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Shi D, Liu F, Li S, Chen L, Jiang X, Gore JC, Zheng Q, Guo H, Xu J. Restriction-induced time-dependent transcytolemmal water exchange: Revisiting the Kӓrger exchange model. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2024; 367:107760. [PMID: 39241283 DOI: 10.1016/j.jmr.2024.107760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 08/21/2024] [Accepted: 08/26/2024] [Indexed: 09/09/2024]
Abstract
The Kӓrger model and its derivatives have been widely used to incorporate transcytolemmal water exchange rate, an essential characteristic of living cells, into analyses of diffusion MRI (dMRI) signals from tissues. The Kӓrger model consists of two homogeneous exchanging components coupled by an exchange rate constant and assumes measurements are made with sufficiently long diffusion time and slow water exchange. Despite successful applications, it remains unclear whether these assumptions are generally valid for practical dMRI sequences and biological tissues. In particular, barrier-induced restrictions to diffusion produce inhomogeneous magnetization distributions in relatively large-sized compartments such as cancer cells, violating the above assumptions. The effects of this inhomogeneity are usually overlooked. We performed computer simulations to quantify how restriction effects, which in images produce edge enhancements at compartment boundaries, influence different variants of the Kӓrger-model. The results show that the edge enhancement effect will produce larger, time-dependent estimates of exchange rates in e.g., tumors with relatively large cell sizes (>10 μm), resulting in overestimations of water exchange as previously reported. Moreover, stronger diffusion gradients, longer diffusion gradient durations, and larger cell sizes, all cause more pronounced edge enhancement effects. This helps us to better understand the feasibility of the Kärger model in estimating water exchange in different tissue types and provides useful guidance on signal acquisition methods that may mitigate the edge enhancement effect. This work also indicates the need to correct the overestimated transcytolemmal water exchange rates obtained assuming the Kärger-model.
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Affiliation(s)
- Diwei Shi
- Center for Nano and Micro Mechanics, Department of Engineering Mechanics, Tsinghua University, Beijing, China
| | - Fan Liu
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Sisi Li
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Li Chen
- Center for Nano and Micro Mechanics, Department of Engineering Mechanics, Tsinghua University, Beijing, China
| | - Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - John C Gore
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States
| | - Quanshui Zheng
- Center for Nano and Micro Mechanics, Department of Engineering Mechanics, Tsinghua University, Beijing, China
| | - Hua Guo
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Junzhong Xu
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States.
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2
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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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Chacko AN, Miller ADC, Dhanabalan KM, Mukherjee A. Exploring the potential of water channels for developing genetically encoded reporters and biosensors for diffusion-weighted MRI. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2024; 365:107743. [PMID: 39053029 DOI: 10.1016/j.jmr.2024.107743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 07/02/2024] [Accepted: 07/17/2024] [Indexed: 07/27/2024]
Abstract
Genetically encoded reporters for magnetic resonance imaging (MRI) offer a valuable technology for making molecular-scale measurements of biological processes within living organisms with high anatomical resolution and whole-organ coverage without relying on ionizing radiation. However, most MRI reporters rely on synthetic contrast agents, typically paramagnetic metals and metal complexes, which often need to be supplemented exogenously to create optimal contrast. To eliminate the need for synthetic contrast agents, we previously introduced aquaporin-1, a mammalian water channel, as a new reporter gene for the fully autonomous detection of genetically labeled cells using diffusion-weighted MRI. In this study, we aimed to expand the toolbox of diffusion-based genetic reporters by modulating aquaporin membrane trafficking and harnessing the evolutionary diversity of water channels across species. We identified a number of new water channels that functioned as diffusion-weighted reporter genes. In addition, we show that loss-of-function variants of yeast and human aquaporins can be leveraged to design first-in-class diffusion-based sensors for detecting the activity of a model protease within living cells.
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Affiliation(s)
- Asish N Chacko
- Department of Chemistry, University of California, Santa Barbara, CA 93106-5080, USA
| | - Austin D C Miller
- Biomolecular Science and Engineering Graduate Program, University of California, Santa Barbara, CA 93106-5080, USA
| | - Kaamini M Dhanabalan
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106-5080, USA
| | - Arnab Mukherjee
- Department of Chemistry, University of California, Santa Barbara, CA 93106-5080, USA; Biomolecular Science and Engineering Graduate Program, University of California, Santa Barbara, CA 93106-5080, USA; Department of Chemical Engineering, University of California, Santa Barbara, CA 93106-5080, USA; Department of Bioengineering, University of California, Santa Barbara, CA 93106-5080, USA.
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Yun J, Huang Y, Miller ADC, Chang BL, Baldini L, Dhanabalan KM, Li E, Li H, Mukherjee A. Destabilized reporters for background-subtracted, chemically-gated, and multiplexed deep-tissue imaging. Chem Sci 2024; 15:11108-11121. [PMID: 39027298 PMCID: PMC11253201 DOI: 10.1039/d4sc00377b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/23/2024] [Indexed: 07/20/2024] Open
Abstract
Tracking gene expression in deep tissues requires genetic reporters that can be unambiguously detected using tissue penetrant techniques. Magnetic resonance imaging (MRI) is uniquely suited for this purpose; however, there is a dearth of reporters that can be reliably linked to gene expression with minimal interference from background tissue signals. Here, we present a conceptually new method for generating background-subtracted, drug-gated, multiplex images of gene expression using MRI. Specifically, we engineered chemically erasable reporters consisting of a water channel, aquaporin-1, fused to destabilizing domains, which are stabilized by binding to cell-permeable small-molecule ligands. We showed that this approach allows for highly specific detection of gene expression through differential imaging. In addition, by engineering destabilized aquaporin-1 variants with orthogonal ligand requirements, it is possible to distinguish distinct subpopulations of cells in mixed cultures. Finally, we demonstrated this approach in a mouse tumor model through differential imaging of gene expression with minimal background.
