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Ulloa P, Methot V, Wottschel V, Koch MA. Extra-axonal contribution to double diffusion encoding-based pore size estimates in the corticospinal tract. MAGMA (NEW YORK, N.Y.) 2023; 36:589-612. [PMID: 36745290 PMCID: PMC10468962 DOI: 10.1007/s10334-022-01058-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 12/14/2022] [Accepted: 12/19/2022] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To study the origin of compartment size overestimation in double diffusion encoding MRI (DDE) in vivo experiments in the human corticospinal tract. Here, the extracellular space is hypothesized to be the origin of the DDE signal. By exploiting the DDE sensitivity to pore shape, it could be possible to identify the origin of the measured signal. The signal difference between parallel and perpendicular diffusion gradient orientation can indicate if a compartment is regular or eccentric in shape. As extracellular space can be considered an eccentric compartment, a positive difference would mean a high contribution to the compartment size estimates. MATERIALS AND METHODS Computer simulations using MISST and in vivo experiments in eight healthy volunteers were performed. DDE experiments using a double spin-echo preparation with eight perpendicular directions were measured in vivo. The difference between parallel and perpendicular gradient orientations was analyzed using a Wilcoxon signed-rank test and a Mann-Whitney U test. RESULTS Simulations and MR experiments showed a statistically significant difference between parallel and perpendicular diffusion gradient orientation signals ([Formula: see text]). CONCLUSION The results suggest that the DDE-based size estimate may be considerably influenced by the extra-axonal compartment. However, the experimental results are also consistent with purely intra-axonal contributions in combination with a large fiber orientation dispersion.
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Affiliation(s)
- Patricia Ulloa
- Institute of Medical Engineering, University of Luebeck, Ratzeburger Allee 160, 23562 Luebeck, Germany
| | - Vincent Methot
- Institute of Medical Engineering, University of Luebeck, Ratzeburger Allee 160, 23562 Luebeck, Germany
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, De Boelelaan 1117, 1081, Amsterdam, The Netherlands
| | - Martin A. Koch
- Institute of Medical Engineering, University of Luebeck, Ratzeburger Allee 160, 23562 Luebeck, Germany
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2
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Warner W, Palombo M, Cruz R, Callaghan R, Shemesh N, Jones DK, Dell'Acqua F, Ianus A, Drobnjak I. Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: Optimisation and pre-clinical demonstration. Neuroimage 2023; 269:119930. [PMID: 36750150 PMCID: PMC7615244 DOI: 10.1016/j.neuroimage.2023.119930] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 01/12/2023] [Accepted: 02/02/2023] [Indexed: 02/07/2023] Open
Abstract
Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general.
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Affiliation(s)
- William Warner
- Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | - Renata Cruz
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | | | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Flavio Dell'Acqua
- NatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
| | - Ivana Drobnjak
- Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom.
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3
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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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4
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Morozov D, Parvin N, Charlton JR, Bennett KM. Mapping kidney tubule diameter ex vivo by diffusion MRI. Am J Physiol Renal Physiol 2021; 320:F934-F946. [PMID: 33719573 DOI: 10.1152/ajprenal.00369.2020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Tubular pathologies are a common feature of kidney disease. Current metrics to assess kidney health, in vivo or in transplant, are generally based on urinary or serum biomarkers and pathological findings from kidney biopsies. Biopsies, usually taken from the kidney cortex, are invasive and prone to sampling error. Tools to directly and noninvasively measure tubular pathology could provide a new approach to assess kidney health. This study used diffusion magnetic resonance imaging (dMRI) as a noninvasive tool to measure the size of the tubular lumen in ex vivo, perfused kidneys. We first used Monte Carlo simulations to demonstrate that dMRI is sensitive to restricted tissue water diffusion at the scale of the kidney tubule. We applied dMRI and biophysical modeling to examine the distribution of tubular diameters in ex vivo, fixed kidneys from mice, rats, and a human donor. The biophysical model to fit the dMRI signal was based on a superposition of freely diffusing water and water diffusing inside infinitely long cylinders of different diameters. Tubular diameters measured by dMRI were within 10% of those measured by histology within the same tissue. Finally, we applied dMRI to investigate kidney pathology in a mouse model of folic-acid-induced acute kidney injury. dMRI detected heterogeneity in the distribution of tubules within the kidney cortex of mice with acute kidney injury compared with control mice. We conclude that dMRI can be used to measure the distribution of tubule diameters in the kidney cortex ex vivo and that dMRI may provide a new noninvasive biomarker of tubular pathology.NEW & NOTEWORTHY Tubular pathologies are a common feature of kidney disease. Current metrics to assess kidney health, in vivo or in transplant, are generally based on urinary or serum biomarkers and pathological findings from kidney biopsies. Diffusion MRI can be used to measure the distribution of tubule diameters in the kidney cortex ex vivo and may provide a new noninvasive biomarker of tubular pathology.
