOI: 10.3348/jksr.2023.0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/13/2023] [Accepted: 04/15/2023] [Indexed: 10/24/2023]
Abstract
Purpose This study aimed to compare the volume and normative percentiles of brain volumetry in the Korean population using quantitative brain volumetric MRI analysis tools NeuroQuant® (NQ) and DeepBrain® (DB), and to evaluate whether the differences in the normative percentiles of brain volumetry between the two tools is related to cranial shape. Materials and Methods In this retrospective study, we analyzed the brain volume reports obtained from NQ and DB in 163 participants without gross structural brain abnormalities. We measured three-dimensional diameters to evaluate the cranial shape on T1-weighted images. Statistical analyses were performed using intra-class correlation coefficients and linear correlations. Results The mean normative percentiles of the thalamus (90.8 vs. 63.3 percentile), putamen (90.0 vs. 60.0 percentile), and parietal lobe (80.1 vs. 74.1 percentile) were larger in the NQ group than in the DB group, whereas that of the occipital lobe (18.4 vs. 68.5 percentile) was smaller in the NQ group than in the DB group. We found a significant correlation between the mean normative percentiles obtained from the NQ and cranial shape: the mean normative percentile of the occipital lobe increased with the anteroposterior diameter and decreased with the craniocaudal diameter. Conclusion The mean normative percentiles obtained from NQ and DB differed significantly for many brain regions, and these differences may be related to cranial shape.
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Yearley AG, Goedmakers CMW, Panahi A, Doucette J, Rana A, Ranganathan K, Smith TR. FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval. Artif Intell Med 2023; 143:102607. [PMID: 37673576 DOI: 10.1016/j.artmed.2023.102607] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 09/08/2023]
Abstract
Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.
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Affiliation(s)
- Alexander G Yearley
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA.
| | - Caroline M W Goedmakers
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Department of Neurosurgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, Netherlands
| | - Armon Panahi
- The George Washington University School of Medicine and Health Sciences, 2300 I St NW, Washington, DC 20052, USA
| | - Joanne Doucette
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; School of Pharmacy, MCPHS University, 179 Longwood Ave, Boston, MA 02115, USA
| | - Aakanksha Rana
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
| | - Kavitha Ranganathan
- Division of Plastic Surgery, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA
| | - Timothy R Smith
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA
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Warntjes JBM, Lundberg P, Tisell A. Brain Parcellation Repeatability and Reproducibility Using Conventional and Quantitative 3D MR Imaging. AJNR Am J Neuroradiol 2023; 44:910-915. [PMID: 37414454 PMCID: PMC10411845 DOI: 10.3174/ajnr.a7937] [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: 11/18/2022] [Accepted: 06/14/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND AND PURPOSE Automatic brain parcellation is typically performed on dedicated MR imaging sequences, which require valuable examination time. In this study, a 3D MR imaging quantification sequence to retrieve R1 and R2 relaxation rates and proton density maps was used to synthesize a T1-weighted image stack for brain volume measurement, thereby combining image data for multiple purposes. The repeatability and reproducibility of using the conventional and synthetic input data were evaluated. MATERIALS AND METHODS Twelve subjects with a mean age of 54 years were scanned twice at 1.5T and 3T with 3D-QALAS and a conventionally acquired T1-weighted sequence. Using SyMRI, we converted the R1, R2, and proton density maps into synthetic T1-weighted images. Both the conventional T1-weighted and the synthetic 3D-T1-weighted inversion recovery images were processed for brain parcellation by NeuroQuant. Bland-Altman statistics were used to correlate the volumes of 12 brain structures. The coefficient of variation was used to evaluate the repeatability. RESULTS A high correlation with medians of 0.97 for 1.5T and 0.92 for 3T was found. A high repeatability was shown with a median coefficient of variation of 1.2% for both T1-weighted and synthetic 3D-T1-weighted inversion recovery at 1.5T, and 1.5% for T1-weighted imaging and 4.4% for synthetic 3D-T1-weighted inversion recovery at 3T. However, significant biases were observed between the methods and field strengths. CONCLUSIONS It is possible to perform MR imaging quantification of R1, R2, and proton density maps to synthesize a 3D-T1-weighted image stack, which can be used for automatic brain parcellation. Synthetic parameter settings should be reinvestigated to reduce the observed bias.