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Affiliation(s)
- Jason Yun
- Department of Chemistry, University of California Santa Barbara CA 93106 USA
| | - Yimeng Huang
- Department of Chemistry, University of California Santa Barbara CA 93106 USA
| | - Austin D C Miller
- Biomolecular Science and Engineering Graduate Program, University of California Santa Barbara CA 93106 USA
| | - Brandon L Chang
- Department of Molecular, Cell, and Developmental Biology, University of California Santa Barbara CA 93106 USA
| | - Logan Baldini
- Department of Chemical Engineering, University of California Santa Barbara CA 93106 USA
| | - Kaamini M Dhanabalan
- Department of Chemical Engineering, University of California Santa Barbara CA 93106 USA
| | - Eugene Li
- Department of Chemical Engineering, University of California Santa Barbara CA 93106 USA
| | - Honghao Li
- Department of Chemistry, University of California Santa Barbara CA 93106 USA
| | - Arnab Mukherjee
- Department of Chemistry, University of California Santa Barbara CA 93106 USA
- Biomolecular Science and Engineering Graduate Program, University of California Santa Barbara CA 93106 USA
- Department of Chemical Engineering, University of California Santa Barbara CA 93106 USA
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Di Gregorio E, Papi C, Conti L, Di Lorenzo A, Cavallari E, Salvatore M, Cavaliere C, Ferrauto G, Aime S. A Magnetic Resonance Imaging-Chemical Exchange Saturation Transfer (MRI-CEST) Method for the Detection of Water Cycling across Cellular Membranes. Angew Chem Int Ed Engl 2024; 63:e202313485. [PMID: 37905585 DOI: 10.1002/anie.202313485] [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: 09/11/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 11/02/2023]
Abstract
Water cycling across the membrane transporters is considered a hallmark of cellular metabolism and it could be of high diagnostic relevance in the characterization of tumors and other diseases. The method relies on the response of intracellular proton exchanging molecules to the presence of extracellular Gd-based contrast agents (GBCAs). Paramagnetic GBCAs enhances the relaxation rate of water molecules in the extracellular compartment and, through membrane exchange, the relaxation enhancement is transferred to intracellular molecules. The effect is detected at the MRI-CEST (Magnetic Resonance Imaging - Chemical Exchange Saturation Transfer) signal of intracellular proton exchanging molecules. The magnitude of the change in the CEST response reports on water cycling across the membrane. The method has been tested on Red Blood Cells and on orthotopic murine models of breast cancer with different degree of malignancy (4T1, TS/A and 168FARN). The distribution of voxels reporting on membrane permeability fits well with the cells' aggressiveness and acts as an early reporter to monitor therapeutic treatments.
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Affiliation(s)
- Enza Di Gregorio
- Department of Molecular Biotechnologies and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Chiara Papi
- Department of Molecular Biotechnologies and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Laura Conti
- Department of Molecular Biotechnologies and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Antonino Di Lorenzo
- Department of Molecular Biotechnologies and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Eleonora Cavallari
- Department of Molecular Biotechnologies and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Marco Salvatore
- IRCCS SDN SynLab, Via E. Gianturco 113, 80143, Napoli, Italy
| | - Carlo Cavaliere
- IRCCS SDN SynLab, Via E. Gianturco 113, 80143, Napoli, Italy
| | - Giuseppe Ferrauto
- Department of Molecular Biotechnologies and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy
| | - Silvio Aime
- IRCCS SDN SynLab, Via E. Gianturco 113, 80143, Napoli, Italy
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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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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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Yun J, Baldini L, Huang Y, Li E, Li H, Chacko AN, Miller AD, Wan J, Mukherjee A. Engineering ligand stabilized aquaporin reporters for magnetic resonance imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.02.543364. [PMID: 37333371 PMCID: PMC10274688 DOI: 10.1101/2023.06.02.543364] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Imaging transgene expression in live tissues requires reporters that are detectable with deeply penetrant modalities, such as magnetic resonance imaging (MRI). Here, we show that LSAqp1, a water channel engineered from aquaporin-1, can be used to create background-free, drug-gated, and multiplex images of gene expression using MRI. LSAqp1 is a fusion protein composed of aquaporin-1 and a degradation tag that is sensitive to a cell-permeable ligand, which allows for dynamic small molecule modulation of MRI signals. LSAqp1 improves specificity for imaging gene expression by allowing reporter signals to be conditionally activated and distinguished from the tissue background by difference imaging. In addition, by engineering destabilized aquaporin-1 variants with different ligand requirements, it is possible to image distinct cell types simultaneously. Finally, we expressed LSAqp1 in a tumor model and showed successful in vivo imaging of gene expression without background activity. LSAqp1 provides a conceptually unique approach to accurately measure gene expression in living organisms by combining the physics of water diffusion and biotechnology tools to control protein stability.
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Affiliation(s)
- Jason Yun
- Department of Chemistry, University of California, Santa Barbara, CA 93106, USA
| | - Logan Baldini
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA
| | - Yimeng Huang
- Department of Chemistry, University of California, Santa Barbara, CA 93106, USA
| | - Eugene Li
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA
| | - Honghao Li
- Department of Chemistry, University of California, Santa Barbara, CA 93106, USA
| | - Asish N. Chacko
- Department of Chemistry, University of California, Santa Barbara, CA 93106, USA
| | - Austin D.C. Miller
- Biomolecular Science and Engineering, University of California, Santa Barbara, CA 93106, USA
| | - Jinyang Wan
- Department of Chemistry, University of California, Santa Barbara, CA 93106, USA
| | - Arnab Mukherjee
- Department of Chemistry, University of California, Santa Barbara, CA 93106, USA
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA
- Biomolecular Science and Engineering, University of California, Santa Barbara, CA 93106, USA
- Biological Engineering, University of California, Santa Barbara, CA 93106, USA
- Neuroscience Research Institute, University of California, Santa Barbara, CA 93106, USA
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9
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Zhou Z, Li Q, Liao C, Cao X, Liang H, Chen Q, Pu R, Ye H, Tong Q, He H, Zhong J. Optimized three-dimensional ultrashort echo time: Magnetic resonance fingerprinting for myelin tissue fraction mapping. Hum Brain Mapp 2023; 44:2209-2223. [PMID: 36629336 PMCID: PMC10028641 DOI: 10.1002/hbm.26203] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 12/12/2022] [Accepted: 01/01/2023] [Indexed: 01/12/2023] Open
Abstract
Quantitative assessment of brain myelination has gained attention for both research and diagnosis of neurological diseases. However, conventional pulse sequences cannot directly acquire the myelin-proton signals due to its extremely short T2 and T2* values. To obtain the myelin-proton signals, dedicated short T2 acquisition techniques, such as ultrashort echo time (UTE) imaging, have been introduced. However, it remains challenging to isolate the myelin-proton signals from tissues with longer T2. In this article, we extended our previous two-dimensional ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) with dual-echo acquisition to three dimensional (3D). Given a relatively low proton density (PD) of myelin-proton, we utilized Cramér-Rao Lower Bound to encode myelin-proton with the maximal SNR efficiency for optimizing the MR fingerprinting design, in order to improve the sensitivity of the sequence to myelin-proton. In addition, with a second echo of approximately 3 ms, myelin-water component can be also captured. A myelin-tissue (myelin-proton and myelin-water) fraction mapping can be thus calculated. The optimized 3D UTE-MRF with dual-echo acquisition is tested in simulations, physical phantom and in vivo studies of both healthy subjects and multiple sclerosis patients. The results suggest that the rapidly decayed myelin-proton and myelin-water signal can be depicted with UTE signals of our method at clinically relevant resolution (1.8 mm isotropic) in 15 min. With its good sensitivity to myelin loss in multiple sclerosis patients demonstrated, our method for the whole brain myelin-tissue fraction mapping in clinical friendly scan time has the potential for routine clinical imaging.
