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Jiang X, Harkins KD, Xie J, Wang J, Zu Z, Gore JC, Xu J. Joint estimation of compartment-specific T 2 relaxation and tumor microstructure using multi-TE IMPULSED MRI. Magn Reson Med 2025; 93:96-107. [PMID: 39164611 DOI: 10.1002/mrm.30254] [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: 01/15/2024] [Revised: 07/08/2024] [Accepted: 07/30/2024] [Indexed: 08/22/2024]
Abstract
PURPOSE This study aims to assess how T2 heterogeneity biases IMPULSED-derived metrics of tissue microstructure in solid tumors and evaluate the potential of estimating multi-compartmental T2 and microstructural parameters simultaneously. METHODS This study quantifies the impact of T2 relaxation on IMPULSED-derived microstructural parameters using computer simulations and in vivo multi-TE IMPULSED MRI in five tumor models, including brain, breast, prostate, melanoma, and colon cancer. A comprehensive T2 + IMPULSED method was developed to fit multi-compartmental T2 and microstructural parameters simultaneously. A Bayesian model selection approach was carried out voxel-wisely to determine if the T2 heterogeneity needs to be included in IMPULSED MRI in cancer. RESULTS Simulations suggest that T2 heterogeneity has a minor effect on the estimation of d in tissues with intermediate or high cell density, but significantly biases the estimation ofv in $$ {v}_{in} $$ with low cell density. For the in vivo animal experiments, all IMPULSED metrics exceptv in $$ {v}_{in} $$ are statistically independent on TE. For B16 tumors, the IMPULSED-derivedv in $$ {v}_{in} $$ exhibited a notable increase with longer TEs. For MDA-MB-231 tumors, IMPULSED-derivedv in $$ {v}_{in} $$ showed a significant increase with increasing TEs. The T2 + IMPULSED-derivedT 2 in $$ {T}_2^{in} $$ of all five tumor models are consistently smaller thanT 2 ex $$ {T}_2^{ex} $$ . CONCLUSIONS The findings from this study highlight two key observations: (i) TE has a negligible impact on IMPULSED-derived cell sizes, and (ii) the TE-dependence of IMPULSED-derived intracellular volume fractions used in T2 + IMPULSED modeling to estimateT 2 in $$ {T}_2^{in} $$ andT 2 ex $$ {T}_2^{ex} $$ . These insights contribute to the ongoing development and refinement of non-invasive MRI techniques for measuring cell sizes.
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Affiliation(s)
- Xiaoyu Jiang
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kevin D Harkins
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jingping Xie
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jian Wang
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, USA
| | - Junzhong Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, USA
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Partridge SC, Xu J. Cellular Characterization of Breast Cancer Using Microstructural Diffusion MRI. Radiology 2024; 313:e242268. [PMID: 39436293 DOI: 10.1148/radiol.242268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2024]
Affiliation(s)
- Savannah C Partridge
- From the Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, 1144 Eastlake Ave E, Seattle, WA 98109 (S.C.P.); and Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn (J.X.)
| | - Junzhong Xu
- From the Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, 1144 Eastlake Ave E, Seattle, WA 98109 (S.C.P.); and Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn (J.X.)
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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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Iima M, Yamamoto A. Editorial for "In Vivo Microstructure Imaging in Oropharyngeal Squamous Cell Carcinoma Using the Random Walk With Barriers Model". J Magn Reson Imaging 2024; 59:939-940. [PMID: 37452628 DOI: 10.1002/jmri.28893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 06/18/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023] Open
Affiliation(s)
- Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, Kyoto, Japan
| | - Akira Yamamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Center for Medical Education and Internationalization, Kyoto University Graduate School of Medicine, Kyoto, Japan
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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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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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7
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Yang T, Li Y, Ye Z, Yao S, Li Q, Yuan Y, Song B. Diffusion Weighted Imaging of the Abdomen and Pelvis: Recent Technical Advances and Clinical Applications. Acad Radiol 2023; 30:470-482. [PMID: 36038417 DOI: 10.1016/j.acra.2022.07.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/20/2022] [Accepted: 07/23/2022] [Indexed: 01/25/2023]
Abstract
Diffusion weighted imaging (DWI) serves as one of the most important functional magnetic resonance imaging techniques in abdominal and pelvic imaging. It is designed to reflect the diffusion of water molecules and is particularly sensitive to the malignancies. Yet, the limitations of image distortion and artifacts in single-shot DWI may hamper its widespread use in clinical practice. With recent technical advances in DWI, such as simultaneous multi-slice excitation, computed or reduced field-of-view techniques, as well as advanced shimming methods, it is possible to achieve shorter acquisition time, better image quality, and higher robustness in abdominopelvic DWI. This review discussed the recent advances of each DWI approach, and highlighted its future perspectives in abdominal and pelvic imaging, hoping to familiarize physicians and radiologists with the technical improvements in this field and provide future research directions.
