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Cao Y, Lu Y, Shao W, Zhai W, Song J, Zhang A, Huang S, Zhao X, Cheng W, Wu F, Chen T. Time-dependent diffusion MRI-based microstructural mapping for differentiating high-grade serous ovarian cancer from serous borderline ovarian tumor. Eur J Radiol 2024; 178:111622. [PMID: 39018648 DOI: 10.1016/j.ejrad.2024.111622] [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: 01/16/2024] [Revised: 06/24/2024] [Accepted: 07/11/2024] [Indexed: 07/19/2024]
Abstract
PURPOSE To investigate the value of microstructural characteristics derived from time-dependent diffusion MRI in distinguishing high-grade serous ovarian cancer (HGSOC) from serous borderline ovarian tumor (SBOT) and the associations of immunohistochemical markers with microstructural features. METHODS Totally 34 HGSOC and 12 SBOT cases who received preoperative pelvic MRI were retrospectively included in this study. Two radiologists delineated the tumors to obtain the regions of interest (ROIs). Time-dependent diffusion MRI signals were fitted by the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model, to extract microstructural parameters, including fraction of the intracellular component (fin), cell diameter (d), cellularity and extracellular diffusivity (Dex). Apparent diffusion coefficient (ADC) values were obtained from standard diffusion-weighted imaging (DWI). The parameters of HGSOCs and SBOTs were compared, and the diagnostic performance was evaluated. The associations of microstructural indexes with immunopathological parameters were assessed, including Ki-67, P53, Pax-8, ER and PR. RESULTS In this study, fin, cellularity, Dex and ADC had good diagnostic performance levels in differentiating HGSOC from SBOT, with AUCs of 0.936, 0.909, 0.902 and 0.914, respectively. There were no significant differences in diagnostic performance among these parameters. Spearman analysis revealed in the HGSOC group, cellularity had a significant positive correlation with P53 expression (P = 0.028, r = 0.389) and Dex had a significant positive correlation with Pax-8 expression (P = 0.018, r = 0.415). ICC showed excellent agreement for all parameters. CONCLUSION Time-dependent diffusion MRI had value in evaluating the microstructures of HGSOC and SBOT and could discriminate between these tumors.
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Affiliation(s)
- Yuwei Cao
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, China
| | - Yao Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, China
| | - Wenhui Shao
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, China
| | - Weiling Zhai
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, China
| | - Jiacheng Song
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, China
| | - Aining Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, China
| | - Shan Huang
- Philips (China) Investment Co. Ltd Building A1, No 718, Ling Shi Road, Jing'an District, Shanghai, China
| | - Xiance Zhao
- Philips (China) Investment Co. Ltd Building A1, No 718, Ling Shi Road, Jing'an District, Shanghai, China
| | - Wenjun Cheng
- Department of Obstetrics & Gynecology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, China
| | - Feiyun Wu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, China.
| | - Ting Chen
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, China.
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Shi D, Li S, Liu F, Jiang X, Wu L, Chen L, Zheng Q, Bao H, Guo H, Xu J. Comprehensive characterization of tumor therapeutic response with simultaneous mapping cell size, density, and transcytolemmal water exchange. ARXIV 2024:arXiv:2408.01918v1. [PMID: 39130198 PMCID: PMC11312621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Early assessment of tumor therapeutic response is an important topic in precision medicine to optimize personalized treatment regimens and reduce unnecessary toxicity, cost, and delay. Although diffusion MRI (dMRI) has shown potential to address this need, its predictive accuracy is limited, likely due to its unspecific sensitivity to overall pathological changes. In this work, we propose a new quantitative dMRI-based method dubbed EXCHANGE (MRI of water Exchange, Confined and Hindered diffusion under Arbitrary Gradient waveform Encodings) for simultaneous mapping of cell size, cell density, and transcytolemmal water exchange. Such rich microstructural information comprehensively evaluates tumor pathologies at the cellular level. Validations using numerical simulations and in vitro cell experiments confirmed that the EXCHANGE method can accurately estimate mean cell size, density, and water exchange rate constants. The results from in vivo animal experiments show the potential of EXCHANGE for monitoring tumor treatment response. Finally, the EXCHANGE method was implemented in breast cancer patients with neoadjuvant chemotherapy, demonstrating its feasibility in assessing tumor therapeutic response in clinics. In summary, a new, quantitative dMRI-based EXCHANGE method was proposed to comprehensively characterize tumor microstructural properties at the cellular level, suggesting a unique means to monitor tumor treatment response in clinical practice.
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Affiliation(s)
- Diwei Shi
- Center for Nano and Micro Mechanics, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China
| | - Sisi Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Fan Liu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Lei Wu
- Qinghai University Affiliated Hospital, Qinghai, Xining 810000, China
| | - Li Chen
- Center for Nano and Micro Mechanics, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China
| | - Quanshui Zheng
- Center for Nano and Micro Mechanics, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China
| | - Haihua Bao
- Qinghai University Affiliated Hospital, Qinghai, Xining 810000, China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Junzhong Xu
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
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Ejima F, Fukukura Y, Kamimura K, Nakajo M, Ayukawa T, Kanzaki F, Yanazume S, Kobayashi H, Kitazono I, Imai H, Feiweier T, Yoshiura T. Oscillating Gradient Diffusion-Weighted MRI for Risk Stratification of Uterine Endometrial Cancer. J Magn Reson Imaging 2024; 60:67-77. [PMID: 37886909 DOI: 10.1002/jmri.29106] [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: 03/08/2023] [Revised: 10/06/2023] [Accepted: 10/06/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Oscillating gradient diffusion-weighted imaging (DWI) enables elucidation of microstructural characteristics in cancers; however, there are limited data to evaluate its utility in patients with endometrial cancer. PURPOSE To investigate the utility of oscillating gradient DWI for risk stratification in patients with uterine endometrial cancer compared with conventional pulsed gradient DWI. STUDY TYPE Retrospective. SUBJECTS Sixty-three women (mean age: 58 [range: 32-85] years) with endometrial cancer. FIELD STRENGTH/SEQUENCE 3 T MRI including DWI using oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE) research sequences. ASSESSMENT Mean value of the apparent diffusion coefficient (ADC) values for OGSE (ADCOGSE) and PGSE (ADCPGSE) as well as the ADC ratio (ADCOGSE/ADCPGSE) within endometrial cancer were measured using regions of interest. Prognostic factors (histological grade, deep myometrial invasion, lymphovascular invasion, International Federation of Gynecology and Obstetrics [FIGO] stage, and prognostic risk classification) were tabulated. STATISTICAL TESTS Interobserver agreement was analyzed by calculating the intraclass correlation coefficient. The associations of ADCOGSE, ADCPGSE, and ADCOGSE/ADCPGSE with prognostic factors were examined using the Kendall rank correlation coefficient, Mann-Whitney U test, and receiver operating characteristic (ROC) curve. A P value of <0.05 was statistically significant. RESULTS Compared with ADCOGSE and ADCPGSE, ADCOGSE/ADCPGSE was significantly and strongly correlated with histological grade (observer 1, τ = 0.563; observer 2, τ = 0.456), FIGO stage (observer 1, τ = 0.354; observer 2, τ = 0.324), and prognostic risk classification (observer 1, τ = 0.456; observer 2, τ = 0.385). The area under the ROC curves of ADCOGSE/ADCPGSE for histological grade (observer 1, 0.92, 95% confidence intervals [CIs]: 0.83-0.98; observer 2, 0.84, 95% CI: 0.73-0.92) and prognostic risk (observer 1, 0.80, 95% CI: 0.68-0.89; observer 2, 0.76, 95% CI: 0.63-0.86) were significantly higher than that of ADCOGSE and ADCPGSE. DATA CONCLUSION The ADC ratio obtained via oscillating gradient and pulsed gradient DWIs might be useful imaging biomarkers for risk stratification in patients with endometrial cancer. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Fumitaka Ejima
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Yoshihiko Fukukura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Kiyohisa Kamimura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Masatoyo Nakajo
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Takuro Ayukawa
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Fumiko Kanzaki
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Shintaro Yanazume
- Department of Obstetrics and Gynecology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Hiroaki Kobayashi
- Department of Obstetrics and Gynecology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Ikumi Kitazono
- Department of Pathology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | | | | | - Takashi Yoshiura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
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Chatterjee A, Dwivedi DK. MRI-based virtual pathology of the prostate. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01163-w. [PMID: 38856839 DOI: 10.1007/s10334-024-01163-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
Prostate cancer poses significant diagnostic challenges, with conventional methods like prostate-specific antigen (PSA) screening and transrectal ultrasound (TRUS)-guided biopsies often leading to overdiagnosis or miss clinically significant cancers. Multiparametric MRI (mpMRI) has emerged as a more reliable tool. However, it is limited by high inter-observer variability and radiologists missing up to 30% of clinically significant cancers. This article summarizes a few of these recent advancements in quantitative MRI techniques that look at the "Virtual Pathology" of the prostate with an aim to enhance prostate cancer detection and characterization. These techniques include T2 relaxation-based techniques such as luminal water imaging, diffusion based such as vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) and restriction spectrum imaging or combined relaxation-diffusion techniques such as hybrid multi-dimensional MRI (HM-MRI), time-dependent diffusion imaging, and diffusion-relaxation correlation spectrum imaging. These methods provide detailed insights into underlying prostate microstructure and tissue composition and have shown improved diagnostic accuracy over conventional MRI. These innovative MRI methods hold potential for augmenting mpMRI, reducing variability in diagnosis, and paving the way for MRI as a 'virtual histology' tool in prostate cancer diagnosis. However, they require further validation in larger multi-center clinical settings and rigorous in-depth radiological-pathology correlation are needed for broader implementation.
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Affiliation(s)
- Aritrick Chatterjee
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL, 60637, USA.
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA.
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Hoffmann E, Masthoff M, Kunz WG, Seidensticker M, Bobe S, Gerwing M, Berdel WE, Schliemann C, Faber C, Wildgruber M. Multiparametric MRI for characterization of the tumour microenvironment. Nat Rev Clin Oncol 2024; 21:428-448. [PMID: 38641651 DOI: 10.1038/s41571-024-00891-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2024] [Indexed: 04/21/2024]
Abstract
Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.