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Affiliation(s)
- Zihan Zhou
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qing Li
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- MR Collaborations, Siemens Healthineers Ltd, Shanghai, China
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Hui Liang
- Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Quan Chen
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Run Pu
- Neusoft Medical Systems, Shanghai, China
| | - Huihui Ye
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qiqi Tong
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
- School of Physics, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
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10
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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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11
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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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12
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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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13
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Olesen JL, Østergaard L, Shemesh N, Jespersen SN. Diffusion time dependence, power-law scaling, and exchange in gray matter. Neuroimage 2022; 251:118976. [PMID: 35168088 PMCID: PMC8961002 DOI: 10.1016/j.neuroimage.2022.118976] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/24/2021] [Accepted: 02/04/2022] [Indexed: 12/27/2022] Open
Abstract
Characterizing neural tissue microstructure is a critical goal for future neuroimaging. Diffusion MRI (dMRI) provides contrasts that reflect diffusing spins' interactions with myriad microstructural features of biological systems. However, the specificity of dMRI remains limited due to the ambiguity of its signals vis-à-vis the underlying microstructure. To improve specificity, biophysical models of white matter (WM) typically express dMRI signals according to the Standard Model (SM) and have more recently in gray matter (GM) taken spherical compartments into account (the SANDI model) in attempts to represent cell soma. The validity of the assumptions underlying these models, however, remains largely undetermined, especially in GM. To validate these assumptions experimentally, observing their unique, functional properties, such as the b-1/2 power-law associated with one-dimensional diffusion, has emerged as a fruitful strategy. The absence of this signature in GM, in turn, has been explained by neurite water exchange, non-linear morphology, and/or by obscuring soma signal contributions. Here, we present diffusion simulations in realistic neurons demonstrating that curvature and branching does not destroy the stick power-law behavior in impermeable neurites, but also that their signal is drowned by the soma signal under typical experimental conditions. Nevertheless, by studying the GM dMRI signal's behavior as a function of diffusion weighting as well as time, we identify an attainable experimental regime in which the neurite signal dominates. Furthermore, we find that exchange-driven time dependence produces a signal behavior opposite to that which would be expected from restricted diffusion, thereby providing a functional signature that disambiguates the two effects. We present data from dMRI experiments in ex vivo rat brain at ultrahigh field of 16.4T and observe a time dependence that is consistent with substantial exchange but also with a GM stick power-law. The first finding suggests significant water exchange between neurites and the extracellular space while the second suggests a small sub-population of impermeable neurites. To quantify these observations, we harness the Kärger exchange model and incorporate the corresponding signal time dependence in the SM and SANDI models.
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Affiliation(s)
- Jonas L Olesen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Leif Østergaard
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Sune N Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark.
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14
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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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15
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Zhang J, Lemberskiy G, Moy L, Fieremans E, Novikov DS, Kim SG. Measurement of cellular-interstitial water exchange time in tumors based on diffusion-time-dependent diffusional kurtosis imaging. NMR IN BIOMEDICINE 2021; 34:e4496. [PMID: 33634508 PMCID: PMC8170918 DOI: 10.1002/nbm.4496] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 02/08/2021] [Indexed: 05/10/2023]
Abstract
PURPOSE To assess the feasibility of using diffusion-time-dependent diffusional kurtosis imaging (tDKI) to measure cellular-interstitial water exchange time (τex ) in tumors, both in animals and in humans. METHODS Preclinical tDKI studies at 7 T were performed with the GL261 glioma model and the 4T1 mammary tumor model injected into the mouse brain. Clinical studies were performed at 3 T with women who had biopsy-proven invasive ductal carcinoma. tDKI measurement was conducted using a diffusion-weighted STEAM pulse sequence with multiple diffusion times (20-800 ms) at a fixed echo time, while keeping the b-values the same (0-3000 s/mm2 ) by adjusting the diffusion gradient strength. The tDKI data at each diffusion time t were used for a weighted linear least-squares fit method to estimate the diffusion-time-dependent diffusivity, D(t), and diffusional kurtosis, K(t). RESULTS Both preclinical and clinical studies showed that, when diffusion time t ≥ 200 ms, D(t) did not have a noticeable change while K(t) decreased monotonically with increasing diffusion time in tumors and t ≥ 100 ms for the cortical ribbon of the mouse brain. The estimated τex averaged median and interquartile range (IQR) of GL261 and 4T1 tumors were 93 (IQR = 89) ms and 68 (78) ms, respectively. For the cortical ribbon, the estimated τex averaged median and IQR were 41 (34) ms for C57BL/6 and 30 (17) ms for BALB/c. For invasive ductal carcinoma, the estimated τex median and IQR of the two breast cancers were 70 (94) and 106 (92) ms. CONCLUSION The results of this proof-of-concept study substantiate the feasibility of using tDKI to measure cellular-interstitial water exchange time without using an exogenous contrast agent.
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Affiliation(s)
- Jin Zhang
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Gregory Lemberskiy
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Els Fieremans
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Sungheon Gene Kim
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
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16
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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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17
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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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18
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Ludwig D, Laun FB, Ladd ME, Bachert P, Kuder TA. Apparent exchange rate imaging: On its applicability and the connection to the real exchange rate. Magn Reson Med 2021; 86:677-692. [PMID: 33749019 DOI: 10.1002/mrm.28714] [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: 03/19/2020] [Revised: 01/11/2021] [Accepted: 01/15/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE Water exchange between the intracellular and extracellular space can be measured using apparent exchange rate (AXR) imaging. The aim of this study was to investigate the relationship between the measured AXR and the geometry of diffusion restrictions, membrane permeability, and the real exchange rate, as well as to explore the applicability of AXR for typical human measurement settings. METHODS The AXR measurements and the underlying exchange rates were simulated using the Monte Carlo method with different geometries, size distributions, packing densities, and a broad range of membrane permeabilities. Furthermore, the influence of SNR and sequence parameters was analyzed. RESULTS The estimated AXR values correspond to the simulated values and show the expected proportionality to membrane permeability, except for fast exchange (ie, AXR > 20 - 30 s - 1 ) and small packing densities. Moreover, it was found that the duration of the filter gradient must be shorter than 2 · AX R - 1 . In cell size and permeability distributions, AXR depends on the average surface-to-volume ratio, permeability, and the packing density. Finally, AXR can be reliably determined in the presence of orientation dispersion in axon-like structures with sufficient gradient sampling (ie, 30 gradient directions). CONCLUSION Currently used experimental settings for in vivo human measurements are well suited for determining AXR, with the exception of single-voxel analysis, due to limited SNR. The detection of changes in membrane permeability in diseased tissue is nonetheless challenging because of the AXR dependence on further factors, such as packing density and geometry, which cannot be disentangled without further knowledge of the underlying cell structure.
