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Balasubramanian AA, Al-Heejawi SMA, Singh A, Breggia A, Ahmad B, Christman R, Ryan ST, Amal S. Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology. Cancers (Basel) 2024; 16:2222. [PMID: 38927927 PMCID: PMC11201924 DOI: 10.3390/cancers16122222] [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: 05/09/2024] [Revised: 06/07/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens.
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Affiliation(s)
| | | | - Akarsh Singh
- College of Engineering, Northeastern University, Boston, MA 02115, USA; (S.M.A.A.-H.); (A.S.)
| | - Anne Breggia
- MaineHealth Institute for Research, Scarborough, ME 04074, USA;
| | - Bilal Ahmad
- Maine Medical Center, Portland, ME 04102, USA; (B.A.); (R.C.); (S.T.R.)
| | - Robert Christman
- Maine Medical Center, Portland, ME 04102, USA; (B.A.); (R.C.); (S.T.R.)
| | - Stephen T. Ryan
- Maine Medical Center, Portland, ME 04102, USA; (B.A.); (R.C.); (S.T.R.)
| | - Saeed Amal
- The Roux Institute, Department of Bioengineering, College of Engineering, Northeastern University, Boston, MA 02115, USA
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Sacher SE, Baral EC, Wright TM, Bauer TW, Li Q, Padgett DE, Potter HG, Koff MF. Association of Total Hip Arthroplasty Flexural Rigidity With Magnetic Resonance Imaging and Histological Findings. J Arthroplasty 2024:S0883-5403(24)00192-X. [PMID: 38428693 DOI: 10.1016/j.arth.2024.02.072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Modular connections in total hip arthroplasty (THA) offer surgical advantages, but can contribute to implant fretting and corrosion due to micromotion at the head-stem interface. Previous studies implicated lower flexural rigidity as a key contributing factor to THA corrosion and fretting, but none associated flexural rigidity with direct histological evaluation or magnetic resonance imaging (MRI) outcomes. The purpose of this study was to determine how implant flexural rigidity is associated with MRI imaging metrics and histopathological outcomes in patients who have a failed THA. METHODS Patients requiring revision THA surgery underwent preoperative MRIs with 3-dimensional multispectral imaging techniques to suppress metal artifacts. The MRI images were graded for adverse local tissue reactions. For each hip, trunnion flexural rigidity was measured from the retrieved femoral stem, and a periprosthetic tissue sample was retrieved and evaluated using semiquantitative histology. Generalized linear models and analyses of variance were used to assess associations between flexural rigidity and MRI and histology outcomes. RESULTS A total of 106 THA stems were retrieved (46 women and 60 men, age: 68 years (range, 60 to 73 years). After adjustment for length of implantation, flexural rigidity was negatively correlated with histologic aseptic lymphocyte-dominant vasculitis-associated lesion severity (β = -26.27, P = .018), Fujishiro lymphocyte grading (β = -13.4, P = .039), perivascular lymphocyte layers (β = -17.8, P = .022), the grade of tissue organization (β = -22.5, P = .009), the presence of diffuse synovitis (β = -66.5, P = .003), and the presence of lymphoid aggregates (β = -75.9, P = .022). No association was found between MRI metrics and flexural rigidity. CONCLUSIONS Among these implants, decreased trunnion stiffness was associated with increased histologic features of adverse host-mediated soft tissue reactions.
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Affiliation(s)
- Sara E Sacher
- Hospital for Special Surgery, Department of Radiology, New York, New York
| | - Elexis C Baral
- Hospital for Special Surgery, Department of Biomechanics, New York, New York
| | - Timothy M Wright
- Hospital for Special Surgery, Department of Biomechanics, New York, New York
| | - Thomas W Bauer
- Hospital for Special Surgery, Department of Pathology and Laboratory Medicine, New York, New York
| | - Qian Li
- Hospital for Special Surgery, Department of Radiology, New York, New York
| | - Douglas E Padgett
- Hospital for Special Surgery, Adult Reconstruction and Joint Replacement, New York, New York
| | - Hollis G Potter
- Hospital for Special Surgery, Department of Radiology, New York, New York
| | - Matthew F Koff
- Hospital for Special Surgery, Department of Radiology, New York, New York
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3
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Enríquez-Mier-Y-Terán FE, Chatterjee A, Antic T, Oto A, Karczmar G, Bourne R. Multi-model sequential analysis of MRI data for microstructure prediction in heterogeneous tissue. Sci Rep 2023; 13:16486. [PMID: 37779137 PMCID: PMC10543593 DOI: 10.1038/s41598-023-43329-x] [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: 12/09/2022] [Accepted: 09/22/2023] [Indexed: 10/03/2023] Open
Abstract
We propose a general method for combining multiple models to predict tissue microstructure, with an exemplar using in vivo diffusion-relaxation MRI data. The proposed method obviates the need to select a single 'optimum' structure model for data analysis in heterogeneous tissues where the best model varies according to local environment. We break signal interpretation into a three-stage sequence: (1) application of multiple semi-phenomenological models to predict the physical properties of tissue water pools contributing to the observed signal; (2) from each Stage-1 semi-phenomenological model, application of a tissue microstructure model to predict the relative volumes of tissue structure components that make up each water pool; and (3) aggregation of the predictions of tissue structure, with weightings based on model likelihood and fractional volumes of the water pools from Stage-1. The multiple model approach is expected to reduce prediction variance in tissue regions where a complex model is overparameterised, and bias where a model is underparameterised. The separation of signal characterisation (Stage-1) from biological assignment (Stage-2) enables alternative biological interpretations of the observed physical properties of the system, by application of different tissue structure models. The proposed method is exemplified with human prostate diffusion-relaxation MRI data, but has potential application to a wide range of analyses where a single model may not be optimal throughout the sampled domain.