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Affiliation(s)
- J B M Warntjes
- From the Centre for Medical Image Science and Visualization (J.B.M.W.)
- SyntheticMR (J.B.M.W.), Linköping, Sweden
| | - P Lundberg
- Department of Radiation Physics (P.L., A.T.)
- Department of Health, Medicine and Caring Sciences (P.L., A.T.), Linköping University, Linköping, Sweden
| | - A Tisell
- Department of Radiation Physics (P.L., A.T.)
- Department of Health, Medicine and Caring Sciences (P.L., A.T.), Linköping University, Linköping, Sweden
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Moon CM, Lee YY, Hyeong KE, Yoon W, Baek BH, Heo SH, Shin SS, Kim SK. Development and validation of deep learning-based automatic brain segmentation for East Asians: A comparison with Freesurfer. Front Neurosci 2023; 17:1157738. [PMID: 37250408 PMCID: PMC10213324 DOI: 10.3389/fnins.2023.1157738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 02/27/2023] [Indexed: 05/31/2023] Open
Abstract
Purpose To develop and validate deep learning-based automatic brain segmentation for East Asians with comparison to data for healthy controls from Freesurfer based on a ground truth. Methods A total of 30 healthy participants were enrolled and underwent T1-weighted magnetic resonance imaging (MRI) using a 3-tesla MRI system. Our Neuro I software was developed based on a three-dimensional convolutional neural networks (CNNs)-based, deep-learning algorithm, which was trained using data for 776 healthy Koreans with normal cognition. Dice coefficient (D) was calculated for each brain segment and compared with control data by paired t-test. The inter-method reliability was assessed by intraclass correlation coefficient (ICC) and effect size. Pearson correlation analysis was applied to assess the relationship between D values for each method and participant ages. Results The D values obtained from Freesurfer (ver6.0) were significantly lower than those from Neuro I. The histogram of the Freesurfer results showed remarkable differences in the distribution of D values from Neuro I. Overall, D values obtained by Freesurfer and Neuro I showed positive correlations, but the slopes and intercepts were significantly different. It was showed the largest effect sizes ranged 1.07-3.22, and ICC also showed significantly poor to moderate correlations between the two methods (0.498 ≤ ICC ≤ 0.688). For Neuro I, D values resulted in reduced residuals when fitting data to a line of best fit, and indicated consistent values corresponding to each age, even in young and older adults. Conclusion Freesurfer and Neuro I were not equivalent when compared to a ground truth, where Neuro I exhibited higher performance. We suggest that Neuro I is a useful alternative for the assessment of the brain volume.