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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, Erlangen, 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
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19
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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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20
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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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21
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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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22
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Demetriou E, Kujawa A, Golay X. Pulse sequences for measuring exchange rates between proton species: From unlocalised NMR spectroscopy to chemical exchange saturation transfer imaging. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2020; 120-121:25-71. [PMID: 33198968 DOI: 10.1016/j.pnmrs.2020.06.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 06/27/2020] [Accepted: 06/30/2020] [Indexed: 06/11/2023]
Abstract
Within the field of NMR spectroscopy, the study of chemical exchange processes through saturation transfer techniques has a long history. In the context of MRI, chemical exchange techniques have been adapted to increase the sensitivity of imaging to small fractions of exchangeable protons, including the labile protons of amines, amides and hydroxyls. The MR contrast is generated by frequency-selective irradiation of the labile protons, which results in a reduction of the water signal associated with transfer of the labile protons' saturated magnetization to the protons of the surrounding free water. The signal intensity depends on the rate of chemical exchange and the concentration of labile protons as well as on the properties of the irradiation field. This methodology is referred to as CEST (chemical exchange saturation transfer) imaging. Applications of CEST include imaging of molecules with short transverse relaxation times and mapping of physiological parameters such as pH, temperature, buffer concentration and chemical composition due to the dependency of this chemical exchange effect on all these parameters. This article aims to describe these effects both theoretically and experimentally. In depth analysis and mathematical modelling are provided for all pulse sequences designed to date to measure the chemical exchange rate. Importantly, it has become clear that the background signal from semi-solid protons and the presence of the Nuclear Overhauser Effect (NOE), either through direct dipole-dipole mechanisms or through exchange-relayed signals, complicates the analysis of CEST effects. Therefore, advanced methods to suppress these confounding factors have been developed, and these are also reviewed. Finally, the experimental work conducted both in vitro and in vivo is discussed and the progress of CEST imaging towards clinical practice is presented.
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Affiliation(s)
- Eleni Demetriou
- Brain Repair & Rehabilitation, Institute of Neurology, University College London, United Kingdom.
| | - Aaron Kujawa
- Brain Repair & Rehabilitation, Institute of Neurology, University College London, United Kingdom.
| | - Xavier Golay
- Brain Repair & Rehabilitation, Institute of Neurology, University College London, United Kingdom.
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23
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McHugh DJ, Lipowska‐Bhalla G, Babur M, Watson Y, Peset I, Mistry HB, Hubbard Cristinacce PL, Naish JH, Honeychurch J, Williams KJ, O'Connor JPB, Parker GJM. Diffusion model comparison identifies distinct tumor sub-regions and tracks treatment response. Magn Reson Med 2020; 84:1250-1263. [PMID: 32057115 PMCID: PMC7317874 DOI: 10.1002/mrm.28196] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 01/13/2020] [Accepted: 01/13/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE MRI biomarkers of tumor response to treatment are typically obtained from parameters derived from a model applied to pre-treatment and post-treatment data. However, as tumors are spatially and temporally heterogeneous, different models may be necessary in different tumor regions, and model suitability may change over time. This work evaluates how the suitability of two diffusion-weighted (DW) MRI models varies spatially within tumors at the voxel level and in response to radiotherapy, potentially allowing inference of qualitatively different tumor microenvironments. METHODS DW-MRI data were acquired in CT26 subcutaneous allografts before and after radiotherapy. Restricted and time-independent diffusion models were compared, with regions well-described by the former hypothesized to reflect cellular tissue, and those well-described by the latter expected to reflect necrosis or oedema. Technical and biological validation of the percentage of tissue described by the restricted diffusion microstructural model (termed %MM) was performed through simulations and histological comparison. RESULTS Spatial and radiotherapy-related variation in model suitability was observed. %MM decreased from a mean of 64% at baseline to 44% 6 days post-radiotherapy in the treated group. %MM correlated negatively with the percentage of necrosis from histology, but overestimated it due to noise. Within MM regions, microstructural parameters were sensitive to radiotherapy-induced changes. CONCLUSIONS There is spatial and radiotherapy-related variation in different models' suitability for describing diffusion in tumor tissue, suggesting the presence of different and changing tumor sub-regions. The biological and technical validation of the proposed %MM cancer imaging biomarker suggests it correlates with, but overestimates, the percentage of necrosis.
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Affiliation(s)
- Damien J. McHugh
- Quantitative Biomedical Imaging LaboratoryThe University of ManchesterManchesterUK
- Division of Cancer SciencesThe University of ManchesterManchesterUK
| | - Grazyna Lipowska‐Bhalla
- Quantitative Biomedical Imaging LaboratoryThe University of ManchesterManchesterUK
- Division of Cancer SciencesThe University of ManchesterManchesterUK
| | - Muhammad Babur
- Division of Pharmacy & OptometryThe University of ManchesterManchesterUK
| | - Yvonne Watson
- Quantitative Biomedical Imaging LaboratoryThe University of ManchesterManchesterUK
| | - Isabel Peset
- Imaging and Flow CytometryCancer Research UK Manchester InstituteManchesterUK
| | - Hitesh B. Mistry
- Division of Cancer SciencesThe University of ManchesterManchesterUK
| | | | - Josephine H. Naish
- Division of Cardiovascular SciencesThe University of ManchesterManchesterUK
- Bioxydyn Ltd.ManchesterUK
| | | | - Kaye J. Williams
- Division of Pharmacy & OptometryThe University of ManchesterManchesterUK
| | - James P. B. O'Connor
- Quantitative Biomedical Imaging LaboratoryThe University of ManchesterManchesterUK
- Division of Cancer SciencesThe University of ManchesterManchesterUK
| | - Geoffrey J. M. Parker
- Bioxydyn Ltd.ManchesterUK
- Division of Neuroscience and Experimental PsychologyThe University of ManchesterManchesterUK
- Centre for Medical Image ComputingUniversity College LondonLondonUK
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24
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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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25
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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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26
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Palombo M, Ianus A, Guerreri M, Nunes D, Alexander DC, Shemesh N, Zhang H. SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI. Neuroimage 2020; 215:116835. [PMID: 32289460 PMCID: PMC8543044 DOI: 10.1016/j.neuroimage.2020.116835] [Citation(s) in RCA: 120] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 03/26/2020] [Accepted: 04/06/2020] [Indexed: 11/29/2022] Open
Abstract
This work introduces a compartment-based model for apparent cell body (namely soma) and neurite density imaging (SANDI) using non-invasive diffusion-weighted MRI (DW-MRI). The existing conjecture in brain microstructure imaging through DW-MRI presents water diffusion in white (WM) and gray (GM) matter as restricted diffusion in neurites, modelled by infinite cylinders of null radius embedded in the hindered extra-neurite water. The extra-neurite pool in WM corresponds to water in the extra-axonal space, but in GM it combines water in the extra-cellular space with water in soma. While several studies showed that this microstructure model successfully describe DW-MRI data in WM and GM at b ≤ 3,000 s/mm2 (or 3 ms/μm2), it has been also shown to fail in GM at high b values (b≫3,000 s/mm2 or 3 ms/μm2). Here we hypothesise that the unmodelled soma compartment (i.e. cell body of any brain cell type: from neuroglia to neurons) may be responsible for this failure and propose SANDI as a new model of brain microstructure where soma of any brain cell type is explicitly included. We assess the effects of size and density of soma on the direction-averaged DW-MRI signal at high b values and the regime of validity of the model using numerical simulations and comparison with experimental data from mouse (bmax = 40,000 s/mm2, or 40 ms/μm2) and human (bmax = 10,000 s/mm2, or 10 ms/μm2) brain. We show that SANDI defines new contrasts representing complementary information on the brain cyto- and myelo-architecture. Indeed, we show maps from 25 healthy human subjects of MR soma and neurite signal fractions, that remarkably mirror contrasts of histological images of brain cyto- and myelo-architecture. Although still under validation, SANDI might provide new insight into tissue architecture by introducing a new set of biomarkers of potential great value for biomedical applications and pure neuroscience.