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Affiliation(s)
- Darya Morozov
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Neda Parvin
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | | | - Kevin M Bennett
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
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5
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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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6
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Harkins KD, Beaulieu C, Xu J, Gore JC, Does MD. A simple estimate of axon size with diffusion MRI. Neuroimage 2020; 227:117619. [PMID: 33301942 PMCID: PMC7949481 DOI: 10.1016/j.neuroimage.2020.117619] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 11/06/2020] [Accepted: 11/29/2020] [Indexed: 12/18/2022] Open
Abstract
Noninvasive estimation of mean axon diameter presents a new opportunity to explore white matter plasticity, development, and pathology. Several diffusion-weighted MRI (DW-MRI) methods have been proposed to measure the average axon diameter in white matter, but they typically require many diffusion encoding measurements and complicated mathematical models to fit the signal to multiple tissue compartments, including intra- and extra-axonal spaces. Here, Monte Carlo simulations uncovered a straightforward DW-MRI metric of axon diameter: the change in radial apparent diffusion coefficient estimated at different effective diffusion times, ΔD⊥. Simulations indicated that this metric increases monotonically within a relevant range of effective mean axon diameter while being insensitive to changes in extra-axonal volume fraction, axon diameter distribution, g-ratio, and influence of myelin water. Also, a monotonic relationship was found to exist for signals coming from both intra- and extra-axonal compartments. The slope in ΔD⊥ with effective axon diameter increased with the difference in diffusion time of both oscillating and pulsed gradient diffusion sequences.
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Affiliation(s)
- Kevin D Harkins
- Biomedical Engineering, Vanderbilt University, United States; Institute of Imaging Science, Vanderbilt University, United States.
| | | | - Junzhong Xu
- Institute of Imaging Science, Vanderbilt University, United States; Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States
| | - John C Gore
- Biomedical Engineering, Vanderbilt University, United States; Institute of Imaging Science, Vanderbilt University, United States; Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States
| | - Mark D Does
- Biomedical Engineering, Vanderbilt University, United States; Institute of Imaging Science, Vanderbilt University, United States
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7
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Henriques RN, Palombo M, Jespersen SN, Shemesh N, Lundell H, Ianuş A. Double diffusion encoding and applications for biomedical imaging. J Neurosci Methods 2020; 348:108989. [PMID: 33144100 DOI: 10.1016/j.jneumeth.2020.108989] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/25/2020] [Accepted: 10/20/2020] [Indexed: 12/11/2022]
Abstract
Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important contemporary non-invasive modalities for probing tissue structure at the microscopic scale. The majority of dMRI techniques employ standard single diffusion encoding (SDE) measurements, covering different sequence parameter ranges depending on the complexity of the method. Although many signal representations and biophysical models have been proposed for SDE data, they are intrinsically limited by a lack of specificity. Advanced dMRI methods have been proposed to provide additional microstructural information beyond what can be inferred from SDE. These enhanced contrasts can play important roles in characterizing biological tissues, for instance upon diseases (e.g. neurodegenerative, cancer, stroke), aging, learning, and development. In this review we focus on double diffusion encoding (DDE), which stands out among other advanced acquisitions for its versatility, ability to probe more specific diffusion correlations, and feasibility for preclinical and clinical applications. Various DDE methodologies have been employed to probe compartment sizes (Section 3), decouple the effects of microscopic diffusion anisotropy from orientation dispersion (Section 4), probe displacement correlations, study exchange, or suppress fast diffusing compartments (Section 6). DDE measurements can also be used to improve the robustness of biophysical models (Section 5) and study intra-cellular diffusion via magnetic resonance spectroscopy of metabolites (Section 7). This review discusses all these topics as well as important practical aspects related to the implementation and contrast in preclinical and clinical settings (Section 9) and aims to provide the readers a guide for deciding on the right DDE acquisition for their specific application.
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Affiliation(s)
- Rafael N Henriques
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Marco Palombo
- Centre for Medical Image Computing and Dept. of Computer Science, University College London, London, UK
| | - 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
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark
| | - Andrada Ianuş
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.