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Affiliation(s)
- Emily Hoffmann
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Max Masthoff
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Wolfgang G Kunz
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Max Seidensticker
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Bobe
- Gerhard Domagk Institute of Pathology, University Hospital Münster, Münster, Germany
| | - Mirjam Gerwing
- Clinic of Radiology, University of Münster, Münster, Germany
| | | | | | - Cornelius Faber
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Moritz Wildgruber
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
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Lewis D, Zhu X, Jackson A. Editorial for "Improving Microstructural Estimation in Time-Dependent Diffusion MRI with a Bayesian Method". J Magn Reson Imaging 2024. [PMID: 38769811 DOI: 10.1002/jmri.29449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 02/26/2024] [Indexed: 05/22/2024] Open
Affiliation(s)
- Daniel Lewis
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, UK
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Xiaoping Zhu
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Alan Jackson
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
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Liu K, Lin Z, Zheng T, Ba R, Zhang Z, Li H, Zhang H, Tal A, Wu D. Improving Microstructural Estimation in Time-Dependent Diffusion MRI With a Bayesian Method. J Magn Reson Imaging 2024. [PMID: 38769739 DOI: 10.1002/jmri.29434] [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: 12/26/2023] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Accurately fitting diffusion-time-dependent diffusion MRI (td-dMRI) models poses challenges due to complex and nonlinear formulas, signal noise, and limited clinical data acquisition. PURPOSE Introduce a Bayesian methodology to refine microstructural fitting within the IMPULSED (Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion) model and optimize the prior distribution within the Bayesian framework. STUDY TYPE Retrospective. POPULATION Involving 69 pediatric patients (median age 6 years, interquartile range [IQR] 3-9 years, 61% male) with 41 low-grade and 28 high-grade gliomas, of which 76.8% were identified within the brainstem or cerebellum. FIELD STRENGTH/SEQUENCE 3 T, oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE). ASSESSMENT The Bayesian method's performance in fitting cell diameter (d $$ d $$ ), intracellular volume fraction (f in $$ {f}_{in} $$ ), and extracellular diffusion coefficient (D ex $$ {D}_{ex} $$ ) was compared against the NLLS method, considering simulated and experimental data. The tumor region-of-interest (ROI) were manually delineated on the b0 images. The diagnostic performance in distinguishing high- and low-grade gliomas was assessed, and fitting accuracy was validated against H&E-stained pathology. STATISTICAL TESTS T-test, receiver operating curve (ROC), area under the curve (AUC) and DeLong's test were conducted. Significance considered at P < 0.05. RESULTS Bayesian methodology manifested increased accuracy with robust estimates in simulation (RMSE decreased by 29.6%, 40.9%, 13.6%, and STD decreased by 29.2%, 43.5%, and 24.0%, respectively ford $$ d $$ ,f in $$ {f}_{in} $$ , andD ex $$ {D}_{ex} $$ compared to NLLS), indicating fewer outliers and reduced error. Diagnostic performance for tumor grade was similar in both methods, however, Bayesian method generated smoother microstructural maps (outliers ratio decreased by 45.3% ± 19.4%) and a marginal enhancement in correlation with H&E staining result (r = 0.721 forf in $$ {f}_{in} $$ compared to r = 0.698 using NLLS, P = 0.5764). DATA CONCLUSION The proposed Bayesian method substantially enhances the accuracy and robustness of IMPULSED model estimation, suggesting its potential clinical utility in characterizing cellular microstructure. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Kuiyuan Liu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Zixuan Lin
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Tianshu Zheng
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Ruicheng Ba
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Zelin Zhang
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Haotian Li
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Hongxi Zhang
- Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Dan Wu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
- Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, 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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Cao Y, Davarani SN, You D, Feiweier T, Casper K, Balis U, Udager A, Balter J, Mierzwa M. In Vivo Microstructure Imaging in Oropharyngeal Squamous Cell Carcinoma Using the Random Walk With Barriers Model. J Magn Reson Imaging 2024; 59:929-938. [PMID: 37366349 DOI: 10.1002/jmri.28831] [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: 02/16/2023] [Revised: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Apparent diffusion coefficient is not specifically sensitive to tumor microstructure and therapy-induced cellular changes. PURPOSE To investigate time-dependent diffusion imaging with the short-time-limit random walk with barriers model (STL-RWBM) for quantifying microstructure parameters and early cancer cellular response to therapy. STUDY TYPE Prospective. POPULATION Twenty-seven patients (median age of 58 years and 7.4% of females) with p16+/p16- oropharyngeal/oral cavity squamous cell carcinomas (OPSCC/OCSCC) underwent MRI scans before therapy, of which 16 patients had second scans at 2 weeks of the 7-weeks chemoradiation therapy (CRT). FIELD STRENGTH/SEQUENCE 3-T, diffusion sequence with oscillating gradient spine echo (OGSE) and pulse gradient spin echo (PGSE). ASSESSMENT Diffusion weighted images were acquired using OGSE and PGSE. Effective diffusion times were derived for the STL-RWBM to estimate free diffusion coefficient D0 , volume-to-surface area ratio of cellular membranes V/S, and cell membrane permeability κ. Mean values of these parameters were calculated in tumor volumes. STATISTICAL TESTS Tumor microstructure parameters were compared with clinical stages of p16+ I-II OPSCC, p16+ III OPSCC, and p16- IV OCSCC by Spearman's rank correlation and with digital pathological analysis of a resected tissue sample. Tumor microstructure parameter responses during CRT in the 16 patients were assessed by paired t-tests. A P-value of <0.05 was considered statistically significant. RESULTS The derived effective diffusion times affected estimated values of V/S and κ by 40%. The tumor V/S values were significantly correlated with clinical stages (r = 0.47) as an increase from low to high clinical stages. The in vivo estimated cell size agreed with one from pathological analysis of a tissue sample. Early tumor cellular responses showed a significant increase in D0 (14%, P = 0.03) and non-significant increases in κ (56%, P = 0.6) and V/S (10%, P = 0.1). DATA CONCLUSION Effective diffusion time estimation might impact microstructure parameter estimation. The tumor V/S was correlated with OPSCC/OCSCC clinical stages. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Daekeun You
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Keith Casper
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan, USA
| | - Ulysses Balis
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Aaron Udager
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - James Balter
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Michelle Mierzwa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
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Jiang X, McKinley ET, Xie J, Gore JC, Xu J. Detection of Treatment Response in Triple-Negative Breast Tumors to Paclitaxel Using MRI Cell Size Imaging. J Magn Reson Imaging 2024; 59:575-584. [PMID: 37218596 PMCID: PMC10665540 DOI: 10.1002/jmri.28774] [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: 12/20/2022] [Revised: 04/28/2023] [Accepted: 04/28/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND Breast cancer treatment response evaluation using the response evaluation criteria in solid tumors (RECIST) guidelines, based on tumor volume changes, has limitations, prompting interest in novel imaging markers for accurate therapeutic effect determination. PURPOSE To use MRI-measured cell size as a new imaging biomarker for assessing chemotherapy response in breast cancer. STUDY TYPE Longitudinal; animal model. STUDY POPULATION Triple-negative human breast cancer cell (MDA-MB-231) pellets (4 groups, n = 7) treated with dimethyl sulfoxide (DMSO) or 10 nM of paclitaxel for 24, 48, and 96 hours, and 29 mice with MDA-MB-231 tumors in right hind limbs treated with paclitaxel (n = 16) or DMSO (n = 13) twice weekly for 3 weeks. FIELD STRENGTH/SEQUENCE Oscillating gradient spin echo and pulsed gradient spin echo sequences at 4.7 T. ASSESSMENT MDA-MB-231 cells were analyzed using flowcytometry and light microscopy to assess cell cycle phases and cell size distribution. MDA-MB-231 cell pellets were MR imaged. Mice were imaged weekly, with 9, 6, and 14 being sacrificed for histology after MRI at weeks 1, 2, and 3, respectively. Microstructural parameters of tumors/cell pellets were derived by fitting diffusion MRI data to a biophysical model. STATISTICAL TESTS One-way ANOVA compared cell sizes and MR-derived parameters between treated and control samples. Repeated measures 2-way ANOVA with Bonferroni post-tests compared temporal changes in MR-derived parameters. A P-value <0.05 was considered statistically significant. RESULTS In vitro experiments showed that the mean MR-derived cell sizes of paclitaxel-treated cells increased significantly with a 24-hours treatment and decreased (P = 0.06) with a 96-hour treatment. For in vivo xenograft experiments, the paclitaxel-treated tumors showed significant decreases in cell size at later weeks. MRI observations were supported by flowcytometry, light microscopy, and histology. DATA CONCLUSIONS MR-derived cell size may characterize the cell shrinkage during treatment-induced apoptosis, and may potentially provide new insights into the assessment of therapeutic response. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 4.
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Affiliation(s)
- Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Eliot T. McKinley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Jingping Xie
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - John C. Gore
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
| | - Junzhong Xu
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
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11
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Fokkinga E, Hernandez-Tamames JA, Ianus A, Nilsson M, Tax CMW, Perez-Lopez R, Grussu F. Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology. J Magn Reson Imaging 2023. [PMID: 38032021 DOI: 10.1002/jmri.29144] [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: 08/11/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state-of-the-art of DW-MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW-MRI method is essential. In this article, we review the current landscape of DW-MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b-tensor diffusion encoding or joint diffusion-relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter-/intra-scanner robustness, and on assessing histological validity in broader contexts. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ella Fokkinga
- Biomedical Engineering, Track Medical Physics, Delft University of Technology, Delft, The Netherlands
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Juan A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Markus Nilsson
- Department of Diagnostic Radiology, Clinical Sciences Lund, Lund, Sweden
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Center (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
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12
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Fennessy FM, Maier SE. Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment. Eur J Radiol 2023; 167:111066. [PMID: 37651828 PMCID: PMC10623580 DOI: 10.1016/j.ejrad.2023.111066] [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: 08/19/2023] [Accepted: 08/24/2023] [Indexed: 09/02/2023]
Abstract
Diffusion-weighted imaging is a dependable method for detection of clinically significant prostate cancer. In prostate tissue, there are several compartments that can be distinguished from each other, based on different water diffusion decay signals observed. Alterations in cell architecture, such as a relative increase in tumor infiltration and decrease in stroma, will influence the observed diffusion signal in a voxel due to impeded random motion of water molecules. The amount of restricted diffusion can be assessed quantitatively by measuring the apparent diffusion coefficient (ADC) value. This is traditionally calculated using a monoexponential decay formula represented by the slope of a line produced between the logarithm of signal intensity decay plotted against selected b-values. However, the choice and number of b-values and their distribution, has a significant effect on the measured ADC values. There have been many models that attempt to use higher-order functions to better describe the observed diffusion signal decay, requiring an increased number and range of b-values. While ADC can probe heterogeneity on a macroscopic level, there is a need to optimize advanced diffusion techniques to better interrogate prostate tissue microstructure. This could be of benefit in clinical challenges such as identifying sparse tumors in normal prostate tissue or better defining tumor margins. This paper reviews the principles of diffusion MRI and novel higher order diffusion signal analysis techniques to improve the detection of prostate cancer.
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Affiliation(s)
- Fiona M Fennessy
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
| | - Stephan E Maier
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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13
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Su CQ, Wang BB, Tang WT, Tao C, Zhao P, Pan MH, Hong XN, Hu WT, Dai YM, Shi HB, Lu SS. Diffusion-relaxation correlation spectrum imaging for predicting tumor consistency and gross total resection in patients with pituitary adenomas: a preliminary study. Eur Radiol 2023; 33:6993-7002. [PMID: 37148353 DOI: 10.1007/s00330-023-09694-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 02/17/2023] [Accepted: 03/09/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVE To evaluate the ability of diffusion-relaxation correlation spectrum imaging (DR-CSI) to predict the consistency and extent of resection (EOR) of pituitary adenomas (PAs). METHODS Forty-four patients with PAs were prospectively enrolled. Tumor consistency was evaluated at surgery as either soft or hard, followed by histological assessment. In vivo DR-CSI was performed and spectra were segmented following to a peak-based strategy into four compartments, designated A (low ADC), B (mediate ADC, short T2), C (mediate ADC, long T2), and D (high ADC). The corresponding volume fractions ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]) along with the ADC and T2 values were calculated and assessed using univariable analysis for discrimination between hard and soft PAs. Predictors of EOR > 95% were analyzed using logistic regression model and receiver-operating-characteristic analysis. RESULTS Tumor consistency was classified as soft (n = 28) or hard (n = 16). Hard PAs presented higher [Formula: see text] (p = 0.001) and lower [Formula: see text] (p = 0.013) than soft PAs, while no significant difference was found in other parameters. [Formula: see text] significantly correlated with the level of collagen content (r = 0.448, p = 0.002). Knosp grade (odds ratio [OR], 0.299; 95% confidence interval [CI], 0.124-0.716; p = 0.007) and [Formula: see text] (OR, 0.834, per 1% increase; 95% CI, 0.731-0.951; p = 0.007) were independently associated with EOR > 95%. A prediction model based on these variables yielded an AUC of 0.934 (sensitivity, 90.9%; specificity, 90.9%), outperforming the Knosp grade alone (AUC, 0.785; p < 0.05). CONCLUSION DR-CSI may serve as a promising tool to predict the consistency and EOR of PAs. CLINICAL RELEVANCE STATEMENT DR-CSI provides an imaging dimension for characterizing tissue microstructure of PAs and may serve as a promising tool to predict the tumor consistency and extent of resection in patients with PAs. KEY POINTS • DR-CSI provides an imaging dimension for characterizing tissue microstructure of PAs by visualizing the volume fraction and corresponding spatial distribution of four compartments ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]). • [Formula: see text] correlated with the level of collagen content and may be the best DR-CSI parameter for discrimination between hard and soft PAs. • The combination of Knosp grade and [Formula: see text] achieved an AUC of 0.934 for predicting the total or near-total resection, outperforming the Knosp grade alone (AUC, 0.785).
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Affiliation(s)
- Chun-Qiu Su
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guang Zhou Road, Gulou District, Nanjing, 210029, Jiangsu Province, China
| | - Bin-Bin Wang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China
| | - Wen-Tian Tang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guang Zhou Road, Gulou District, Nanjing, 210029, Jiangsu Province, China
| | - Chao Tao
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China
| | - Peng Zhao
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China
| | - Min-Hong Pan
- Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China
| | - Xun-Ning Hong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guang Zhou Road, Gulou District, Nanjing, 210029, Jiangsu Province, China
| | - Wen-Tao Hu
- Central Research Institute, MR Collaboration, United Imaging Healthcare, Shanghai, China
| | - Yong-Ming Dai
- Central Research Institute, MR Collaboration, United Imaging Healthcare, Shanghai, China
- School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
| | - Hai-Bin Shi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guang Zhou Road, Gulou District, Nanjing, 210029, Jiangsu Province, China.
| | - Shan-Shan Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guang Zhou Road, Gulou District, Nanjing, 210029, Jiangsu Province, China.
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14
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Kamimura K, Kamimura Y, Nakano T, Hasegawa T, Nakajo M, Yamada C, Akune K, Ejima F, Ayukawa T, Ito S, Nagano H, Takumi K, Nakajo M, Uchida H, Tabata K, Iwanaga T, Imai H, Feiweier T, Yoshiura T. Differentiating brain metastasis from glioblastoma by time-dependent diffusion MRI. Cancer Imaging 2023; 23:75. [PMID: 37553578 PMCID: PMC10410879 DOI: 10.1186/s40644-023-00595-2] [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: 05/14/2023] [Accepted: 07/24/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND This study was designed to investigate the use of time-dependent diffusion magnetic resonance imaging (MRI) parameters in distinguishing between glioblastomas and brain metastases. METHODS A retrospective study was conducted involving 65 patients with glioblastomas and 27 patients with metastases using a diffusion-weighted imaging sequence with oscillating gradient spin-echo (OGSE, 50 Hz) and a conventional pulsed gradient spin-echo (PGSE, 0 Hz) sequence. In addition to apparent diffusion coefficient (ADC) maps from two sequences (ADC50Hz and ADC0Hz), we generated maps of the ADC change (cADC): ADC50Hz - ADC0Hz and the relative ADC change (rcADC): (ADC50Hz - ADC0Hz)/ ADC0Hz × 100 (%). RESULTS The mean and the fifth and 95th percentile values of each parameter in enhancing and peritumoral regions were compared between glioblastomas and metastases. The area under the receiver operating characteristic curve (AUC) values of the best discriminating indices were compared. In enhancing regions, none of the indices of ADC0Hz and ADC50Hz showed significant differences between metastases and glioblastomas. The mean cADC and rcADC values of metastases were significantly higher than those of glioblastomas (0.24 ± 0.12 × 10-3mm2/s vs. 0.14 ± 0.03 × 10-3mm2/s and 23.3 ± 9.4% vs. 14.0 ± 4.7%; all p < 0.01). In peritumoral regions, no significant difference in all ADC indices was observed between metastases and glioblastomas. The AUC values for the mean cADC (0.877) and rcADC (0.819) values in enhancing regions were significantly higher than those for ADC0Hz5th (0.595; all p < 0.001). CONCLUSIONS The time-dependent diffusion MRI parameters may be useful for differentiating brain metastases from glioblastomas.