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Affiliation(s)
- Marco Palombo
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK.
| | - Andrada Ianus
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK; Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Michele Guerreri
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
| | - Daniel Nunes
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Daniel C Alexander
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Hui Zhang
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
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27
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Ianuş A, Santiago I, Galzerano A, Montesinos P, Loução N, Sanchez-Gonzalez J, Alexander DC, Matos C, Shemesh N. Higher-order diffusion MRI characterization of mesorectal lymph nodes in rectal cancer. Magn Reson Med 2019; 84:348-364. [PMID: 31850546 DOI: 10.1002/mrm.28102] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 11/05/2019] [Accepted: 11/07/2019] [Indexed: 01/02/2023]
Abstract
PURPOSE Mesorectal lymph node staging plays an important role in treatment decision making. Here, we explore the benefit of higher-order diffusion MRI models accounting for non-Gaussian diffusion effects to classify mesorectal lymph nodes both 1) ex vivo at ultrahigh field correlated with histology and 2) in vivo in a clinical scanner upon patient staging. METHODS The preclinical investigation included 54 mesorectal lymph nodes, which were scanned at 16.4 T with an extensive diffusion MRI acquisition. Eight diffusion models were compared in terms of goodness of fit, lymph node classification ability, and histology correlation. In the clinical part of this study, 10 rectal cancer patients were scanned with diffusion MRI at 1.5 T, and 72 lymph nodes were analyzed with Apparent Diffusion Coefficient (ADC), Intravoxel Incoherent Motion (IVIM), Kurtosis, and IVIM-Kurtosis. RESULTS Compartment models including restricted and anisotropic diffusion improved the preclinical data fit, as well as the lymph node classification, compared to standard ADC. The comparison with histology revealed only moderate correlations, and the highest values were observed between diffusion anisotropy metrics and cell area fraction. In the clinical study, the diffusivity from IVIM-Kurtosis was the only metric showing significant differences between benign (0.80 ± 0.30 μm2 /ms) and malignant (1.02 ± 0.41 μm2 /ms, P = .03) nodes. IVIM-Kurtosis also yielded the largest area under the receiver operating characteristic curve (0.73) and significantly improved the node differentiation when added to the standard visual analysis by experts based on T2 -weighted imaging. CONCLUSION Higher-order diffusion MRI models perform better than standard ADC and may be of added value for mesorectal lymph node classification in rectal cancer patients.
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Affiliation(s)
- Andrada Ianuş
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.,Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Ines Santiago
- Champalimaud Clinical Centre, Champalimaud Centre for the Unknown, Lisbon, Portugal.,Nova Medical School, Lisbon, Portugal
| | - Antonio Galzerano
- Champalimaud Clinical Centre, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | | | | | | | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Celso Matos
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.,Champalimaud Clinical Centre, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
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28
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Jiang X, McKinley ET, Xie J, Li H, Xu J, Gore JC. In vivo magnetic resonance imaging of treatment-induced apoptosis. Sci Rep 2019; 9:9540. [PMID: 31266982 PMCID: PMC6606573 DOI: 10.1038/s41598-019-45864-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 06/03/2019] [Indexed: 01/06/2023] Open
Abstract
Imaging apoptosis could provide an early and specific means to monitor tumor responses to treatment. To date, despite numerous attempts to develop molecular imaging approaches, there is still no widely-accepted and reliable method for in vivo imaging of apoptosis. We hypothesized that the distinct cellular morphologic changes associated with treatment-induced apoptosis, such as cell shrinkage, cytoplasm condensation, and DNA fragmentation, can be detected by temporal diffusion spectroscopy imaging (TDSI). Cetuximab-induced apoptosis was assessed in vitro and in vivo with cetuximab-sensitive (DiFi) and insensitive (HCT-116) human colorectal cancer cell lines by TDSI. TDSI findings were complemented by flow cytometry and immunohistochemistry. Cell cycle analysis and flow cytometry detected apoptotic cell shrinkage in cetuximab-treated DiFi cells, and significant apoptosis was confirmed by histology. TDSI-derived parameters quantified key morphological changes including cell size decreases during apoptosis in responsive tumors that occurred earlier than gross tumor volume regression. TDSI provides a unique measurement of apoptosis by identifying cellular characteristics, particularly cell shrinkage. The method will assist in understanding the underlying biology of solid tumors and predict tumor response to therapies. TDSI is free of any exogenous agent or radiation, and hence is very suitable to be incorporated into clinical applications.
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Affiliation(s)
- Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University, 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, Nashville, TN, 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, 37232, USA
| | - Hua Li
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, 37232, USA
| | - Junzhong Xu
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, 37232, USA.
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, 37232, USA.
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, 37232, USA.
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, 37232, USA.
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37232, USA.
| | - John C Gore
- Institute of Imaging Science, Vanderbilt University, Nashville, TN, 37232, USA.
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, 37232, USA.
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, 37232, USA.
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, 37232, USA.
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37232, USA.
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA.
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Bailey C, Bourne RM, Siow B, Johnston EW, Brizmohun Appayya M, Pye H, Heavey S, Mertzanidou T, Whitaker H, Freeman A, Patel D, Shaw GL, Sridhar A, Hawkes DJ, Punwani S, Alexander DC, Panagiotaki E. VERDICT MRI validation in fresh and fixed prostate specimens using patient-specific moulds for histological and MR alignment. NMR IN BIOMEDICINE 2019; 32:e4073. [PMID: 30779863 PMCID: PMC6519204 DOI: 10.1002/nbm.4073] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 01/07/2019] [Accepted: 01/09/2019] [Indexed: 06/09/2023]
Abstract
The VERDICT framework for modelling diffusion MRI data aims to relate parameters from a biophysical model to histological features used for tumour grading in prostate cancer. Validation of the VERDICT model is necessary for clinical use. This study compared VERDICT parameters obtained ex vivo with histology in five specimens from radical prostatectomy. A patient-specific 3D-printed mould was used to investigate the effects of fixation on VERDICT parameters and to aid registration to histology. A rich diffusion data set was acquired in each ex vivo prostate before and after fixation. At both time points, data were best described by a two-compartment model: the model assumes that an anisotropic tensor compartment represents the extracellular space and a restricted sphere compartment models the intracellular space. The effect of fixation on model parameters associated with tissue microstructure was small. The patient-specific mould minimized tissue deformations and co-localized slices, so that rigid registration of MRI to histology images allowed region-based comparison with histology. The VERDICT estimate of the intracellular volume fraction corresponded to histological indicators of cellular fraction, including high values in tumour regions. The average sphere radius from VERDICT, representing the average cell size, was relatively uniform across samples. The primary diffusion direction from the extracellular compartment of the VERDICT model aligned with collagen fibre patterns in the stroma obtained by structure tensor analysis. This confirmed the biophysical relationship between ex vivo VERDICT parameters and tissue microstructure from histology.
