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Bautin P, Fortier MA, Sean M, Little G, Martel M, Descoteaux M, Léonard G, Tétreault P. What has brain diffusion magnetic resonance imaging taught us about chronic primary pain: a narrative review. Pain 2024:00006396-990000000-00689. [PMID: 39172945 DOI: 10.1097/j.pain.0000000000003345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 06/13/2024] [Indexed: 08/24/2024]
Abstract
ABSTRACT Chronic pain is a pervasive and debilitating condition with increasing implications for public health, affecting millions of individuals worldwide. Despite its high prevalence, the underlying neural mechanisms and pathophysiology remain only partly understood. Since its introduction 35 years ago, brain diffusion magnetic resonance imaging (MRI) has emerged as a powerful tool to investigate changes in white matter microstructure and connectivity associated with chronic pain. This review synthesizes findings from 58 articles that constitute the current research landscape, covering methods and key discoveries. We discuss the evidence supporting the role of altered white matter microstructure and connectivity in chronic primary pain conditions, highlighting the importance of studying multiple chronic pain syndromes to identify common neurobiological pathways. We also explore the prospective clinical utility of diffusion MRI, such as its role in identifying diagnostic, prognostic, and therapeutic biomarkers. Furthermore, we address shortcomings and challenges associated with brain diffusion MRI in chronic primary pain studies, emphasizing the need for the harmonization of data acquisition and analysis methods. We conclude by highlighting emerging approaches and prospective avenues in the field that may provide new insights into the pathophysiology of chronic pain and potential new therapeutic targets. Because of the limited current body of research and unidentified targeted therapeutic strategies, we are forced to conclude that further research is required. However, we believe that brain diffusion MRI presents a promising opportunity for enhancing our understanding of chronic pain and improving clinical outcomes.
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Affiliation(s)
- Paul Bautin
- Department of Anesthesiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Marc-Antoine Fortier
- Department of Anesthesiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Monica Sean
- Department of Anesthesiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Graham Little
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Marylie Martel
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Guillaume Léonard
- School of Rehabilitation, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
- Research Centre on Aging du Centre intégré universitaire de santé et de services sociaux de l'Estrie-Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Pascal Tétreault
- Department of Anesthesiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Medical Imaging and Radiation Sciences, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
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da S Senra Filho AC, Murta Junior LO, Monteiro Paschoal A. Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01185-4. [PMID: 39068635 DOI: 10.1007/s10334-024-01185-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/24/2024] [Accepted: 06/28/2024] [Indexed: 07/30/2024]
Abstract
OBJECT Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of López-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC). MATERIALS AND METHODS The OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials. RESULTS The DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps. DISCUSSION In the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.
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Nakai H, Takahashi N, Sugi MD, Wellnitz CV, Thompson CP, Kawashima A. Image quality comparison of 1.5T and 3T prostate MRIs of the same post-hip arthroplasty patients: multi-rater assessments including PI-QUAL version 2. Abdom Radiol (NY) 2024:10.1007/s00261-024-04483-6. [PMID: 38980403 DOI: 10.1007/s00261-024-04483-6] [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: 04/15/2024] [Revised: 06/27/2024] [Accepted: 06/30/2024] [Indexed: 07/10/2024]
Abstract
OBJECTIVES To compare the image quality of 1.5T and 3T prostate MRIs of the same post-hip arthroplasty patients, with a specific focus on the degree of susceptibility artifacts. METHODS This single-center retrospective study included post-hip arthroplasty patients who underwent 1.5T prostate MRIs between 2021 and 2023, as well as comparative 3T prostate MRIs. Three blinded abdominal radiologists retrospectively reviewed their diffusion-weighted imaging (DWI, 50 s/mm2), T2-weighted imaging (T2WI), and dynamic contrast-enhanced imaging (DCE) to evaluate the image quality. The degree of susceptibility artifacts was categorized using a three-point scale, with 3 indicating the least artifact and 1 indicating the most. Image quality was also evaluated using Prostate Imaging Quality (PI-QUAL) version 2. The median of the three raters' scores was compared between 1.5T and 3T prostate MRIs using the Wilcoxon signed-rank test. The inter-rater agreement was evaluated using the multi-rater generalized kappa. RESULTS Twenty pairs of 1.5T and 3T prostate MRI examinations from 20 unique patients were included. The DWI susceptibility artifact score at 1.5T was significantly higher than at 3T (mean score ± standard deviation, 2.80 ± 0.41 vs. 2.35 ± 0.93, p = 0.014). In contrast, no significant differences were observed in the susceptibility artifact scores in T2WI and DCE, or in the PI-QUAL score. The inter-reader agreement in the susceptibility artifact score was moderate (multi-rater generalized kappa: 0.60) in DWI, perfect in T2WI (not applicable), and substantial (0.65) in DCE. The inter-reader agreement was fair (0.27) in the PI-QUAL score. CONCLUSION Using 1.5T scanners may be preferable to reduce susceptibility artifacts from hip prostheses in DWI.
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Affiliation(s)
| | | | - Mark D Sugi
- Department of Radiology, Mayo Clinic Arizona, 13400 E. Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Clinton V Wellnitz
- Department of Radiology, Mayo Clinic Arizona, 13400 E. Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Cole P Thompson
- Department of Radiology, Mayo Clinic Arizona, 13400 E. Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Akira Kawashima
- Department of Radiology, Mayo Clinic Arizona, 13400 E. Shea Blvd, Scottsdale, AZ, 85259, USA.
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Liu S, Zhang Y, Liu W, Yin T, Yuan J, Ran J, Li X. Simultaneous multi-slice technique for reducing acquisition times in diffusion tensor imaging of the knee: a feasibility study. Skeletal Radiol 2024:10.1007/s00256-024-04719-y. [PMID: 38913177 DOI: 10.1007/s00256-024-04719-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/30/2024] [Accepted: 05/30/2024] [Indexed: 06/25/2024]
Abstract
OBJECTIVES To explore the feasibility of simultaneous multi-slice (SMS) technique for reducing acquisition times in readout-segmented echo planar imaging (RESOLVE) for diffusion tensor imaging (DTI) of the knee. MATERIALS AND METHODS A total of 30 healthy volunteers and 23 patients with knee acute injury (12 cases with anterior ligament (ACL) tears and 16 cases with patellar cartilage (PC) injury) were enrolled in this prospective study. Three DTI protocols were used: conventional RESOLVE-DTI with 12 directions (protocol 1), SMS-RESOLVE-DTI with 12 directions (protocol 2) and 20 directions (protocol 3). DTI parameters of gastrocnemius, ACL and posterior cruciate ligament (PCL), and PC from three protocols were quantitatively assessed. RESULTS For volunteers, protocol 2 significantly reduced acquisition time by 38.6% and 34.2% compared to protocols 1 and 3 while maintaining similar high-quality images and similar diffusive parameters, except for the fractional anisotropy (FA) and axial diffusivity (AD) of the PC between protocols 2 and 1 (P < 0.05). For injured ACL and PC, protocols 1 and 2 showed similar accurate diffusive parameters (except for AD, P = 0.025) and similar diagnostic efficacy, which demonstrated significantly lower FA and higher radial diffusivity (RD) in protocols 1 and 2 compared to volunteers (P < 0.05). CONCLUSIONS The 12-direction SMS-RESOLVE-DTI demonstrated a favorable balance between acquisition time and image quality, making it a promising alternative to conventional DTI for evaluating ligament and cartilage injuries. ADVANCES IN KNOWLEDGE The SMS technique greatly reduces acquisition time while maintaining image quality, which signified the possibility of DTI's clinical application.
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Affiliation(s)
- Simin Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan, Hubei Province, China
| | - Yao Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan, Hubei Province, China
| | - Wei Liu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., No. 32 Gaoxin C. Ave., 2nd, Shenzhen, China
| | - Ting Yin
- MR Collaborations, Siemens Healthineers Ltd., Chengdu, China
| | - Jie Yuan
- Department of Radiology, Zhongxiang People's Hospital, Zhongxiang City, China
| | - Jun Ran
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan, Hubei Province, China.
| | - Xiaoming Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan, Hubei Province, China.
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Li Y, Yu R, Chang H, Yan W, Wang D, Li F, Cui Y, Wang Y, Wang X, Yan Q, Liu X, Jia W, Zeng Q. Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:976-987. [PMID: 38347392 PMCID: PMC11169103 DOI: 10.1007/s10278-024-00988-0] [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: 10/04/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 06/13/2024]
Abstract
The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.
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Affiliation(s)
- Yuting Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ruize Yu
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Huan Chang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Wanying Yan
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Dawei Wang
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Fuyan Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yi Cui
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Yong Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xiao Wang
- Department of Radiology, Jining No. 1 People's Hospital, Jining, China
| | - Qingqing Yan
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Xinhui Liu
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Wenjing Jia
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Qianfoshan Hospital, Shandong, Jinan, China.
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Gul E, Atalar MH, Atik I. Evaluation of the contralateral hemisphere with DWI in pediatric patients with Dyke-Davidoff-Masson syndrome. Acta Neurol Belg 2024; 124:911-918. [PMID: 38361171 DOI: 10.1007/s13760-024-02473-5] [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: 08/19/2023] [Accepted: 01/03/2024] [Indexed: 02/17/2024]
Abstract
INTRODUCTION Dyke-Davidoff-Masson Syndrome (DDMS) is a clinical syndrome that causes different clinical symptoms and is defined by volume decrement in one cerebral hemisphere. In this study, we aimed to evaluate the involvement of the normal-appearing contralateral hemisphere in 16 pediatric patients with DDMS using diffusion-weighted imaging (DWI). MATERIALS AND METHODS Brain MRIs were retrospectively reviewed between January 2014 and January 2023. Sixteen pediatric patients radiologically compatible with DDMS were included in the study. Sixteen children who had undergone brain MRI, most commonly for headaches and whose MRI findings had been completely normal, were included as the control group. Apparent diffusion coefficient (ADC) values of the deep gray and white matter of the normal-appearing hemisphere in the patient group were calculated and compared with that of the control group. RESULTS The ADC values of the gray and white matters of the patient and control groups were not statistically different. However, in the patient group, the ADC values of the gray and white matters in males were remarkably lower than in females (p = 0.038, p = 0.037, respectively). CONCLUSION The difference in the ADC values of the contralateral hemisphere between females and males in the patient group suggests that the normal-appearing hemisphere may have been affected by DDMS. Although, the exact mechanism of this effect is not known. Therefore, in patients with DDMS, contralateral hemisphere involvement in cerebral hemiatrophy and hemispherectomy should be evaluated clinically and radiologically.
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Affiliation(s)
- Enes Gul
- Sivas Cumhuriyet Universitesi, Sivas, Sivas, Turkey.
| | | | - Irfan Atik
- Sivas Cumhuriyet Universitesi, Sivas, Sivas, Turkey
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Bhandari A, Gu B, Kashkooli FM, Zhan W. Image-based predictive modelling frameworks for personalised drug delivery in cancer therapy. J Control Release 2024; 370:721-746. [PMID: 38718876 DOI: 10.1016/j.jconrel.2024.05.004] [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: 02/04/2024] [Revised: 04/11/2024] [Accepted: 05/02/2024] [Indexed: 05/19/2024]
Abstract
Personalised drug delivery enables a tailored treatment plan for each patient compared to conventional drug delivery, where a generic strategy is commonly employed. It can not only achieve precise treatment to improve effectiveness but also reduce the risk of adverse effects to improve patients' quality of life. Drug delivery involves multiple interconnected physiological and physicochemical processes, which span a wide range of time and length scales. How to consider the impact of individual differences on these processes becomes critical. Multiphysics models are an open system that allows well-controlled studies on the individual and combined effects of influencing factors on drug delivery outcomes while accommodating the patient-specific in vivo environment, which is not economically feasible through experimental means. Extensive modelling frameworks have been developed to reveal the underlying mechanisms of drug delivery and optimise effective delivery plans. This review provides an overview of currently available models, their integration with advanced medical imaging modalities, and code packages for personalised drug delivery. The potential to incorporate new technologies (i.e., machine learning) in this field is also addressed for development.
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Affiliation(s)
- Ajay Bhandari
- Biofluids Research Lab, Department of Mechanical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India
| | - Boram Gu
- School of Chemical Engineering, Chonnam National University, Gwangju, Republic of Korea
| | | | - Wenbo Zhan
- School of Engineering, University of Aberdeen, Aberdeen, UK.
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Blocker SJ, Mowery YM, Everitt JI, Cook J, Cofer GP, Qi Y, Bassil AM, Xu ES, Kirsch DG, Badea CT, Johnson GA. MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma. Front Oncol 2024; 14:1287479. [PMID: 38884083 PMCID: PMC11176416 DOI: 10.3389/fonc.2024.1287479] [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: 12/29/2023] [Accepted: 05/15/2024] [Indexed: 06/18/2024] Open
Abstract
Purpose To identify significant relationships between quantitative cytometric tissue features and quantitative MR (qMRI) intratumorally in preclinical undifferentiated pleomorphic sarcomas (UPS). Materials and methods In a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting of matched multi-contrast in vivo MRI, three-dimensional (3D) multi-contrast high-resolution ex vivo MR histology (MRH), and two-dimensional (2D) tissue slides. From digitized histology we generated quantitative cytometric feature maps from whole-slide automated nuclear segmentation. We automatically segmented intratumoral regions of distinct qMRI values and measured corresponding cytometric features. Linear regression analysis was performed to compare intratumoral qMRI and tissue cytometric features, and results were corrected for multiple comparisons. Linear correlations between qMRI and cytometric features with p values of <0.05 after correction for multiple comparisons were considered significant. Results Three features correlated with ex vivo apparent diffusion coefficient (ADC), and no features correlated with in vivo ADC. Six features demonstrated significant linear relationships with ex vivo T2*, and fifteen features correlated significantly with in vivo T2*. In both cases, nuclear Haralick texture features were the most prevalent type of feature correlated with T2*. A small group of nuclear topology features also correlated with one or both T2* contrasts, and positive trends were seen between T2* and nuclear size metrics. Conclusion Registered multi-parametric imaging datasets can identify quantitative tissue features which contribute to UPS MR signal. T2* may provide quantitative information about nuclear morphology and pleomorphism, adding histological insights to radiological interpretation of UPS.
