Article
Neurosciences
Yen-Peng Liao, Shin-ichi Urayama, Tadashi Isa, Hidenao Fukuyama
Summary: The study proposed an optimal model mapping method to improve the reliability of perfusion parameter estimation in IVIM study, finding Gaussian, Kurtosis, and Gamma models optimal for different brain tissues. Using the optimal model mapping method resulted in more reliable estimations of perfusion fraction and pseudo diffusion coefficient compared to conventional methods, potentially providing additional information for clinical diagnosis.
FRONTIERS IN HUMAN NEUROSCIENCE
(2021)
Article
Mathematics
Jingting Yao, Muhammad Ali Raza Anjum, Anshuman Swain, David A. Reiter
Summary: This study introduces the IVIM and FFD models based on DWI for skeletal muscle perfusion and provides a mathematical framework for direct transformation of parameters between the two models. In vivo DWI measurements in skeletal muscle are analyzed using both models, demonstrating the difficulty of model selection based on goodness of fit to experimental data. This analysis offers a framework for interpreting and harmonizing perfusion parameters using IVIM and FFD models.
Article
Radiology, Nuclear Medicine & Medical Imaging
Martin Loh, Tobit Fuehres, Christoph Stuprich, Michael Uder, Marc Saake, Frederik Bernd Laun
Summary: This study compared the differences in IVIM parameters in the liver between different slice settings, and found that there were no significant differences in the parameters between studies using few slices and many slices.
MAGNETIC RESONANCE IN MEDICINE
(2023)
Article
Multidisciplinary Sciences
Qing Hu, Peipei Jiang, Yongjing Feng, Yan Xu, Nan Zhou, Weibo Chen, Li Zhu, Yali Hu, Zhengyang Zhou
Summary: IVIM MR imaging shows potential in evaluating endometrial fibrosis, with a combination model of ADC, D, and f values demonstrating good diagnostic accuracy. ADC performed better than ET in diagnosis, and reproducibility of IVIM parameters was good to excellent, indicating their potential as imaging biomarkers for noninvasive assessment of endometrial fibrosis.
SCIENTIFIC REPORTS
(2021)
Review
Radiology, Nuclear Medicine & Medical Imaging
Erin K. Englund, David A. Reiter, Bahar Shahidi, Eric E. Sigmund
Summary: IVIM MRI is a noninvasive method for evaluating blood flow and tissue diffusion, with prior research focusing on highly vascularized organs, but skeletal muscle characteristics may affect the interpretation of IVIM data. In healthy individuals, there were no significant differences in IVIM parameters among different muscle locations, and exercise led to positive changes in all IVIM parameters.
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Giada Ercolani, Silvia Capuani, Amanda Antonelli, Arianna Camilli, Sandra Ciulla, Roberta Petrillo, Serena Satta, Robert Grimm, Antonella Giancotti, Paolo Ricci, Carlo Catalano, Lucia Manganaro
Summary: The study investigated the microstructural tissue changes in fetal lung and kidney during gestation using IVIM MRI in 34 normal pregnancies. Results showed correlations between IVIM parameters and gestational age, with the perfusion fraction f potentially serving as a marker for pulmonary and renal maturation.
EUROPEAN JOURNAL OF RADIOLOGY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Tobit Fuehres, Andreas Julian Riexinger, Martin Loh, Jan Martin, Andreas Wetscherek, Tristan Anselm Kuder, Michael Uder, Bernhard Hensel, Frederik Bernd Laun
Summary: The purpose of this study was to investigate the TE-dependence of IVIM parameters and to check if the triexponential pseudodiffusion compartments are associated with arterial and venous blood. TE-dependence was observed for f, f(1) and f(2), while D, D*, D1* and D2* showed no significant TE-dependence. It was found that f(1) and f(2) exhibit a similar TE-dependence as f, suggesting that the triexponential pseudodiffusion compartments are most probably not associated with arterial and venous blood.