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Affiliation(s)
- Kiyohisa Kamimura
- Department of Advanced Radiological Imaging, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
| | - Yoshiki Kamimura
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Tsubasa Nakano
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Tomohito Hasegawa
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Masanori Nakajo
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Chihiro Yamada
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Kentaro Akune
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Fumitaka Ejima
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takuro Ayukawa
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Soichiro Ito
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Hiroaki Nagano
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Koji Takumi
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Masatoyo Nakajo
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Hiroyuki Uchida
- Department of Neurosurgery, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Kazuhiro Tabata
- Department of Pathology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takashi Iwanaga
- Department of Radiological Technology, Kagoshima University Hospital, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Hiroshi Imai
- Siemens Healthcare K.K., Gate City Osaki West Tower, 1-11-1 Osaki, Shinagawa-Ku, Tokyo, 141-8644, Japan
| | | | - Takashi Yoshiura
- Department of Advanced Radiological Imaging, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
- Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
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15
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Cao Y, Cuneo KC, Evans J, Ten Haken RK, Chang DT, Lawrence TS. Is Apparent Diffusion Coefficient Established as an Imaging Biomarker for Stereotactic Body Radiation Therapy Assessment in Hepatocellular Carcinoma? Cancer J 2023; 29:238-242. [PMID: 37471615 PMCID: PMC10372684 DOI: 10.1097/ppo.0000000000000668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
ABSTRACT In this article, as part of this special issue on biomarkers of early response, we review currently available reports regarding magnetic resonance imaging apparent diffusion coefficient (ADC) changes in hepatocellular carcinoma (HCC) in response to stereotactic body radiation therapy. We compare diffusion image acquisition, ADC analysis, methods for HCC response assessment, and statistical methods for prediction of local tumor progression by ADC metrics. We discuss the pros and cons of these studies. Following detailed analyses of existing investigations, we cannot conclude that ADC is established as an imaging biomarker for stereotactic body radiation therapy assessment in HCC.
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Affiliation(s)
- Yue Cao
- From the Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
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16
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Zhang H, Liu K, Ba R, Zhang Z, Zhang Y, Chen Y, Gu W, Shen Z, Shu Q, Fu J, Wu D. Histological and molecular classifications of pediatric glioma with time-dependent diffusion MRI-based microstructural mapping. Neuro Oncol 2023; 25:1146-1156. [PMID: 36617263 PMCID: PMC10237431 DOI: 10.1093/neuonc/noad003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Gliomas are the most common type of central nervous system tumors in children, and the combination of histological and molecular classification is essential for prognosis and treatment. Here, we proposed a newly developed microstructural mapping technique based on diffusion-time-dependent diffusion MRI td-dMRI theory to quantify tumor cell properties and tested these microstructural markers in identifying histological grade and molecular alteration of H3K27. METHODS This prospective study included 69 pediatric glioma patients aged 6.14 ± 3.25 years old, who underwent td-dMRI with pulsed and oscillating gradient diffusion sequences on a 3T scanner. dMRI data acquired at varying tds were fitted into a 2-compartment microstructural model to obtain intracellular fraction (fin), cell diameter, cellularity, etc. Apparent diffusivity coefficient (ADC) and T1 and T2 relaxation times were also obtained. H&E stained histology was used to validate the estimated microstructural properties. RESULTS For histological classification of low- and high-grade pediatric gliomas, the cellularity index achieved the highest area under the receiver-operating-curve (AUC) of 0.911 among all markers, while ADC, T1, and T2 showed AUCs of 0.906, 0.885, and 0.886. For molecular classification of H3K27-altered glioma in 39 midline glioma patients, cell diameter showed the highest discriminant power with an AUC of 0.918, and the combination of cell diameter and extracellular diffusivity further improved AUC to 0.929. The td-dMRI estimated fin correlated well with the histological ground truth with r = 0.7. CONCLUSIONS The td-dMRI-based microstructural properties outperformed routine MRI measurements in diagnosing pediatric gliomas, and the different microstructural features showed complementary strength in histological and molecular classifications.
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Affiliation(s)
- Hongxi Zhang
- Department of Radiology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Kuiyuan Liu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Ruicheng Ba
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Zelin Zhang
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yi Zhang
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Ye Chen
- Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Weizhong Gu
- Department of Pathology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Zhipeng Shen
- Department of Neurosurgery, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Qiang Shu
- Department of Cardiology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Junfen Fu
- Department of Endocrinology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Dan Wu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
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17
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Xu J, Xie J, Semmineh NB, Devan SP, Jiang X, Gore JC. Diffusion time dependency of extracellular diffusion. Magn Reson Med 2023; 89:2432-2440. [PMID: 36740894 PMCID: PMC10392121 DOI: 10.1002/mrm.29594] [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: 04/24/2022] [Revised: 12/10/2022] [Accepted: 01/09/2023] [Indexed: 02/07/2023]
Abstract
PURPOSE To quantify the variations of the power-law dependences on diffusion time t or gradient frequencyf $$ f $$ of extracellular water diffusion measured by diffusion MRI (dMRI). METHODS Model cellular systems containing only extracellular water were used to investigate thet / f $$ t/f $$ dependence ofD ex $$ {D}_{ex} $$ , the extracellular diffusion coefficient. Computer simulations used a randomly packed tissue model with realistic intracellular volume fractions and cell sizes. DMRI measurements were performed on samples consisting of liposomes containing heavy water(D2 O, deuterium oxide) dispersed in regular water (H2 O).D ex $$ {D}_{ex} $$ was obtained over a broadt $$ t $$ range (∼1-1000 ms) and then fit power-law equationsD ex ( t ) = D const + const · t - ϑ t $$ {D}_{ex}(t)={D}_{\mathrm{const}}+\mathrm{const}\cdotp {t}^{-{\vartheta}_t} $$ andD ex ( f ) = D const + const · f ϑ f $$ {D}_{ex}(f)={D}_{\mathrm{const}}+\mathrm{const}\cdotp {f}^{\vartheta_f} $$ . RESULTS Both simulated and experimental results suggest that no single power-law adequately describes the behavior ofD ex $$ {D}_{ex} $$ over the range of diffusion times of most interest in practical dMRI. Previous theoretical predictions are accurate over only limitedt $$ t $$ ranges; for example,θ t = θ f = - 1 2 $$ {\theta}_t={\theta}_f=-\frac{1}{2} $$ is valid only for short times, whereasθ t = 1 $$ {\theta}_t=1 $$ orθ f = 3 2 $$ {\theta}_f=\frac{3}{2} $$ is valid only for long times but cannot describe other ranges simultaneously. For the specifict $$ t $$ range of 5-70 ms used in typical human dMRI measurements,θ t = θ f = 1 $$ {\theta}_t={\theta}_f=1 $$ matches the data well empirically. CONCLUSION The optimal power-law fit of extracellular diffusion varies with diffusion time. The dependency obtained at short or longt $$ t $$ limits cannot be applied to typical dMRI measurements in human cancer or liver. It is essential to determine the appropriate diffusion time range when modeling extracellular diffusion in dMRI-based quantitative microstructural imaging.
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Affiliation(s)
- Junzhong Xu
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee
| | - Jingping Xie
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Sean P. Devan
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - John C. Gore
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee
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18
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Figini M, Castellano A, Bailo M, Callea M, Cadioli M, Bouyagoub S, Palombo M, Pieri V, Mortini P, Falini A, Alexander DC, Cercignani M, Panagiotaki E. Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology. Cancers (Basel) 2023; 15:2490. [PMID: 37173965 PMCID: PMC10177485 DOI: 10.3390/cancers15092490] [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: 02/27/2023] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
The aim of this work was to extend the VERDICT-MRI framework for modelling brain tumours, enabling comprehensive characterisation of both intra- and peritumoural areas with a particular focus on cellular and vascular features. Diffusion MRI data were acquired with multiple b-values (ranging from 50 to 3500 s/mm2), diffusion times, and echo times in 21 patients with brain tumours of different types and with a wide range of cellular and vascular features. We fitted a selection of diffusion models that resulted from the combination of different types of intracellular, extracellular, and vascular compartments to the signal. We compared the models using criteria for parsimony while aiming at good characterisation of all of the key histological brain tumour components. Finally, we evaluated the parameters of the best-performing model in the differentiation of tumour histotypes, using ADC (Apparent Diffusion Coefficient) as a clinical standard reference, and compared them to histopathology and relevant perfusion MRI metrics. The best-performing model for VERDICT in brain tumours was a three-compartment model accounting for anisotropically hindered and isotropically restricted diffusion and isotropic pseudo-diffusion. VERDICT metrics were compatible with the histological appearance of low-grade gliomas and metastases and reflected differences found by histopathology between multiple biopsy samples within tumours. The comparison between histotypes showed that both the intracellular and vascular fractions tended to be higher in tumours with high cellularity (glioblastoma and metastasis), and quantitative analysis showed a trend toward higher values of the intracellular fraction (fic) within the tumour core with increasing glioma grade. We also observed a trend towards a higher free water fraction in vasogenic oedemas around metastases compared to infiltrative oedemas around glioblastomas and WHO 3 gliomas as well as the periphery of low-grade gliomas. In conclusion, we developed and evaluated a multi-compartment diffusion MRI model for brain tumours based on the VERDICT framework, which showed agreement between non-invasive microstructural estimates and histology and encouraging trends for the differentiation of tumour types and sub-regions.
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Affiliation(s)
- Matteo Figini
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Michele Bailo
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Marcella Callea
- Pathology Unit, IRCCS Ospedale San Raffaele, 20132 Milan, Italy
| | | | - Samira Bouyagoub
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton BN1 9RR, UK
| | - Marco Palombo
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
| | - Valentina Pieri
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Pietro Mortini
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, IRCCS Ospedale San Raffaele, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Daniel C. Alexander
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Mara Cercignani
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton BN1 9RR, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1V 6LJ, UK
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19
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Hoffmann E, Gerwing M, Niland S, Niehoff R, Masthoff M, Geyer C, Wachsmuth L, Wilken E, Höltke C, Heindel WL, Hoerr V, Schinner R, Berger P, Vogl T, Eble JA, Maus B, Helfen A, Wildgruber M, Faber C. Profiling specific cell populations within the inflammatory tumor microenvironment by oscillating-gradient diffusion-weighted MRI. J Immunother Cancer 2023; 11:jitc-2022-006092. [PMID: 36918222 PMCID: PMC10016257 DOI: 10.1136/jitc-2022-006092] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND The inflammatory tumor microenvironment (TME) is formed by various immune cells, being closely associated with tumorigenesis. Especially, the interaction between tumor-infiltrating T-cells and macrophages has a crucial impact on tumor progression and metastatic spread. The purpose of this study was to investigate whether oscillating-gradient diffusion-weighted MRI (OGSE-DWI) enables a cell size-based discrimination between different cell populations of the TME. METHODS Sine-shaped OGSE-DWI was combined with the Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion (IMPULSED) approach to measure microscale diffusion distances, here relating to cell sizes. The accuracy of IMPULSED-derived cell radii was evaluated using in vitro spheroid models, consisting of either pure cancer cells, macrophages, or T-cells. Subsequently, in vivo experiments aimed to assess changes within the TME and its specific immune cell composition in syngeneic murine breast cancer models with divergent degrees of malignancy (4T1, 67NR) during tumor progression, clodronate liposome-mediated depletion of macrophages, and immune checkpoint inhibitor (ICI) treatment. Ex vivo analysis of IMPULSED-derived cell radii was conducted by immunohistochemical wheat germ agglutinin staining of cell membranes, while intratumoral immune cell composition was analyzed by CD3 and F4/80 co-staining. RESULTS OGSE-DWI detected mean cell radii of 8.8±1.3 µm for 4T1, 8.2±1.4 µm for 67NR, 13.0±1.7 for macrophage, and 3.8±1.8 µm for T-cell spheroids. While T-cell infiltration during progression of 4T1 tumors was observed by decreasing mean cell radii from 9.7±1.0 to 5.0±1.5 µm, increasing amount of intratumoral macrophages during progression of 67NR tumors resulted in increasing mean cell radii from 8.9±1.2 to 12.5±1.1 µm. After macrophage depletion, mean cell radii decreased from 6.3±1.7 to 4.4±0.5 µm. T-cell infiltration after ICI treatment was captured by decreasing mean cell radii in both tumor models, with more pronounced effects in the 67NR tumor model. CONCLUSIONS OGSE-DWI provides a versatile tool for non-invasive profiling of the inflammatory TME by assessing the dominating cell type T-cells or macrophages.