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Affiliation(s)
- Chung-Man Moon
- Research Institute of Medical Sciences, Chonnam National University, Gwangju, Republic of Korea
| | - Yun Young Lee
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
| | | | - Woong Yoon
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
- Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Byung Hyun Baek
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
- Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Suk-Hee Heo
- Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea
| | - Sang-Soo Shin
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
- Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Seul Kee Kim
- Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea
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Song H, Lee SA, Jo SW, Chang SK, Lim Y, Yoo YS, Kim JH, Choi SH, Sohn CH. Agreement and Reliability between Clinically Available Software Programs in Measuring Volumes and Normative Percentiles of Segmented Brain Regions. Korean J Radiol 2022; 23:959-975. [PMID: 36175000 PMCID: PMC9523231 DOI: 10.3348/kjr.2022.0067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 07/15/2022] [Accepted: 07/18/2022] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVE To investigate the agreement and reliability of estimating the volumes and normative percentiles (N%) of segmented brain regions among NeuroQuant (NQ), DeepBrain (DB), and FreeSurfer (FS) software programs, focusing on the comparison between NQ and DB. MATERIALS AND METHODS Three-dimensional T1-weighted images of 145 participants (48 healthy participants, 50 patients with mild cognitive impairment, and 47 patients with Alzheimer's disease) from a single medical center (SMC) dataset and 130 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset were included in this retrospective study. All images were analyzed with DB, NQ, and FS software to obtain volume estimates and N% of various segmented brain regions. We used Bland-Altman analysis, repeated measures ANOVA, reproducibility coefficient, effect size, and intraclass correlation coefficient (ICC) to evaluate inter-method agreement and reliability. RESULTS Among the three software programs, the Bland-Altman plot showed a substantial bias, the ICC showed a broad range of reliability (0.004-0.97), and repeated-measures ANOVA revealed significant mean volume differences in all brain regions. Similarly, the volume differences of the three software programs had large effect sizes in most regions (0.73-5.51). The effect size was largest in the pallidum in both datasets and smallest in the thalamus and cerebral white matter in the SMC and ADNI datasets, respectively. N% of NQ and DB showed an unacceptably broad Bland-Altman limit of agreement in all brain regions and a very wide range of ICC values (-0.142-0.844) in most brain regions. CONCLUSION NQ and DB showed significant differences in the measured volume and N%, with limited agreement and reliability for most brain regions. Therefore, users should be aware of the lack of interchangeability between these software programs when they are applied in clinical practice.
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Affiliation(s)
- Huijin Song
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Seun Ah Lee
- Department of Radiology, Dongtan Sacred Heart Hospital, Hallym University Medical Center, Hwaseong, Korea
| | - Sang Won Jo
- Department of Radiology, Dongtan Sacred Heart Hospital, Hallym University Medical Center, Hwaseong, Korea.
| | - Suk-Ki Chang
- Department of Radiology, Dongtan Sacred Heart Hospital, Hallym University Medical Center, Hwaseong, Korea
| | - Yunji Lim
- Department of Radiology, Dongtan Sacred Heart Hospital, Hallym University Medical Center, Hwaseong, Korea
| | - Yeong Seo Yoo
- Department of Radiology, Dongtan Sacred Heart Hospital, Hallym University Medical Center, Hwaseong, Korea
| | - Jae Ho Kim
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University Medical Center, Hwaseong, Korea
| | - Seung Hong Choi
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
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12
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Jeong SY, Suh CH, Park HY, Heo H, Shim WH, Kim SJ. [Brain MRI-Based Artificial Intelligence Software in Patients with Neurodegenerative Diseases: Current Status]. TAEHAN YONGSANG UIHAKHOE CHI 2022; 83:473-485. [PMID: 36238504 PMCID: PMC9514516 DOI: 10.3348/jksr.2022.0048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/05/2022] [Accepted: 05/15/2022] [Indexed: 11/28/2022]
Abstract
The incidence of neurodegenerative diseases in the older population has increased in recent years. A considerable number of studies have been performed to characterize these diseases. Imaging analysis is an important biomarker for the diagnosis of neurodegenerative disease. Objective and reliable assessment and precise detection are important for the early diagnosis of neurodegenerative diseases. Artificial intelligence (AI) using brain MRI applied to the study of neurodegenerative diseases could promote early diagnosis and optimal decisions for treatment plans. MRI-based AI software have been developed and studied worldwide. Representatively, there are MRI-based volumetry and segmentation software. In this review, we present the development process of brain volumetry analysis software in neurodegenerative diseases, currently used and developed AI software for neurodegenerative disease in the Republic of Korea, probable uses of AI in the future, and AI software limitations.