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8
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Benjamini D, Hutchinson EB, Komlosh ME, Comrie CJ, Schwerin SC, Zhang G, Pierpaoli C, Basser PJ. Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging. Neuroimage 2020; 221:117195. [PMID: 32726643 PMCID: PMC7805019 DOI: 10.1016/j.neuroimage.2020.117195] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 07/17/2020] [Accepted: 07/21/2020] [Indexed: 12/17/2022] Open
Abstract
We describe a practical two-dimensional (2D) diffusion MRI framework to deliver specificity and improve sensitivity to axonal injury in the spinal cord. This approach provides intravoxel distributions of correlations of water mobilities in orthogonal directions, revealing sub-voxel diffusion components. Here we use it to investigate water diffusivities along axial and radial orientations within spinal cord specimens with confirmed, tract-specific axonal injury. First, we show using transmission electron microscopy and immunohistochemistry that tract-specific axonal beading occurs following Wallerian degeneration in the cortico-spinal tract as direct sequelae to closed head injury. We demonstrate that although some voxel-averaged diffusion tensor imaging (DTI) metrics are sensitive to this axonal injury, they are non-specific, i.e., they do not reveal an underlying biophysical mechanism of injury. Then we employ 2D diffusion correlation imaging (DCI) to improve discrimination of different water microenvironments by measuring and mapping the joint water mobility distributions perpendicular and parallel to the spinal cord axis. We determine six distinct diffusion spectral components that differ according to their microscopic anisotropy and mobility. We show that at the injury site a highly anisotropic diffusion component completely disappears and instead becomes more isotropic. Based on these findings, an injury-specific MR image of the spinal cord was generated, and a radiological-pathological correlation with histological silver staining % area was performed. The resulting strong and significant correlation (r=0.70,p < 0.0001) indicates the high specificity with which DCI detects injury-induced tissue alterations. We predict that the ability to selectively image microstructural changes following axonal injury in the spinal cord can be useful in clinical and research applications by enabling specific detection and increased sensitivity to injury-induced microstructural alterations. These results also encourage us to translate DCI to higher spatial dimensions to enable assessment of traumatic axonal injury, and possibly other diseases and disorders in the brain.
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Affiliation(s)
- Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20817, USA; The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD 20814, USA.
| | - Elizabeth B Hutchinson
- The Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona 85721, USA
| | - Michal E Komlosh
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20817, USA; The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD 20814, USA
| | - Courtney J Comrie
- The Department of Biomedical Engineering, The University of Arizona, Tucson, Arizona 85721, USA
| | - Susan C Schwerin
- The Center for Neuroscience and Regenerative Medicine, Uniformed Service University of the Health Sciences, Bethesda, MD 20814, USA; Department of Anatomy, Physiology, and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
| | - Guofeng Zhang
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD 20817, USA
| | - Carlo Pierpaoli
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD 20817, USA
| | - Peter J Basser
- The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20817, USA
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9
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Jiang X, Dudzinski S, Beckermann KE, Young K, McKinley E, J McIntyre O, Rathmell JC, Xu J, Gore JC. MRI of tumor T cell infiltration in response to checkpoint inhibitor therapy. J Immunother Cancer 2020; 8:e000328. [PMID: 32581044 PMCID: PMC7312343 DOI: 10.1136/jitc-2019-000328] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/16/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Immune checkpoint inhibitors, the most widespread class of immunotherapies, have demonstrated unique response patterns that are not always adequately captured by traditional response criteria such as the Response Evaluation Criteria in Solid Tumors or even immune-specific response criteria. These response metrics rely on monitoring tumor growth, but an increase in tumor size and/or appearance after starting immunotherapy does not always represent tumor progression, but also can be a result of T cell infiltration and thus positive treatment response. Therefore, non-invasive and longitudinal monitoring of T cell infiltration are needed to assess the effects of immunotherapies such as checkpoint inhibitors. Here, we proposed an innovative concept that a sufficiently large influx of tumor infiltrating T cells, which have a smaller diameter than cancer cells, will change the diameter distribution and decrease the average size of cells within a volume to a degree that can be quantified by non-invasive MRI. METHODS We validated our hypothesis by studying tumor response to combination immune-checkpoint blockade (ICB) of anti-PD-1 and anti-CTLA4 in a mouse model of colon adenocarcinoma (MC38). The response was monitored longitudinally using Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion (IMPULSED), a diffusion MRI-based method which has been previously shown to non-invasively map changes in intracellular structure and cell sizes with the spatial resolution of MRI, in cell cultures and in animal models. Tumors were collected for immunohistochemical and flow cytometry analyzes immediately after the last imaging session. RESULTS Immunohistochemical analysis revealed that increased T cell infiltration of the tumors results in a decrease in mean cell size (eg, a 10% increase of CD3+ T cell fraction results a ~1 µm decrease in the mean cell size). IMPULSED showed that the ICB responders, mice with tumor volumes were less than 250 mm3 or had tumors with stable or decreased volumes, had significantly smaller mean cell sizes than both Control IgG-treated tumors and ICB non-responder tumors. CONCLUSIONS IMPULSED-derived cell size could potentially serve as an imaging marker for differentiating responsive and non-responsive tumors after checkpoint inhibitor therapies, a current clinical challenge that is not solved by simply monitoring tumor growth.
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Affiliation(s)
- Xiaoyu Jiang
- Vanderbilt University Institute of Imaging Science, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Stephanie Dudzinski
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, United States
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Kathryn E Beckermann
- Division of Hematology/Oncology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Kirsten Young
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Eliot McKinley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Oliver J McIntyre
- Vanderbilt University Institute of Imaging Science, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, United States
- Department of Cancer Biology, Vanderbilt University, Nashville, TN 37232, United States
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN 37232, United States
| | - Jeffrey C Rathmell
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Vanderbilt Center for Immunobiology, Vanderbilt University School of Medicine, Nashville, TN 37232, United States
| | - Junzhong Xu
- Vanderbilt University Institute of Imaging Science, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, United States
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, United States
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, United States
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, United States
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