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232 results on '"Vardhanabhuti, Varut"'

4. Intraventricular 4D flow cardiovascular magnetic resonance for assessing patients with heart failure with preserved ejection fraction: a pilot study

Author

Kwan, Chi Ting, Ching, On Hang Samuel, Yap, Pui Min, Fung, Sau Yung, Tang, Hok Shing, Tse, Wan Wai Vivian, Kwan, Cheuk Nam Felix, Chow, Yin Hay Phoebe, Yiu, Nga Ching, Lee, Yung Pok, Lau, Jessica Wing Ka, Fong, Ambrose Ho Tung, Ren, Qing-Wen, Wu, Mei-Zhen, Wan, Eric Yuk Fai, Lee, Ka Chun Kevin, Leung, Chun Yu, Li, Andrew, Montero, David, Vardhanabhuti, Varut, Hai, Jojo Siu Han, Siu, Chung-Wah, Tse, Hung-Fat, Zingan, Valentin, Zhao, Xiaoxi, Wang, Haonan, Pennell, Dudley John, Mohiaddin, Raad, Senior, Roxy, Yiu, Kai-Hang, and Ng, Ming-Yen

Published
2023
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8. Lung Cancer Screening in Asia: An Expert Consensus Report

Author

Lam, David Chi-Leung, Liam, Chong-Kin, Andarini, Sita, Park, Samina, Tan, Daniel S.W., Singh, Navneet, Jang, Seung Hun, Vardhanabhuti, Varut, Ramos, Antonio B., Nakayama, Tomio, Nhung, Nguyen Viet, Ashizawa, Kazuto, Chang, Yeun-Chung, Tscheikuna, Jamsak, Van, Cong Cung, Chan, Wai Yee, Lai, Yeur-Hur, and Yang, Pan-Chyr

Published
2023
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15. Author Correction: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

Author

Dou, Qi, So, Tiffany Y., Jiang, Meirui, Liu, Quande, Vardhanabhuti, Varut, Kaissis, Georgios, Li, Zeju, Si, Weixin, Lee, Heather H. C., Yu, Kevin, Feng, Zuxin, Dong, Li, Burian, Egon, Jungmann, Friederike, Braren, Rickmer, Makowski, Marcus, Kainz, Bernhard, Rueckert, Daniel, Glocker, Ben, Yu, Simon C. H., and Heng, Pheng Ann

Published
2022
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16. Use of model-based iterative reconstruction (MBIR) techniques in computed tomography (CT)

Author

Vardhanabhuti, Varut

Subjects
616.07
Abstract

Over the past decades, the use of computed tomography (CT) has risen exponentially due to expanding need in medical diagnosis and treatment planning. The use of CT is not entirely without risk and involves theoretical risk from radiation. This has led to several advancement in dose reduction techniques. Most promising is the use of so called “iterative reconstruction” which is a post image acquisition step combining a software/hardware configuration that allows the images acquired to be processed more efficiently, eliminating noise which increases with low dose techniques. This thesis examines the use of a specific iterative reconstruction technique known as model-based iterative reconstruction (MBIR) and assesses image quality, diagnostic accuracy, and feasibility of implementation in routine clinical practice. With no prior guidelines for implementation, first we assessed this technique in pre-clinical setting by scanning a realistic model of a human (known as a phantom). This allows us to scan at various tube current/voltage settings as well as fixed/automatic adjustments to assess the most optimal parameters by assessing image quality. Second, we assessed if image quality can be improved using the same dose for routine patients. Third, we assessed image quality and diagnostic accuracy in 3 body parts by scanning patients with matched intra-individual comparison of standard and low dose techniques. This was done for chest, abdomen/pelvis, and colon. Fourth, we tested the use of low dose techniques in specific clinical situations, assessing diagnostic accuracy in the context of lung nodule follow-up and renal stone detection in patients presenting with renal colic.

Published
2018

20. Refining TNM-8 M1 categories with anatomic subgroups for previously untreated de novo metastatic nasopharyngeal carcinoma

Author

Chan, Sik Kwan, Lin, Cheng, Huang, Shao Hui, Chau, Tin Ching, Guo, Qiao Juan, O'Sullivan, Brian, Lam, Ka On, Chau, Sze Chun, Chan, Sum Yin, Tong, Chi Chung, Vardhanabhuti, Varut, Kwong, Dora Lai Wan, So, Tsz Him, Ng, Chor Yi, Leung, To Wai, Luk, Mai Yee, Lee, Anne Wing Mui, Choi, Horace Cheuk Wai, Pan, Jian Ji, and Lee, Victor Ho Fun