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Affiliation(s)
- Emily Hoffmann
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Mirjam Gerwing
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Stephan Niland
- Institute of Physiological Chemistry and Pathobiochemistry, University of Münster, Münster, Germany
| | - Rolf Niehoff
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Max Masthoff
- Clinic of Radiology, University of Münster, Münster, Germany
| | | | - Lydia Wachsmuth
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Enrica Wilken
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Carsten Höltke
- Clinic of Radiology, University of Münster, Münster, Germany
| | | | - Verena Hoerr
- Clinic of Radiology, University of Münster, Münster, Germany.,Department of Internal Medicine II, Heart Center Bonn, University Hospital Bonn, Bonn, Germany
| | - Regina Schinner
- Department of Radiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Philipp Berger
- Institute of Immunology, University of Münster, Münster, Germany
| | - Thomas Vogl
- Institute of Immunology, University of Münster, Münster, Germany
| | - Johannes A Eble
- Institute of Physiological Chemistry and Pathobiochemistry, University of Münster, Münster, Germany
| | - Bastian Maus
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Anne Helfen
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Moritz Wildgruber
- Clinic of Radiology, University of Münster, Münster, Germany.,Department of Radiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Cornelius Faber
- Clinic of Radiology, University of Münster, Münster, Germany
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20
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Choi BK, Katoch N, Park JA, Kim JW, Oh TI, Kim HJ, Woo EJ. Measurement of extracellular volume fraction using magnetic resonance-based conductivity tensor imaging. Front Physiol 2023. [DOI: 10.3389/fphys.2023.132911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023] Open
Abstract
Conductivity tensor imaging (CTI) using MRI is an advanced method that can non-invasively measure the electrical properties of living tissues. The contrast of CTI is based on underlying hypothesis about the proportionality between the mobility and diffusivity of ions and water molecules inside tissues. The experimental validation of CTI in both in vitro and in vivo settings is required as a reliable tool to assess tissue conditions. The changes in extracellular space can be indicators for disease progression, such as fibrosis, edema, and cell swelling. In this study, we conducted a phantom imaging experiment to test the feasibility of CTI for measuring the extracellular volume fraction in biological tissue. To mimic tissue conditions with different extracellular volume fractions, four chambers of giant vesicle suspension (GVS) with different vesicle densities were included in the phantom. The reconstructed CTI images of the phantom were compared with the separately-measured conductivity spectra of the four chambers using an impedance analyzer. Moreover, the values of the estimated extracellular volume fraction in each chamber were compared with those measured by a spectrophotometer. As the vesicle density increased, we found that the extracellular volume fraction, extracellular diffusion coefficient, and low-frequency conductivity decreased, while the intracellular diffusion coefficient slightly increased. On the other hand, the high-frequency conductivity could not clearly distinguish the four chambers. The extracellular volume fraction measured by the spectrophotometer and CTI method in each chamber were quite comparable, i.e., (1.00, 0.98 ± 0.01), (0.59, 0.63 ± 0.02), (0.40, 0.40 ± 0.05), and (0.16, 0.18 ± 0.02). The prominent factor influencing the low-frequency conductivity at different GVS densities was the extracellular volume fraction. Further studies are needed to validate the CTI method as a tool to measure the extracellular volume fractions in living tissues with different intracellular and extracellular compartments.
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21
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Li H, Zu T, Hsu YC, Zhao Z, Liu R, Zheng T, Li Q, Sun Y, Liu D, Zhang J, Zhang Y, Wu D. Inversion-Recovery-Prepared Oscillating Gradient Sequence Improves Diffusion-Time Dependency Measurements in the Human Brain. J Magn Reson Imaging 2023; 57:446-453. [PMID: 35723048 DOI: 10.1002/jmri.28311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 06/06/2022] [Accepted: 06/06/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Oscillating gradient diffusion MRI (dMRI) enables measurements at a short diffusion-time (td ), but it is challenging for clinical systems. Particularly, the low b-value and low resolution may give rise to cerebrospinal fluid (CSF) contamination. PURPOSE To assess the effect of CSF partial volume on td -dMRI measurements and efficacy of inversion-recovery (IR) prepared oscillating and pulsed gradient dMRI sequence to improve td -dMRI measurements in the human brain. STUDY TYPE Prospective. SUBJECTS Ten normal volunteers and six glioma patients. FIELD STRENGTH/SEQUENCE A 3 T; three-dimensional (3D) IR-prepared oscillating gradient-prepared gradient spin-echo (GRASE) and two-dimensional (2D) IR-prepared oscillating gradient echo-planar imaging (EPI) sequences. ASSESSMENT We assessed the td -dependent patterns of apparent diffusion coefficient (ADC) in several gray and white matter structures, including the hippocampal subfields (head, body, and tail), cortical gray matter, thalamus, and posterior white matter in normal volunteers. Pulsed gradient (0 Hz) and oscillating gradients at frequencies of 20 Hz, 40 Hz, and 60 Hz dMRI were acquired with GRASE and EPI sequences with or without the IR module. We also tested the td -dependency patterns in glioma patients using the EPI sequence with or without the IR module. STATISTICAL TESTS The differences in ADC across the different td s were compared by one-way ANOVA followed by post hoc pairwise t-tests with Bonferroni correction. RESULTS In the healthy subjects, brain regions that were possibly contaminated by CSF signals, such as the hippocampus (head, body, and tail) and cortical gray matter, td -dependent ADC changes were only significant with the IR-prepared 2D and 3D sequences but not with the non-IR sequences. In brain glioblastomas patients, significantly higher td -dependence was observed in the tumor region with the IR module than that without IR (slope = 0.0196 μm2 /msec2 vs. 0.0034 μm2 /msec2 ). CONCLUSION The IR-prepared sequence effectively suppressed the CSF partial volume effect and significantly improved the td -dependent measurements in the human brain. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Haotian Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tao Zu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi-Cheng Hsu
- MR Collaboration, Siemens Healthcare China, Shanghai, China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ruibin Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tianshu Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qing Li
- MR Collaboration, Siemens Healthcare China, Shanghai, China
| | - Yi Sun
- MR Collaboration, Siemens Healthcare China, Shanghai, China
| | - Daihong Liu
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
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22
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Springer CS, Baker EM, Li X, Moloney B, Pike MM, Wilson GJ, Anderson VC, Sammi MK, Garzotto MG, Kopp RP, Coakley FV, Rooney WD, Maki JH. Metabolic activity diffusion imaging (MADI): II. Noninvasive, high-resolution human brain mapping of sodium pump flux and cell metrics. NMR IN BIOMEDICINE 2023; 36:e4782. [PMID: 35654761 DOI: 10.1002/nbm.4782] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
We introduce a new 1 H2 O magnetic resonance approach: metabolic activity diffusion imaging (MADI). Numerical diffusion-weighted imaging decay simulations characterized by the mean cellular water efflux (unidirectional) rate constant (kio ), mean cell volume (V), and cell number density (ρ) are produced from Monte Carlo random walks in virtual stochastically sized/shaped cell ensembles. Because of active steady-state trans-membrane water cycling (AWC), kio reflects the cytolemmal Na+ , K+ ATPase (NKA) homeostatic cellular metabolic rate (c MRNKA ). A digital 3D "library" contains thousands of simulated single diffusion-encoded (SDE) decays. Library entries match well with disparate, animal, and human experimental SDE decays. The V and ρ values are consistent with estimates from pertinent in vitro cytometric and ex vivo histopathological literature: in vivo V and ρ values were previously unavailable. The library allows noniterative pixel-by-pixel experimental SDE decay library matchings that can be used to advantage. They yield proof-of-concept MADI parametric mappings of the awake, resting human brain. These reflect the tissue morphology seen in conventional MRI. While V is larger in gray matter (GM) than in white matter (WM), the reverse is true for ρ. Many brain structures have kio values too large for current, invasive methods. For example, the median WM kio is 22s-1 ; likely reflecting mostly exchange within myelin. The kio •V product map displays brain tissue c MRNKA variation. The GM activity correlates, quantitatively and qualitatively, with the analogous resting-state brain 18 FDG-PET tissue glucose consumption rate (t MRglucose ) map; but noninvasively, with higher spatial resolution, and no pharmacokinetic requirement. The cortex, thalamus, putamen, and caudate exhibit elevated metabolic activity. MADI accuracy and precision are assessed. The results are contextualized with literature overall homeostatic brain glucose consumption and ATP production/consumption measures. The MADI/PET results suggest different GM and WM metabolic pathways. Preliminary human prostate results are also presented.
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Affiliation(s)
- Charles S Springer
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, Oregon, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Department of Chemical Physiology and Biochemistry, Oregon Health & Science University, Portland, Oregon, USA
| | - Eric M Baker
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, Oregon, USA
| | - Brendan Moloney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Martin M Pike
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
| | - Gregory J Wilson
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Valerie C Anderson
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Manoj K Sammi
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Mark G Garzotto
- Department of Urology, Portland VA Center, Portland, Oregon, USA
- Department of Urology, Oregon Health & Science University, Portland, Oregon, USA
| | - Ryan P Kopp
- Department of Urology, Portland VA Center, Portland, Oregon, USA
- Department of Urology, Oregon Health & Science University, Portland, Oregon, USA
| | - Fergus V Coakley
- Department of Radiology, Oregon Health & Science University, Portland, Oregon, USA
| | - William D Rooney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA
| | - Jeffrey H Maki
- Department of Radiology, Anschutz Medical Center, University of Colorado, Aurora, Colorado, USA
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23
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Springer CS, Baker EM, Li X, Moloney B, Wilson GJ, Pike MM, Barbara TM, Rooney WD, Maki JH. Metabolic activity diffusion imaging (MADI): I. Metabolic, cytometric modeling and simulations. NMR IN BIOMEDICINE 2023; 36:e4781. [PMID: 35654608 DOI: 10.1002/nbm.4781] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
Evidence mounts that the steady-state cellular water efflux (unidirectional) first-order rate constant (kio [s-1 ]) magnitude reflects the ongoing, cellular metabolic rate of the cytolemmal Na+ , K+ -ATPase (NKA), c MRNKA (pmol [ATP consumed by NKA]/s/cell), perhaps biology's most vital enzyme. Optimal 1 H2 O MR kio determinations require paramagnetic contrast agents (CAs) in model systems. However, results suggest that the homeostatic metabolic kio biomarker magnitude in vivo is often too large to be reached with allowable or possible CA living tissue distributions. Thus, we seek a noninvasive (CA-free) method to determine kio in vivo. Because membrane water permeability has long been considered important in tissue water diffusion, we turn to the well-known diffusion-weighted MRI (DWI) modality. To analyze the diffusion tensor magnitude, we use a parsimoniously primitive model featuring Monte Carlo simulations of water diffusion in virtual ensembles comprising water-filled and -immersed randomly sized/shaped contracted Voronoi cells. We find this requires two additional, cytometric properties: the mean cell volume (V [pL]) and the cell number density (ρ [cells/μL]), important biomarkers in their own right. We call this approach metabolic activity diffusion imaging (MADI). We simulate water molecule displacements and transverse MR signal decays covering the entirety of b-space from pure water (ρ = V = 0; kio undefined; diffusion coefficient, D0 ) to zero diffusion. The MADI model confirms that, in compartmented spaces with semipermeable boundaries, diffusion cannot be described as Gaussian: the nanoscopic D (Dn ) is diffusion time-dependent, a manifestation of the "diffusion dispersion". When the "well-mixed" (steady-state) condition is reached, diffusion becomes limited, mainly by the probabilities of (1) encountering (ρ, V), and (2) permeating (kio ) cytoplasmic membranes, and less so by Dn magnitudes. Importantly, for spaces with large area/volume (A/V; claustrophobia) ratios, this can happen in less than a millisecond. The model matches literature experimental data well, with implications for DWI interpretations.
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Affiliation(s)
- Charles S Springer
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Department of Chemical Physiology and Biochemistry, Oregon Health & Science University, Portland, Oregon, USA
| | - Eric M Baker
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, Oregon, USA
| | - Brendan Moloney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Gregory J Wilson
- Department of Radiology, University of Washington, Seattle, Washington, USA
- Bayer Healthcare, Radiology, New Jersey, USA
| | - Martin M Pike
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
| | - Thomas M Barbara
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - William D Rooney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA
| | - Jeffrey H Maki
- Anschutz Medical Center Department of Radiology, University of Colorado, Aurora, Colorado, USA
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24
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Choi BK, Katoch N, Park JA, Kim JW, Oh TI, Kim HJ, Woo EJ. Measurement of extracellular volume fraction using magnetic resonance-based conductivity tensor imaging. Front Physiol 2023; 14:1132911. [PMID: 36875031 PMCID: PMC9983119 DOI: 10.3389/fphys.2023.1132911] [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: 01/05/2023] [Accepted: 02/02/2023] [Indexed: 02/19/2023] Open
Abstract
Conductivity tensor imaging (CTI) using MRI is an advanced method that can non-invasively measure the electrical properties of living tissues. The contrast of CTI is based on underlying hypothesis about the proportionality between the mobility and diffusivity of ions and water molecules inside tissues. The experimental validation of CTI in both in vitro and in vivo settings is required as a reliable tool to assess tissue conditions. The changes in extracellular space can be indicators for disease progression, such as fibrosis, edema, and cell swelling. In this study, we conducted a phantom imaging experiment to test the feasibility of CTI for measuring the extracellular volume fraction in biological tissue. To mimic tissue conditions with different extracellular volume fractions, four chambers of giant vesicle suspension (GVS) with different vesicle densities were included in the phantom. The reconstructed CTI images of the phantom were compared with the separately-measured conductivity spectra of the four chambers using an impedance analyzer. Moreover, the values of the estimated extracellular volume fraction in each chamber were compared with those measured by a spectrophotometer. As the vesicle density increased, we found that the extracellular volume fraction, extracellular diffusion coefficient, and low-frequency conductivity decreased, while the intracellular diffusion coefficient slightly increased. On the other hand, the high-frequency conductivity could not clearly distinguish the four chambers. The extracellular volume fraction measured by the spectrophotometer and CTI method in each chamber were quite comparable, i.e., (1.00, 0.98 ± 0.01), (0.59, 0.63 ± 0.02), (0.40, 0.40 ± 0.05), and (0.16, 0.18 ± 0.02). The prominent factor influencing the low-frequency conductivity at different GVS densities was the extracellular volume fraction. Further studies are needed to validate the CTI method as a tool to measure the extracellular volume fractions in living tissues with different intracellular and extracellular compartments.