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Affiliation(s)
- Ting Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ying Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Qing Li
- MR Collaborations, Siemens Healthcare, Shanghai, China
| | - Yuan Yuan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China; Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China.
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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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9
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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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10
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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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Ludwig D, Laun FB, Klika KD, Rauch J, Ladd ME, Bachert P, Kuder TA. Diffusion pore imaging in the presence of extraporal water. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 339:107219. [PMID: 35533642 DOI: 10.1016/j.jmr.2022.107219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 03/23/2022] [Accepted: 04/13/2022] [Indexed: 06/14/2023]
Abstract
Diffusion-weighted imaging (DWI) is a powerful, non-invasive tool which is widely used in clinical routine. Mostly, apparent diffusion coefficient maps are acquired, which cannot be related directly to cellular structure. More recently it was shown that DWI is able to reconstruct pore shapes using a specialized magnetic field gradient scheme so that cell size distributions may be obtained. So far, artificial systems have been used for experimental demonstration without extraporal signal components and relatively low gradient amplitudes. The aim of this study was to investigate the feasibility of diffusion pore imaging in the presence of extraporal fluids and to develop correction methods for the effects arising from extraporal signal contributions. Monte Carlo simulations and validation experiments on a 14.1 T NMR spectrometer equipped with a dedicated diffusion probe head were performed. Both by using a filter gradient approach suppressing extraporal signal components as well as by using post-processing methods relying on the Gaussian phase approximation, it was possible to reconstruct pore space functions in the presence of extraporal fluids with little to no deviations from the expectations. These results may be a significant step towards application of diffusion pore imaging to biological samples.
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Affiliation(s)
- Dominik Ludwig
- Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Frederik Bernd Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Karel D Klika
- Molecular Structure Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Julian Rauch
- Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany; Max-Planck-Institute for Nuclear Physics, Heidelberg, Germany
| | - Mark Edward Ladd
- Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany; Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
| | - Peter Bachert
- Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Tristan Anselm Kuder
- Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany.
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12
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Li Y, Kim MM, Wahl DR, Lawrence TS, Parmar H, Cao Y. Survival Prediction Analysis in Glioblastoma With Diffusion Kurtosis Imaging. Front Oncol 2021; 11:690036. [PMID: 34336676 PMCID: PMC8316991 DOI: 10.3389/fonc.2021.690036] [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: 04/02/2021] [Accepted: 06/24/2021] [Indexed: 11/13/2022] Open
Abstract
SIMPLE SUMMARY Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Diffusion kurtosis imaging (DKI) has characterized non-Gaussian diffusion behaviors in brain normal tissue and gliomas, but there are very limited efforts in investigating treatment responses of kurtosis in GBM. This study aimed to investigate whether any parameter derived from the DKI is a significant predictor of overall survival (OS). We found that the large mean, 80 and 90 percentile kurtosis values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1-weighted images pre-RT were significantly associated with reduced OS. In the multivariate Cox model, the mean kurtosis Gd-GTV pre-RT after considering effects of age, extent of surgery, and methylation were significant predictors of OS. In addition, the 80 and 90 percentile kurtosis values in Gd-GTV post RT were significantly associated with progression free survival (PFS). The DKI model demonstrates the potential to predict outcomes in the patients with GBM. PURPOSE Non-Gaussian diffusion behaviors in gliomas have been characterized by diffusion kurtosis imaging (DKI). But there are very limited efforts in investigating the kurtosis in glioblastoma (GBM) and its prognostic and predictive values. This study aimed to investigate whether any of the diffusion kurtosis parameters derived from DKI is a significant predictor of overall survival. METHODS AND MATERIALS Thirty-three patients with GBM had pre-radiation therapy (RT) and mid-RT diffusion weighted (DW) images. Kurtosis and diffusion coefficient (DC) values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1 weighted images pre-RT and mid-RT were calculated. Univariate and multivariate Cox models were used to evaluate the DKI parameters and clinical factors for prediction of OS and PFS. RESULTS The large mean kurtosis values in the Gd-GTV pre-RT were significantly associated with reduced OS (p = 0.02), but the values at mid-RT were not (p > 0.8). In the multivariate Cox model, the mean kurtosis in the Gd-GTV pre-RT (p = 0.009) was still a significant predictor of OS after adjusting effects of age, O6-Methylguanine-DNA Methyl transferase (MGMT) methylation and extent of resection. In Gd-GTV post-RT, 80 and 90 percentile kurtosis values were significant predictors (p ≤ 0.05) for progression free survival (PFS). CONCLUSION The DKI model demonstrates the potential to predict OS and PFS in the patients with GBM. Further development and histopathological validation of the DKI model will warrant its role in clinical management of GBM.
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Affiliation(s)
- Yuan Li
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Michelle M. Kim
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Daniel R. Wahl
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Theodore S. Lawrence
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Hemant Parmar
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
| | - Yue Cao
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
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