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Affiliation(s)
- Stephanie J Blocker
- Department of Radiology, Duke University Medical Center, Duke University, Durham, NC, United States
| | - Yvonne M Mowery
- Department of Radiation Oncology, Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Jeffrey I Everitt
- Department of Pathology, Duke University Medical Center, Duke University, Durham, NC, United States
| | - James Cook
- Department of Radiology, Duke University Medical Center, Duke University, Durham, NC, United States
| | - Gary Price Cofer
- Department of Radiology, Duke University Medical Center, Duke University, Durham, NC, United States
| | - Yi Qi
- Department of Radiology, Duke University Medical Center, Duke University, Durham, NC, United States
| | - Alex M Bassil
- Department of Radiation Oncology, Duke University Medical Center, Duke University, Durham, NC, United States
| | - Eric S Xu
- Duke University Medical Center, Duke University, Durham, NC, United States
| | - David G Kirsch
- Departments of Radiation Oncology and Medical Biophysics, Princess Margaret Cancer Centre, University Health Network (UHN), Toronto, ON, Canada
| | - Cristian T Badea
- Department of Radiology, Duke University Medical Center, Duke University, Durham, NC, United States
| | - G Allan Johnson
- Department of Radiology, Duke University Medical Center, Duke University, Durham, NC, United States
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Zheng F, Zhang L, Chen H, Zang Y, Chen X, Li Y. Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences. JOURNAL OF RADIATION RESEARCH 2024; 65:350-359. [PMID: 38650477 PMCID: PMC11115443 DOI: 10.1093/jrr/rrae007] [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: 06/19/2023] [Revised: 08/12/2023] [Indexed: 04/25/2024]
Abstract
Using radiomics to predict O6-methylguanine-DNA methyltransferase promoter methylation status in patients with newly diagnosed glioblastoma and compare the performances of different MRI sequences. Preoperative MRI scans from 215 patients were included in this retrospective study. After image preprocessing and feature extraction, two kinds of machine-learning models were established and compared for their performances. One kind was established using all MRI sequences (T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient), and the other kind was based on single MRI sequence as listed above. For the machine-learning model based on all sequences, a total of seven radiomic features were selected with the Maximum Relevance and Minimum Redundancy algorithm. The predictive accuracy was 0.993 and 0.750 in the training and validation sets, respectively, and the area under curves were 1.000 and 0.754 in the two sets, respectively. For the machine-learning model based on single sequence, the numbers of selected features were 8, 10, 10, 13, 9, 7 and 6 for T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient, respectively, with predictive accuracies of 0.797-1.000 and 0.583-0.694 in the training and validation sets, respectively, and the area under curves of 0.874-1.000 and 0.538-0.697 in the two sets, respectively. Specifically, T1-weighted image-based model performed best, while contrast enhancement-based model performed worst in the independent validation set. The machine-learning models based on seven different single MRI sequences performed differently in predicting O6-methylguanine-DNA methyltransferase status in glioblastoma, while the machine-learning model based on the combination of all sequences performed best.
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Affiliation(s)
- Fei Zheng
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
- Department of Radiology, Peking University People’s Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, 100044, P. R. China
| | - Lingling Zhang
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
| | - Hongyan Chen
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
| | - Yuying Zang
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
| | - Xuzhu Chen
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
| | - Yiming Li
- Department of Neurosurgery, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
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Johansson J, Lagerstrand K, Björkman-Burtscher IM, Laesser M, Hebelka H, Maier SE. Normal Brain and Brain Tumor ADC: Changes Resulting From Variation of Diffusion Time and/or Echo Time in Pulsed-Gradient Spin Echo Diffusion Imaging. Invest Radiol 2024:00004424-990000000-00206. [PMID: 38587357 DOI: 10.1097/rli.0000000000001081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
OBJECTIVES Increasing gradient performance on modern magnetic resonance imaging scanners has profoundly reduced the attainable diffusion and echo times for clinically available pulsed-gradient spin echo (PGSE) sequences. This study investigated how this may impact the measured apparent diffusion coefficient (ADC), which is considered an important diagnostic marker for differentiation between normal and abnormal brain tissue and for therapeutic follow-up. MATERIALS AND METHODS Diffusion time and echo time dependence of the ADC were evaluated on a high-performance 3 T magnetic resonance imaging scanner. Diffusion PGSE brain scans were performed in 10 healthy volunteers and in 10 brain tumor patients using diffusion times of 16, 40, and 70 ms, echo times of 60, 75, and 104 ms at 3 b-values (0, 100, and 1000 s/mm 2 ), and a maximum gradient amplitude of 68 mT/m. A low gradient performance system was also emulated by reducing the diffusion encoding gradient amplitude to 19 mT/m. In healthy subjects, the ADC was measured in 6 deep gray matter regions and in 6 white matter regions. In patients, the ADC was measured in the solid part of the tumor. RESULTS With increasing diffusion time, a small but significant ADC increase of up to 2.5% was observed for 6 aggregate deep gray matter structures. With increasing echo time or reduced gradient performance, a small but significant ADC decrease of up to 2.6% was observed for 6 aggregate white matter structures. In tumors, diffusion time-related ADC changes were inconsistent without clear trend. For tumors with diffusivity above 1.0 μm 2 /ms, with prolonged echo time, there was a pronounced ADC increase of up to 12%. Meanwhile, for tumors with diffusivity at or below 1.0 μm 2 /ms, no change or a reduction was observed. Similar results were observed for gradient performance reduction, with an increase of up to 21%. The coefficient of variation determined in repeat experiments was 2.4%. CONCLUSIONS For PGSE and the explored parameter range, normal tissue ADC changes seem negligible. Meanwhile, observed tumor ADC changes can be relevant if ADC is used as a quantitative biomarker and not merely assessed by visual inspection. This highlights the importance of reporting all pertinent timing parameters in ADC studies and of considering these effects when building scan protocols for use in multicenter investigations.
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Affiliation(s)
- Jens Johansson
- From the Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden (J.J., I.M.B.-B., M.L., H.H., S.E.M.); Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden (K.L.); Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden (J.J., K.L.); Department of Radiology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden (I.M.B.-B., M.L., H.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (S.E.M.)
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11
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Barbosa MA, Pereira EGR, da Mata Pereira PJ, Guasti AA, Andreiuolo F, Chimelli L, Kasuki L, Ventura N, Gadelha MR. Diffusion-weighted imaging does not seem to be a predictor of consistency in pituitary adenomas. Pituitary 2024; 27:187-196. [PMID: 38273189 DOI: 10.1007/s11102-023-01377-6] [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] [Accepted: 12/20/2023] [Indexed: 01/27/2024]
Abstract
PURPOSE To prospectively evaluate the usefulness of T1-weighted imaging (T1WI) and diffusion-weighted imaging (DWI) sequences in predicting the consistency of macroadenomas. In addition, to determine their values as prognostic factors of surgical outcomes. METHODS Patients with pituitary macroadenoma and surgical indication were included. All patients underwent pre-surgical magnetic resonance imaging (MRI) that included the sequences T1WI before and after contrast administration and DWI with the apparent diffusion coefficient (ADC) map. Post-surgical MRI was performed at least 3 months after surgery. The consistency of the macroadenomas was evaluated at surgery, and they were grouped into soft and intermediate/hard adenomas. Mean ADC values, signal on T1WI and the ratio of tumor ADC values to pons (ADCR) were compared with tumor consistency and grade of surgical resection. RESULTS A total of 80 patients were included. A softened consistency was found at surgery in 53 patients and hardened in 27 patients. The median ADC in the soft consistency group was 0.532 × 10-3 mm2/sec (0.306 - 1.096 × 10-3 mm2/sec), and in the intermediate/hard consistency group was 0.509 × 10-3 mm2/sec (0.308 - 0.818 × 10-3 mm2/sec). There was no significant difference between the median values of ADC, ADCR and signal on T1W between the soft and hard tumor groups, or between patients with and without tumor residue. CONCLUSION Our results did not show usefulness of the DWI and T1WI for assessing the consistency of pituitary macroadenomas, nor as a predictor of the degree of surgical resection.
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Affiliation(s)
- Monique Alvares Barbosa
- Radiology Unit, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, Brazil.
- MRI Unit, Clínica de Diagnóstico por Imagem, DASA, Rio de Janeiro, Brazil.
- Serviço de Radiologia, Instituto Estadual do Cérebro Paulo Niemeyer, Rua do Rezende, 156, Centro, Rio de Janeiro, 20231-092, Brazil.
| | | | - Paulo José da Mata Pereira
- Neurosurgery Unit, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, Brazil
| | - André Accioly Guasti
- Neurosurgery Unit, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, Brazil
| | - Felipe Andreiuolo
- Neuropathology and Molecular Genetics Laboratory, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, Brazil
| | - Leila Chimelli
- Neuropathology and Molecular Genetics Laboratory, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, Brazil
| | - Leandro Kasuki
- Neuroendocrinology Research Center/Endocrinology Division, Medical School and Hospital Universitário Clementino Fraga Filho, Rio de Janeiro, Brazil
- Neuroendocrine Unit, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, Brazil
- Endocrinology Division, Hospital Federal de Bonsucesso, Rio de Janeiro, Brazil
| | - Nina Ventura
- Radiology Unit, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, Brazil
- Neuroradiology Division, Medical School and Hospital Universitário Clementino Fraga Filho, Rio de Janeiro, Brazil
- Neuroradiology Unit, Samaritano Hospital, Grupo Fleury, Rio de Janeiro, Brazil
| | - Monica R Gadelha
- Neuropathology and Molecular Genetics Laboratory, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, Brazil
- Neuroendocrinology Research Center/Endocrinology Division, Medical School and Hospital Universitário Clementino Fraga Filho, Rio de Janeiro, Brazil
- Neuroendocrine Unit, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde, Rio de Janeiro, Brazil
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12
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Altmann S, Grauhan NF, Mercado MAA, Steinmetz S, Kronfeld A, Paul R, Benkert T, Uphaus T, Groppa S, Winter Y, Brockmann MA, Othman AE. Deep Learning Accelerated Brain Diffusion-Weighted MRI with Super Resolution Processing. Acad Radiol 2024:S1076-6332(24)00139-9. [PMID: 38521612 DOI: 10.1016/j.acra.2024.02.049] [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: 12/31/2023] [Revised: 02/20/2024] [Accepted: 02/26/2024] [Indexed: 03/25/2024]
Abstract
OBJECTIVES To investigate the clinical feasibility and image quality of accelerated brain diffusion-weighted imaging (DWI) with deep learning image reconstruction and super resolution. METHODS 85 consecutive patients with clinically indicated MRI at a 3 T scanner were prospectively included. Conventional diffusion-weighted data (c-DWI) with four averages were obtained. Reconstructions of one and two averages, as well as deep learning diffusion-weighted imaging (DL-DWI), were accomplished. Three experienced readers evaluated the acquired data using a 5-point Likert scale regarding overall image quality, overall contrast, diagnostic confidence, occurrence of artefacts and evaluation of the central region, basal ganglia, brainstem, and cerebellum. To assess interrater agreement, Fleiss' kappa (ϰ) was determined. Signal intensity (SI) levels for basal ganglia and the central region were estimated via automated segmentation, and SI values of detected pathologies were measured. RESULTS Intracranial pathologies were identified in 35 patients. DL-DWI was significantly superior for all defined parameters, independently from applied averages (p-value <0.001). Optimum image quality was achieved with DL-DWI by utilizing a single average (p-value <0.001), demonstrating very good (80.9%) to excellent image quality (14.5%) in nearly all cases, compared to 12.5% with very good and 0% with excellent image quality for c-MRI (p-value <0.001). Comparable results could be shown for diagnostic confidence. Inter-rater Fleiss' Kappa demonstrated moderate to substantial agreement for virtually all defined parameters, with good accordance, particularly for the assessment of pathologies (p = 0.74). Regarding SI values, no significant difference was found. CONCLUSION Ultra-fast diffusion-weighted imaging with super resolution is feasible, resulting in highly accelerated brain imaging while increasing diagnostic image quality.
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Affiliation(s)
- Sebastian Altmann
- Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany.
| | - Nils F Grauhan
- Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany
| | - Mario Alberto Abello Mercado
- Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany
| | - Sebastian Steinmetz
- Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany
| | - Andrea Kronfeld
- Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany
| | - Roman Paul
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Mainz, Johannes Gutenberg University, Rhabanusstr. 3/Tower A, 55118 Mainz, Germany
| | | | - Timo Uphaus
- Department of Neurology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany
| | - Sergiu Groppa
- Department of Neurology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany
| | - Yaroslav Winter
- Department of Neurology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany; Department of Neurology, Philipps-University Marburg, Baldingerstr, 35043 Marburg, Germany
| | - Marc A Brockmann
- Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany
| | - Ahmed E Othman
- Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckstr. 1, 55131 Mainz, Germany
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13
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Xu XQ, Cao LL, Ma G, Shen GC, Lu SS, Zhang YX, Zhang Y, Shi HB, Liu S, Wu FY. Potential Approach to Quantifying the Volume of the Ischemic Core in Truncated Computed Tomography Perfusion Scans: A Preliminary Study. J Comput Assist Tomogr 2024; 48:298-302. [PMID: 37757843 DOI: 10.1097/rct.0000000000001552] [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: 09/29/2023]
Abstract
OBJECTIVE This study aimed to provide an alternative approach for quantifying the volume of the ischemic core (IC) if truncation of computed tomography perfusion (CTP) occurs in clinical practice. METHODS Baseline CTP and follow-up diffusion-weighted imaging (DWI) data from 88 patients with stroke were retrospectively collected. CTP source images (CTPSI) from the unenhanced phase to the peak arterial phase (CTPSI-A) or the peak venous phase (CTPSI-V) were collected to simulate the truncation of CTP in the arterial or venous phases, respectively. The volume of IC on CTPSI-A (V CTPSI-A ) or CTPSI-V (V CTPSI-V ) was defined as the volume of the brain tissue with >65% reduction in attenuation compared with that of the normal tissue. The volume of IC on the baseline CTP (V CTP ) was defined as a relative cerebral blood flow of <30% of that in the normal tissue. The volume of the posttreatment infarct on the follow-up DWI (V DWI ) image was manually delineated and calculated. One-way analysis of variance, Bland-Altman plots, and Spearman correlation analyses were used for the statistical analysis. RESULTS V CTPSI-A was significantly higher than V DWI ( P < 0.001); however, no significant difference was observed between V CTP and V DWI ( P = 0.073) or between V CTPSI-V and V DWI ( P > 0.999). The mean differences between V DWI and V CTPSI-V , V DWI and V CTP , and V DWI and V CTPSI-A were 1.70 mL (limits of agreement [LoA], -56.40 to 59.70), 8.30 mL (LoA, -40.70 to 57.30), and -68.10 mL (LoA, -180.90 to 44.70), respectively. Significant correlations were observed between V DWI and V CTP ( r = 0.68, P < 0.001) and between V DWI and V CTPSI-V ( r = 0.39, P < 0.001); however, no significant correlation was observed between V DWI and V CTPSI-A ( r = 0.20, P = 0.068). CONCLUSIONS V CTPSI-V may be a promising method for quantifying the volume of the IC if truncation of CTP occurs.