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13
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Ross DE, Seabaugh J, Seabaugh JM, Barcelona J, Seabaugh D, Wright K, Norwind L, King Z, Graham TJ, Baker J, Lewis T. Updated Review of the Evidence Supporting the Medical and Legal Use of NeuroQuant ® and NeuroGage ® in Patients With Traumatic Brain Injury. Front Hum Neurosci 2022; 16:715807. [PMID: 35463926 PMCID: PMC9027332 DOI: 10.3389/fnhum.2022.715807] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 03/03/2022] [Indexed: 02/05/2023] Open
Abstract
Over 40 years of research have shown that traumatic brain injury affects brain volume. However, technical and practical limitations made it difficult to detect brain volume abnormalities in patients suffering from chronic effects of mild or moderate traumatic brain injury. This situation improved in 2006 with the FDA clearance of NeuroQuant®, a commercially available, computer-automated software program for measuring MRI brain volume in human subjects. More recent strides were made with the introduction of NeuroGage®, commercially available software that is based on NeuroQuant® and extends its utility in several ways. Studies using these and similar methods have found that most patients with chronic mild or moderate traumatic brain injury have brain volume abnormalities, and several of these studies found-surprisingly-more abnormal enlargement than atrophy. More generally, 102 peer-reviewed studies have supported the reliability and validity of NeuroQuant® and NeuroGage®. Furthermore, this updated version of a previous review addresses whether NeuroQuant® and NeuroGage® meet the Daubert standard for admissibility in court. It concludes that NeuroQuant® and NeuroGage® meet the Daubert standard based on their reliability, validity, and objectivity. Due to the improvements in technology over the years, these brain volumetric techniques are practical and readily available for clinical or forensic use, and thus they are important tools for detecting signs of brain injury.
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Affiliation(s)
- David E. Ross
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
| | - John Seabaugh
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
- Department of Radiology, St. Mary’s Hospital School of Medical Imaging, Richmond, VA, United States
| | - Jan M. Seabaugh
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
| | - Justis Barcelona
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
| | - Daniel Seabaugh
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
| | - Katherine Wright
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
| | - Lee Norwind
- Karp, Wigodsky, Norwind, Kudel & Gold, P.A., Rockville, MD, United States
| | - Zachary King
- Karp, Wigodsky, Norwind, Kudel & Gold, P.A., Rockville, MD, United States
| | | | - Joseph Baker
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
- Department of Neuroscience, Christopher Newport University, Newport News, VA, United States
| | - Tanner Lewis
- Virginia Institute of Neuropsychiatry, Midlothian, VA, United States
- NeuroGage LLC, Midlothian, VA, United States
- Department of Undergraduate Studies, University of Virginia, Charlottesville, VA, United States
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14
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Pemberton HG, Zaki LAM, Goodkin O, Das RK, Steketee RME, Barkhof F, Vernooij MW. Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review. Neuroradiology 2021; 63:1773-1789. [PMID: 34476511 PMCID: PMC8528755 DOI: 10.1007/s00234-021-02746-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 06/02/2021] [Indexed: 12/22/2022]
Abstract
Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis.
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Affiliation(s)
- Hugh G Pemberton
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Lara A M Zaki
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Olivia Goodkin
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ravi K Das
- Clinical, Educational and Health Psychology, University College London, London, UK
| | - Rebecca M E Steketee
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
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15
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Lee J, Lee JY, Oh SW, Chung MS, Park JE, Moon Y, Jeon HJ, Moon WJ. Evaluation of Reproducibility of Brain Volumetry between Commercial Software, Inbrain and Established Research Purpose Method, FreeSurfer. J Clin Neurol 2021; 17:307-316. [PMID: 33835753 PMCID: PMC8053534 DOI: 10.3988/jcn.2021.17.2.307] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 02/02/2021] [Accepted: 02/02/2021] [Indexed: 01/18/2023] Open
Abstract
Background and Purpose We aimed to determine the intermethod reproducibility between the commercial software Inbrain (MIDAS IT) and the established research-purpose method FreeSurfer, as well as the effect of MRI resolution and the pathological condition of subjects on their intermethod reproducibility. Methods This study included 45 healthy volunteers and 85 patients with mild cognitive impairment (MCI). In 43 of the 85 patients with MCI, three-dimensional, T1-weighted MRI data were obtained at an in-plane resolution of 1.2 mm. The data of the remaining 42 patients with MCI and the healthy volunteers were obtained at an in-plane resolution of 1.0 mm. The within-subject coefficient of variation (CoV), intraclass correlation coefficient (ICC), and effect size were calculated, and means were compared using paired t-tests. The parameters obtained at 1.0-mm and 1.2-mm resolutions in patients with MCI were compared to evaluate the effect of the in-plane resolution on the intermethod reproducibility. The parameters obtained at a 1.0-mm in-plane resolution in patients with MCI and healthy volunteers were used to analyze the effect of subject condition on intermethod reproducibility. Results Overall the two methods showed excellent reproducibility across all regions of the brain (CoV=0.5–3.9, ICC=0.93 to >0.99). In the subgroup of healthy volunteers, the intermethod reliability was only good in some regions (frontal, temporal, cingulate, and insular). The intermethod reproducibility was better in the 1.0-mm group than the 1.2-mm group in all regions other than the nucleus accumbens. Conclusions Inbrain and FreeSurfer showed good-to-excellent intermethod reproducibility for volumetric measurements. Nevertheless, some noticeable differences were found based on subject condition, image resolution, and brain region.