Published
2022
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21. USPSTF2013 versus PLCOm2012 lung cancer screening eligibility criteria (International Lung Screening Trial): interim analysis of a prospective cohort study

Author

Tammemägi, Martin C, Ruparel, Mamta, Tremblay, Alain, Myers, Renelle, Mayo, John, Yee, John, Atkar-Khattra, Sukhinder, Yuan, Ren, Cressman, Sonya, English, John, Bedard, Eric, MacEachern, Paul, Burrowes, Paul, Quaife, Samantha L, Marshall, Henry, Yang, Ian, Bowman, Rayleen, Passmore, Linda, McWilliams, Annette, Brims, Fraser, Lim, Kuan Pin, Mo, Lin, Melsom, Stephen, Saffar, Bann, Teh, Mark, Sheehan, Ramon, Kuok, Yijin, Manser, Renee, Irving, Louis, Steinfort, Daniel, McCusker, Mark, Pascoe, Diane, Fogarty, Paul, Stone, Emily, Lam, David C L, Ng, Ming-Yen, Vardhanabhuti, Varut, Berg, Christine D, Hung, Rayjean J, Janes, Samuel M, Fong, Kwun, and Lam, Stephen

Published
2022
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32. Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

Author

Dou, Qi, So, Tiffany Y., Jiang, Meirui, Liu, Quande, Vardhanabhuti, Varut, Kaissis, Georgios, Li, Zeju, Si, Weixin, Lee, Heather H. C., Yu, Kevin, Feng, Zuxin, Dong, Li, Burian, Egon, Jungmann, Friederike, Braren, Rickmer, Makowski, Marcus, Kainz, Bernhard, Rueckert, Daniel, Glocker, Ben, Yu, Simon C. H., and Heng, Pheng Ann

Published
2021
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33. Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph

Author

Du, Richard, Tsougenis, Efstratios D., Ho, Joshua W. K., Chan, Joyce K. Y., Chiu, Keith W. H., Fang, Benjamin X. H., Ng, Ming Yen, Leung, Siu-Ting, Lo, Christine S. Y., Wong, Ho-Yuen F., Lam, Hiu-Yin S., Chiu, Long-Fung J., So, Tiffany Y, Wong, Ka Tak, Wong, Yiu Chung I., Yu, Kevin, Yeung, Yiu-Cheong, Chik, Thomas, Pang, Joanna W. K., Wai, Abraham Ka-chung, Kuo, Michael D., Lam, Tina P. W., Khong, Pek-Lan, Cheung, Ngai-Tseung, and Vardhanabhuti, Varut

Published
2021
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36. Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs

Author

Chiu, Wan Hang Keith, Vardhanabhuti, Varut, Poplavskiy, Dmytro, Yu, Philip Leung Ho, Du, Richard, Yap, Alistair Yun Hee, Zhang, Sailong, Fong, Ambrose Ho-Tung, Chin, Thomas Wing-Yan, Lee, Jonan Chun Yin, Leung, Siu Ting, Lo, Christine Shing Yen, Lui, Macy Mei-Sze, Fang, Benjamin Xin Hao, Ng, Ming-Yen, and Kuo, Michael D.

Published
2020
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37. Diagnostic Performance of Cardiovascular Magnetic Resonance Phase Contrast Analysis to Identify Heart Failure With Preserved Ejection Fraction.

Author

Lin, Lu, Kwan, Chi Ting, Yap, Pui Min, Fung, Sau Yung, Tang, Hok Shing, Tse, Wan Wai Vivian, Kwan, Cheuk Nam Felix, Chow, Yin Hay Phoebe, Yiu, Nga Ching, Lee, Yung Pok, Fong, Ambrose Ho Tung, Ren, Qing-Wen, Wu, Mei-Zhen, Lee, Ka Chun Kevin, Leung, Chun Yu, Li, Andrew, Montero, David, Vardhanabhuti, Varut, Hai, JoJo, and Siu, Chung-Wah

Published
2024
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39. Photon-Counting CT Material Decomposition in Bone Imaging.

Author

Bhattarai, Abhisek, Tanaka, Ray, Yeung, Andy Wai Kan, and Vardhanabhuti, Varut

Subjects
DUAL-energy X-ray absorptiometry, BONE density, SPECTRAL sensitivity, THREE-dimensional imaging, MEDICAL screening, PHOTON counting, COMPUTED tomography
Abstract