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Affiliation(s)
- Colleen Bailey
- Centre for Medical Image ComputingUniversity College LondonLondonUK
- Sunnybrook Research InstituteTorontoONCanada
| | - Roger M. Bourne
- Discipline of Medical Radiation SciencesThe University of SydneySydneyAustralia
| | - Bernard Siow
- Centre for Advanced Biomedical ImagingUniversity College LondonLondonUK
- ImagingFrancis Crick InstituteLondonUK
| | | | | | - Hayley Pye
- Division of Surgery and Interventional ScienceUniversity College LondonLondonUK
- Department of UrologyUniversity College London HospitalsLondonUK
| | - Susan Heavey
- Division of Surgery and Interventional ScienceUniversity College LondonLondonUK
- Department of UrologyUniversity College London HospitalsLondonUK
| | | | - Hayley Whitaker
- Division of Surgery and Interventional ScienceUniversity College LondonLondonUK
| | - Alex Freeman
- Department of Research PathologyUniversity College LondonLondonUK
| | - Dominic Patel
- Department of Research PathologyUniversity College LondonLondonUK
| | - Greg L. Shaw
- Division of Surgery and Interventional ScienceUniversity College LondonLondonUK
- Department of UrologyUniversity College London HospitalsLondonUK
| | - Ashwin Sridhar
- Division of Surgery and Interventional ScienceUniversity College LondonLondonUK
- Department of UrologyUniversity College London HospitalsLondonUK
| | - David J. Hawkes
- Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Shonit Punwani
- Centre for Medical ImagingUniversity College LondonLondonUK
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30
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Karunanithy G, Wheeler RJ, Tear LR, Farrer NJ, Faulkner S, Baldwin AJ. INDIANA: An in-cell diffusion method to characterize the size, abundance and permeability of cells. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 302:1-13. [PMID: 30904779 PMCID: PMC7611012 DOI: 10.1016/j.jmr.2018.12.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 11/30/2018] [Accepted: 12/03/2018] [Indexed: 05/13/2023]
Abstract
NMR and MRI diffusion experiments contain information describing the shape, size, abundance, and membrane permeability of cells although extracting this information can be challenging. Here we present the INDIANA (IN-cell DIffusion ANAlysis) method to simultaneously and non-invasively measure cell abundance, effective radius, permeability and intrinsic relaxation rates and diffusion coefficients within the inter- and intra-cellular populations. The method couples an experimental dataset comprising stimulated-echo diffusion measurements, varying both the gradient strength and the diffusion delay, together with software to fit a model based on the Kärger equations to robustly extract the relevant parameters. A detailed error analysis is presented by comparing the results from fitting simulated data from Monte Carlo simulations, establishing its effectiveness. We note that for parameters typical of mammalian cells the approach is particularly effective, and the shape of the underlying cells does not unduly affect the results. Finally, we demonstrate the performance of the experiment on systems of suspended yeast and mammalian cells. The extracted parameters describing cell abundance, size, permeability and relaxation are independently validated.
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Affiliation(s)
- Gogulan Karunanithy
- Department of Chemistry, Physical and Theoretical Chemistry Laboratory, University of Oxford, South Parks Road, Oxford OX1 3QZ, United Kingdom
| | - Richard J Wheeler
- Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine, University of Oxford, Oxford OX1 3SY, United Kingdom
| | - Louise R Tear
- Chemistry Research Laboratory, University of Oxford, 12 Mansfield Road, Oxford OX1 3TA, United Kingdom
| | - Nicola J Farrer
- Chemistry Research Laboratory, University of Oxford, 12 Mansfield Road, Oxford OX1 3TA, United Kingdom
| | - Stephen Faulkner
- Chemistry Research Laboratory, University of Oxford, 12 Mansfield Road, Oxford OX1 3TA, United Kingdom
| | - Andrew J Baldwin
- Department of Chemistry, Physical and Theoretical Chemistry Laboratory, University of Oxford, South Parks Road, Oxford OX1 3QZ, United Kingdom.
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31
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Jiang X, Xu J, Gore JC. Quantitative temporal diffusion spectroscopy as an early imaging biomarker of radiation therapeutic response in gliomas: A preclinical proof of concept. Adv Radiat Oncol 2019; 4:367-376. [PMID: 31011683 PMCID: PMC6460331 DOI: 10.1016/j.adro.2018.11.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 11/07/2018] [Indexed: 12/17/2022] Open
Abstract
PURPOSE This study aims to test the ability of quantitative temporal diffusion spectroscopy (qTDS) to assess cellular changes associated with radiation-induced cell death in a rat glioma model. METHODS AND MATERIALS Tumor response to a single fraction of 20 Gy of x-ray radiation was investigated in a rat glioma (9L) model. Tumor response was monitored longitudinally at postinoculation days 21, 23, and 25, using a specific implementation of qTDS with acronym IMPULSED (Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion), as well as conventional diffusion and high-resolution anatomic imaging. IMPULSED method combines diffusion-weighted signals acquired over a range of diffusion times that are then analyzed and interpreted using a theoretical model of water diffusion in tissues, which generates parametric maps depicting cellular and subcellular structural information on a voxel-wise basis. Results from different metrics were compared statistically. RESULTS A single dose of 20 Gy x-ray radiation significantly prolonged survival of 9L-bearing rats. The mean cell sizes of irradiated tumors decreased (P < .005) after radiation treatment, which we associate with cell shrinkage and the formation of small cellular bodies during apoptosis and necrosis. A combination of IMPULSED-derived parameters (mean cell size d and extracellular structural parameter β ex ) separated 90% of irradiated tumors from the nonirradiated cases at post inoculation day 23, whereas a combination of tumor growth and conventional apparent diffusion coefficient did not differentiate irradiated tumors from nonirradiated tumors. CONCLUSIONS This proof-of-concept study demonstrates the IMPULSED method to be a new method for deriving quantitative microstructural parameters in a preclinical tumor model. The method provides unique information based on the diffusion time dependency of diffusion magnetic resonance imaging, which cannot be obtained by conventional diffusion weighted imaging methods, and the results have a close correlation with primary biologic markers of treatment efficacy, such as cell death and survival.