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Affiliation(s)
- Xiao-Quan Xu
- From the Department of Radiology, The First Affiliated Hospital of Nanjing Medical University
| | - Lin-Li Cao
- Department of Medical Imaging, Jiangsu Second Hospital of Traditional Chinese Medicine, Nanjing
| | - Gao Ma
- From the Department of Radiology, The First Affiliated Hospital of Nanjing Medical University
| | - Guang-Chen Shen
- From the Department of Radiology, The First Affiliated Hospital of Nanjing Medical University
| | - Shan-Shan Lu
- From the Department of Radiology, The First Affiliated Hospital of Nanjing Medical University
| | | | - Yu Zhang
- Shukun Network Technology, Co, Ltd, Beijing
| | - Hai-Bin Shi
- Department of Interventional Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Sheng Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fei-Yun Wu
- From the Department of Radiology, The First Affiliated Hospital of Nanjing Medical University
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14
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Fan H, Bunker L, Wang Z, Durfee AZ, Lin DDM, Yedavalli V, Ge Y, Zhou XJ, Hillis AE, Lu H. Simultaneous perfusion, diffusion, T 2 *, and T 1 mapping with MR fingerprinting. Magn Reson Med 2024; 91:558-569. [PMID: 37749847 PMCID: PMC10872728 DOI: 10.1002/mrm.29880] [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: 07/05/2023] [Revised: 08/27/2023] [Accepted: 09/12/2023] [Indexed: 09/27/2023]
Abstract
PURPOSE Quantitative mapping of brain perfusion, diffusion, T2 *, and T1 has important applications in cerebrovascular diseases. At present, these sequences are performed separately. This study aims to develop a novel MRI technique to simultaneously estimate these parameters. METHODS This sequence to measure perfusion, diffusion, T2 *, and T1 mapping with magnetic resonance fingerprinting (MRF) was based on a previously reported MRF-arterial spin labeling (ASL) sequence, but the acquisition module was modified to include different TEs and presence/absence of bipolar diffusion-weighting gradients. We compared parameters derived from the proposed method to those derived from reference methods (i.e., separate sequences of MRF-ASL, conventional spin-echo DWI, and T2 * mapping). Test-retest repeatability and initial clinical application in two patients with stroke were evaluated. RESULTS The scan time of our proposed method was 24% shorter than the sum of the reference methods. Parametric maps obtained from the proposed method revealed excellent image quality. Their quantitative values were strongly correlated with those from reference methods and were generally in agreement with values reported in the literature. Repeatability assessment revealed that ADC, T2 *, T1 , and B1 + estimation was highly reliable, with voxelwise coefficient of variation (CoV) <5%. The CoV for arterial transit time and cerebral blood flow was 16% ± 3% and 25% ± 9%, respectively. The results from the two patients with stroke demonstrated that parametric maps derived from the proposed method can detect both ischemic and hemorrhagic stroke. CONCLUSION The proposed method is a promising technique for multi-parametric mapping and has potential use in patients with stroke.
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Affiliation(s)
- Hongli Fan
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Lisa Bunker
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Zihan Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Alexandra Zezinka Durfee
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Doris Da May Lin
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Vivek Yedavalli
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Yulin Ge
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, Unites States
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research and Department of Radiology, University of Illinois at Chicago, Chicago, IL, United States
| | - Argye E. Hillis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Hanzhang Lu
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
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15
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Fatima K, Naik S, Jain M, Bhoi SK, Padhi S, Bag ND, Panigrahi A, Mohakud S. Diffusion-Weighted Imaging and Chemical Shift Imaging to Differentiate Benign and Malignant Vertebral Lesion: A Hospital-Based Cross-Sectional Study. Indian J Radiol Imaging 2024; 34:76-84. [PMID: 38106853 PMCID: PMC10723945 DOI: 10.1055/s-0043-1772848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023] Open
Abstract
Objective The aim of this study was to evaluate the role of diffusion-weighted imaging (DWI) and chemical shift imaging (CSI) for the differentiation of benign and malignant vertebral lesions. Methods Patients with vertebral lesions underwent routine magnetic resonance imaging (MRI) along with DWI and CSI. Qualitative analysis of the morphological features was done by routine MRI. Quantitative analysis of apparent diffusion coefficient (ADC) from DWI and fat fraction (FF) from CSI was done and compared between benign and malignant vertebral lesions. Results Seventy-two patients were included. No significant difference was noted in signal intensities of benign and malignant lesions on conventional MRI sequences. Posterior element involvement, paravertebral soft-tissue lesion, and posterior vertebral bulge were common in malignant lesion, whereas epidural/paravertebral collection, absence of posterior vertebral bulge, and multiple compression fractures were common in benign vertebral lesion ( p < 0.001). The mean ADC value was 1.25 ± 0.27 mm 2 /s for benign lesions and 0.9 ± 0.19 mm 2 /s for malignant vertebral lesions ( p ≤ 0.001). The mean value of FF was 12.7 ± 7.49 for the benign group and 4.04 ± 2.6 for the malignant group ( p < 0.001). A receiver operating characteristic (ROC) curve analysis showed that an ADC cutoff of 1.05 × 10 -3 mm 2 /s and an FF cutoff of 6.9 can differentiate benign from malignant vertebral lesions, with the former having 86% sensitivity and 82.8% specificity and the latter having 93% sensitivity and 96.6% specificity. Conclusion The addition of DWI and CSI to routine MRI protocol in patients with vertebral lesions promises to be very helpful in differentiating benign from malignant vertebral lesions when difficulty in qualitative interpretation of conventional MR images arises.
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Affiliation(s)
- Kaneez Fatima
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Suprava Naik
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Mantu Jain
- Department of Orthopaedics, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Sanjeev Kumar Bhoi
- Department of Neurology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Somnath Padhi
- Department of Pathology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Nerbadyswari Deep Bag
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Ashutosh Panigrahi
- Department of Haematology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Sudipta Mohakud
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
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16
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Joseph C. Guess What Is in My Brain. J Adv Pract Oncol 2024; 15:60-64. [PMID: 39119082 PMCID: PMC11308535 DOI: 10.6004/jadpro.2024.15.1.7] [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] [Indexed: 08/10/2024] Open
Abstract
Magnetic resonance imaging (MRI) of the brain is an important diagnostic tool used by neurologists. This article explores the workup and management for a patient with a brain lesion and highlights the importance of neuroimaging. Similarities and differences in MRI findings for meningioma, central nervous system lymphoma, and glioblastomas are discussed, along with common MRI sequences and their utility.
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Affiliation(s)
- Catherine Joseph
- From Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
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17
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Tejani AS, Berson E, Phillip J, Feltrin FS, Bazan C, Raj KM, Agarwal AK, Maldjian JA, Lee WC, Yu FF. Diffusion-weighted imaging of the orbit. Clin Radiol 2024; 79:10-18. [PMID: 37926649 DOI: 10.1016/j.crad.2023.10.010] [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: 03/27/2023] [Revised: 09/14/2023] [Accepted: 10/02/2023] [Indexed: 11/07/2023]
Abstract
Orbital lesions compose a heterogeneous group of pathologies that often present with non-specific imaging findings on conventional magnetic resonance imaging (MRI) sequences (T1-and T2-weighted). Accordingly, the application of diffusion MRI offers an opportunity to further distinguish between lesions along this spectrum. Diffusion-weighted imaging (DWI) represents the simplest and most frequent clinically utilised diffusion imaging technique. Recent advances in DWI techniques have extended its application to the evaluation of a wider spectrum of neurological pathology, including orbital lesions. This review details the manifestations of select orbital pathology on DWI and underscores specific situations where diffusion imaging allows for increased diagnostic sensitivity compared to more conventional MRI techniques. These examples also describe preferred management for orbital lesions identified by DWI.
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Affiliation(s)
- A S Tejani
- Department of Raddsiology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - E Berson
- Department of Radiology, Yale School of Medicine, New Haven, CT, USA
| | - J Phillip
- Department of Raddsiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - F S Feltrin
- Department of Raddsiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - C Bazan
- Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - K M Raj
- Department of Raddsiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - A K Agarwal
- Department of Raddsiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - J A Maldjian
- Department of Raddsiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - W-C Lee
- Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - F F Yu
- Department of Raddsiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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18
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Weaver JM, DiPiero M, Rodrigues PG, Cordash H, Davidson RJ, Planalp EM, Dean DC. Automated motion artifact detection in early pediatric diffusion MRI using a convolutional neural network. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:10.1162/imag_a_00023. [PMID: 38344118 PMCID: PMC10854394 DOI: 10.1162/imag_a_00023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Diffusion MRI (dMRI) is a widely used method to investigate the microstructure of the brain. Quality control (QC) of dMRI data is an important processing step that is performed prior to analysis using models such as diffusion tensor imaging (DTI) or neurite orientation dispersion and density imaging (NODDI). When processing dMRI data from infants and young children, where intra-scan motion is common, the identification and removal of motion artifacts is of the utmost importance. Manual QC of dMRI data is (1) time-consuming due to the large number of diffusion directions, (2) expensive, and (3) prone to subjective errors and observer variability. Prior techniques for automated dMRI QC have mostly been limited to adults or school-age children. Here, we propose a deep learning-based motion artifact detection tool for dMRI data acquired from infants and toddlers. The proposed framework uses a simple three-dimensional convolutional neural network (3DCNN) trained and tested on an early pediatric dataset of 2,276 dMRI volumes from 121 exams acquired at 1 month and 24 months of age. An average classification accuracy of 95% was achieved following four-fold cross-validation. A second dataset with different acquisition parameters and ages ranging from 2-36 months (consisting of 2,349 dMRI volumes from 26 exams) was used to test network generalizability, achieving 98% classification accuracy. Finally, to demonstrate the importance of motion artifact volume removal in a dMRI processing pipeline, the dMRI data were fit to the DTI and NODDI models and the parameter maps were compared with and without motion artifact removal.
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Affiliation(s)
- Jayse Merle Weaver
- Department of Medical Physics, University of Wisconsin–Madison, Madison, WI, United States
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
| | - Marissa DiPiero
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin–Madison, Madison, WI, United States
| | | | - Hassan Cordash
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
| | - Richard J. Davidson
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, United States
- Center for Healthy Minds, University of Wisconsin–Madison, Madison WI, United States
- Department of Psychiatry, University of Wisconsin–Madison, Madison, WI, United States
| | - Elizabeth M. Planalp
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Department of Medicine, University of Wisconsin–Madison, Madison, WI, United States
| | - Douglas C. Dean
- Department of Medical Physics, University of Wisconsin–Madison, Madison, WI, United States
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Department of Pediatrics, University of Wisconsin–Madison, Madison, WI, United States
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19
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Martucci A, Di Giuliano F, Minosse S, Pocobelli G, Nucci C, Garaci F. MRI and Clinical Biomarkers Overlap between Glaucoma and Alzheimer's Disease. Int J Mol Sci 2023; 24:14932. [PMID: 37834380 PMCID: PMC10573932 DOI: 10.3390/ijms241914932] [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: 09/04/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023] Open
Abstract
Glaucoma is the leading cause of blindness worldwide. It is classically associated with structural and functional changes in the optic nerve head and retinal nerve fiber layer, but the damage is not limited to the eye. The involvement of the central visual pathways and disruption of brain network organization have been reported using advanced neuroimaging techniques. The brain structural changes at the level of the areas implied in processing visual information could justify the discrepancy between signs and symptoms and underlie the analogy of this disease with neurodegenerative dementias, such as Alzheimer's disease, and with the complex group of pathologies commonly referred to as "disconnection syndromes." This review aims to summarize the current state of the art on the use of advanced neuroimaging techniques in glaucoma and Alzheimer's disease, highlighting the emerging biomarkers shared by both diseases.
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Affiliation(s)
- Alessio Martucci
- Ophthalmology Unit, Department of Experimental Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (A.M.); (G.P.)
| | - Francesca Di Giuliano
- Neuroradiology Unit, Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, Italy;
| | - Silvia Minosse
- Diagnostic Imaging Unit, Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, Italy; (S.M.); (F.G.)
| | - Giulio Pocobelli
- Ophthalmology Unit, Department of Experimental Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (A.M.); (G.P.)
| | - Carlo Nucci
- Ophthalmology Unit, Department of Experimental Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (A.M.); (G.P.)
| | - Francesco Garaci
- Diagnostic Imaging Unit, Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, Italy; (S.M.); (F.G.)