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Affiliation(s)
- Bup Kyung Choi
- Department of Biomedical Engineering, Kyung Hee University, Seoul, Republic of Korea
| | - Nitish Katoch
- Department of Biomedical Engineering, Kyung Hee University, Seoul, Republic of Korea
| | - Ji Ae Park
- Division of Applied RI, Korea Institute of Radiological and Medical Science, Seoul, Republic of Korea
| | - Jin Woong Kim
- Department of Radiology, Chosun University Hospital and Chosun University College of Medicine, Gwangju, Republic of Korea
| | - Tong In Oh
- Department of Biomedical Engineering, Kyung Hee University, Seoul, Republic of Korea
| | - Hyung Joong Kim
- Department of Biomedical Engineering, Kyung Hee University, Seoul, Republic of Korea
| | - Eung Je Woo
- Department of Biomedical Engineering, Kyung Hee University, Seoul, Republic of Korea
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25
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Chakwizira A, Westin C, Brabec J, Lasič S, Knutsson L, Szczepankiewicz F, Nilsson M. Diffusion MRI with pulsed and free gradient waveforms: Effects of restricted diffusion and exchange. NMR IN BIOMEDICINE 2023; 36:e4827. [PMID: 36075110 PMCID: PMC10078514 DOI: 10.1002/nbm.4827] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 08/27/2022] [Accepted: 09/06/2022] [Indexed: 05/06/2023]
Abstract
Monitoring time dependence with diffusion MRI yields observables sensitive to compartment sizes (restricted diffusion) and membrane permeability (water exchange). However, restricted diffusion and exchange have opposite effects on the diffusion-weighted signal, which can lead to errors in parameter estimates. In this work, we propose a signal representation that incorporates the effects of both restricted diffusion and exchange up to second order in b-value and is compatible with gradient waveforms of arbitrary shape. The representation features mappings from a gradient waveform to two scalars that separately control the sensitivity to restriction and exchange. We demonstrate that these scalars span a two-dimensional space that can be used to choose waveforms that selectively probe restricted diffusion or exchange, eliminating the correlation between the two phenomena. We found that waveforms with specific but unconventional shapes provide an advantage over conventional pulsed and oscillating gradient acquisitions. We also show that parametrization of waveforms into a two-dimensional space can be used to understand protocols from other approaches that probe restricted diffusion and exchange. For example, we found that the variation of mixing time in filter-exchange imaging corresponds to variation of our exchange-weighting scalar at a fixed value of the restriction-weighting scalar. The proposed signal representation was evaluated using Monte Carlo simulations in identical parallel cylinders with hexagonal and random packing as well as parallel cylinders with gamma-distributed radii. Results showed that the approach is sensitive to sizes in the interval 4-12 μm and exchange rates in the simulated range of 0 to 20 s - 1 , but also that there is a sensitivity to the extracellular geometry. The presented theory constitutes a simple and intuitive description of how restricted diffusion and exchange influence the signal as well as a guide to protocol design capable of separating the two effects.
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Affiliation(s)
- Arthur Chakwizira
- Department of Medical Radiation Physics, LundLund UniversityLundSweden
| | - Carl‐Fredrik Westin
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jan Brabec
- Department of Medical Radiation Physics, LundLund UniversityLundSweden
| | - Samo Lasič
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital ‐ Amager and HvidovreCopenhagenDenmark
- Random Walk Imaging ABLundSweden
| | - Linda Knutsson
- Department of Medical Radiation Physics, LundLund UniversityLundSweden
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- F. M. Kirby Research Center for Functional Brain ImagingKennedy Krieger InstituteBaltimoreMarylandUSA
| | | | - Markus Nilsson
- Department of Clinical Sciences Lund, RadiologyLund UniversityLundSweden
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Jiang X, Devan SP, Xie J, Gore JC, Xu J. Improving MR cell size imaging by inclusion of transcytolemmal water exchange. NMR IN BIOMEDICINE 2022; 35:e4799. [PMID: 35794795 PMCID: PMC10124991 DOI: 10.1002/nbm.4799] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 06/27/2022] [Accepted: 07/05/2022] [Indexed: 05/12/2023]
Abstract
The goal of the current study is to include transcytolemmal water exchange in MR cell size imaging using the IMPULSED model for more accurate characterization of tissue cellular properties (e.g., apparent volume fraction of intracellular space v in ) and quantification of indicators of transcytolemmal water exchange. We propose a heuristic model that incorporates transcytolemmal water exchange into a multicompartment diffusion-based method (IMPULSED) that was developed previously to extract microstructural parameters (e.g., mean cell size d and apparent volume fraction of intracellular space v in ) assuming no water exchange. For t diff ≤ 5 ms, the water exchange can be ignored, and the signal model is the same as the IMPULSED model. For t diff ≥ 30 ms, we incorporated the modified Kärger model that includes both restricted diffusion and exchange between compartments. Using simulations and previously published in vitro cell data, we evaluated the accuracy and precision of model-derived parameters and determined how they are dependent on SNR and imaging parameters. The joint model provides more accurate d values for cell sizes ranging from 10 to 12 microns when water exchange is fast (e.g., intracellular water pre-exchange lifetime τ in ≤ 100 ms) than IMPULSED, and reduces the bias of IMPULSED-derived estimates of v in , especially when water exchange is relatively slow (e.g., τ in > 200 ms). Indicators of transcytolemmal water exchange derived from the proposed joint model are linearly correlated with ground truth τ in values and can detect changes in cell membrane permeability induced by saponin treatment in murine erythroleukemia cancer cells. Our results suggest this joint model not only improves the accuracy of IMPULSED-derived microstructural parameters, but also provides indicators of water exchange that are usually ignored in diffusion models of tissues.
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Affiliation(s)
- Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Sean P Devan
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN 37232, USA
| | - Jingping Xie
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - John C. Gore
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
| | - Junzhong Xu
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
- Corresponding author: Address: Vanderbilt University, Institute of Imaging Science, 1161 21 Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, United States. Fax: +1 615 322 0734. (Junzhong Xu). Twitter: @JunzhongXu
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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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Devan SP, Jiang X, Luo G, Xie J, Quirk JD, Engelbach JA, Harmsen H, McKinley ET, Cui J, Zu Z, Attia A, Garbow JR, Gore JC, McKnight CD, Kirschner AN, Xu J. Selective Cell Size MRI Differentiates Brain Tumors from Radiation Necrosis. Cancer Res 2022; 82:3603-3613. [PMID: 35877201 PMCID: PMC9532360 DOI: 10.1158/0008-5472.can-21-2929] [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: 08/30/2021] [Revised: 02/05/2022] [Accepted: 07/21/2022] [Indexed: 11/16/2022]
Abstract
Brain metastasis is a common characteristic of late-stage lung cancers. High doses of targeted radiotherapy can control tumor growth in the brain but can also result in radiotherapy-induced necrosis. Current methods are limited for distinguishing whether new parenchymal lesions following radiotherapy are recurrent tumors or radiotherapy-induced necrosis, but the clinical management of these two classes of lesions differs significantly. Here, we developed, validated, and evaluated a new MRI technique termed selective size imaging using filters via diffusion times (SSIFT) to differentiate brain tumors from radiotherapy necrosis in the brain. This approach generates a signal filter that leverages diffusion time dependence to establish a cell size-weighted map. Computer simulations in silico, cultured cancer cells in vitro, and animals with brain tumors in vivo were used to comprehensively validate the specificity of SSIFT for detecting typical large cancer cells and the ability to differentiate brain tumors from radiotherapy necrosis. SSIFT was also implemented in patients with metastatic brain cancer and radiotherapy necrosis. SSIFT showed high correlation with mean cell sizes in the relevant range of less than 20 μm. The specificity of SSIFT for brain tumors and reduced contrast in other brain etiologies allowed SSIFT to differentiate brain tumors from peritumoral edema and radiotherapy necrosis. In conclusion, this new, cell size-based MRI method provides a unique contrast to differentiate brain tumors from other pathologies in the brain. SIGNIFICANCE This work introduces and provides preclinical validation of a new diffusion MRI method that exploits intrinsic differences in cell sizes to distinguish brain tumors and radiotherapy necrosis.
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Affiliation(s)
- Sean P Devan
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37232, USA
| | - Xiaoyu Jiang
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Guozhen Luo
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Jingping Xie
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - James D Quirk
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO 63110, USA
| | - John A Engelbach
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO 63110, USA
| | - Hannah Harmsen
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Eliot T McKinley
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, 37232, USA
| | - Jing Cui
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Albert Attia
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Joel R Garbow
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO 63110, USA
- Alvin J Siteman Cancer Center, Washington University, St. Louis, MO, 63110, USA
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
| | - Colin D McKnight
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Austin N Kirschner
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Junzhong Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
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Wu J, Kang T, Lan X, Chen X, Wu Z, Wang J, Lin L, Cai C, Lin J, Ding X, Cai S. IMPULSED model based cytological feature estimation with U-Net: Application to human brain tumor at 3T. Magn Reson Med 2022; 89:411-422. [PMID: 36063493 DOI: 10.1002/mrm.29429] [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: 04/24/2022] [Revised: 07/06/2022] [Accepted: 08/08/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE This work introduces and validates a deep-learning-based fitting method, which can rapidly provide accurate and robust estimation of cytological features of brain tumor based on the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model fitting with diffusion-weighted MRI data. METHODS The U-Net was applied to rapidly quantify extracellular diffusion coefficient (Dex ), cell size (d), and intracellular volume fraction (vin ) of brain tumor. At the training stage, the image-based training data, synthesized by randomizing quantifiable microstructural parameters within specific ranges, was used to train U-Net. At the test stage, the pre-trained U-Net was applied to estimate the microstructural parameters from simulated data and the in vivo data acquired on patients at 3T. The U-Net was compared with conventional non-linear least-squares (NLLS) fitting in simulations in terms of estimation accuracy and precision. RESULTS Our results confirm that the proposed method yields better fidelity in simulations and is more robust to noise than the NLLS fitting. For in vivo data, the U-Net yields obvious quality improvement in parameter maps, and the estimations of all parameters are in good agreement with the NLLS fitting. Moreover, our method is several orders of magnitude faster than the NLLS fitting (from about 5 min to <1 s). CONCLUSION The image-based training scheme proposed herein helps to improve the quality of the estimated parameters. Our deep-learning-based fitting method can estimate the cell microstructural parameters fast and accurately.
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Affiliation(s)
- Jian Wu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Taishan Kang
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xinli Lan
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Xinran Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Zhigang Wu
- MSC Clinical & Technical Solutions, Philips Healthcare, Beijing, China
| | - Jiazheng Wang
- MSC Clinical & Technical Solutions, Philips Healthcare, Beijing, China
| | - Liangjie Lin
- MSC Clinical & Technical Solutions, Philips Healthcare, Beijing, China
| | - Congbo Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Jianzhong Lin
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xin Ding
- Department of Pathology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Shuhui Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
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Wu D, Jiang K, Li H, Zhang Z, Ba R, Zhang Y, Hsu YC, Sun Y, Zhang YD. Time-Dependent Diffusion MRI for Quantitative Microstructural Mapping of Prostate Cancer. Radiology 2022; 303:578-587. [PMID: 35258368 DOI: 10.1148/radiol.211180] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Recently developed time-dependent diffusion MRI has potential in characterizing cellular tissue microstructures; however, its value in imaging prostate cancer (PCa) remains unknown. Purpose To investigate the feasibility of time-dependent diffusion MRI-based microstructural mapping for noninvasively characterizing cellular properties of PCa and for discriminating between clinically significant PCa and clinically insignificant disease. Materials and Methods Men with a clinical suspicion of PCa were enrolled prospectively between October 2019 and August 2020. Time-dependent diffusion MRI data were acquired with pulsed and oscillating gradient diffusion MRI sequences at an equivalent diffusion time of 7.5-30 msec on a 3.0-T scanner. Time-dependent diffusion MRI-based microstructural parameters, including cell diameter, intracellular volume fraction, cellularity, and diffusivities, were estimated with a two-compartment model. These were compared for different International Society of Urological Pathology grade groups (GGs), and their performance in discriminating clinically significant PCa (GG >1) from clinically insignificant disease (benign and GG 1) was determined with a linear discriminant analysis. The fitted microstructural parameters were validated by means of correlation with histopathologic measurements. Results In the 48 enrolled men, the time-dependent diffusion MRI measurements showed that higher GG was correlated with higher intracellular volume fraction and higher cellularity (intracellular volume fraction = 0.22, 0.36, 0.34, 0.37, and 0.40 in GGs 1-5, respectively; P < .001 at one-way analysis of variance), while lower cell diameter was found at higher GGs (diameter = 23.4, 18.3, 19.2, 17.9, and 18.5 μm in GGs 1-5, respectively; P = .002). Among all measurements derived from time-dependent diffusion MRI, cellularity achieved the highest diagnostic performance, with an accuracy of 92% (44 of 48 participants) and area under the receiver operating characteristic curve of 0.96 (95% CI: 0.87, 0.99) in discriminating clinically significant PCa from clinically insignificant disease. Microstructural mapping was supported by positive correlations between time-dependent diffusion MRI-based and pathologic examination-based intracellular volume fraction (r = 0.83; P < .001). Conclusion Time-dependent diffusion MRI-based microstructural mapping correlates with pathologic findings and demonstrates promise for characterizing prostate cancer. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chatterjee and Oto in this issue.