The accurate screening of osteoporosis is important for identifying persons at risk. The diagnosis of bone conditions using dual X-ray absorptiometry is limited to extracting areal bone mineral density (BMD) and fails to provide any structural information. Computed tomography (CT) is excellent for morphological imaging but not ideal for material quantification. Advanced photon-counting detector CT (PCD-CT) possesses high spectral sensitivity and material decomposition capabilities to simultaneously determine qualitative and quantitative information. In this study, we explored the diagnostic utility of PCD-CT to provide high-resolution 3-D imaging of bone microarchitecture and composition for the sensitive diagnosis of bone in untreated and ovariectomized rats. PCD-CT accurately decomposed the calcium content within hydroxyapatite phantoms (r = 0.99). MicroCT analysis of tibial bone revealed significant differences in the morphological parameters between the untreated and ovariectomized samples. However, differences in the structural parameters of the mandible between the treatment groups were not observed. BMD determined with microCT and calcium concentration decomposed using PCD-CT differed significantly between the treatment groups in both the tibia and mandible. Quantitative analysis with PCD-CT is sensitive in determining the distribution of calcium and water components in bone and may have utility in the screening and diagnosis of bone conditions such as osteoporosis. [ABSTRACT FROM AUTHOR]

Published
2023
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40. Automatic Multiparametric Magnetic Resonance Imaging‐Based Prostate Lesions Assessment with Unsupervised Domain Adaptation.

Author

Dai, Jing, Wang, Xiaomei, Li, Yingqi, Liu, Zhiyu, Ng, Yui-Lun, Xiao, Jiaren, Fan, Joe King Man, Lam, James, Dou, Qi, Vardhanabhuti, Varut, and Kwok, Ka-Wai

Abstract

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a valuable diagnostic tool in prostate lesion assessment. However, training convolutional neural networks (CNNs) inevitably involves magnetic resonance (MR) images from multiple cohorts. There always exists variation in scanning protocol among cohorts, inducing significant changes in data distribution between source and target domains. This challenge has greatly limited clinical adoption on a large scale. Herein, a coarse mask‐guided deep domain adaptation network (CMD2A‐Net) is proposed to develop a fully automated framework for prostate lesion detection and classification (PLDC). No category or mask label is required from the target domain. A coarse segmentation module is trained to cover the possible lesion‐related regions, so that attention maps can be generated to dedicate the local feature extraction of lesions within those regions. Experiments are performed on 512 mpMRI sets from datasets of PROSTATEx (330 sets) and two cohorts, A (74 sets) and B (108 sets). Using ensemble learning, CMD2A‐Net accomplishes an AUC of 0.921 in cohort A and 0.913 in cohort B, demonstrating its transferability from a large‐scale public dataset PROSTATEx to small‐scale target domains. Results from an ablation study also support its effectiveness in classification between benign and malignant lesions, compared to the state‐of‐the‐art models. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.166081031.11420810/v1. [ABSTRACT FROM AUTHOR]

Published
2023
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43. Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning

Author

Wang, Fuying, Zhou, Yuyin, Wang, Shujun, Vardhanabhuti, Varut, and Yu, Lequan

Subjects
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Computation and Language (cs.CL)
Abstract

Learning medical visual representations directly from paired radiology reports has become an emerging topic in representation learning. However, existing medical image-text joint learning methods are limited by instance or local supervision analysis, ignoring disease-level semantic correspondences. In this paper, we present a novel Multi-Granularity Cross-modal Alignment (MGCA) framework for generalized medical visual representation learning by harnessing the naturally exhibited semantic correspondences between medical image and radiology reports at three different levels, i.e., pathological region-level, instance-level, and disease-level. Specifically, we first incorporate the instance-wise alignment module by maximizing the agreement between image-report pairs. Further, for token-wise alignment, we introduce a bidirectional cross-attention strategy to explicitly learn the matching between fine-grained visual tokens and text tokens, followed by contrastive learning to align them. More important, to leverage the high-level inter-subject relationship semantic (e.g., disease) correspondences, we design a novel cross-modal disease-level alignment paradigm to enforce the cross-modal cluster assignment consistency. Extensive experimental results on seven downstream medical image datasets covering image classification, object detection, and semantic segmentation tasks demonstrate the stable and superior performance of our framework., NeurIPS 2022

Published
2022

45. Towards visceral fat estimation at population scale: correlation of visceral adipose tissue assessment using three-dimensional cross-sectional imaging with BIA, DXA, and single-slice CT.