MAGNETIC RESONANCE IN MEDICINE
(2022)
Article
Endocrinology & Metabolism
Wenqi Wu, Tong Gong, Jinliang Niu, Wenjin Li, Jianting Li, Xiaoli Song, Sha Cui, Wenjin Bian, Jun Wang
Summary: The evaluation of bone marrow microstructure and gender-related cellular and capillary networks in healthy young adults can help to better understand the process of bone metabolism. IVIM provides a non-invasive method to assess bone marrow microstructure, such as cellularity and perfusion, without requiring intravenous contrast agent injection.
FRONTIERS IN ENDOCRINOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Augustin Lecler, Loic Duron, Mathieu Zmuda, Kevin Zuber, Olivier Berges, Marc Putterman, Julien Savatovsky, Laure Fournier
Summary: This study focused on the diagnostic accuracy of MRI IVIM in characterizing orbital lesions, showing that IVIM might help better characterize these lesions. By measuring parameters such as ADC, D, and D*, significant differences were found between malignant and benign lesions.
EUROPEAN RADIOLOGY
(2021)
Article
Clinical Neurology
K. Yamashita, R. Kamei, H. Sugimori, T. Kuwashiro, S. Tokunaga, K. Kawamata, K. Furuya, S. Harada, J. Maehara, Y. Okada, T. Noguchi
Summary: The study evaluated the usefulness of intravoxel incoherent motion MR imaging in patients with acute ischemic stroke, showing high interobserver agreement and potential clinical utility in determining treatment strategies for patients, particularly in assessing the presence of presumed penumbral regions.
AMERICAN JOURNAL OF NEURORADIOLOGY
(2022)
Review
Radiology, Nuclear Medicine & Medical Imaging
Qi Wang, Guanghui Yu, Jianfeng Qiu, Weizhao Lu
Summary: Intravoxel incoherent motion (IVIM) modeling is a widely used technique for evaluating liver diseases by describing diffusion-weighted imaging (DWI) signals. However, there is still instability in assessing liver fibrosis and liver tumors using IVIM.
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2023)
Article
Medicine, General & Internal
Takayuki Arakane, Masahiro Okada, Yujiro Nakazawa, Kenichiro Tago, Hiroki Yoshikawa, Mariko Mizuno, Hayato Abe, Tokio Higaki, Yukiyasu Okamura, Tadatoshi Takayama
Summary: IVIM and SV are both effective in predicting HF. SV/BSA is more accurate in evaluating severe HF, while IVIM is more accurate in evaluating mild or no HF.
Article
Oncology
Weiquan Wu, Jun Xia, Bin Li, Wenci Liu, Zhan Ge, Zhi Tan, Qiujin Bu, Wubiao Chen, Yuange Li
Summary: The feasibility of IVIM-DWI in the diagnosis of skull-base invasion in nasopharyngeal carcinoma was evaluated in this study. MRI and IVIM-DWI scans were performed on 50 NPC patients and 40 controls, and the D, D*, and f values were obtained and analyzed. The results suggest that IVIM-DWI can be used as a noninvasive method for diagnosing SBI in NPC.
Article
Radiology, Nuclear Medicine & Medical Imaging
Tao Lu, Mou Li, Hang Li, Yishuang Wang, Xinyi Zhao, Yan Zhao, Na Wang
Summary: The use of magnetic resonance imaging (MRI) parameters can be helpful in evaluating estimated blood loss (EBL) and predicting postpartum hemorrhage (PPH) in patients with placenta accreta spectrum (PAS) disorders.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2023)
Article
Biophysics
Susanne S. Rauh, Oliver Maier, Oliver J. Gurney-Champion, Melissa T. Hooijmans, Rudolf Stollberger, Aart J. Nederveen, Gustav J. Strijkers
Summary: Model-based reconstruction improves the precision of parameter estimation in IVIM and IVIM-DTI imaging, particularly for f and D* maps. The feasibility of this method was demonstrated through simulations and in vivo data.