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Affiliation(s)
- Dan Wu
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Kewen Jiang
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Hai Li
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Zelin Zhang
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Ruicheng Ba
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Yi Zhang
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Yi-Cheng Hsu
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Yi Sun
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Yu-Dong Zhang
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
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Grussu F, Bernatowicz K, Casanova-Salas I, Castro N, Nuciforo P, Mateo J, Barba I, Perez-Lopez R. Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology. Magn Reson Med 2022; 88:365-379. [PMID: 35181943 PMCID: PMC9303340 DOI: 10.1002/mrm.29174] [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: 07/29/2021] [Revised: 12/21/2021] [Accepted: 01/07/2022] [Indexed: 11/09/2022]
Abstract
Purpose Relationships between diffusion‐weighted MRI signals and hepatocyte microstructure were investigated to inform liver diffusion MRI modeling, focusing on the following question: Can cell size and diffusivity be estimated at fixed diffusion time, realistic SNR, and negligible contribution from extracellular/extravascular water and exchange? Methods Monte Carlo simulations were performed within synthetic hepatocytes for varying cell size/diffusivity L/D0, and clinical protocols (single diffusion encoding; maximum b‐value: {1000, 1500, 2000} s/mm2; 5 unique gradient duration/separation pairs; SNR = {∞, 100, 80, 40, 20}), accounting for heterogeneity in (D0,L) and perfusion contamination. Diffusion (D) and kurtosis (K) coefficients were calculated, and relationships between (D0,L) and (D,K) were visualized. Functions mapping (D,K) to (D0,L) were computed to predict unseen (D0,L) values, tested for their ability to classify discrete cell‐size contrasts, and deployed on 9.4T ex vivo MRI‐histology data of fixed mouse livers Results Relationships between (D,K) and (D0,L) are complex and depend on the diffusion encoding. Functions mapping D,K to (D0,L) captures salient characteristics of D0(D,K) and L(D,K) dependencies. Mappings are not always accurate, but they enable just under 70% accuracy in a three‐class cell‐size classification task (for SNR = 20, bmax = 1500 s/mm2, δ = 20 ms, and Δ = 75 ms). MRI detects cell‐size contrasts in the mouse livers that are confirmed by histology, but overestimates the largest cell sizes. Conclusion Salient information about liver cell size and diffusivity may be retrieved from minimal diffusion encodings at fixed diffusion time, in experimental conditions and pathological scenarios for which extracellular, extravascular water and exchange are negligible.
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Affiliation(s)
- Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Kinga Bernatowicz
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Irene Casanova-Salas
- Prostate Cancer Translational Research Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Natalia Castro
- Prostate Cancer Translational Research Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Paolo Nuciforo
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Joaquin Mateo
- Prostate Cancer Translational Research Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Ignasi Barba
- NMR Lab, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.,Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain
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The relationship between diffusion heterogeneity and microstructural changes in high-grade gliomas using Monte Carlo simulations. Magn Reson Imaging 2021; 85:108-120. [PMID: 34653578 DOI: 10.1016/j.mri.2021.10.001] [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: 06/24/2021] [Revised: 09/17/2021] [Accepted: 10/07/2021] [Indexed: 11/21/2022]
Abstract
PURPOSE Diffusion-weighted imaging (DWI) may aid accurate tumor grading. Decreased diffusivity and increased diffusion heterogeneity measures have been observed in high-grade gliomas using the non-monoexponential models for DWI. However, DWI measures concerning tissue characteristics in terms of pathophysiological and structural changes are yet to be established. Thus, this study aims to investigate the relationship between the diffusion measurements and microstructural changes in the presence of high-grade gliomas using a three-dimensional Monte Carlo simulation with systematic changes of microstructural parameters. METHODS Water diffusion was simulated in a microenvironment along with changes associated with the presence of high-grade gliomas, including increases in cell density, nuclear volume, extracellular volume (VFex), and extracellular tortuosity (λex), and changes in membrane permeability (Pmem). DWI signals were simulated using a pulsed gradient spin-echo sequence. The sequence parameters, including the maximum gradient strength and diffusion time, were set to be comparable to those of clinical scanners and advanced human MRI systems. The DWI signals were fitted using the gamma distribution and diffusional kurtosis models with b-values up to 6000 and 2500 s/mm2, respectively. RESULTS The diffusivity measures (apparent diffusion coefficients (ADC), Dgamma of the gamma distribution model and Dapp of the diffusional kurtosis model) decreased with increases in cell density and λex, and a decrease in Pmem. These diffusivity measures increased with increases in nuclear volume and VFex. The diffusion heterogeneity measures (σgamma of the gamma distribution model and Kapp of the diffusional kurtosis model) increased with increases in cell density or nuclear volume at the low Pmem, and a decrease in Pmem. Increased σgamma was also associated with an increase in VFex. CONCLUSION Among simulated microstructural changes, only increases in cell density at low Pmem or decreases in Pmem corresponded to both the decreased diffusivity and increased diffusion heterogeneity measures. The results suggest that increases in cell density at low Pmem or decreases in Pmem may be associated with the diffusion changes observed in high-grade gliomas.
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Hernando D, Zhang Y, Pirasteh A. Quantitative diffusion MRI of the abdomen and pelvis. Med Phys 2021; 49:2774-2793. [PMID: 34554579 DOI: 10.1002/mp.15246] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 08/05/2021] [Accepted: 09/15/2021] [Indexed: 12/14/2022] Open
Abstract
Diffusion MRI has enormous potential and utility in the evaluation of various abdominal and pelvic disease processes including cancer and noncancer imaging of the liver, prostate, and other organs. Quantitative diffusion MRI is based on acquisitions with multiple diffusion encodings followed by quantitative mapping of diffusion parameters that are sensitive to tissue microstructure. Compared to qualitative diffusion-weighted MRI, quantitative diffusion MRI can improve standardization of tissue characterization as needed for disease detection, staging, and treatment monitoring. However, similar to many other quantitative MRI methods, diffusion MRI faces multiple challenges including acquisition artifacts, signal modeling limitations, and biological variability. In abdominal and pelvic diffusion MRI, technical acquisition challenges include physiologic motion (respiratory, peristaltic, and pulsatile), image distortions, and low signal-to-noise ratio. If unaddressed, these challenges lead to poor technical performance (bias and precision) and clinical outcomes of quantitative diffusion MRI. Emerging and novel technical developments seek to address these challenges and may enable reliable quantitative diffusion MRI of the abdomen and pelvis. Through systematic validation in phantoms, volunteers, and patients, including multicenter studies to assess reproducibility, these emerging techniques may finally demonstrate the potential of quantitative diffusion MRI for abdominal and pelvic imaging applications.
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Affiliation(s)
- Diego Hernando
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Yuxin Zhang
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ali Pirasteh
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Asperud J, Arous D, Edin NFJ, Malinen E. Spatially fractionated radiotherapy: tumor response modelling including immunomodulation. Phys Med Biol 2021; 66. [PMID: 34298527 DOI: 10.1088/1361-6560/ac176b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 07/23/2021] [Indexed: 01/20/2023]
Abstract
A mathematical tumor response model has been developed, encompassing the interplay between immune cells and cancer cells initiated by either partial or full tumor irradiation. The iterative four-compartment model employs the linear-quadratic radiation response theory for four cell types: active and inactive cytotoxic T lymphocytes (immune cells, CD8+T cells in particular), viable cancer cells (undamaged and reparable cells) and doomed cells (irreparably damaged cells). The cell compartment interactions are calculated per day, with total tumor volume (TV) as the main quantity of interest. The model was fitted to previously published data on syngeneic xenografts (67NR breast carcinoma and Lewis lung carcinoma; (Markovskyet al2019Int. J. Radiat. Oncol. Biol. Phys.103697-708)) subjected to single doses of 10 or 15 Gy by 50% (partial) or 100% (full) TV irradiation. The experimental data included effects from anti-CD8+antibodies and immunosuppressive drugs. Using a new optimization method, promising fits were obtained where the lowest and highest root-mean-squared error values were observed for anti-CD8+treatment and unirradiated control data, respectively, for both cell types. Additionally, predictive capabilities of the model were tested by using the estimated model parameters to predict scenarios for higher doses and different TV irradiation fractions. Here, mean relative deviations in the range of 19%-34% from experimental data were found. However, more validation data is needed to conclude on the model's predictive capabilities. In conclusion, the model was found useful in evaluating the impact from partial and full TV irradiation on the immune response and subsequent tumor growth. The model shows potential to support and guide spatially fractionated radiotherapy in future pre-clinical and clinical studies.
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Affiliation(s)
- Jonas Asperud
- Department of Physics, University of Oslo, PO Box 1048 Blindern, N-0316 Oslo, Norway
| | - Delmon Arous
- Department of Physics, University of Oslo, PO Box 1048 Blindern, N-0316 Oslo, Norway.,Department of Medical Physics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4953 Nydalen, N-0424 Oslo, Norway
| | | | - Eirik Malinen
- Department of Physics, University of Oslo, PO Box 1048 Blindern, N-0316 Oslo, Norway.,Department of Medical Physics, The Norwegian Radium Hospital, Oslo University Hospital, PO Box 4953 Nydalen, N-0424 Oslo, Norway
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35
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Gao F, Shen X, Zhang H, Ba R, Ma X, Lai C, Zhang J, Zhang Y, Wu D. Feasibility of oscillating and pulsed gradient diffusion MRI to assess neonatal hypoxia-ischemia on clinical systems. J Cereb Blood Flow Metab 2021; 41:1240-1250. [PMID: 32811261 PMCID: PMC8142137 DOI: 10.1177/0271678x20944353] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Diffusion-time- (td) dependent diffusion MRI (dMRI) extends our ability to characterize brain microstructure by measuring dMRI signals at varying td. The use of oscillating gradient (OG) is essential for accessing short td but is technically challenging on clinical MRI systems. This study aims to investigate the clinical feasibility and value of td-dependent dMRI in neonatal hypoxic-ischemic encephalopathy (HIE). Eighteen HIE neonates and six normal term-born neonates were scanned on a 3 T scanner, with OG-dMRI at an oscillating frequency of 33 Hz (equivalent td ≈ 7.5 ms) and pulsed gradient (PG)-dMRI at a td of 82.8 ms and b-value of 700 s/mm2. The td-dependence, as quantified by the difference in apparent diffusivity coefficients between OG- and PG-dMRI (ΔADC), was observed in the normal neonatal brains, and the ΔADC was higher in the subcortical white matter than the deep grey matter. In HIE neonates with severe and moderate injury, ΔADC significantly increased in the basal ganglia (BG) compared to the controls (23.7% and 10.6%, respectively). In contrast, the conventional PG-ADC showed a 12.6% reduction only in the severe HIE group. White matter edema regions also demonstrated increased ΔADC, where PG-ADC did not show apparent changes. Our result demonstrated that td-dependent dMRI provided high sensitivity in detecting moderate-to-severe HIE.