Author

Chan, Benjamin, Yan Yu, Fan Huang, and Vardhanabhuti, Varut

Subjects
CROSS-sectional imaging, THREE-dimensional imaging, DUAL-energy X-ray absorptiometry, ADIPOSE tissues, FAT, VOLUME measurements, BODY composition
Abstract

Background: In terms of assessing obesity-associated risk, quantification of visceral adipose tissue (VAT) has become increasingly important in risk assessment for cardiovascular and metabolic diseases. However, differences exist in the accuracy of various modalities, with a lack of up-to-date comparison with three-dimensional whole volume assessment. Aims: Using CT or MRI three-dimensional whole volume VAT as a reference, we evaluated the correlation of various commonly used modalities and techniques namely body impedance analysis (BIA), dual-energy x-ray absorptiometry (DXA) as well as single slice CT to establish how these methods compare. Methods: We designed the study in two parts. First, we performed an intraindividual comparison of the 4558 participants from the UK Biobank cohorts with matching data of MRI abdominal body composition, DXA with VAT estimation, and BIA. Second, we evaluated 174 CT scans from the publicly available dataset to assess the correlation of the commonly used single-slice technique compared to three-dimensional VAT volume. Results: Across the UK Biobank cohort, the DXA-derived VAT measurement correlated better (R² 0.94, p<0.0001) than BIA (R² 0.49, p<0.0001) with reference three-dimensional volume on MRI. However, DXA-derived VAT correlation was worse for participants with a BMI of < 20 (R² = 0.62, p=0.0013). A commonly used single slice method on CT demonstrated a modest correlation (R² between 0.51 - 0.64), with best values at L3- and L4 (R2 L3 = 0.63, p<0.0001; L4 = 0.64, p<0.0001) compared to reference three-dimensional volume. Combining multiple slices yielded a better correlation, with a strong correlation when L2- L3 levels were combined (R² = 0.92, p<0.0001). Conclusion: When deployed at scale, DXA-derived VAT volume measurement shows excellent correlation with three-dimensional volume on MRI based on the UK Biobank cohort. Whereas a single slice CT technique demonstrated moderate correlation with three-dimensional volume on CT, with a stronger correlation achieved when multiple levels were combined. [ABSTRACT FROM AUTHOR]

Published
2023
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48. Semisupervised white matter hyperintensities segmentation on MRI.

Author

Huang, Fan, Xia, Peng, Vardhanabhuti, Varut, Hui, Sai‐Kam, Lau, Kui‐Kai, Ka‐Fung Mak, Henry, and Cao, Peng

Subjects
WHITE matter (Nerve tissue), MAGNETIC resonance imaging, ALZHEIMER'S disease, COMPUTER-assisted image analysis (Medicine), MEDICAL databases
Abstract

This study proposed a semisupervised loss function named level‐set loss (LSLoss) for cerebral white matter hyperintensities (WMHs) segmentation on fluid‐attenuated inversion recovery images. The training procedure did not require manually labeled WMH masks. Our image preprocessing steps included biased field correction, skull stripping, and white matter segmentation. With the proposed LSLoss, we trained a V‐Net using the MRI images from both local and public databases. Local databases were the small vessel disease cohort (HKU‐SVD, n = 360) and the multiple sclerosis cohort (HKU‐MS, n = 20) from our institutional imaging center. Public databases were the Medical Image Computing Computer‐assisted Intervention (MICCAI) WMH challenge database (MICCAI‐WMH, n = 60) and the normal control cohort of the Alzheimer's Disease Neuroimaging Initiative database (ADNI‐CN, n = 15). We achieved an overall dice similarity coefficient (DSC) of 0.81 on the HKU‐SVD testing set (n = 20), DSC = 0.77 on the HKU‐MS testing set (n = 5), and DSC = 0.78 on MICCAI‐WMH testing set (n = 30). The segmentation results obtained by our semisupervised V‐Net were comparable with the supervised methods and outperformed the unsupervised methods in the literature. [ABSTRACT FROM AUTHOR]

Published
2023
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49. Photon counting spectral computed tomography for localization and quantitative assessment of adipose tissue and vasculature

Author

Vardhanabhuti, Varut

Subjects
Segmentation, Hybrid Imaging, PET-CT, Molecular imaging, Osteoporosis, Tissue characterisation, Kv imaging, Diagnostic procedure, CT-Quantitative, Contrast agents, CT
Abstract

Purpose Methods and materials Results Conclusion Personal information and conflict of interest References, Purpose: Obesity has reached epidemic levels, and as a potential treatment, utilizing the thermogenic property of brown adipose tissues (BAT) has drawn attention [1], [2]. BAT differs from white adipose tissue (WAT) in lipid-water content. To detect the...

Published
2022
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50. A meshfree approach for liver and vessel segmentation

Author

Vardhanabhuti, Varut

Subjects
Transplantation, Segmentation, Liver, CAD, CT
Abstract

Purpose Methods and materials Results Conclusion Personal information and conflict of interest References, Purpose: Identification of the liver structures by automatic segmentation is crucial for preoperative liver surgical planning in living donor liver transplantation. This study was intended to develop an automatic meshfree segmentation scheme (MSS) to solve...

Published
2022
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