- San Raffaele Cassino, 03043 Frosinone, Italy
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Karmakar DK, Badhe PV, Mhatre P, Shrivastava S, Sultan M, Shankar G, Tekriwal K, Moharkar S. Utility of Diffusion Tensor Imaging in Assessing Corticospinal Tracts for the Management of Brain Tumors: A Cross-Sectional Observational Study. Cureus 2023; 15:e47811. [PMID: 38021806 PMCID: PMC10679788 DOI: 10.7759/cureus.47811] [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] [Accepted: 10/27/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Intra-axial brain tumors are a significant health problem and present several diagnostic and treatment challenges. Conventional magnetic resonance imaging (MRI) has posed several limitations, such as the inability to delineate the detailed anatomy of fibers in structures like the brainstem and the inability to accurately judge the extent of tumor infiltration. Diffusion tensor imaging (DTI), based on the concepts of isotropic and anisotropic diffusion, is capable of visualizing and segmenting white fiber bundles in high detail and providing crucial information about tumor boundaries, extent, neighboring tracts, and more. This information can be very useful in initial non-invasive diagnosis, preoperative tumor grading, biopsy planning, surgical planning, and prognosis. Methods and materials This is a cross-sectional observational study in a tertiary care setup, conducted over a one-year period. The study was performed in Seth Gordhandas Sunderdas Medical College (Seth G.S. Medical College) and King Edward VII Memorial Hospital (K.E.M. Hospital), a tertiary care hospital located in Mumbai, India. Fiber tractography was performed and was used to visualize the corticospinal tracts passing through the length of the brainstem. Changes in the degree of infiltration, destruction, and displacement of the corticospinal tracts were observed carefully. Adult patients who were diagnosed with brain tumors, willing to participate in the study, and capable of providing written informed consent prior to study registration were included. The DTI findings along with information from other investigations were used to decide the best course of management for each case. Results The study included 30 participants with a mean age of 46.0 ± 17.1 years, 63.3% and 37.7% being male and female, respectively. According to the lesion's location, the pons was found to be the most often affected area in 23.33% of cases, followed by the temporo-parietal region (13.3%) and the frontal region (13.3%). These lesions had heterogenous enhancement in 63.3% of the instances and homogeneous enhancement in 36.7% of the cases, according to a contrast study. According to their consistency, the lesions were further divided into two categories: solid lesions, which were present in 66.7% of instances, and cystic lesions, which were present in 90% of cases. Results from the diffusion tensor technique revealed that infiltration accounted for 40.0% of cases, displacement for 76.7%, and loss of white fiber tracts for 20.0%. DTI findings were significantly associated with the type of planned management and with the presence of post-management neurological deficit. Conclusion DTI played a complementary role in the assessment of tumors and can be used to improve surgical planning and therapeutic decision making. Preservation of corticospinal tracts is vital to prevent motor impairment. Availability of qualitative data with the depiction of corticospinal tracts in a three-dimensional projection and their relation with the brain tumors by DTI greatly helps in preoperative decision making and surgical approach.
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Affiliation(s)
- Deepmala K Karmakar
- Radiology, Seth Gordhandas Sunderdas Medical College and King Edward Memorial Hospital, Mumbai, IND
| | - Padma V Badhe
- Radiology, Seth Gordhandas Sunderdas Medical College and King Edward Memorial Hospital, Mumbai, IND
| | - Pauras Mhatre
- Radiology, Seth Gordhandas Sunderdas Medical College and King Edward Memorial Hospital, Mumbai, IND
| | - Shashwat Shrivastava
- Radiology, Seth Gordhandas Sunderdas Medical College and King Edward Memorial Hospital, Mumbai, IND
| | | | - Gautham Shankar
- Radiology, Seth Gordhandas Sunderdas Medical College and King Edward Memorial Hospital, Mumbai, IND
| | - Khushboo Tekriwal
- Radiology, Seth Gordhandas Sunderdas Medical College and King Edward Memorial Hospital, Mumbai, IND
| | - Swapnil Moharkar
- Radiology, Seth Gordhandas Sunderdas Medical College and King Edward Memorial Hospital, Mumbai, IND
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Li YH, Lin SC, Chung HW, Chang CC, Peng HH, Huang TY, Shen WC, Tsai CH, Lo YC, Lee TY, Juan CH, Juan CE, Chang HC, Liu YJ, Juan CJ. The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net. Eur Radiol 2023; 33:6157-6167. [PMID: 37095361 DOI: 10.1007/s00330-023-09622-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: 08/24/2022] [Revised: 02/11/2023] [Accepted: 02/17/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. METHODS This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10-3 mm2/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal-Wallis test with Tukey-Kramer post-hoc tests were used for comparison. A p < .05 was considered statistically significant. RESULTS The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10-3 mm2/s and 0.8 × 10-3 mm2/s (p < .001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10-3 mm2/s (p = .062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10-3 mm2/s achieved the highest DSC in the segmentation of AIS lesion. CONCLUSIONS The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10-3 mm2/s in segmentating AIS lesion with highest DSC. KEY POINTS • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10-3 mm2/s.
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Affiliation(s)
- Ya-Hui Li
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, Republic of China
- Department of Medical Imaging, China Medical University Hsinchu Hospital, No. 199, Sec. 1, Xinglong Rd., Zhubei City, Hsinchu County 302, Hsinchu, Taiwan, Republic of China
| | - Shao-Chieh Lin
- Department of Medical Imaging, China Medical University Hsinchu Hospital, No. 199, Sec. 1, Xinglong Rd., Zhubei City, Hsinchu County 302, Hsinchu, Taiwan, Republic of China
- Ph.D. Program in Electrical and Communication Engineering, Feng Chia University, Taichung, Taiwan, Republic of China
| | - Hsiao-Wen Chung
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, Republic of China
- Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, Republic of China
| | - Chia-Ching Chang
- Department of Medical Imaging, China Medical University Hsinchu Hospital, No. 199, Sec. 1, Xinglong Rd., Zhubei City, Hsinchu County 302, Hsinchu, Taiwan, Republic of China
- Department of Management Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, Republic of China
| | - Hsu-Hsia Peng
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, Republic of China
| | - Teng-Yi Huang
- Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, Republic of China
| | - Wu-Chung Shen
- Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan, Republic of China
- Department of Medical Imaging, Medical University Hospital, Taichung, Taiwan, Republic of China
| | - Chon-Haw Tsai
- Department of Neurology, China Medical University Hospital, Taichung, Taiwan, Republic of China
| | - Yu-Chien Lo
- Department of Medical Imaging, Medical University Hospital, Taichung, Taiwan, Republic of China
| | - Tung-Yang Lee
- Cheng Ching Hospital, Taichung, Taiwan, Republic of China
- Master's Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, Republic of China
| | - Cheng-Hsuan Juan
- Cheng Ching Hospital, Taichung, Taiwan, Republic of China
- Master's Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, Republic of China
| | - Cheng-En Juan
- Master's Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, Republic of China
| | - Hing-Chiu Chang
- Department of Biomedical Engineering, The Chinese University of Hong Kong, ERB1112, 11/F, William M.W. Mong Engineering Building, Shatin, N.T, Hong Kong.
- Multi-Scale Medical Robotics Center, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong.
| | - Yi-Jui Liu
- Department of Automatic Control Engineering, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, 40724, Taichung, Taiwan, Republic of China.
| | - Chun-Jung Juan
- Department of Medical Imaging, China Medical University Hsinchu Hospital, No. 199, Sec. 1, Xinglong Rd., Zhubei City, Hsinchu County 302, Hsinchu, Taiwan, Republic of China.
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, Republic of China.
- Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan, Republic of China.
- Department of Medical Imaging, Medical University Hospital, Taichung, Taiwan, Republic of China.
- Department of Biomedical Engineering, National Defense Medical Center, Taipei, Taiwan, Republic of China.
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China.
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Lampros M, Vlachos N, Tsitsopoulos PP, Zikou AK, Argyropoulou MI, Voulgaris S, Alexiou GA. The Role of Novel Imaging and Biofluid Biomarkers in Traumatic Axonal Injury: An Updated Review. Biomedicines 2023; 11:2312. [PMID: 37626808 PMCID: PMC10452517 DOI: 10.3390/biomedicines11082312] [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: 06/01/2023] [Revised: 08/02/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023] Open
Abstract
Traumatic brain injury (TBI) is a leading cause of disability worldwide. Traumatic axonal injury (TAI) is a subtype of TBI resulting from high-impact forces that cause shearing and/or stretching of the axonal fibers in white matter tracts. It is present in almost half of cases of severe TBI and frequently associated with poor functional outcomes. Axonal injury results from axonotomy due to mechanical forces and the activation of a biochemical cascade that induces the activation of proteases. It occurs at a cellular level; hence, conventional imaging modalities often fail to display TAI lesions. However, the advent of novel imaging modalities, such as functional magnetic resonance imaging and fiber tractography, has significantly improved the detection and characteristics of TAI. Furthermore, the significance of several fluid and structural biomarkers has also been researched, while the contribution of omics in the detection of novel biomarkers is currently under investigation. In the present review, we discuss the role of imaging modalities and potential biomarkers in diagnosing, classifying, and predicting the outcome in patients with TAI.
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Affiliation(s)
- Marios Lampros
- Department of Neurosurgery, School of Medicine, University of Ioannina, St. Niarhou Avenue, 45500 Ioannina, Greece; (M.L.); (N.V.); (S.V.)
| | - Nikolaos Vlachos
- Department of Neurosurgery, School of Medicine, University of Ioannina, St. Niarhou Avenue, 45500 Ioannina, Greece; (M.L.); (N.V.); (S.V.)
| | - Parmenion P. Tsitsopoulos
- Department of Neurosurgery, Hippokratio General Hospital, Aristotle University of Thessaloniki School of Medicine, 54942 Thessaloniki, Greece;
| | - Anastasia K. Zikou
- Department of Radiology, University of Ioannina, 45110 Ioannina, Greece; (A.K.Z.); (M.I.A.)
| | - Maria I. Argyropoulou
- Department of Radiology, University of Ioannina, 45110 Ioannina, Greece; (A.K.Z.); (M.I.A.)
| | - Spyridon Voulgaris
- Department of Neurosurgery, School of Medicine, University of Ioannina, St. Niarhou Avenue, 45500 Ioannina, Greece; (M.L.); (N.V.); (S.V.)
| | - George A. Alexiou
- Department of Neurosurgery, School of Medicine, University of Ioannina, St. Niarhou Avenue, 45500 Ioannina, Greece; (M.L.); (N.V.); (S.V.)
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Ulloa P, Methot V, Wottschel V, Koch MA. Extra-axonal contribution to double diffusion encoding-based pore size estimates in the corticospinal tract. MAGMA (NEW YORK, N.Y.) 2023; 36:589-612. [PMID: 36745290 PMCID: PMC10468962 DOI: 10.1007/s10334-022-01058-8] [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: 05/25/2022] [Revised: 12/14/2022] [Accepted: 12/19/2022] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To study the origin of compartment size overestimation in double diffusion encoding MRI (DDE) in vivo experiments in the human corticospinal tract. Here, the extracellular space is hypothesized to be the origin of the DDE signal. By exploiting the DDE sensitivity to pore shape, it could be possible to identify the origin of the measured signal. The signal difference between parallel and perpendicular diffusion gradient orientation can indicate if a compartment is regular or eccentric in shape. As extracellular space can be considered an eccentric compartment, a positive difference would mean a high contribution to the compartment size estimates. MATERIALS AND METHODS Computer simulations using MISST and in vivo experiments in eight healthy volunteers were performed. DDE experiments using a double spin-echo preparation with eight perpendicular directions were measured in vivo. The difference between parallel and perpendicular gradient orientations was analyzed using a Wilcoxon signed-rank test and a Mann-Whitney U test. RESULTS Simulations and MR experiments showed a statistically significant difference between parallel and perpendicular diffusion gradient orientation signals ([Formula: see text]). CONCLUSION The results suggest that the DDE-based size estimate may be considerably influenced by the extra-axonal compartment. However, the experimental results are also consistent with purely intra-axonal contributions in combination with a large fiber orientation dispersion.
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Affiliation(s)
- Patricia Ulloa
- Institute of Medical Engineering, University of Luebeck, Ratzeburger Allee 160, 23562 Luebeck, Germany
| | - Vincent Methot
- Institute of Medical Engineering, University of Luebeck, Ratzeburger Allee 160, 23562 Luebeck, Germany
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, De Boelelaan 1117, 1081, Amsterdam, The Netherlands
| | - Martin A. Koch
- Institute of Medical Engineering, University of Luebeck, Ratzeburger Allee 160, 23562 Luebeck, Germany
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24
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Boerger TF, Pahapill P, Butts AM, Arocho-Quinones E, Raghavan M, Krucoff MO. Large-scale brain networks and intra-axial tumor surgery: a narrative review of functional mapping techniques, critical needs, and scientific opportunities. Front Hum Neurosci 2023; 17:1170419. [PMID: 37520929 PMCID: PMC10372448 DOI: 10.3389/fnhum.2023.1170419] [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: 02/20/2023] [Accepted: 05/16/2023] [Indexed: 08/01/2023] Open
Abstract
In recent years, a paradigm shift in neuroscience has been occurring from "localizationism," or the idea that the brain is organized into separately functioning modules, toward "connectomics," or the idea that interconnected nodes form networks as the underlying substrates of behavior and thought. Accordingly, our understanding of mechanisms of neurological function, dysfunction, and recovery has evolved to include connections, disconnections, and reconnections. Brain tumors provide a unique opportunity to probe large-scale neural networks with focal and sometimes reversible lesions, allowing neuroscientists the unique opportunity to directly test newly formed hypotheses about underlying brain structural-functional relationships and network properties. Moreover, if a more complete model of neurological dysfunction is to be defined as a "disconnectome," potential avenues for recovery might be mapped through a "reconnectome." Such insight may open the door to novel therapeutic approaches where previous attempts have failed. In this review, we briefly delve into the most clinically relevant neural networks and brain mapping techniques, and we examine how they are being applied to modern neurosurgical brain tumor practices. We then explore how brain tumors might teach us more about mechanisms of global brain dysfunction and recovery through pre- and postoperative longitudinal connectomic and behavioral analyses.