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Affiliation(s)
- Francisco E Enríquez-Mier-Y-Terán
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, 2008, Australia
- The Brain and Mind Centre, The University of Sydney, Sydney, 2050, Australia
| | - Aritrick Chatterjee
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, 60637, IL, USA
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, 60637, IL, USA
| | - Tatjana Antic
- Department of Pathology, University of Chicago, Chicago, 60637, IL, USA
| | - Aytekin Oto
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, 60637, IL, USA
| | - Gregory Karczmar
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, 60637, IL, USA
| | - Roger Bourne
- Discipline of Medical Imaging Science, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, 2006, Australia.
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Al-Amri AM. Recent Progress in Printed Photonic Devices: A Brief Review of Materials, Devices, and Applications. Polymers (Basel) 2023; 15:3234. [PMID: 37571128 PMCID: PMC10422352 DOI: 10.3390/polym15153234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
Printing electronics incorporates several significant technologies, such as semiconductor devices produced by various printing techniques on flexible substrates. With the growing interest in printed electronic devices, new technologies have been developed to make novel devices with inexpensive and large-area printing techniques. This review article focuses on the most recent developments in printed photonic devices. Photonics and optoelectronic systems may now be built utilizing materials with specific optical properties and 3D designs achieved through additive printing. Optical and architected materials that can be printed in their entirety are among the most promising future research topics, as are platforms for multi-material processing and printing technologies that can print enormous volumes at a high resolution while also maintaining a high throughput. Significant advances in innovative printable materials create new opportunities for functional devices to act efficiently, such as wearable sensors, integrated optoelectronics, and consumer electronics. This article provides an overview of printable materials, printing methods, and the uses of printed electronic devices.
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Affiliation(s)
- Amal M Al-Amri
- Physics Department, Collage of Science & Arts, King Abdulaziz University, Rabigh 25724, Saudi Arabia
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Clark A, Flouri D, Mufti N, James J, Clements E, Aughwane R, Aertsen M, David A, Melbourne A. Developments in functional imaging of the placenta. Br J Radiol 2023; 96:20211010. [PMID: 35234516 PMCID: PMC10321248 DOI: 10.1259/bjr.20211010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 01/26/2022] [Accepted: 02/22/2022] [Indexed: 12/21/2022] Open
Abstract
The placenta is both the literal and metaphorical black box of pregnancy. Measurement of the function of the placenta has the potential to enhance our understanding of this enigmatic organ and serve to support obstetric decision making. Advanced imaging techniques are key to support these measurements. This review summarises emerging imaging technology being used to measure the function of the placenta and new developments in the computational analysis of these data. We address three important examples where functional imaging is supporting our understanding of these conditions: fetal growth restriction, placenta accreta, and twin-twin transfusion syndrome.
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Affiliation(s)
- Alys Clark
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | | | | | - Joanna James
- Department of Obstetrics and Gynaecology, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Eleanor Clements
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Rosalind Aughwane
- Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London, UK
| | - Michael Aertsen
- Department of Radiology, University Hospitals KU Leuven, Leuven, Belgium
| | - Anna David
- Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London, UK
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Berger C, Bauer M, Scheurer E, Lenz C. Temperature correction of post mortem quantitative magnetic resonance imaging using real-time forehead temperature acquisitions. Forensic Sci Int 2023; 348:111738. [PMID: 37263059 DOI: 10.1016/j.forsciint.2023.111738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/24/2023] [Accepted: 05/22/2023] [Indexed: 06/03/2023]
Abstract
Performing magnetic resonance imaging (MRI) of deceased is challenging due to altered body temperatures compared to in vivo temperatures and, hence, requires a temperature correction. This study investigates the possibility to correct brain MRI parameters real-time and non invasively based on the forehead temperature. 17 post mortem cases were included and their forehead temperatures were measured continuously during the in situ brain MRI protocol consisting of a diffusion tensor imaging, multi-contrast spin echo, multi-echo gradient echo and inversion recovery spin echo sequence. Linear models were fitted to the quantitative MRI parameters in a forensically interesting temperature range for white matter, cerebral cortex and deep gray matter, separately, and the influence of the forehead temperature on the MRI parameters was determined. A statistically significant temperature sensitivity was found for T2 and mean diffusivity in white matter, for T1 in cerebral cortex, as well as for T1 and mean diffusivity in deep gray matter. Linear models were computed to temperature correct these MRI parameters in in situ post mortem scans to allow their comparison regardless of temperature. The here presented real-time and non invasive temperature correction method for the brain presents a crucial precondition for quantitative in situ post mortem MRI.