NMR IN BIOMEDICINE
(2023)
Review
Radiology, Nuclear Medicine & Medical Imaging
Jing Yuan, Darren M. C. Poon, Gladys Lo, Oi Lei Wong, Kin Yin Cheung, Siu Ki Yu
Summary: Magnetic resonance guided radiotherapy (MRgRT) is a novel technique for prostate cancer treatment that aims to improve clinical outcomes and reduce toxicity. The role of prostate MRI has expanded to screening, treatment, and surveillance, showing its importance in the management of prostate cancer.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2022)
Article
Medicine, General & Internal
Manohar Karki, Karthik Kantipudi, Feng Yang, Hang Yu, Yi Xiang J. Wang, Ziv Yaniv, Stefan Jaeger
Summary: The classification of drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) from chest radiographs is still an unresolved problem. Previous performance on publicly available chest X-ray data achieved 85% AUC with a deep convolutional neural network (CNN), but significant performance degradation was observed when applied to unseen data. In this paper, the generalizability of the models on images from a different country's dataset is investigated, and the lack of good generalization is explored. Comparisons between radiologist-annotated lesion locations and the trained model's localization using GradCAM show little overlap.
Article
Radiology, Nuclear Medicine & Medical Imaging
Max W. K. Law, Jing Yuan, Oilei O. L. Wong, Abby Y. Ding, Yihang Zhou, Kin Y. Cheung, Siu K. Yu
Summary: This study evaluated the machine-dependent three-dimensional geometric distortion images acquired from a 1.5T 700 mm-wide bore MR-simulator based on a large geometric accuracy phantom. By optimizing acquisition parameters and retrospective correction, more accurate geometric distortion images can be obtained. The results of this study are of great significance for optimizing sequence selection for different radiation therapy applications.
BIOMEDICAL PHYSICS & ENGINEERING EXPRESS
(2022)
Article
Computer Science, Artificial Intelligence
Jin Hong, Simon Chun-Ho Yu, Weitian Chen
Summary: This paper proposes a novel unsupervised domain adaptation framework for cross-modality liver segmentation using joint adversarial learning and self-learning. Experimental results show that the framework achieves excellent segmentation results on public datasets.
APPLIED SOFT COMPUTING
(2022)
Article
Oncology
Darren M. C. Poon, Bin Yang, Hui Geng, Oi Lei Wong, Sin Ting Chiu, Kin Yin Cheung, Siu Ki Yu, George Chiu, Jing Yuan
Summary: This study retrospectively analyzed and characterized the online plan adaptation of 1.5T magnetic resonance-guided stereotactic body radiotherapy (MRgSBRT) in prostate cancer patients. The study found significant differences in the adoption frequency of ATS fractions between different treatment fractions, and investigated the rationale and criteria for ATS implementation.
JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Sheheryar Khan, Basim Azam, Yongcheng Yao, Weitian Chen
Summary: This paper proposes a deep learning-based automatic segmentation framework for precise knee tissue segmentation. By combining an encoder-decoder-based segmentation network with a low rank tensor-reconstructed segmentation network, the proposed method utilizes low rank reconstruction in MRI tensor sub-blocks to improve accuracy. Trimap generation is used to effectively model tissue boundary regions and utilize superimposed regions, avoiding incomplete segmentation results. Experimental results demonstrate the effectiveness of the proposed method in knee tissue segmentation.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Jian Hou, Vincent W-S Wong, Yurui Qian, Baiyan Jiang, Anthony W-H Chan, Howard H-W Leung, Grace L-H Wong, Simon C-H Yu, Winnie C-W Chu, Weitian Chen
Summary: This study used MPF-SL technology to measure macromolecule levels in the liver and found that it can detect early-stage liver fibrosis, and is not confounded by liver iron concentration or fat fraction.