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Affiliation(s)
- Timothy F. Boerger
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Peter Pahapill
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Alissa M. Butts
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, United States
- Mayo Clinic, Rochester, MN, United States
| | - Elsa Arocho-Quinones
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Max O. Krucoff
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States
- Department of Biomedical Engineering, Medical College of Wisconsin, Marquette University, Milwaukee, WI, United States
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25
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Vanherp L, Poelmans J, Govaerts K, Hillen A, Lagrou K, Vande Velde G, Himmelreich U. In vivo assessment of differences in fungal cell density in cerebral cryptococcomas of mice infected with Cryptococcus neoformans or Cryptococcus gattii. Microbes Infect 2023; 25:105127. [PMID: 36940783 DOI: 10.1016/j.micinf.2023.105127] [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: 09/09/2022] [Revised: 03/07/2023] [Accepted: 03/15/2023] [Indexed: 03/22/2023]
Abstract
In cerebral cryptococcomas caused by Cryptococcus neoformans or Cryptococcus gattii, the density of fungal cells within lesions can contribute to the overall brain fungal burden. In cultures, cell density is inversely related to the size of the cryptococcal capsule, a dynamic polysaccharide layer surrounding the cell. Methods to investigate cell density or related capsule size within fungal lesions of a living host are currently unavailable, precluding in vivo studies on longitudinal changes. Here, we assessed whether intravital microscopy and quantitative magnetic resonance imaging techniques (diffusion MRI and MR relaxometry) would enable non-invasive investigation of fungal cell density in cerebral cryptococcomas in mice. We compared lesions caused by type strains C. neoformans H99 and C. gattii R265 and evaluated potential relations between observed imaging properties, fungal cell density, total cell and capsule size. The observed inverse correlation between apparent diffusion coefficient and cell density permitted longitudinal investigation of cell density changes. Using these imaging methods, we were able to study the multicellular organization and cell density within brain cryptococcomas in the intact host environment of living mice. Since the MRI techniques are also clinically available, the same approach could be used to assess fungal cell density in brain lesions of patients.
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Affiliation(s)
- Liesbeth Vanherp
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Jennifer Poelmans
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Kristof Govaerts
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Amy Hillen
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Katrien Lagrou
- Laboratory of Clinical Bacteriology and Mycology, Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium; National Reference Centre for Mycosis, Department of Laboratory Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Greetje Vande Velde
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Uwe Himmelreich
- Biomedical MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.
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Li H, Wang H, Fu Q, Liu Y, Song B, Zhao J, Lin J. Association of Bun/Cr ratio-based dehydration status with infarct volumes and stroke severity in acute ischemic stroke. Clin Neurol Neurosurg 2023; 229:107741. [PMID: 37119656 DOI: 10.1016/j.clineuro.2023.107741] [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: 03/16/2023] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 05/01/2023]
Abstract
BACKGROUND Only a few clinical research had previously investigated the dehydration status to predict the evolution of the ischemic core. The aim of this study is to clarify the association between blood urea nitrogen (BUN)/creatinine (Cr)ratio-based dehydration and infarct volume measured using DWI (Diffusion-weighted imaging) at admission in patients with AIS (Acute Ischemic Stroke). METHODS We retrospectively recruited a total of 203 consecutive patients who were hospitalized through emergency or outpatient services within 72 h of acute ischemic stroke onset between October 2015 and September 2019. Stroke severity was measured by assessing the National Institutes of Health Stroke Scale (NIHSS) on admission. Infarct volume was measured using DWI with MATLAB software. RESULTS In this study, 203 patients who met the study criteria were enrolled. Patients in the dehydration group (Bun/Cr ratio>15) had a higher median NIHSS score (6(IQR:4-10) VS. 5(3-7); P = 0.0015)and larger DWI infarct volume (1.55 ml (IQR:0.51-6.79) VS. (0.37 ml (0.05-1.22); P < 0.001) on admission compared with patients in normal group. Further, a statistically significant correlation was found between DWI infarct volumes and NIHSS score with nonparametric Spearman rank correlation (r = 0.77; P < 0.001). The median NIHSS scores for the DWI infarct volumes quartiles were 3 ml (IQR, 2-4), 5 ml (4-7), 6 ml (5-8), and12 ml (8-17) from lowest to highest. However, the second quartile group did not show any significant correlation with the third quartile group (P = 0.4268). Multivariable linear and logistic regression analyses were used to test dehydration (Bun/Cr ratio>15), representing a predictor of infarct volume and stroke severity. CONCLUSION Bun/Cr ratio-based dehydration is associated with larger volumes of ischemic tissue measured using DWI and worse neurological deficit assessed by the NIHSS score in acute ischemic stroke.
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Affiliation(s)
- Huanyin Li
- Department of Neurology, Minhang Hospital, Fudan University, Shanghai 201101, China.
| | - Hao Wang
- Department of Radiology, Minhang Hospital, Fudan University, Shanghai 201101, China.
| | - Qingyin Fu
- Department of Ultrasonography, Minhang Hospital, Fudan University, Shanghai 201101, China.
| | - Yang Liu
- Department of Neurology, Minhang Hospital, Fudan University, Shanghai 201101, China.
| | - Bin Song
- Department of Radiology, Minhang Hospital, Fudan University, Shanghai 201101, China.
| | - Jing Zhao
- Department of Neurology, Minhang Hospital, Fudan University, Shanghai 201101, China.
| | - Jixian Lin
- Department of Neurology, Minhang Hospital, Fudan University, Shanghai 201101, China.
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Guadilla I, González S, Cerdán S, Lizarbe B, López-Larrubia P. Magnetic resonance imaging to assess the brain response to fasting in glioblastoma-bearing rats as a model of cancer anorexia. Cancer Imaging 2023; 23:36. [PMID: 37038232 PMCID: PMC10088192 DOI: 10.1186/s40644-023-00553-y] [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: 11/15/2022] [Accepted: 04/03/2023] [Indexed: 04/12/2023] Open
Abstract
BACKGROUND Global energy balance is a vital process tightly regulated by the brain that frequently becomes dysregulated during the development of cancer. Glioblastoma (GBM) is one of the most investigated malignancies, but its appetite-related disorders, like anorexia/cachexia symptoms, remain poorly understood. METHODS We performed manganese enhanced magnetic resonance imaging (MEMRI) and subsequent diffusion tensor imaging (DTI), in adult male GBM-bearing (n = 13) or control Wistar rats (n = 12). A generalized linear model approach was used to assess the effects of fasting in different brain regions involved in the regulation of the global energy metabolism: cortex, hippocampus, hypothalamus and thalamus. The regions were selected on the contralateral side in tumor-bearing animals, and on the left hemisphere in control rats. An additional DTI-only experiment was completed in two additional GBM (n = 5) or healthy cohorts (n = 6) to assess the effects of manganese infusion on diffusion measurements. RESULTS MEMRI results showed lower T1 values in the cortex (p-value < 0.001) and thalamus (p-value < 0.05) of the fed ad libitum GBM animals, as compared to the control cohort, consistent with increased Mn2+ accumulation. No MEMRI-detectable differences were reported between fed or fasting rats, either in control or in the GBM group. In the MnCl2-infused cohorts, DTI studies showed no mean diffusivity (MD) variations from the fed to the fasted state in any animal cohort. However, the DTI-only set of acquisitions yielded remarkably decreased MD values after fasting only in the healthy control rats (p-value < 0.001), and in all regions, but thalamus, of GBM compared to control animals in the fed state (p-value < 0.01). Fractional anisotropy (FA) decreased in tumor-bearing rats due to the infiltrate nature of the tumor, which was detected in both diffusion sets, with (p-value < 0.01) and without Mn2+ administration (p-value < 0.001). CONCLUSIONS Our results revealed that an altered physiological brain response to fasting occurred in hunger related regions in GBM animals, detectable with DTI, but not with MEMRI acquisitions. Furthermore, the present results showed that Mn2+ induces neurotoxic inflammation, which interferes with diffusion MRI to detect appetite-induced responses through MD changes.
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Affiliation(s)
- Irene Guadilla
- Biomedical Magnetic Resonance Group, Instituto de Investigaciones Biomédicas Alberto Sols, CSIC-UAM, C/ Arturo Duperier 4, 28029, Madrid, Spain
| | - Sara González
- Biomedical Magnetic Resonance Group, Instituto de Investigaciones Biomédicas Alberto Sols, CSIC-UAM, C/ Arturo Duperier 4, 28029, Madrid, Spain
| | - Sebastián Cerdán
- Biomedical Magnetic Resonance Group, Instituto de Investigaciones Biomédicas Alberto Sols, CSIC-UAM, C/ Arturo Duperier 4, 28029, Madrid, Spain
| | - Blanca Lizarbe
- Biomedical Magnetic Resonance Group, Instituto de Investigaciones Biomédicas Alberto Sols, CSIC-UAM, C/ Arturo Duperier 4, 28029, Madrid, Spain
- Departamento de Bioquímica, Universidad Autónoma de Madrid, 28029, Madrid, Spain
| | - Pilar López-Larrubia
- Biomedical Magnetic Resonance Group, Instituto de Investigaciones Biomédicas Alberto Sols, CSIC-UAM, C/ Arturo Duperier 4, 28029, Madrid, Spain.
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Sabidussi ER, Klein S, Jeurissen B, Poot DHJ. dtiRIM: A generalisable deep learning method for diffusion tensor imaging. Neuroimage 2023; 269:119900. [PMID: 36702213 DOI: 10.1016/j.neuroimage.2023.119900] [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: 07/07/2022] [Revised: 01/19/2023] [Accepted: 01/21/2023] [Indexed: 01/25/2023] Open
Abstract
Diffusion weighted MRI is an indispensable tool for routine patient screening and diagnostics of pathology. Recently, several deep learning methods have been proposed to quantify diffusion parameters, but poor generalisation to new data prevents broader use of these methods, as they require retraining of the neural network for each new scan protocol. In this work, we present the dtiRIM, a new deep learning method for Diffusion Tensor Imaging (DTI) based on the Recurrent Inference Machines. Thanks to its ability to learn how to solve inverse problems and to use the diffusion tensor model to promote data consistency, the dtiRIM can generalise to variations in the acquisition settings. This enables a single trained network to produce high quality tensor estimates for a variety of cases. We performed extensive validation of our method using simulation and in vivo data, and compared it to the Iterated Weighted Linear Least Squares (IWLLS), the approach of the state-of-the-art MRTrix3 software, and to an implementation of the Maximum Likelihood Estimator (MLE). Our results show that dtiRIM predictions present low dependency on tissue properties, anatomy and scanning parameters, with results comparable to or better than both IWLLS and MLE. Further, we demonstrate that a single dtiRIM model can be used for a diversity of data sets without significant loss in quality, representing, to our knowledge, the first generalisable deep learning based solver for DTI.
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Affiliation(s)
- E R Sabidussi
- Erasmus MC University Medical Center, Department of Radiology and Nuclear Medicine, Rotterdam, the Netherlands.
| | - S Klein
- Erasmus MC University Medical Center, Department of Radiology and Nuclear Medicine, Rotterdam, the Netherlands
| | - B Jeurissen
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium; Lab for Equilibrium Investigations and Aerospace, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - D H J Poot
- Erasmus MC University Medical Center, Department of Radiology and Nuclear Medicine, Rotterdam, the Netherlands
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Ratcliffe C, Adan G, Marson A, Solomon T, Saini J, Sinha S, Keller SS. Neurocysticercosis-related Seizures: Imaging Biomarkers. Seizure 2023; 108:13-23. [PMID: 37060627 DOI: 10.1016/j.seizure.2023.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 04/08/2023] Open
Abstract
Neurocysticercosis (NCC)-a parasitic CNS infection endemic to developing nations-has been called the leading global cause of acquired epilepsy yet remains understudied. It is currently unknown why a large proportion of patients develop recurrent seizures, often following the presentation of acute seizures. Furthermore, the presentation of NCC is heterogenous and the features that predispose to the development of an epileptogenic state remain uncertain. Perilesional factors (such as oedema and gliosis) have been implicated in NCC-related ictogenesis, but the effects of cystic factors, including lesion load and location, seem not to play a role in the development of habitual epilepsy. In addition, the cytotoxic consequences of the cyst's degenerative stages are varied and the majority of research, relying on retrospective data, lacks the necessary specificity to distinguish between acute symptomatic and unprovoked seizures. Previous research has established that epileptogenesis can be the consequence of abnormal network connectivity, and some imaging studies have suggested that a causative link may exist between NCC and aberrant network organisation. In wider epilepsy research, network approaches have been widely adopted; studies benefiting predominantly from the rich, multimodal data provided by advanced MRI methods are at the forefront of the field. Quantitative MRI approaches have the potential to elucidate the lesser-understood epileptogenic mechanisms of NCC. This review will summarise the current understanding of the relationship between NCC and epilepsy, with a focus on MRI methodologies. In addition, network neuroscience approaches with putative value will be highlighted, drawing from current imaging trends in epilepsy research.
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Affiliation(s)
- Corey Ratcliffe
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK; Department of Neuro Imaging and Interventional Radiology, National Institute of Mental Health and Neuro Sciences, Bangalore, India.
| | - Guleed Adan
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK; The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Anthony Marson
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Tom Solomon
- The Walton Centre NHS Foundation Trust, Liverpool, UK; Veterinary and Ecological Sciences, National Institute for Health Research Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection, University of Liverpool, Liverpool, UK; Tropical and Infectious Diseases Unit, Royal Liverpool and Broadgreen University Hospitals NHS Trust, Liverpool, UK
| | - Jitender Saini
- Department of Neuro Imaging and Interventional Radiology, National Institute of Mental Health and Neuro Sciences, Bangalore, India
| | - Sanjib Sinha
- Department of Neurology, National Institute of Mental Health and Neuro Sciences, Bangalore, India
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK; The Walton Centre NHS Foundation Trust, Liverpool, UK
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Warner W, Palombo M, Cruz R, Callaghan R, Shemesh N, Jones DK, Dell'Acqua F, Ianus A, Drobnjak I. Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: Optimisation and pre-clinical demonstration. Neuroimage 2023; 269:119930. [PMID: 36750150 PMCID: PMC7615244 DOI: 10.1016/j.neuroimage.2023.119930] [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: 07/22/2022] [Revised: 01/12/2023] [Accepted: 02/02/2023] [Indexed: 02/07/2023] Open
Abstract
Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general.