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Affiliation(s)
- Jungbin Lee
- Department of Radiology, Soonchunghyang University Bucheon Hospital, Bucheon, Korea
| | - Ji Young Lee
- Department of Radiology, Hanyang University Medical Center, Seoul, Korea
| | - Se Won Oh
- Department of Radiology, Soonchunhyang University Cheonan Hospital, Cheonan, Korea
| | - Mi Sun Chung
- Department of Radiology, Chung-Ang University Hospital, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, Korea
| | - Yeonsil Moon
- Department of Neurology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Hong Jun Jeon
- Department of Psychiatry, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Won Jin Moon
- Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea.
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16
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Lee JY, Park JE, Chung MS, Oh SW, Moon WJ. Expert Opinions and Recommendations for the Clinical Use of Quantitative Analysis Software for MRI-Based Brain Volumetry. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2021; 82:1124-1139. [PMID: 36238415 PMCID: PMC9432367 DOI: 10.3348/jksr.2020.0174] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 12/31/2020] [Accepted: 01/21/2021] [Indexed: 11/25/2022]
Abstract
치매를 비롯한 퇴행성 신경 질환의 초기 진단에 자기공명영상을 이용한 뇌 위축 평가와 정량적 용적 분석이 중요하다. 뇌 위축의 시각적 평가는 주관적으로 평가자에 따라 다른 결과를 보여주기 때문에, 객관적인 결과를 제공하면서 임상 적용도 가능한 소프트웨어의 수요와 개발이 늘어나고 있다. 이러한 임상용 소프트웨어의 실제 임상 적용은 영상 검사의 표준화가 선행되어야 하고, 개발된 소프트웨어의 검증이 반드시 필요하다. 따라서 대한신경두경부영상의학회는 뇌용적 분석 임상용 소프트웨어의 임상적 활용에 대한 의견을 제시하기 위해 전문위원회를 구성하고 현재까지 발표된 연구를 정리하였다. 그리고, 정량화 분석을 위한 영상 검사의 표준화 및 소프트웨어의 임상 적용에 대한 전문가 의견을 제시하기 위하여 공동 작업을 수행하였다. 본 종설에서는 뇌 자기공명영상의 정량화 분석의 필요성 및 배경, 정량화 분석을 위한 임상용 소프트웨어의 소개 및 기존의 표준품(reference standard)과의 진단능 비교, 영상 획득의 표준화, 분석 및 평가의 표준화, 소프트웨어의 임상 적용에 대한 전문가 의견, 제한점 및 대처 방법 등 대한신경두경부영상의학회의 전문가 권고안을 소개하는 것이 목적이다.
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Affiliation(s)
- Ji Young Lee
- Department of Radiology, Hanyang University Medical Center, Hanyang University Medical College, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Mi Sun Chung
- Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea
| | - Se Won Oh
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Won-Jin Moon
- Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
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