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Affiliation(s)
- Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee
| | - Junzhong Xu
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
| | - John C. Gore
- Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee
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32
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McHugh DJ, Zhou F, Wimpenny I, Poologasundarampillai G, Naish JH, Hubbard Cristinacce PL, Parker GJM. A biomimetic tumor tissue phantom for validating diffusion-weighted MRI measurements. Magn Reson Med 2018; 80:147-158. [PMID: 29154442 PMCID: PMC5900984 DOI: 10.1002/mrm.27016] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 09/22/2017] [Accepted: 10/27/2017] [Indexed: 12/20/2022]
Abstract
PURPOSE To develop a biomimetic tumor tissue phantom which more closely reflects water diffusion in biological tissue than previously used phantoms, and to evaluate the stability of the phantom and its potential as a tool for validating diffusion-weighted (DW) MRI measurements. METHODS Coaxial-electrospraying was used to generate micron-sized hollow polymer spheres, which mimic cells. The bulk structure was immersed in water, providing a DW-MRI phantom whose apparent diffusion coefficient (ADC) and microstructural properties were evaluated over a period of 10 months. Independent characterization of the phantom's microstructure was performed using scanning electron microscopy (SEM). The repeatability of the construction process was investigated by generating a second phantom, which underwent high resolution synchrotron-CT as well as SEM and MR scans. RESULTS ADC values were stable (coefficients of variation (CoVs) < 5%), and varied with diffusion time, with average values of 1.44 ± 0.03 µm2 /ms (Δ = 12 ms) and 1.20 ± 0.05 µm2 /ms (Δ = 45 ms). Microstructural parameters showed greater variability (CoVs up to 13%), with evidence of bias in sphere size estimates. Similar trends were observed in the second phantom. CONCLUSION A novel biomimetic phantom has been developed and shown to be stable over 10 months. It is envisaged that such phantoms will be used for further investigation of microstructural models relevant to characterizing tumor tissue, and may also find application in evaluating acquisition protocols and comparing DW-MRI-derived biomarkers obtained from different scanners at different sites. Magn Reson Med 80:147-158, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Damien J. McHugh
- Division of Informatics, Imaging and Data SciencesThe University of ManchesterManchesterUK
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and ManchesterCambridge and ManchesterUK
| | - Feng‐Lei Zhou
- Division of Informatics, Imaging and Data SciencesThe University of ManchesterManchesterUK
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and ManchesterCambridge and ManchesterUK
- The School of MaterialsThe University of ManchesterManchesterUK
| | - Ian Wimpenny
- Division of Informatics, Imaging and Data SciencesThe University of ManchesterManchesterUK
- The School of MaterialsThe University of ManchesterManchesterUK
| | | | - Josephine H. Naish
- Division of Informatics, Imaging and Data SciencesThe University of ManchesterManchesterUK
| | | | - Geoffrey J. M. Parker
- Division of Informatics, Imaging and Data SciencesThe University of ManchesterManchesterUK
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and ManchesterCambridge and ManchesterUK
- Bioxydyn Ltd.ManchesterUK
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33
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Bailey C, Collins DJ, Tunariu N, Orton MR, Morgan VA, Feiweier T, Hawkes DJ, Leach MO, Alexander DC, Panagiotaki E. Microstructure Characterization of Bone Metastases from Prostate Cancer with Diffusion MRI: Preliminary Findings. Front Oncol 2018; 8:26. [PMID: 29503808 PMCID: PMC5820304 DOI: 10.3389/fonc.2018.00026] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 01/29/2018] [Indexed: 01/23/2023] Open
Abstract
PURPOSE To examine the usefulness of rich diffusion protocols with high b-values and varying diffusion time for probing microstructure in bone metastases. Analysis techniques including biophysical and mathematical models were compared with the clinical apparent diffusion coefficient (ADC). METHODS Four patients were scanned using 13 b-values up to 3,000 s/mm2 and diffusion times ranging 18-52 ms. Data were fitted to mono-exponential ADC, intravoxel incoherent motion (IVIM), Kurtosis and Vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) models. Parameters from the models were compared using correlation plots. RESULTS ADC and IVIM did not fit the data well, failing to capture the signal at high b-values. The Kurtosis model best explained the data in many voxels, but in voxels exhibiting a more time-dependent signal, the VERDICT model explained the data best. The ADC correlated significantly (p < 0.004) with the intracellular diffusion coefficient (r = 0.48), intracellular volume fraction (r = -0.21), and perfusion fraction (r = 0.46) parameters from VERDICT, suggesting that these factors all contribute to ADC contrast. The mean kurtosis correlated with the intracellular volume fraction parameter (r = 0.26) from VERDICT, consistent with the hypothesis that kurtosis relates to cellularity, but also correlated weakly with the intracellular diffusion coefficient (r = 0.18) and cell radius (r = 0.16) parameters, suggesting that it may be difficult to attribute physical meaning to kurtosis. CONCLUSION Both Kurtosis and VERDICT explained the diffusion signal better than ADC and IVIM, primarily due to poor fitting at high b-values in the latter two models. The Kurtosis and VERDICT models captured information at high b using parameters (Kurtosis or intracellular volume fraction and radius) that do not have a simple relationship with ADC and that may provide additional microstructural information in bone metastases.
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Affiliation(s)
- Colleen Bailey
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Radiation Oncology, Odette Cancer Centre, Toronto, Canada
| | - David J. Collins
- CR-UK and EPSRC Cancer Imaging Centre, Institute of Cancer Research, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Nina Tunariu
- Radiology, Royal Marsden NHS Foundation Trust, Institute of Cancer Research, Sutton, United Kingdom
| | - Matthew R. Orton
- CR-UK and EPSRC Cancer Imaging Centre, Institute of Cancer Research, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Veronica A. Morgan
- CR-UK and EPSRC Cancer Imaging Centre, Institute of Cancer Research, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | | | - David J. Hawkes
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Martin O. Leach
- CR-UK and EPSRC Cancer Imaging Centre, Institute of Cancer Research, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Clinical Imaging Research Centre, National University of Singapore, Singapore, Singapore
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34
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Lasič S, Lundell H, Topgaard D, Dyrby TB. Effects of imaging gradients in sequences with varying longitudinal storage time—Case of diffusion exchange imaging. Magn Reson Med 2017; 79:2228-2235. [DOI: 10.1002/mrm.26856] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Revised: 06/28/2017] [Accepted: 07/07/2017] [Indexed: 12/20/2022]
Affiliation(s)
- Samo Lasič
- Danish Research Centre for Magnetic ResonanceCentre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital HvidovreHvidovre Copenhagen Denmark
- CR Development ABLundSweden
| | - Henrik Lundell
- Danish Research Centre for Magnetic ResonanceCentre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital HvidovreHvidovre Copenhagen Denmark
| | | | - Tim B. Dyrby
- Danish Research Centre for Magnetic ResonanceCentre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital HvidovreHvidovre Copenhagen Denmark
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens Lyngby Denmark
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35
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Yang DM, Huettner JE, Bretthorst GL, Neil JJ, Garbow JR, Ackerman JJH. Intracellular water preexchange lifetime in neurons and astrocytes. Magn Reson Med 2017; 79:1616-1627. [PMID: 28675497 DOI: 10.1002/mrm.26781] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 04/20/2017] [Accepted: 05/17/2017] [Indexed: 01/06/2023]
Abstract
PURPOSE To determine the intracellular water preexchange lifetime, τi , the "average residence time" of water, in the intracellular milieu of neurons and astrocytes. The preexchange lifetime is important for modeling a variety of MR data sets, including relaxation, diffusion-sensitive, and dynamic contrast-enhanced data sets. METHODS Herein, τi in neurons and astrocytes is determined in a microbead-adherent, cultured cell system. In concert with thin-slice selection, rapid flow of extracellular media suppresses extracellular signal, allowing determination of the transcytolemmal-exchange-dominated, intracellular T1 . With this knowledge, and that of the intracellular T1 in the absence of exchange, τi can be derived. RESULTS Under normal culture conditions, τi for neurons is 0.75 ± 0.05 s versus 0.57 ± 0.03 s for astrocytes. Both neuronal and astrocytic τi s decrease within 30 min after the onset of oxygen-glucose deprivation, with the astrocytic τi showing a substantially greater decrease than the neuronal τi . CONCLUSIONS Given an approximate intra- to extracellular volume ratio of 4:1 in the brain, these data imply that, under normal physiological conditions, an MR experimental characteristic time of less than 0.012 s is required for a nonexchanging, two-compartment (intra- and extracellular) model to be valid for MR studies. This characteristic time shortens significantly (i.e., 0.004 s) under injury conditions. Magn Reson Med 79:1616-1627, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Donghan M Yang