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Affiliation(s)
- William Warner
- Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | - Renata Cruz
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | | | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Flavio Dell'Acqua
- NatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
| | - Ivana Drobnjak
- Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom.
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Hnilicova P, Kantorova E, Sutovsky S, Grofik M, Zelenak K, Kurca E, Zilka N, Parvanovova P, Kolisek M. Imaging Methods Applicable in the Diagnostics of Alzheimer's Disease, Considering the Involvement of Insulin Resistance. Int J Mol Sci 2023; 24:ijms24043325. [PMID: 36834741 PMCID: PMC9958721 DOI: 10.3390/ijms24043325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/29/2023] [Accepted: 01/30/2023] [Indexed: 02/10/2023] Open
Abstract
Alzheimer's disease (AD) is an incurable neurodegenerative disease and the most frequently diagnosed type of dementia, characterized by (1) perturbed cerebral perfusion, vasculature, and cortical metabolism; (2) induced proinflammatory processes; and (3) the aggregation of amyloid beta and hyperphosphorylated Tau proteins. Subclinical AD changes are commonly detectable by using radiological and nuclear neuroimaging methods such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). Furthermore, other valuable modalities exist (in particular, structural volumetric, diffusion, perfusion, functional, and metabolic magnetic resonance methods) that can advance the diagnostic algorithm of AD and our understanding of its pathogenesis. Recently, new insights into AD pathoetiology revealed that deranged insulin homeostasis in the brain may play a role in the onset and progression of the disease. AD-related brain insulin resistance is closely linked to systemic insulin homeostasis disorders caused by pancreas and/or liver dysfunction. Indeed, in recent studies, linkages between the development and onset of AD and the liver and/or pancreas have been established. Aside from standard radiological and nuclear neuroimaging methods and clinically fewer common methods of magnetic resonance, this article also discusses the use of new suggestive non-neuronal imaging modalities to assess AD-associated structural changes in the liver and pancreas. Studying these changes might be of great clinical importance because of their possible involvement in AD pathogenesis during the prodromal phase of the disease.
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Affiliation(s)
- Petra Hnilicova
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
- Correspondence: (P.H.); (M.K.)
| | - Ema Kantorova
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Stanislav Sutovsky
- 1st Department of Neurology, Faculty of Medicine, Comenius University in Bratislava and University Hospital, 813 67 Bratislava, Slovakia
| | - Milan Grofik
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Kamil Zelenak
- Clinic of Radiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Egon Kurca
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Norbert Zilka
- Institute of Neuroimmunology, Slovak Academy of Sciences, 845 10 Bratislava, Slovakia
| | - Petra Parvanovova
- Department of Medical Biochemistry, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Martin Kolisek
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
- Correspondence: (P.H.); (M.K.)
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Speckter H, Palque-Santos S, Mota-Gonzalez R, Bido J, Hernandez G, Rivera D, Suazo L, Valenzuela S, Gonzalez-Curi M, Stoeter P. Can Apparent Diffusion Coefficient (ADC) maps replace Diffusion Tensor Imaging (DTI) maps to predict the volumetric response of meningiomas to Gamma Knife Radiosurgery? J Neurooncol 2023; 161:547-554. [PMID: 36745271 DOI: 10.1007/s11060-023-04243-4] [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: 12/22/2022] [Accepted: 01/17/2023] [Indexed: 02/07/2023]
Abstract
PURPOSE Noninvasive methods are desired to predict the treatment response to Stereotactic Radiosurgery (SRS) to improve individual tumor management. In a previous study, we demonstrated that Diffusion Tensor Imaging (DTI)-derived parameter maps significantly correlate to SRS response. This study aimed to analyze and compare the predictive value of intratumoral ADC and DTI parameters in patients with meningiomas undergoing radiosurgery. METHODS MR images of 70 patients treated with Gamma Knife SRS for WHO grade I meningiomas were retrospectively reviewed. MR acquisition included pre- and post-treatment DWI and DTI sequences, and subtractions were calculated to assess for radiation-induced changes in the parameter values. RESULTS After a mean follow-up period (FUP) of 52.7 months, 69 of 70 meningiomas were controlled, with a mean volume reduction of 34.9%. Whereas fractional anisotropy (FA) values of the initial exam showed the highest correlation to tumor volume change at the last FU (CC = - 0.607), followed by the differences between first and second FU values of FA (CC = - 0.404) and the first longitudinal diffusivity (LD) value (CC = - 0.375), the correlation coefficients of all ADC values were comparably low. Nevertheless, all these correlations, except for ADC measured at the first follow-up, reached significance. CONCLUSION For the first time, the prognostic value of ADC maps measured in meningiomas before and at first follow-up after Gamma Knife SRS, was compared to simultaneously acquired DTI parameter maps. Quantities assessed from ADC maps present significant correlations to the volumetric meningioma response but are less effective than correlations with DTI parameters.
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Affiliation(s)
- Herwin Speckter
- Centro Gamma Knife Dominicano, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic. .,Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic.
| | - Sarai Palque-Santos
- Centro Gamma Knife Dominicano, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Ruben Mota-Gonzalez
- Centro Gamma Knife Dominicano, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Jose Bido
- Centro Gamma Knife Dominicano, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Giancarlo Hernandez
- Centro Gamma Knife Dominicano, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Diones Rivera
- Centro Gamma Knife Dominicano, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Luis Suazo
- Centro Gamma Knife Dominicano, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Santiago Valenzuela
- Centro Gamma Knife Dominicano, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Maria Gonzalez-Curi
- Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
| | - Peter Stoeter
- Centro Gamma Knife Dominicano, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic.,Department of Radiology, CEDIMAT, Plaza de la Salud, Santo Domingo, Dominican Republic
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Romano A, Palizzi S, Romano A, Moltoni G, Di Napoli A, Maccioni F, Bozzao A. Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review. Cancers (Basel) 2023; 15:cancers15030618. [PMID: 36765575 PMCID: PMC9913305 DOI: 10.3390/cancers15030618] [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: 12/19/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.
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Affiliation(s)
- Andrea Romano
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Serena Palizzi
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Allegra Romano
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
| | - Giulia Moltoni
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
- Correspondence: ; Tel.: +39-3347906958
| | - Alberto Di Napoli
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Francesca Maccioni
- Department of Radiology, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Alessandro Bozzao
- NESMOS Department, U.O.C. Neuroradiology, “Sant’Andrea” University Hospital, 00189 Rome, Italy
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Otikovs M, Basak A, Frydman L. Spatiotemporal encoding MRI using subspace-constrained sampling and locally-low-rank regularization: Applications to diffusion weighted and diffusion kurtosis imaging of human brain and prostate. Magn Reson Imaging 2022; 94:151-160. [PMID: 36216145 DOI: 10.1016/j.mri.2022.09.011] [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: 06/26/2022] [Revised: 09/21/2022] [Accepted: 09/28/2022] [Indexed: 06/16/2023]
Abstract
The benefits of performing locally low-rank (LLR) reconstructions on subsampled diffusion weighted and diffusion kurtosis imaging data employing spatiotemporal encoding (SPEN) methods, is investigated. SPEN allows for self-referenced correction of motion-induced phase errors in case of interleaved diffusion-oriented acquisitions, and allows one to overcome distortions otherwise observed along EPI's phase-encoded dimension. In combination with LLR-based reconstructions of the pooled imaging data and with a joint subsampling of b-weighted and interleaved images, additional improvements in terms of sensitivity as well as shortened acquisition times are demonstrated, without noticeable penalties. Details on how the LLR-regularized, subspace-constrained image reconstructions were adapted to SPEN are given; the improvements introduced by adopting these reconstruction frameworks for the accelerated acquisition of diffusivity and of kurtosis imaging data in both relatively homogeneous regions like the human brain and in more challenging regions like the human prostate, are presented and discussed within the context of similar efforts in the field.
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Affiliation(s)
- Martins Otikovs
- Department of Chemical and Biological Physics and Azrieli National Center for Brain Imaging, Weizmann Institute of Science, Rehovot, Israel
| | - Ankit Basak
- Department of Chemical and Biological Physics and Azrieli National Center for Brain Imaging, Weizmann Institute of Science, Rehovot, Israel
| | - Lucio Frydman
- Department of Chemical and Biological Physics and Azrieli National Center for Brain Imaging, Weizmann Institute of Science, Rehovot, Israel.
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Boudreau E, Kerwin SC, DuPont EB, Levine JM, Griffin JF. Temporal and sequence-related variability in diffusion-weighted imaging of presumed cerebrovascular accidents in the dog brain. Front Vet Sci 2022; 9:1008447. [PMID: 36419725 PMCID: PMC9676236 DOI: 10.3389/fvets.2022.1008447] [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: 07/31/2022] [Accepted: 10/20/2022] [Indexed: 11/09/2022] Open
Abstract
Diffusion-weighted MRI (DWI) is often used to guide clinical interpretation of intraparenchymal brain lesions when there is suspicion for a cerebrovascular accident (CVA). Despite widespread evidence that imaging and patient parameters can influence diffusion-weighted measurements, such as apparent diffusion coefficient (ADC), there is little published data on such measurements for naturally occurring CVA in clinical cases in dogs. We describe a series of 22 presumed and confirmed spontaneous canine CVA with known time of clinical onset imaged on a single 3T magnet between 2011 and 2021. Median ADC values of < 1.0x10−3 mm2/s were seen in normal control tissues as well as within CVAs. Absolute and relative ADC values in CVAs were well-correlated (R2 = 0.82). Absolute ADC values < 1.0x10−3 mm2/s prevailed within ischemic CVAs, though there were exceptions, including some lesions of < 5 days age. Some lesions showed reduced absolute but not relative ADC values when compared to matched normal contralateral tissue. CVAs with large hemorrhagic components did not show restricted diffusion. Variation in the DWI sequence used impacted the ADC values obtained. Failure to identify a region of ADC < 1.0x10−3 mm2/s should not exclude CVA from the differential list when clinical suspicion is high.
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Affiliation(s)
- Elizabeth Boudreau
- Department of Small Animal Clinical Sciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
- *Correspondence: Elizabeth Boudreau
| | - Sharon C. Kerwin
- Department of Small Animal Clinical Sciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - Emily B. DuPont
- Department of Small Animal Clinical Sciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - Jonathan M. Levine
- Department of Small Animal Clinical Sciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - John F. Griffin
- Department of Large Animal Clinical Sciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
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Paquier Z, Chao SL, Bregni G, Sanchez AV, Guiot T, Dhont J, Gulyban A, Levillain H, Sclafani F, Reynaert N, Bali MA. Pre-trial quality assurance of diffusion-weighted MRI for radiomic analysis and the role of harmonisation. Phys Med 2022; 103:138-146. [DOI: 10.1016/j.ejmp.2022.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/30/2022] [Accepted: 10/08/2022] [Indexed: 11/17/2022] Open
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Brancato V, Cavaliere C, Garbino N, Isgrò F, Salvatore M, Aiello M. The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study. Front Oncol 2022; 12:1005805. [PMID: 36276163 PMCID: PMC9582951 DOI: 10.3389/fonc.2022.1005805] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/21/2022] [Indexed: 12/01/2022] Open
Abstract
Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman's correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as "virtual biopsy", introducing new insights for omics integration toward a personalized medicine approach.
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Affiliation(s)
| | | | | | - Francesco Isgrò
- Department of Electrical Engineering and Information Technologies, University of Napoli Federico II, Napoli, Italy
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Multinuclear MRI in Drug Discovery. Molecules 2022; 27:molecules27196493. [PMID: 36235031 PMCID: PMC9572840 DOI: 10.3390/molecules27196493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 09/17/2022] [Accepted: 09/29/2022] [Indexed: 11/05/2022] Open
Abstract
The continuous development of magnetic resonance imaging broadens the range of applications to newer areas. Using MRI, we can not only visualize, but also track pharmaceutical substances and labeled cells in both in vivo and in vitro tests. 1H is widely used in the MRI method, which is determined by its high content in the human body. The potential of the MRI method makes it an excellent tool for imaging the morphology of the examined objects, and also enables registration of changes at the level of metabolism. There are several reports in the scientific publications on the use of clinical MRI for in vitro tracking. The use of multinuclear MRI has great potential for scientific research and clinical studies. Tuning MRI scanners to the Larmor frequency of a given nucleus, allows imaging without tissue background. Heavy nuclei are components of both drugs and contrast agents and molecular complexes. The implementation of hyperpolarization techniques allows for better MRI sensitivity. The aim of this review is to present the use of multinuclear MRI for investigations in drug delivery.
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Bonde A, Andreazza Dal Lago E, Foster B, Javadi S, Palmquist S, Bhosale P. Utility of the Diffusion Weighted Sequence in Gynecological Imaging: Review Article. Cancers (Basel) 2022; 14:cancers14184468. [PMID: 36139628 PMCID: PMC9496793 DOI: 10.3390/cancers14184468] [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: 07/28/2022] [Revised: 08/30/2022] [Accepted: 09/08/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Diffusion weighted imaging (DWI) is a magnetic resonance imaging sequence with diverse clinical applications in malignant and nonmalignant gynecological conditions. It provides vital supplemental information in the diagnosis and management of various gynecological conditions. Radiologists should be aware of fundamental concepts, clinical applications and pitfalls of DWI. Additionally we briefly discuss potential scope of newer advanced techniques based on DWI including diffusion tensor imaging and diffusion-weighted whole-body imaging with background signal suppression. Abstract Functional imaging with diffusion-weighted imaging (DWI) is a complementary tool to conventional diagnostic magnetic resonance imaging sequences. It is being increasingly investigated to predict tumor response and assess tumor recurrence. We elucidate the specific technical modifications of DWI preferred for gynecological imaging, including the different b-values and planes for image acquisition. Additionally, we discuss the problems and potential pitfalls encountered during DWI interpretation and ways to overcome them. DWI has a wide range of clinical applications in malignant and non-malignant gynecological conditions. It provides supplemental information helpful in diagnosing and managing tubo-ovarian abscess, uterine fibroids, endometriosis, adnexal torsion, and dermoid. Similarly, DWI has diverse applications in gynecological oncology in diagnosis, staging, detection of recurrent disease, and tumor response assessment. Quantitative evaluation with apparent diffusion coefficient (ADC) measurement is being increasingly evaluated for correlation with various tumor parameters in managing gynecological malignancies aiding in preoperative treatment planning. Newer advanced DWI techniques of diffusion tensor imaging (DTI) and whole body DWI with background suppression (DWIBS) and their potential uses in pelvic nerve mapping, preoperative planning, and fertility-preserving surgeries are briefly discussed.
