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11 results on '"Fang, Wanxuan"'

2. A Deep Registration Method for Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis

Author

Wang, Haolin, Ou, Yafei, Fang, Wanxuan, Ambalathankandy, Prasoon, Goto, Naoto, Ota, Gen, Ikebe, Masayuki, and Kamishima, Tamotsu

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, 68T45, I.4
Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that results in progressive articular destruction and severe disability. Joint space narrowing (JSN) progression has been regarded as an important indicator for RA progression and has received sustained attention. In the diagnosis and monitoring of RA, radiology plays a crucial role to monitor joint space. A new framework for monitoring joint space by quantifying JSN progression through image registration in radiographic images has been developed. This framework offers the advantage of high accuracy, however, challenges do exist in reducing mismatches and improving reliability. In this work, a deep intra-subject rigid registration network is proposed to automatically quantify JSN progression in the early stage of RA. In our experiments, the mean-square error of Euclidean distance between moving and fixed image is 0.0031, standard deviation is 0.0661 mm, and the mismatching rate is 0.48\%. The proposed method has sub-pixel level accuracy, exceeding manual measurements by far, and is equipped with immune to noise, rotation, and scaling of joints. Moreover, this work provides loss visualization, which can aid radiologists and rheumatologists in assessing quantification reliability, with important implications for possible future clinical applications. As a result, we are optimistic that this proposed work will make a significant contribution to the automatic quantification of JSN progression in RA., Comment: 11 pages, 9 figures, 7 tables

Published
2023
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4. Quantification of Joint Space Width Difference on Radiography Via Phase-Only Correlation (POC) Analysis: a Phantom Study Comparing with Various Tomographical Modalities Using Conventional Margin-Contouring

Author

Taguchi, Aimi, Shishido, Shun, Ou, Yafei, Ikebe, Masayuki, Zeng, Tianyu, Fang, Wanxuan, Murakami, Koichi, Ueda, Toshikazu, Yasojima, Nobutoshi, Sato, Keitaro, Tamura, Kenichi, Sutherland, Kenneth, Oki, Nozomi, Chiba, Ko, Minowa, Kazuyuki, Uetani, Masataka, and Kamishima, Tamotsu

Published
2021
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7. Giant Intraosseous Cyst-Like Lesions of the Metacarpal Bones in Rheumatoid Arthritis

Author

Fang, Wanxuan, Nakagawa, Ikuma, 1000030451446, Sutherland, Kenneth, Tanimura, Kazuhide, 1000010399868, Kamishima, Tamotsu, Fang, Wanxuan, Nakagawa, Ikuma, 1000030451446, Sutherland, Kenneth, Tanimura, Kazuhide, 1000010399868, and Kamishima, Tamotsu

Abstract

The purpose of this study was to illustrate the clinical and imaging properties of giant intraosseous cyst-like lesions (GICLs) of the metacarpal bones extending beyond the central diaphysis in rheumatoid arthritis (RA) patients on magnetic resonance (MR) images. A keyword search was conducted to extract GICLs of the metacarpal bones out of MR reports in RA patients. There were nine GICLs extending from the subchondral bone region beyond the central diaphysis of the metacarpal bones on MR images in eight subjects with RA (seven females, one male). The age range was from 60 to 87 years with a median age of 65.5 years. The average disease duration was 13.1 years. As for the disease activity, one was low, six were moderate and one was high. None of the nine lesions were visible on radiography. The Steinbrocker stage distribution was as follows: I (n = 3), II (n = 2), and III (n = 3). Intraosseous cyst-like lesion of the metacarpal bones on MR images is a relatively rare manifestation in patients with long-standing RA. Although the lesion seems to be derived from subcortical bone break, it is not necessarily erosive in nature.

Published
2021

9. Quantification of Joint Space Width Difference on Radiography Via Phase-Only Correlation (POC) Analysis: a Phantom Study Comparing with Various Tomographical Modalities Using Conventional Margin-Contouring

Author

Taguchi, Aimi, primary, Shishido, Shun, additional, Ou, Yafei, additional, Ikebe, Masayuki, additional, Zeng, Tianyu, additional, Fang, Wanxuan, additional, Murakami, Koichi, additional, Ueda, Toshikazu, additional, Yasojima, Nobutoshi, additional, Sato, Keitaro, additional, Tamura, Kenichi, additional, Sutherland, Kenneth, additional, Oki, Nozomi, additional, Chiba, Ko, additional, Minowa, Kazuyuki, additional, Uetani, Masataka, additional, and Kamishima, Tamotsu, additional

Published
2020
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11. Artificial Intelligence Quantification of Enhanced Synovium Throughout the Entire Hand in Rheumatoid Arthritis on Dynamic Contrast-Enhanced MRI.

Author

Mao Y, Imahori K, Fang W, Sugimori H, Kiuch S, Sutherland K, and Kamishima T

Subjects
Humans, Female, Male, Middle Aged, Retrospective Studies, Aged, Adult, Synovitis diagnostic imaging, ROC Curve, Image Interpretation, Computer-Assisted methods, Image Processing, Computer-Assisted methods, Arthritis, Rheumatoid diagnostic imaging, Magnetic Resonance Imaging methods, Contrast Media, Artificial Intelligence, Synovial Membrane diagnostic imaging, Synovial Membrane pathology, Hand diagnostic imaging
Abstract

Background: Challenges persist in achieving automatic and efficient inflammation quantification using dynamic contrast-enhanced (DCE) MRI in rheumatoid arthritis (RA) patients., Purpose: To investigate an automatic artificial intelligence (AI) approach and an optimized dynamic MRI protocol for quantifying disease activity in RA in whole hands while excluding arterial pixels., Study Type: Retrospective., Subjects: Twelve RA patients underwent DCE-MRI with 27 phases for creating the AI model and tested on images with a variable number of phases from 35 RA patients., Field Strength/sequence: 3.0 T/DCE T1-weighted gradient echo sequence (mDixon, water image)., Assessment: The model was trained with various DCE-MRI time-intensity number of phases. Evaluations were conducted for similarity between AI segmentation and manual outlining in 51 ROIs with synovitis. The relationship between synovial volume via AI segmentation with rheumatoid arthritis magnetic resonance imaging scoring (RAMRIS) across whole hands was then evaluated. The reference standard was determined by an experienced musculoskeletal radiologist., Statistical Test: Area under the curve (AUC) of receiver operating characteristic (ROC), Dice and Spearman's rank correlation coefficients, and interclass correlation coefficient (ICC). A P-value <0.05 was considered statistically significant., Results: A minimum of 15 phases (acquisition time at least 2.5 minutes) was found to be necessary. AUC ranged from 0.941 ± 0.009 to 0.965 ± 0.009. The Dice score was 0.557-0.615. Spearman's correlation coefficients between the AI model and ground truth were 0.884-0.927 and 0.736-0.831, for joint ROIs and whole hands, respectively. The Spearman's correlation coefficient for the additional test set between the model trained with 15 phases and RAMRIS was 0.768., Conclusion: The AI-based classification model effectively identified synovitis pixels while excluding arteries. The optimal performance was achieved with at least 15 phases, providing a quantitative assessment of inflammatory activity in RA while minimizing acquisition time., Evidence Level: 3 TECHNICAL EFFICACY: Stage 2., (© 2024 International Society for Magnetic Resonance in Medicine.)

Published
2025
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