- Department of Chemistry, Washington University, St. Louis, Missouri, USA.,Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - James E Huettner
- Department of Cell Biology and Physiology, Washington University, St. Louis, Missouri, USA
| | - G Larry Bretthorst
- Department of Radiology, Washington University, St. Louis, Missouri, USA
| | - Jeffrey J Neil
- Department of Neurology, Washington University, St. Louis, Missouri, USA.,Department of Pediatrics, Washington University, St. Louis, Missouri, USA.,Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Joel R Garbow
- Department of Radiology, Washington University, St. Louis, Missouri, USA.,Alvin J. Siteman Cancer Center, Washington University, St. Louis, Missouri, USA
| | - Joseph J H Ackerman
- Department of Chemistry, Washington University, St. Louis, Missouri, USA.,Department of Radiology, Washington University, St. Louis, Missouri, USA.,Alvin J. Siteman Cancer Center, Washington University, St. Louis, Missouri, USA.,Department of Internal Medicine, Washington University, St. Louis, Missouri, USA
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36
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Jiang X, Li H, Xie J, McKinley ET, Zhao P, Gore JC, Xu J. In vivo imaging of cancer cell size and cellularity using temporal diffusion spectroscopy. Magn Reson Med 2017; 78:156-164. [PMID: 27495144 PMCID: PMC5293685 DOI: 10.1002/mrm.26356] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Revised: 06/29/2016] [Accepted: 07/02/2016] [Indexed: 01/17/2023]
Abstract
PURPOSE A temporal diffusion MRI spectroscopy based approach has been developed to quantify cancer cell size and density in vivo. METHODS A novel imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED) method selects a specific limited diffusion spectral window for an accurate quantification of cell sizes ranging from 10 to 20 μm in common solid tumors. In practice, it is achieved by a combination of a single long diffusion time pulsed gradient spin echo (PGSE) and three low-frequency oscillating gradient spin echo (OGSE) acquisitions. To validate our approach, hematoxylin and eosin staining and immunostaining of cell membranes, in concert with whole slide imaging, were used to visualize nuclei and cell boundaries, and hence, enabled accurate estimates of cell size and cellularity. RESULTS Based on a two compartment model (incorporating intra- and extracellular spaces), accurate estimates of cell sizes were obtained in vivo for three types of human colon cancers. The IMPULSED-derived apparent cellularities showed a stronger correlation (r = 0.81; P < 0.0001) with histology-derived cellularities than conventional ADCs (r = -0.69; P < 0.03). CONCLUSION The IMPULSED approach samples a specific region of temporal diffusion spectra with enhanced sensitivity to length scales of 10-20 μm, and enables measurements of cell sizes and cellularities in solid tumors in vivo. Magn Reson Med 78:156-164, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA
| | - Hua Li
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
| | - Jingping Xie
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA
| | - Eliot T. McKinley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Ping Zhao
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA
| | - John C. Gore
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
| | - Junzhong Xu
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
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37
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Harms RL, Fritz FJ, Tobisch A, Goebel R, Roebroeck A. Robust and fast nonlinear optimization of diffusion MRI microstructure models. Neuroimage 2017; 155:82-96. [PMID: 28457975 PMCID: PMC5518773 DOI: 10.1016/j.neuroimage.2017.04.064] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 04/07/2017] [Accepted: 04/09/2017] [Indexed: 02/07/2023] Open
Abstract
Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of run time, fit, accuracy and precision. Parameter initialization approaches were found to be relevant especially for more complex models, such as those involving several fiber orientations per voxel. For these, a fitting cascade initializing or fixing parameter values in a later optimization step from simpler models in an earlier optimization step further improved run time, fit, accuracy and precision compared to a single step fit. This establishes and makes available standards by which robust fit and accuracy can be achieved in shorter run times. This is especially relevant for the use of diffusion microstructure modeling in large group or population studies and in combining microstructure parameter maps with tractography results. Evaluate robustness of fit, accuracy, precision for diffusion microstructure models Test three optimization algorithms and three parameter initialization strategies GPU implementation removes run time constraints; whole brain fit within minutes Powell conjugate-direction algorithm has superior fit, accuracy, precision Initialization approaches are important for crossing fiber microstructure models
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Affiliation(s)
- R L Harms
- Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands; Brain Innovation B.V., Maastricht, The Netherlands.
| | - F J Fritz
- Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
| | - A Tobisch
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - R Goebel
- Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
| | - A Roebroeck
- Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
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38
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Tian X, Li H, Jiang X, Xie J, Gore JC, Xu J. Evaluation and comparison of diffusion MR methods for measuring apparent transcytolemmal water exchange rate constant. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2017; 275:29-37. [PMID: 27960105 PMCID: PMC5266627 DOI: 10.1016/j.jmr.2016.11.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 11/29/2016] [Accepted: 11/30/2016] [Indexed: 05/08/2023]
Abstract
Two diffusion-based approaches, CG (constant gradient) and FEXI (filtered exchange imaging) methods, have been previously proposed for measuring transcytolemmal water exchange rate constant kin, but their accuracy and feasibility have not been comprehensively evaluated and compared. In this work, both computer simulations and cell experiments in vitro were performed to evaluate these two methods. Simulations were done with different cell diameters (5, 10, 20μm), a broad range of kin values (0.02-30s-1) and different SNR's, and simulated kin's were directly compared with the ground truth values. Human leukemia K562 cells were cultured and treated with saponin to selectively change cell transmembrane permeability. The agreement between measured kin's of both methods was also evaluated. The results suggest that, without noise, the CG method provides reasonably accurate estimation of kin especially when it is smaller than 10s-1, which is in the typical physiological range of many biological tissues. However, although the FEXI method overestimates kin even with corrections for the effects of extracellular water fraction, it provides reasonable estimates with practical SNR's and more importantly, the fitted apparent exchange rate AXR showed approximately linear dependence on the ground truth kin. In conclusion, either CG or FEXI method provides a sensitive means to characterize the variations in transcytolemmal water exchange rate constant kin, although the accuracy and specificity is usually compromised. The non-imaging CG method provides more accurate estimation of kin, but limited to large volume-of-interest. Although the accuracy of FEXI is compromised with extracellular volume fraction, it is capable of spatially mapping kin in practice.
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Affiliation(s)
- Xin Tian
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA; Department of Radiology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, PR China
| | - Hua Li
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA
| | - Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA
| | - Jingping Xie
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA
| | - John C Gore
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University, 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; Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA; Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN 37232, USA
| | - Junzhong Xu
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University, 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; Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN 37232, USA.
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