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2023)
Article
Oncology
Darren M. C. Poon, Jing Yuan, Bin Yang, Oi-Lei Wong, Sin-Ting Chiu, George Chiu, Kin-Yin Cheung, Siu-Ki Yu, Raymond W. H. Yung
Summary: This study reports the preliminary clinical experiences and treatment outcomes of 1.5 Tesla adaptive MR-guided stereotactic body radiotherapy (MRgSBRT) with concomitant whole-pelvic nodal radiotherapy (WPRT) in high-risk prostate cancer (HR-PC) patients. The early results showed favorable treatment-related toxicities and encouraging patient-reported quality of life (QoL), but long-term follow-up is needed for confirmation.
Article
Biology
Shutian Zhao, Donal G. Cahill, Siyue Li, Fan Xiao, Thierry Blu, James F. Griffith, Weitian Chen
Summary: This study developed a deep learning-based denoising method using true noise information from 2-NEX acquisitions to suppress noise in 3D FSE MR images of knee joints. The method showed promise for improving denoising performance and image quality.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Editorial Material
Radiology, Nuclear Medicine & Medical Imaging
Weitian Chen
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Lei Wang, Weitian Chen, Yurui Qian, Tiffany Y. So
Summary: This study aimed to assess the repeatability of T1rho imaging in normal brain grey and white matter. The results showed high test-retest repeatability of T1rho imaging for whole brain imaging, indicating its reliability for quantitative brain imaging.
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
(2023)
Article
Engineering, Biomedical
Chaoxing Huang, Vincent Wai-Sun Wong, Queenie Chan, Winnie Chiu-Wing Chu, Weitian Chen
Summary: This study proposes a parametric map refinement approach for learning-based T-1 rho mapping. By training the model probabilistically to model uncertainty and utilizing uncertainty maps for weighted training of an improved mapping network, the mapping performance is enhanced and unreliable values are effectively removed. The results demonstrate the potential of this method to provide a trustworthy learning-based quantitative MRI system for T-1 rho mapping of the liver.
PHYSICS IN MEDICINE AND BIOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Junru Zhong, Yongcheng Yao, Donal G. Cahill, Fan Xiao, Siyue Li, Jack Lee, Kevin Ki-Wai Ho, Michael Tim-Yun Ong, James F. Griffith, Weitian Chen
Summary: This study demonstrates the utility of unsupervised domain adaptation (UDA) for automated osteoarthritis (OA) phenotype classification. By using a large, high-quality source dataset for training, the proposed UDA approach improves the performance of automated OA phenotype classification on small target datasets. This technique is important for improving downstream analysis of locally collected datasets with a small sample size.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2023)
Article
Computer Science, Interdisciplinary Applications
Cindy Xue, Jing Yuan, Yihang Zhou, Oi Lei Wong, Kin Yin Cheung, Siu Ki Yu
Summary: Radiomics has been investigated as a potential biomarker for personalized diagnosis and treatment of head and neck cancer. However, the reliability of radiomics features is a major obstacle for its broad application in the heterogeneous head and neck tissues. This study investigated the repeatability of MRI radiomics features in healthy volunteers and found that the acquisition variability and uncertainty of these features depend on feature types, tissues, and pulse sequences. Only a small fraction of features showed excellent repeatability and low within-subject variability, and multiple MRI scans improved the accuracy and confidence in identifying reliable features compared to simple repeated scans. This study contributes to the understanding of the reliability of radiomics features in MRI acquisition and the selection of reliable features for future head and neck cancer treatment.
VISUAL COMPUTING FOR INDUSTRY BIOMEDICINE AND ART
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Tiffany Y. So, Apurva Sawhney, Lei Wang, Yi Xiang J. Wang
Summary: BCVI is a often overlooked injury that occurs in the carotid or vertebral arteries, and is associated with a risk of ischemic stroke and poor neurological outcome or death. Computed tomographic angiography (CTA) is the most common modality for initial screening and diagnosis, and various potential findings should be considered in imaging.