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Affiliation(s)
- Fusheng Gao
- Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Xiaoxia Shen
- Department of Neonatal Intensive Care Unit, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Hongxi Zhang
- Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Ruicheng Ba
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Xiaolu Ma
- Department of Neonatal Intensive Care Unit, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Can Lai
- Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Jiangyang Zhang
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
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36
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Hormuth DA, Jarrett AM, Davis T, Yankeelov TE. Towards an Image-Informed Mathematical Model of In Vivo Response to Fractionated Radiation Therapy. Cancers (Basel) 2021; 13:cancers13081765. [PMID: 33917080 PMCID: PMC8067722 DOI: 10.3390/cancers13081765] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/01/2021] [Accepted: 04/04/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Using medical imaging data and computational models, we develop a modeling framework to provide personalized treatment response forecasts to fractionated radiation therapy for individual tumors. We evaluate this approach in an animal model of brain cancer and forecast changes in tumor cellularity and vasculature. Abstract Fractionated radiation therapy is central to the treatment of numerous malignancies, including high-grade gliomas where complete surgical resection is often impractical due to its highly invasive nature. Development of approaches to forecast response to fractionated radiation therapy may provide the ability to optimize or adapt treatment plans for radiotherapy. Towards this end, we have developed a family of 18 biologically-based mathematical models describing the response of both tumor and vasculature to fractionated radiation therapy. Importantly, these models can be personalized for individual tumors via quantitative imaging measurements. To evaluate this family of models, rats (n = 7) with U-87 glioblastomas were imaged with magnetic resonance imaging (MRI) before, during, and after treatment with fractionated radiotherapy (with doses of either 2 Gy/day or 4 Gy/day for up to 10 days). Estimates of tumor and blood volume fractions, provided by diffusion-weighted MRI and dynamic contrast-enhanced MRI, respectively, were used to calibrate tumor-specific model parameters. The Akaike Information Criterion was employed to select the most parsimonious model and determine an ensemble averaged model, and the resulting forecasts were evaluated at the global and local level. At the global level, the selected model’s forecast resulted in less than 16.2% error in tumor volume estimates. At the local (voxel) level, the median Pearson correlation coefficient across all prediction time points ranged from 0.57 to 0.87 for all animals. While the ensemble average forecast resulted in increased error (ranging from 4.0% to 1063%) in tumor volume predictions over the selected model, it increased the voxel wise correlation (by greater than 12.3%) for three of the animals. This study demonstrates the feasibility of calibrating a model of response by serial quantitative MRI data collected during fractionated radiotherapy to predict response at the conclusion of treatment.
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Affiliation(s)
- David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (A.M.J.); (T.E.Y.)
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
- Correspondence:
| | - Angela M. Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (A.M.J.); (T.E.Y.)
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Tessa Davis
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (A.M.J.); (T.E.Y.)
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Li Y, Kim M, Lawrence TS, Parmar H, Cao Y. Microstructure Modeling of High b-Value Diffusion-Weighted Images in Glioblastoma. ACTA ACUST UNITED AC 2021; 6:34-43. [PMID: 32280748 PMCID: PMC7138521 DOI: 10.18383/j.tom.2020.00018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Apparent diffusion coefficient has limits to differentiate solid tumor from normal tissue or edema in glioblastoma (GBM). This study investigated a microstructure model (MSM) in GBM using a clinically available diffusion imaging technique. The MSM was modified to integrate with bi-polar diffusion gradient waveforms, and applied to 30 patients with newly diagnosed GBM. Diffusion-weighted (DW) images acquired on a 3 T scanner with b-values from 0 to 2500 s/mm2 were fitted in volumes of interest (VOIs) of solid tumor to obtain the apparent restriction size of intracellular water (ARS), the fractional volume of intracellular water (Vin), and extracellular (Dex) water diffusivity. The parameters in solid tumor were compared with those of other tissue types by Students’ t test. For comparison, DW images were fitted by conventional mono-exponential and bi-exponential models. ARS, Dex, and Vin from the MSM in tumor VOIs were significantly greater than those in WM, GM, and edema (P values of .01–.001). ARS values in solid tumors (from 21.6 to 34.5 um) had absolutely no overlap with those in all other tissue types (from 0.9 to 3.5 um). Vin values showed a descending order from solid tumor (from 0.32 to 0.52) to WM, GM, and edema (from 0.05 to 0.25), consisting with the descending cellularity in these tissue types. The parameters from mono-exponential and bi-exponential models could not significantly differentiate solid tumor from all other tissue types, particularly from edema. Further development and histopathological validation of the MSM will warrant its role in clinical management of GBM.
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Affiliation(s)
- Yuan Li
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Michelle Kim
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Theodore S Lawrence
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Hemant Parmar
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Yue Cao
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
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38
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Wu D, Zhang Y, Cheng B, Mori S, Reeves RH, Gao FJ. Time-dependent diffusion MRI probes cerebellar microstructural alterations in a mouse model of Down syndrome. Brain Commun 2021; 3:fcab062. [PMID: 33937769 PMCID: PMC8063586 DOI: 10.1093/braincomms/fcab062] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 02/09/2021] [Accepted: 02/22/2021] [Indexed: 01/27/2023] Open
Abstract
The cerebellum is a complex system with distinct cortical laminar organization. Alterations in cerebellar microstructure are common and associated with many factors such as genetics, cancer and ageing. Diffusion MRI (dMRI) provides a non-invasive tool to map the brain structural organization, and the recently proposed diffusion-time (td )-dependent dMRI further improves its capability to probe the cellular and axonal/dendritic microstructures by measuring water diffusion at multiple spatial scales. The td -dependent diffusion profile in the cerebellum and its utility in detecting cerebellar disorders, however, are not yet elucidated. Here, we first deciphered the spatial correspondence between dMRI contrast and cerebellar layers, based on which the cerebellar layer-specific td -dependent dMRI patterns were characterized in both euploid and Ts65Dn mice, a mouse model of Down syndrome. Using oscillating gradient dMRI, which accesses diffusion at short td 's by modulating the oscillating frequency, we detected subtle changes in the apparent diffusivity coefficient of the cerebellar internal granular layer and Purkinje cell layer of Ts65Dn mice that were not detectable by conventional pulsed gradient dMRI. The detection sensitivity of oscillating gradient dMRI increased with the oscillating frequency at both the neonatal and adult stages. The td -dependence, quantified by ΔADC map, was reduced in Ts65Dn mice, likely associated with the reduced granule cell density and abnormal dendritic arborization of Purkinje cells as revealed from histological evidence. Our study demonstrates superior sensitivity of short-td diffusion using oscillating gradient dMRI to detect cerebellar microstructural changes in Down syndrome, suggesting the potential application of this technique in cerebellar disorders.
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Affiliation(s)
- Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Bei Cheng
- Department of Radiology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Susumu Mori
- Department of Radiology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Roger H Reeves
- Department of Physiology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Feng J Gao
- Department of Physiology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
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MR cell size imaging with temporal diffusion spectroscopy. Magn Reson Imaging 2021; 77:109-123. [PMID: 33338562 PMCID: PMC7878439 DOI: 10.1016/j.mri.2020.12.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 12/10/2020] [Accepted: 12/13/2020] [Indexed: 02/07/2023]
Abstract
Cytological features such as cell size and intracellular morphology provide fundamental information on cell status and hence may provide specific information on changes that arise within biological tissues. Such information is usually obtained by invasive biopsy in current clinical practice, which suffers several well-known disadvantages. Recently, novel MRI methods such as IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) have been developed for direct measurements of mean cell size non-invasively. The IMPULSED protocol is based on using temporal diffusion spectroscopy (TDS) to combine measurements of water diffusion over a wide range of diffusion times to probe cellular microstructure over varying length scales. IMPULSED has been shown to provide rapid, robust, and reliable mapping of mean cell size and is suitable for clinical imaging. More recently, cell size distributions have also been derived by appropriate analyses of data acquired with IMPULSED or similar sequences, which thus provides MRI-cytometry. This review summarizes the basic principles, practical implementations, validations, and example applications of MR cell size imaging based on TDS and demonstrates how cytometric information can be used in various applications. In addition, the limitations and potential future directions of MR cytometry are identified including the diagnosis of nonalcoholic steatohepatitis of the liver and the assessment of treatment response of cancers.
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40
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Xing S, Levesque IR. A simulation study of cell size and volume fraction mapping for tissue with two underlying cell populations using diffusion-weighted MRI. Magn Reson Med 2021; 86:1029-1044. [PMID: 33644889 DOI: 10.1002/mrm.28694] [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: 06/09/2020] [Revised: 12/23/2020] [Accepted: 01/04/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE To propose a method for voxel-wise estimation of cell radii and volume fractions of two cell populations when they coexist in the same MR voxel using the combination of diffusion-weighted MRI and microstructural modeling. METHOD Microstructure models were investigated using diffusion data simulated with the matrix method for a range of microstructures mimicking tumor tissue with two cell populations, using acquisition parameters available on preclinical scanners. The effect of noise was investigated for a subset of these microstructures. The accuracy and precision of the estimated radii and volume fractions for large and small cells R l , R s , v i n , l , v i n , s were evaluated by comparing the estimates to their true values. The stability of model fitting was characterized by the percentage of accepted fits. RESULTS The estimation accuracy and precision, and thus the ability to robustly distinguish the two cell populations, depended on the microstructural properties and SNR. For a SNR of 50, a minimum difference of 3 μm between the radius of the large and small cell populations was required for differentiation. Proposed modifications to the two cell population microstructure model, including constrained fits, improved the stability of fits. CONCLUSIONS This proof-of-concept study proposed a diffusion MRI-based method for voxel-wise estimation of cell radii and volume fractions of two cell populations when they coexist in the same MR voxel. The ability to reliably characterize tissue with two cell populations opens exciting avenues of potential applications in both tumor diagnosis and treatment monitoring.
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Affiliation(s)
- Shu Xing
- Medical Physics Unit, McGill University, Montreal, Quebec, Canada.,Department of Physics, McGill University, Montreal, Quebec, Canada
| | - Ives R Levesque
- Medical Physics Unit, McGill University, Montreal, Quebec, Canada.,Department of Physics, McGill University, Montreal, Quebec, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada.,Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
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41
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Mazzoli V, Moulin K, Kogan F, Hargreaves BA, Gold GE. Diffusion Tensor Imaging of Skeletal Muscle Contraction Using Oscillating Gradient Spin Echo. Front Neurol 2021; 12:608549. [PMID: 33658976 PMCID: PMC7917051 DOI: 10.3389/fneur.2021.608549] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 01/08/2021] [Indexed: 01/01/2023] Open
Abstract
Diffusion tensor imaging (DTI) measures water diffusion in skeletal muscle tissue and allows for muscle assessment in a broad range of neuromuscular diseases. However, current DTI measurements, typically performed using pulsed gradient spin echo (PGSE) diffusion encoding, are limited to the assessment of non-contracted musculature, therefore providing limited insight into muscle contraction mechanisms and contraction abnormalities. In this study, we propose the use of an oscillating gradient spin echo (OGSE) diffusion encoding strategy for DTI measurements to mitigate the effect of signal voids in contracted muscle and to obtain reliable diffusivity values. Two OGSE sequences with encoding frequencies of 25 and 50 Hz were tested in the lower leg of five healthy volunteers with relaxed musculature and during active dorsiflexion and plantarflexion, and compared with a conventional PGSE approach. A significant reduction of areas of signal voids using OGSE compared with PGSE was observed in the tibialis anterior for the scans obtained in active dorsiflexion and in the soleus during active plantarflexion. The use of PGSE sequences led to unrealistically elevated axial diffusivity values in the tibialis anterior during dorsiflexion and in the soleus during plantarflexion, while the corresponding values obtained using the OGSE sequences were significantly reduced. Similar findings were seen for radial diffusivity, with significantly higher diffusivity measured in plantarflexion in the soleus muscle using the PGSE sequence. Our preliminary results indicate that DTI with OGSE diffusion encoding is feasible in human musculature and allows to quantitatively assess diffusion properties in actively contracting skeletal muscle. OGSE holds great potential to assess microstructural changes occurring in the skeletal muscle during contraction, and for non-invasive assessment of contraction abnormalities in patients with muscle diseases.
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Affiliation(s)
- Valentina Mazzoli
- Department of Radiology, Stanford University, Stanford, CA, United States
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42
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Xu J, Jiang X, Devan SP, Arlinghaus LR, McKinley ET, Xie J, Zu Z, Wang Q, Chakravarthy AB, Wang Y, Gore JC. MRI-cytometry: Mapping nonparametric cell size distributions using diffusion MRI. Magn Reson Med 2021; 85:748-761. [PMID: 32936478 PMCID: PMC7722100 DOI: 10.1002/mrm.28454] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 06/29/2020] [Accepted: 07/10/2020] [Indexed: 02/05/2023]
Abstract
PURPOSE This report introduces and validates a new diffusion MRI-based method, termed MRI-cytometry, which can noninvasively map intravoxel, nonparametric cell size distributions in tissues. METHODS MRI was used to acquire diffusion MRI signals with a range of diffusion times and gradient factors, and a model was fit to these data to derive estimates of cell size distributions. We implemented a 2-step fitting method to avoid noise-induced artificial peaks and provide reliable estimates of tumor cell size distributions. Computer simulations in silico, experimental measurements on cultured cells in vitro, and animal xenografts in vivo were used to validate the accuracy and precision of the method. Tumors in 7 patients with breast cancer were also imaged and analyzed using this MRI-cytometry approach on a clinical 3 Tesla MRI scanner. RESULTS Simulations and experimental results confirm that MRI-cytometry can reliably map intravoxel, nonparametric cell size distributions and has the potential to discriminate smaller and larger cells. The application in breast cancer patients demonstrates the feasibility of direct translation of MRI-cytometry to clinical applications. CONCLUSION The proposed MRI-cytometry method can characterize nonparametric cell size distributions in human tumors, which potentially provides a practical imaging approach to derive specific histopathological information on biological tissues.