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Affiliation(s)
- Apurva Bonde
- Department of Radiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Correspondence:
| | | | - Bryan Foster
- Department of Radiology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Sanaz Javadi
- Department of Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sarah Palmquist
- Department of Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Priya Bhosale
- Department of Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Moreno-Reina C, Martínez-Moya M, Piñero-González de la Peña P, Caro-Domínguez P. Neuroinvasive disease due to West Nile virus: Clinical and imaging findings associated with a re-emerging pathogen. RADIOLOGIA 2022; 64:473-483. [PMID: 36243447 DOI: 10.1016/j.rxeng.2021.06.007] [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: 12/12/2020] [Accepted: 06/21/2021] [Indexed: 06/16/2023]
Abstract
The West Nile virus (WNV) is an arbovirus than can infect human beings and cause severe neuroinvasive disease. Taking the outbreak that occurred in Spain in 2020 as a reference, this article reviews the clinical and imaging findings for neuroinvasive disease due to WNV. We collected demographic, clinical, laboratory, and imaging (CT and MRI) variables for 30 patients with WNV infection diagnosed at our center. The main clinical findings were fever, headache, and altered levels of consciousness. Neuroimaging studies, especially MRI, are very useful in the diagnosis and follow-up of these patients. The most common imaging findings were foci of increased signal intensity in the thalamus and brainstem in T2-weighted sequences; we illustrate these findings in cases from our hospital.
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Affiliation(s)
- C Moreno-Reina
- Unidad de Radiodiagnóstico, Hospital Universitario Virgen del Rocío, Sevilla, Spain.
| | - M Martínez-Moya
- Unidad de Radiodiagnóstico, Hospital Universitario Virgen del Rocío, Sevilla, Spain
| | | | - P Caro-Domínguez
- Unidad de Radiodiagnóstico, Hospital Universitario Virgen del Rocío, Sevilla, Spain
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Development of a standard phantom for diffusion-weighted magnetic resonance imaging quality control studies: A review. POLISH JOURNAL OF MEDICAL PHYSICS AND ENGINEERING 2022. [DOI: 10.2478/pjmpe-2022-0020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Abstract
Various materials and compounds have been used in the design of diffusion-weighted magnetic resonance imaging (DWMRI) phantoms to mimic biological tissue properties, including diffusion. This review thus provides an overview of the preparations of the various DW-MRI phantoms available in relation to the limitations and strengths of materials/solutions used to fill them. The narrative review conducted from relevant databases shows that synthesizing all relevant compounds from individual liquids, gels, and solutions based on their identified strengths could contribute to the development of a novel multifunctional DW-MRI phantom. The proposed multifunctional material at varied concentrations, when filled into a multi-compartment Perspex container of cylindrical or spherical geometry, could serve as a standard DW-MRI phantom. The standard multifunctional phantom could potentially provide DW-MRI quality control test parameters in one study session.
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Chen Y, Pan Z, Meng F, Xu Q, Huang L, Pu X, Yu X, Wu Y, Lyu H, Lin X. Assessment of Rat Sciatic Nerve Using Diffusion-Tensor Imaging With Readout-Segmented Echo Planar Imaging. Front Neurosci 2022; 16:938674. [PMID: 35812234 PMCID: PMC9260505 DOI: 10.3389/fnins.2022.938674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesThis study aimed to compare readout-segmented-3, readout-segmented-5, and readout-segmented-7 echo-planar imaging (RS3-EPI, RS5-EPI, and RS7-EPI) of DTI in the assessment of rat sciatic nerve at 3T MR.MethodsEight male adult healthy Sprague-Dawley rats were scanned at 3T MR with RS-3 EPI, RS5-EPI, and RS-7 EPI DTI. The image quality of RS-3 EPI, RS-5 EPI, and RS-7 EPI in terms of the nerve morphology, distortions of the nearby femur, muscles, and homogeneity of neuromuscular were evaluated by two experienced radiologists. The correlations between the histopathological and DTI parameters, including fractional anisotropy (FA) and radial diffusivity (RD), were calculated, respectively, and compared in RS-3, RS-5, and RS-7 EPI. The image quality scores for RS-3 EPI, RS-5 EPI, and RS-7 EPI were compared using the Wilcoxon rank-sum test. The correlation between DTI and histopathological parameters was calculated using the Pearson correlation coefficient.ResultsRS-5 EPI yielded the best SNR-values corrected for the acquisition time compared to RS3-EPI and RS7-EPI. The image quality scores of RS-5 EPI were superior to those of RS-3 and RS-7 EPI (P = 0.01–0.014) and lower artifacts of the ventral/dorsal margin and femur (P = 0.008–0.016) were shown. DTT analysis yielded a significantly higher number of tracts for RS5-EPI compared to RS3-EPI (P = 0.007) but no significant difference with RS7-EPI (P = 0.071). For the three sequences, FA and RD were well-correlated with the myelin-related histopathological parameters (|r| 0.709–0.965, P = 0.001–0.049). The overall correlation coefficients of FA and RD obtained from RS-5 EPI were numerically higher than that with both RS3-EPI and RS7-EPI.ConclusionFor the rat sciatic nerve DTI imaging, RS-5 EPI offered the best image quality and SNR-values corrected for the acquisition time. The FA and RD derived from the RS-5 EPI were the most sensitive quantitative biomarkers to detect rat sciatic nerve histopathological change.
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Affiliation(s)
- Yueyao Chen
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Zhongxian Pan
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Fanqi Meng
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Qian Xu
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Leyu Huang
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Xuejia Pu
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Xuewen Yu
- Department of Pathology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | | | - Hanqing Lyu
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
- *Correspondence: Hanqing Lyu,
| | - Xiaofeng Lin
- Department of Nuclear Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- Xiaofeng Lin,
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Caroli A. Diffusion-Weighted Magnetic Resonance Imaging: Clinical Potential and Applications. J Clin Med 2022; 11:3339. [PMID: 35743409 PMCID: PMC9224775 DOI: 10.3390/jcm11123339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 06/09/2022] [Indexed: 02/05/2023] Open
Abstract
Since its discovery in the 1980s [...].
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Affiliation(s)
- Anna Caroli
- Bioengineering Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 24020 Ranica, BG, Italy
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Boban J, Thurnher MM, Boban N, Law M, Jahanshad N, Nir TM, Lendak DF, Kozic D. Gradient Patterns of Age-Related Diffusivity Changes in Cerebral White Matter. Front Neurol 2022; 13:870909. [PMID: 35720102 PMCID: PMC9201287 DOI: 10.3389/fneur.2022.870909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 04/25/2022] [Indexed: 11/24/2022] Open
Abstract
The current concept of brain aging proposes three gradient patterns of changes in white matter that occur during healthy brain aging: antero-posterior, supero-inferior, and the myelodegeneration-retrogenesis (or the “last-in-first-out”) concept. The aim of this study was to correlate white matter diffusivity measures (fractional anisotropy-FA, mean diffusivity-MD, radial diffusivity-RD, and axial diffusivity-AD) in healthy volunteers with chronological age and education level, in order to potentially incorporate the findings with proposed patterns of physiological brain aging. The study was performed on 75 healthy participants of both sexes, with an average age of 37.32 ± 11.91 years underwent brain magnetic resonance imaging (MRI) with diffusion tensor imaging (DTI). DTI was performed using tract-based spatial statistics (TBSS), with the analysis of four parameters: FA, MD, RD, and AD. Skeletonized measures were averaged in 29 regions of interest in white matter. Correlations between age and DTI measures and between education-level and DTI measures were performed using Pearson's correlation test. To correct for multiple comparisons, we applied a Bonferroni correction to the p-values. Significance was set at p ≤ 0.001. A significant negative correlation of FA with age was observed in posterior thalamic radiation (PTR) (p< 0.001). A significant positive correlation between age and MD was observed in sagittal stratum (SS) (p< 0.001), between age and RD in PTR, SS, and retrolenticular internal capsule (p< 0.001), and between age and AD in the body of the corpus callosum (p< 0.001). There were no significant correlations of DTI parameters with educational level. According to our study, RD showed the richest correlations with age, out of all DTI metrics. FA, MD, and RD showed significant changes in the diffusivity of projection fibers, while AD presented diffusivity changes in the commissural fibers. The observed heterogeneity in diffusivity changes across the brain cannot be explained by a single aging gradient pattern, since it seems that different patterns of degradation are true for different fiber tracts that no currently available theory can globally explain age-related changes in the brain. Additional factors, such as the effect of somatosensory decline, should be included as one of the important covariables to the existing patterns.
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Affiliation(s)
- Jasmina Boban
- Faculty of Medicine Novi Sad, Department of Radiology, University of Novi Sad, Novi Sad, Serbia
- Vojvodina Institute of Oncology, Center for Diagnostic Imaging, Sremska Kamenica, Serbia
- *Correspondence: Jasmina Boban
| | - Majda M. Thurnher
- Department for Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - Nikola Boban
- Clinical Center of Vojvodina, Center for Radiology, Novi Sad, Serbia
| | - Meng Law
- Department for Neuroscience, The Alfred Centre, Central Clinical School, Monash University, Melbourne, VIC, United States
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Talia M. Nir
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Dajana F. Lendak
- Faculty of Medicine Novi Sad, Department of Infectious Diseases, University of Novi Sad, Novi Sad, Serbia
- Clinical Center of Vojvodina, Clinic for Infectious Diseases, Novi Sad, Serbia
| | - Dusko Kozic
- Faculty of Medicine Novi Sad, Department of Radiology, University of Novi Sad, Novi Sad, Serbia
- Vojvodina Institute of Oncology, Center for Diagnostic Imaging, Sremska Kamenica, Serbia
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Chen Y, Pan Z, Meng F, Li Z, Hu Y, Yu X, Gao J, Guo Y, Lyu H, Lin X. Performance of Single-Shot Echo-Planar Imaging in Diffusion Tensor Imaging in Rat Sciatic Nerve Compared With Readout-Segmented Echo-Planar Imaging. Front Neurosci 2022; 16:844408. [PMID: 35645709 PMCID: PMC9133884 DOI: 10.3389/fnins.2022.844408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 03/29/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives To compare the performances of single-shot echo-planar imaging (SS–EPI) and readout-segmented echo-planar imaging (RS–EPI) for diffusion tensor imaging (DTI) of the rat sciatic nerve. Methods Eight healthy adult male Sprague-Dawley rats were anesthetized and scanned with a 3T MRI scanner using SS–EPI and RS–EPI DTI sequences. The image quality in terms of the morphology of the nerve, distortions of the nearby femur, muscles, and homogeneity of neuromuscular were evaluated and scored. The correlations between the DTI parameters including fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), apparent diffusion coefficient (ADC), and histopathological parameters were calculated by using the Pearson correlation coefficient and compared by the modified Fisher Z-transform, respectively. Results The quality scores were higher for the images from the SS–EPI sequence compared with the RS–EPI sequence for characteristics such as sharpness of the sciatic nerve margin (P = 0.008), artifacts of the sciatic nerve (P = 0.008), and homogeneity of the neuromuscular region (P = 0.007), as well as the contrast-to-noise ratio (CNR) of DW images (P < 0.001). The correlation coefficients were higher for the FA and RD values from the SS–EPI sequence compared with those from the RS–EPI sequence. Furthermore, the correlation coefficients between FA and myelin thickness (P = 0.027), FA and diameter of the myelinated fiber (P = 0.036), as well as RD and myelin thickness (P = 0.05) were statistically higher for the SS–EPI sequence compared with those for the RS–EPI sequence. Conclusion Diffusion tensor imaging analysis of the rat sciatic nerve showed that the image quality from the SS–EPI sequence was significantly higher compared with that from the RS–EPI sequence. Furthermore, the FA and RD derived from the SS–EPI sequence are promising and sensitive biomarkers to detect the histopathological changes in the rat sciatic nerve.
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Affiliation(s)
- Yueyao Chen
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Zhongxian Pan
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Fanqi Meng
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Zhujing Li
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Yuanming Hu
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Xuewen Yu
- Department of Pathology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Jinyun Gao
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Yihao Guo
- Magnetic Resonance Collaboration, Siemens Healthcare, Guangzhou, China
| | - Hanqing Lyu
- Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
- *Correspondence: Hanqing Lyu,
| | - Xiaofeng Lin
- Department of Nuclear Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- Xiaofeng Lin,
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Liu P, Luo B, Zhai LH, Wu HY, Wang QX, Yuan G, Jiang GH, Chen L, Zhang J. Multi-Parametric Diffusion Tensor Imaging of The Optic Nerve for Detection of Dysthyroid Optic Neuropathy in Patients With Thyroid-Associated Ophthalmopathy. Front Endocrinol (Lausanne) 2022; 13:851143. [PMID: 35592782 PMCID: PMC9110867 DOI: 10.3389/fendo.2022.851143] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 03/21/2022] [Indexed: 12/13/2022] Open
Abstract
Objective To evaluate the microstructural changes of the orbital optic nerve in thyroid-associated ophthalmopathy (TAO) patients with or without dysthyroid optic neuropathy (DON) using diffusion tensor imaging (DTI) and investigate whether DTI can be used to detect DON. Materials and Methods 59 bilateral TAO patients with (n= 23) and without DON (non-DON, n= 36) who underwent pretreatment DTI were included and 118 orbits were analyzed. The clinical features of all patients were collected. DTI parameters, including mean, axial, and radial diffusivity (MD, AD, and RD, respectively) and fractional anisotropy (FA) of the intra-orbital optic nerve for each orbit were calculated and compared between the DON and non-DON groups. ROC curves were generated to evaluate the diagnostic performance of single or combined DTI parameters. Correlations between DTI parameters and ophthalmological characteristics were analyzed using correlation analysis. Results Compared with non-DON, the DON group showed decreased FA and increased MD, RD, and AD (P < 0.01). In the differentiation of DON from non-DON, the MD was optimal individually, and the combination of the four parameters had the best diagnostic performance. There were significant correlations between the optic nerve's four DTI metrics and the visual acuity and clinical active score (P < 0.05). In addition, optic nerve FA was significantly associated with the amplitude of visual evoked potentials (P = 0.022). Conclusions DTI is a promising technique in assessing microstructural changes of optic nerve in patients with DON, and it facilitates differentiation of DON from non-DON eyes in patients with TAO.
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Affiliation(s)
- Ping Liu
- Department of Radiology, The Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Ban Luo
- Department of Ophthalmology, The Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lin-han Zhai
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Hong-Yu Wu
- Department of Radiology, The Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiu-Xia Wang
- Department of Radiology, The Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Gang Yuan
- Department of Endocrinology and Metabolism, The Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Gui-Hua Jiang
- Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Lang Chen
- Department of Radiology, The Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jing Zhang
- Department of Radiology, The Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Lao Y, Ruan D, Vassantachart A, Fan Z, Ye JC, Chang EL, Chin R, Kaprealian T, Zada G, Shiroishi MS, Sheng K, Yang W. Voxelwise Prediction of Recurrent High-Grade Glioma via Proximity Estimation-Coupled Multidimensional Support Vector Machine. Int J Radiat Oncol Biol Phys 2022; 112:1279-1287. [PMID: 34963559 PMCID: PMC8923952 DOI: 10.1016/j.ijrobp.2021.12.153] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 12/09/2021] [Accepted: 12/16/2021] [Indexed: 01/28/2023]
Abstract
PURPOSE To provide early and localized glioblastoma (GBM) recurrence prediction, we introduce a novel postsurgery multiparametric magnetic resonance-based support vector machine (SVM) method coupling with stem cell niche (SCN) proximity estimation. METHODS AND MATERIALS This study used postsurgery magnetic resonance imaging (MRI) scans from 50 patients with recurrent GBM, obtained approximately 2 months before clinically diagnosed recurrence. The main prediction pipeline consisted of a proximity-based estimator to identify regions with high risk of recurrence (HRRs) and an SVM classifier to provide voxelwise prediction in HRRs. The HRRs were estimated using the weighted sum of inverse distances to 2 possible origins of recurrence-the SCN and the tumor cavity. Subsequently, multiparametric voxels (from T1, T1 contrast-enhanced, fluid-attenuated inversion recovery, T2, and apparent diffusion coefficient) within the HRR were grouped into recurrent (warped from the clinical diagnosis) and nonrecurrent subregions and fed into the proximity estimation-coupled SVM classifier (SVMPE). The cohort was randomly divided into 40% and 60% for training and testing, respectively. The trained SVMPE was then extrapolated to an earlier time point for earlier recurrence prediction. As an exploratory analysis, the SVMPE predictive cluster sizes and the image intensities from the 5 magnetic resonance sequences were compared across time to assess the progressive subclinical traces. RESULTS On 2-month prerecurrence MRI scans from 30 test cohort patients, the SVMPE classifier achieved a recall of 0.80, a precision of 0.69, an F1-score of 0.73, and a mean boundary distance of 7.49 mm. Exploratory analysis at early time points showed spatially consistent but significantly smaller subclinical clusters and significantly increased T1 contrast-enhanced and apparent diffusion coefficient values over time. CONCLUSIONS We demonstrated a novel voxelwise early prediction method, SVMPE, for GBM recurrence based on clinical follow-up MR scans. The SVMPE is promising in localizing subclinical traces of recurrence 2 months ahead of clinical diagnosis and may be used to guide more effective personalized early salvage therapy.
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Affiliation(s)
- Yi Lao
- Department of Radiation Oncology, University of California - Los Angeles, USA
| | - Dan Ruan
- Department of Radiation Oncology, University of California - Los Angeles, USA
| | - April Vassantachart
- Department of Radiation Oncology, Keck School of Medicine of USC, Los Angeles, USA
| | - Zhaoyang Fan
- Department of Radiology, Keck School of Medicine of USC, Los Angeles, USA
| | - Jason C. Ye
- Department of Radiation Oncology, Keck School of Medicine of USC, Los Angeles, USA
| | - Eric L. Chang
- Department of Radiation Oncology, Keck School of Medicine of USC, Los Angeles, USA
| | - Robert Chin
- Department of Radiation Oncology, University of California - Los Angeles, USA
| | - Tania Kaprealian
- Department of Radiation Oncology, University of California - Los Angeles, USA
| | - Gabriel Zada
- Department of Neurosurgery, Keck School of Medicine of USC, Los Angeles, USA
| | - Mark S Shiroishi
- Department of Radiology, Keck School of Medicine of USC, Los Angeles, USA
| | - Ke Sheng
- Department of Radiation Oncology, University of California - Los Angeles, USA
| | - Wensha Yang
- Department of Radiation Oncology, Keck School of Medicine of USC, Los Angeles, USA
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Mizutani T, Kotani T, Kato N, Imai K, Ushida T, Nakano-Kobayashi T, Kinoshita Y, Ito M, Kinoshita F, Yamamuro O, Kajiyama H. Assessment of placental abruption with diffusion-weighted imaging. J Obstet Gynaecol Res 2022; 48:930-937. [PMID: 35194877 DOI: 10.1111/jog.15183] [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: 09/21/2021] [Revised: 12/28/2021] [Accepted: 01/26/2022] [Indexed: 11/30/2022]
Abstract
AIM To investigate whether placental abruption without fetal distress could be assessed by apparent diffusion coefficient (ADC) values in magnetic resonance imaging (MRI). METHODS We conducted a retrospective case-control study at a single center. ADC values at the lesions of placental abruption in the abruption group (n = 8) were compared to those in the control group (n = 32). In the abruption group, ADC values at the sites of abruption were also compared to those at the nonabruption sites within the same placenta. RESULTS The ADC values in the placental area above the abruption site in the abruption group showed lower values than those in the control group when the slice containing the umbilical cord insertion site was set as the reference, and those values were compared in each corresponding slice. Compared with average ADC values, those above the abruption site in the abruption group were also significantly lower than those in the control group (p < 0.001). Furthermore, ADC values at the area above abruption were lower than those at the nonabruption area of all planes in the abruption group. CONCLUSIONS ADC values at the lesions above the placental abruption site were reduced compared to those in the normal placenta and those in the nonabruption area. Thus, it would be helpful to understand the pathophysiology of placental abruption in expectant management, although further investigations would be needed.
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Affiliation(s)
- Teruyuki Mizutani
- Department of Obstetrics and Gynaecology, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Department of Obstetrics and Gynaecology, Japanese Red Cross Aichi Medical Center Nagoya Daini Hospital, Nagoya, Japan
| | - Tomomi Kotani
- Department of Obstetrics and Gynaecology, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Center for Maternal-Neonatal Care, Nagoya University Hospital, Nagoya, Japan
| | - Noriko Kato
- Department of Obstetrics and Gynaecology, Japanese Red Cross Aichi Medical Center Nagoya Daini Hospital, Nagoya, Japan
| | - Kenji Imai
- Department of Obstetrics and Gynaecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Takafumi Ushida
- Department of Obstetrics and Gynaecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Tomoko Nakano-Kobayashi
- Department of Obstetrics and Gynaecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yoshimi Kinoshita
- Diagnostic Radiology, Japanese Red Cross Aichi Medical Center Nagoya Daini Hospital, Nagoya, Japan
| | - Masato Ito
- Diagnostic Radiology, Japanese Red Cross Aichi Medical Center Nagoya Daini Hospital, Nagoya, Japan
| | - Fumie Kinoshita
- Data Science Division, Data Coordinating Center, Department of Advanced Medicine, Nagoya University Hospital, Nagoya, Japan
| | - Osamu Yamamuro
- Department of Obstetrics and Gynaecology, Japanese Red Cross Aichi Medical Center Nagoya Daini Hospital, Nagoya, Japan
| | - Hiroaki Kajiyama
- Department of Obstetrics and Gynaecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
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Peralta C, Strafella AP, van Eimeren T, Ceravolo R, Seppi K, Kaasinen V, Arena JE, Lehericy S. Pragmatic Approach on Neuroimaging Techniques for the Differential Diagnosis of Parkinsonisms. Mov Disord Clin Pract 2022; 9:6-19. [PMID: 35005060 DOI: 10.1002/mdc3.13354] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 08/26/2021] [Accepted: 09/16/2021] [Indexed: 12/17/2022] Open
Abstract
Background Rapid advances in neuroimaging technologies in the exploration of the living human brain also apply to movement disorders. However, the accurate diagnosis of Parkinson's disease (PD) and atypical parkinsonian disorders (APDs) still remains a challenge in daily practice. Methods We review the literature and our own experience as the Movement Disorder Society-Neuroimaging Study Group in Movement Disorders with the aim of providing a practical approach to the use of imaging technologies in the clinical setting. Results The enormous amount of articles published so far and our increasing recognition of imaging technologies contrast with a lack of imaging protocols and updated algorithms for differential diagnosis. The distinctive pathological involvement in different brain structures and the correlation with imaging findings obtained with magnetic resonance, positron emission tomography, or single-photon emission computed tomography illustrate what qualitative and quantitative measures may be useful in the clinical setting. Conclusion We delineate a pragmatic approach to discuss imaging technologies, updated imaging algorithms, and their implications for differential diagnoses in PD and APDs.
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Affiliation(s)
- Cecilia Peralta
- Movement Disorders Clinic, Neuroscience Department Hospital Universitario CEMIC, Centro de Educación Médica e Investigaciones Clínicas "Norberto Quirno" Buenos Aires Argentina
| | - Antonio P Strafella
- Morton and Gloria Shulman Movement Disorder Unit & E.J. Safra Parkinson Disease Program, Division of Neurology/Department of Medicine, Toronto Western Hospital University Health Network Toronto Ontario Canada.,Krembil Brain Institute, University Health Network Toronto Ontario Canada.,Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health University of Toronto Toronto Ontario Canada
| | - Thilo van Eimeren
- Department of Nuclear Medicine University of Cologne Cologne Germany.,Department of Neurology University of Cologne Cologne Germany
| | - Roberto Ceravolo
- Department of Clinical and Experimental Medicine University of Pisa Pisa Italy
| | - Klaus Seppi
- Department of Neurology Medical University Innsbruck Innsbruck Austria
| | - Valtteri Kaasinen
- Clinical Neurosciences University of Turku and Turku University Hospital Turku Finland
| | - Julieta E Arena
- Movement Disorders Section, Department of Neurology, Fleni Buenos Aires Argentina
| | - Stephane Lehericy
- Institut du Cerveau-ICM, Team "Movement Investigations and Therapeutics," Centre de NeuroImagerie de Recherche-CENIR, Neuroradiology Department Paris France.,Sorbonne Université, INSERM U, Institut national de la santé et de la recherche médicale 1127, National Centre for Scientific Research, Unité mixte de recherche 7225 Paris France.,Department of Neuroradiology Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris Paris France
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Johansson J, Lagerstrand K, Ivarsson L, Svensson PA, Hebelka H, Maier SE. Brain diffusion MRI with multiplexed sensitivity encoding for reduced distortion in a pediatric patient population. Magn Reson Imaging 2022; 87:97-103. [PMID: 34999160 DOI: 10.1016/j.mri.2022.01.003] [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: 09/10/2021] [Revised: 12/30/2021] [Accepted: 01/04/2022] [Indexed: 10/19/2022]
Abstract
BACKGROUND Diffusion-weighted imaging (DWI) is a valuable tool for routine imaging of the pediatric brain. However, the commonly used single-shot (ss) echo-planar imaging (EPI) DWI sequence is prone to geometric distortions and T2*-blurring. This study aimed to investigate in a pediatric population the benefits of using multiplexed sensitivity-encoding (MUSE) without and with reversed polarity gradients (RPG) instead. METHODS This retrospective study compared image quality, geometric distortions, and diffusion values between three different approaches for DWI (ssEPI, MUSE, and RPG-MUSE) in 14 patients (median age = 4 (0.6-15) years, 11 males). Distortion levels were quantified and compared in two brain regions, i.e., the brain stem and the temporal lobes, using the Dice Coefficient and the Hausdorff Distance, with T2-weighted images as reference. Expected geometrical distortion was further evaluated by comparing the effective echo spacing between the DWI sequences. Apparent diffusion coefficient (ADC) values were determined in the genu of the corpus callosum and the optic nerves. Two raters graded overall image quality and image distortions on a Likert scale. RESULTS Distortion levels assessed with Dice coefficient and Hausdorff distance were significantly lower for MUSE (p < 0.05) and RPG-MUSE (p < 0.01) compared to ssEPI. No significant difference in ADC values was observed between methods. The RPG-MUSE method was graded by one rater as significantly higher in overall image quality than ssEPI (p < 0.05) and by both raters as significantly lower in levels of image distortions than both MUSE (p < 0.05) and ssEPI (p < 0.05). These results were in agreement with the reduced effective echo spacing was that was attained with MUSE and RPG-MUSE. CONCLUSION For imaging of the pediatric brain, MUSE and even more so RPG-MUSE offers both improved geometric fidelity and image quality compared to ssEPI.
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Affiliation(s)
- Jens Johansson
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
| | - Kerstin Lagerstrand
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Liz Ivarsson
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Pär-Arne Svensson
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Hanna Hebelka
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden; Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Stephan E Maier
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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