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Affiliation(s)
- Junzhong Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA,Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA,Corresponding author: Junzhong Xu. Vanderbilt University Medical Center, Institute of Imaging Science, 1161 21 Avenue South, AAA 3113 MCN, Nashville, TN 37232-2310, United States. Fax: +1 615 322 0734. (Junzhong Xu). Twitter: @JunzhongXu
| | - Xiaoyu Jiang
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Sean P Devan
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Lori R. Arlinghaus
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Eliot T. McKinley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Jingping Xie
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Qing Wang
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
| | - A. Bapsi Chakravarthy
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yong Wang
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA,Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
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Pediatric Molecular Imaging. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00075-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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44
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Xu J. Probing neural tissues at small scales: Recent progress of oscillating gradient spin echo (OGSE) neuroimaging in humans. J Neurosci Methods 2020; 349:109024. [PMID: 33333089 PMCID: PMC10124150 DOI: 10.1016/j.jneumeth.2020.109024] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 12/04/2020] [Accepted: 12/07/2020] [Indexed: 12/16/2022]
Abstract
The detection sensitivity of diffusion MRI (dMRI) is dependent on diffusion times. A shorter diffusion time can increase the sensitivity to smaller length scales. However, the conventional dMRI uses the pulse gradient spin echo (PGSE) sequence that probes relatively long diffusion times only. To overcome this, the oscillating gradient spin echo (OGSE) sequence has been developed to probe much shorter diffusion times with hardware limitations on preclinical and clinical MRI systems. The OGSE sequence has been previously used on preclinical animal MRI systems. Recently, several studies have translated the OGSE sequence to humans on clinical MRI systems and achieved new information that is invisible using conventional PGSE sequence. This paper provides an overview of the recent progress of the OGSE neuroimaging in humans, including the technical improvements in the translation of the OGSE sequence to human imaging and various applications in different neurological disorders and stroke. Some possible future directions of the OGSE sequence are also discussed.
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Affiliation(s)
- Junzhong Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, 37232, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37232, USA; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, 37232, USA.
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45
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Novikov DS. The present and the future of microstructure MRI: From a paradigm shift to normal science. J Neurosci Methods 2020; 351:108947. [PMID: 33096152 DOI: 10.1016/j.jneumeth.2020.108947] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/29/2020] [Accepted: 09/10/2020] [Indexed: 12/29/2022]
Abstract
The aspiration of imaging tissue microstructure with MRI is to uncover micrometer-scale tissue features within millimeter-scale imaging voxels, in vivo. This kind of super-resolution has fueled a paradigm shift within the biomedical imaging community. However, what feels like an ongoing revolution in MRI, has been conceptually experienced in physics decades ago; from this point of view, our current developments can be seen as Thomas Kuhn's "normal science" stage of progress. While the concept of model-based quantification below the nominal imaging resolution is not new, its possibilities in neuroscience and neuroradiology are only beginning to be widely appreciated. This disconnect calls for communicating the progress of tissue microstructure MR imaging to its potential users. Here, a number of recent research developments are outlined in terms of the overarching concept of coarse-graining the tissue structure over an increasing diffusion length. A variety of diffusion models and phenomena are summarized on the phase diagram of diffusion MRI, with the unresolved problems and future directions corresponding to its unexplored domains.
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Affiliation(s)
- Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.
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46
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Jelescu IO, Palombo M, Bagnato F, Schilling KG. Challenges for biophysical modeling of microstructure. J Neurosci Methods 2020; 344:108861. [PMID: 32692999 PMCID: PMC10163379 DOI: 10.1016/j.jneumeth.2020.108861] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/10/2020] [Accepted: 07/14/2020] [Indexed: 02/07/2023]
Abstract
The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifying both hurdles that have been already overcome and outstanding issues. First, we describe the critical initial task of selecting which features of tissue microstructure can be estimated using a model and which acquisition protocol needs to be implemented to make the estimation possible. The model performance should necessarily be tested in realistic numerical simulations and in experimental data - adapting the fitting strategy accordingly, and parameter estimates should be validated against complementary techniques, when/if available. Secondly, the model performance and validity should be explored in pathological conditions, and, if appropriate, dedicated models for pathology should be developed. We build on examples from tumors, ischemia and demyelinating diseases. We then discuss the challenges associated with clinical translation and added value. Finally, we single out four major unresolved challenges that are related to: the availability of a microstructural ground truth, the validation of model parameters which cannot be accessed with complementary techniques, the development of a generalized standard model for any brain region and pathology, and the seamless communication between different parties involved in the development and application of biophysical models of diffusion.
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Wu D, Liu D, Hsu YC, Li H, Sun Y, Qin Q, Zhang Y. Diffusion-prepared 3D gradient spin-echo sequence for improved oscillating gradient diffusion MRI. Magn Reson Med 2020; 85:78-88. [PMID: 32643240 DOI: 10.1002/mrm.28401] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/03/2020] [Accepted: 06/07/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE Oscillating gradient (OG) enables the access of short diffusion times for time-dependent diffusion MRI (dMRI); however, it poses several technical challenges for clinical use. This study proposes a 3D oscillating gradient-prepared gradient spin-echo (OGprep-GRASE) sequence to improve SNR and shorten acquisition time for OG dMRI on clinical scanners. METHODS The 3D OGprep-GRASE sequence consisted of global saturation, diffusion encoding, fat saturation, and GRASE readout modules. Multiplexed sensitivity-encoding reconstruction was used to correct the phase errors between multiple shots. We compared the scan time and SNR of the proposed sequence and the conventional 2D-EPI sequence for OG dMRI at 30-90-mm slice coverage. We also examined the time-dependent diffusivity changes with OG dMRI acquired at frequencies of 50 Hz and 25 Hz and pulsed-gradient dMRI at diffusion times of 30 ms and 60 ms. RESULTS The OGprep-GRASE sequence reduced the scan time by a factor of 1.38, and increased the SNR by 1.74-2.27 times compared with 2D EPI for relatively thick slice coverage (60-90 mm). The SNR gain led to improved diffusion-tensor reconstruction in the multishot protocols. Image distortion in 2D-EPI images was also reduced in GRASE images. Diffusivity measurements from the pulsed-gradient dMRI and OG dMRI showed clear diffusion-time dependency in the white matter and gray matter of the human brain, using both the GRASE and EPI sequences. CONCLUSION The 3D OGprep-GRASE sequence improved scan time and SNR and reduced image distortion compared with the 2D multislice acquisition for OG dMRI on a 3T clinical system, which may facilitate the clinical translation of time-dependent dMRI.
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Affiliation(s)
- Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dapeng Liu
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Csenter for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yi-Cheng Hsu
- MR Collaboration, Siemens Healthcare China, Shanghai, China
| | - Haotian Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi Sun
- MR Collaboration, Siemens Healthcare China, Shanghai, China
| | - Qin Qin
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Csenter for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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Xu J, Jiang X, Li H, Arlinghaus LR, McKinley ET, Devan SP, Hardy BM, Xie J, Kang H, Chakravarthy AB, Gore JC. Magnetic resonance imaging of mean cell size in human breast tumors. Magn Reson Med 2020; 83:2002-2014. [PMID: 31765494 PMCID: PMC7047520 DOI: 10.1002/mrm.28056] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 10/08/2019] [Accepted: 10/08/2019] [Indexed: 02/05/2023]
Abstract
PURPOSE Cell size is a fundamental characteristic of all tissues, and changes in cell size in cancer reflect tumor status and response to treatments, such as apoptosis and cell-cycle arrest. Unfortunately, cell size can currently be obtained only by pathological evaluation of tumor tissue samples obtained invasively. Previous imaging approaches are limited to preclinical MRI scanners or require relatively long acquisition times that are impractical for clinical imaging. There is a need to develop cell-size imaging for clinical applications. METHODS We propose a clinically feasible IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) approach that can characterize mean cell sizes in solid tumors. We report the use of a combination of pulse sequences, using different gradient waveforms implemented on clinical MRI scanners and analytical equations based on these waveforms to analyze diffusion-weighted MRI signals and derive specific microstructural parameters such as cell size. We also describe comprehensive validations of this approach using computer simulations, cell experiments in vitro, and animal experiments in vivo and demonstrate applications in preoperative breast cancer patients. RESULTS With fast acquisitions (~7 minutes), IMPULSED can provide high-resolution (1.3 mm in-plane) mapping of mean cell size of human tumors in vivo on clinical 3T MRI scanners. All validations suggest that IMPULSED provides accurate and reliable measurements of mean cell size. CONCLUSION The proposed IMPULSED method can assess cell-size variations in tumors of breast cancer patients, which may have the potential to assess early response to neoadjuvant therapy.
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Affiliation(s)
- Junzhong Xu
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA,Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA,Corresponding author: Address: Vanderbilt University, Institute of Imaging Science, 1161 21 Avenue South, AAA 3113 MCN, Nashville, TN 37232-2310, United States. Fax: +1 615 322 0734. (Junzhong Xu), Twitter: @JunzhongXu
| | - Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hua Li
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Lori R. Arlinghaus
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Eliot T. McKinley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Sean P. Devan
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Benjamin M. Hardy
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
| | - Jingping Xie
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - A. Bapsi Chakravarthy
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - John C. Gore
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA,Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
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Jiang X, Dudzinski S, Beckermann KE, Young K, McKinley E, J McIntyre O, Rathmell JC, Xu J, Gore JC. MRI of tumor T cell infiltration in response to checkpoint inhibitor therapy. J Immunother Cancer 2020; 8:e000328. [PMID: 32581044 PMCID: PMC7312343 DOI: 10.1136/jitc-2019-000328] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/16/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Immune checkpoint inhibitors, the most widespread class of immunotherapies, have demonstrated unique response patterns that are not always adequately captured by traditional response criteria such as the Response Evaluation Criteria in Solid Tumors or even immune-specific response criteria. These response metrics rely on monitoring tumor growth, but an increase in tumor size and/or appearance after starting immunotherapy does not always represent tumor progression, but also can be a result of T cell infiltration and thus positive treatment response. Therefore, non-invasive and longitudinal monitoring of T cell infiltration are needed to assess the effects of immunotherapies such as checkpoint inhibitors. Here, we proposed an innovative concept that a sufficiently large influx of tumor infiltrating T cells, which have a smaller diameter than cancer cells, will change the diameter distribution and decrease the average size of cells within a volume to a degree that can be quantified by non-invasive MRI. METHODS We validated our hypothesis by studying tumor response to combination immune-checkpoint blockade (ICB) of anti-PD-1 and anti-CTLA4 in a mouse model of colon adenocarcinoma (MC38). The response was monitored longitudinally using Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion (IMPULSED), a diffusion MRI-based method which has been previously shown to non-invasively map changes in intracellular structure and cell sizes with the spatial resolution of MRI, in cell cultures and in animal models. Tumors were collected for immunohistochemical and flow cytometry analyzes immediately after the last imaging session. RESULTS Immunohistochemical analysis revealed that increased T cell infiltration of the tumors results in a decrease in mean cell size (eg, a 10% increase of CD3+ T cell fraction results a ~1 µm decrease in the mean cell size). IMPULSED showed that the ICB responders, mice with tumor volumes were less than 250 mm3 or had tumors with stable or decreased volumes, had significantly smaller mean cell sizes than both Control IgG-treated tumors and ICB non-responder tumors. CONCLUSIONS IMPULSED-derived cell size could potentially serve as an imaging marker for differentiating responsive and non-responsive tumors after checkpoint inhibitor therapies, a current clinical challenge that is not solved by simply monitoring tumor growth.
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Affiliation(s)
- Xiaoyu Jiang
- Vanderbilt University Institute of Imaging Science, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Stephanie Dudzinski
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, United States
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Kathryn E Beckermann
- Division of Hematology/Oncology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Kirsten Young
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Eliot McKinley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Oliver J McIntyre
- Vanderbilt University Institute of Imaging Science, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, United States
- Department of Cancer Biology, Vanderbilt University, Nashville, TN 37232, United States
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN 37232, United States
| | - Jeffrey C Rathmell
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Vanderbilt Center for Immunobiology, Vanderbilt University School of Medicine, Nashville, TN 37232, United States
| | - Junzhong Xu
- Vanderbilt University Institute of Imaging Science, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, United States
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, United States
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, United States
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, United States
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Jiang X, Xu J, Gore JC. Mapping hepatocyte size in vivo using temporal diffusion spectroscopy MRI. Magn Reson Med 2020; 84:2671-2683. [PMID: 32333469 DOI: 10.1002/mrm.28299] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 03/11/2020] [Accepted: 04/03/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE The goal of this study is to implement a noninvasive method for in vivo mapping of hepatocyte size. This method will have a broad range of clinical and preclinical applications, as pathological changes in hepatocyte sizes are relevant for the accurate diagnosis and assessments of treatment response of liver diseases. METHODS Building on the concepts of temporal diffusion spectroscopy in MRI, a clinically feasible imaging protocol named IMPULSED (Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion) has been developed, which is able to report measurements of cell sizes noninvasively. This protocol acquires a selected set of diffusion imaging data and fits them to a model of water compartments in tissues to derive robust estimates of the cellular structures that restrict free diffusion. Here, we adapt and further develop this approach to measure hepatocyte sizes in vivo. We validated IMPULSED in livers of mice and rats and implemented it to image healthy human subjects using a clinical 3T MRI scanner. RESULTS The IMPULSED-derived mean hepatocyte sizes for rats and mice are about 15-20 µm and agree well with histological findings. Maps of mean hepatocyte size for humans can be achieved in less than 15 minutes, a clinically feasible scan time. CONCLUSION Our results suggest that this method has potential to overcome major limitations of liver biopsy and provide noninvasive mapping of hepatocyte sizes in clinical applications.
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Affiliation(s)
- Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA
| | - Junzhong Xu
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA.,Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - John C Gore
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA.,Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, USA.,Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA
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