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Affiliation(s)
- Celine Berger
- Institute of Forensic Medicine, Department of Biomedical Engineering, University of Basel, Basel, Switzerland; Institute of Forensic Medicine, Health Department Basel-Stadt, Basel, Switzerland
| | - Melanie Bauer
- Institute of Forensic Medicine, Department of Biomedical Engineering, University of Basel, Basel, Switzerland; Institute of Forensic Medicine, Health Department Basel-Stadt, Basel, Switzerland
| | - Eva Scheurer
- Institute of Forensic Medicine, Department of Biomedical Engineering, University of Basel, Basel, Switzerland; Institute of Forensic Medicine, Health Department Basel-Stadt, Basel, Switzerland
| | - Claudia Lenz
- Institute of Forensic Medicine, Department of Biomedical Engineering, University of Basel, Basel, Switzerland; Institute of Forensic Medicine, Health Department Basel-Stadt, Basel, Switzerland.
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Wang N, Maharjan S, Tsai AP, Lin PB, Qi Y, Wallace A, Jewett M, Liu F, Landreth GE, Oblak AL. Integrating multimodality magnetic resonance imaging to the Allen Mouse Brain Common Coordinate Framework. NMR IN BIOMEDICINE 2023; 36:e4887. [PMID: 36454009 PMCID: PMC10106385 DOI: 10.1002/nbm.4887] [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: 02/16/2022] [Revised: 11/28/2022] [Accepted: 11/30/2022] [Indexed: 05/07/2023]
Abstract
High-resolution magnetic resonance imaging (MRI) affords unique image contrasts to nondestructively probe the tissue microstructure; validation of MRI findings with conventional histology is essential to better understand the MRI contrasts. However, the dramatic difference in the spatial resolution and image contrast of these two techniques impedes accurate comparison between MRI metrics and traditional histology. To better validate various MRI metrics, we acquired whole mouse brain multigradient recalled-echo and multishell diffusion MRI datasets at 25-μm isotropic resolution. The recently developed Allen Mouse Brain Common Coordinate Framework (CCFv3) provides opportunities to integrate multimodal and multiscale datasets of the whole mouse brain in a common three-dimensional (3D) space. The T2*, quantitative susceptibility mapping, diffusion tensor imaging, and neurite orientation dispersion and density imaging parameters were compared with both serial two-photon tomography images and 3D Nissl staining images in the CCFv3 at the same spatial resolution. The correlation between MRI and Nissl staining strongly depends on different metrics and different regions of the brain. Integrating different imaging modalities to the same space may substantially improve our understanding of the complexity of the brain at different scales.
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Affiliation(s)
- Nian Wang
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
| | - Surendra Maharjan
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
| | - Andy P. Tsai
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
| | - Peter B. Lin
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
| | - Yi Qi
- Center for In Vivo Microscopy, Department of Radiology, Duke University, Durham, North Carolina, USA
| | - Abigail Wallace
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
| | - Megan Jewett
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
| | - Fang Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Gary E. Landreth
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
| | - Adrian L. Oblak
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, Indiana, USA
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8
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Rendell VR, Winslow ER, Colgan TJ, Kovacs SK, Mühler MR, Knobloch G, Loeffler AG, Agni RM, Reeder SB. Radiologic-pathologic correlation of lesions in resected liver specimens with an ex vivo MRI-compatible localization device. Eur Radiol 2023; 33:535-544. [PMID: 35864349 PMCID: PMC10876158 DOI: 10.1007/s00330-022-08990-2] [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: 02/21/2022] [Revised: 06/23/2022] [Accepted: 06/29/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE Liver lesion characterization is limited by the lack of an established gold standard for precise correlation of radiologic characteristics with their histologic features. The objective of this study was to demonstrate the feasibility of using an ex vivo MRI-compatible sectioning device for radiologic-pathologic co-localization of lesions in resected liver specimens. METHODS In this prospective feasibility study, adults undergoing curative partial hepatectomy from February 2018 to January 2019 were enrolled. Gadoxetic acid was administered intraoperatively prior to hepatic vascular inflow ligation. Liver specimens were stabilized in an MRI-compatible acrylic lesion localization device (27 × 14 × 14 cm3) featuring slicing channels and a silicone gel 3D matrix. High-resolution 3D T1-weighted fast spoiled gradient echo and 3D T2-weighted fast-spin-echo images were acquired using a single channel quadrature head coil. Radiologic lesion coordinates guided pathologic sectioning. A final histopathologic diagnosis was prepared for all lesions. The proportion of successfully co-localized lesions was determined. RESULTS A total of 57 lesions were identified radiologically and sectioned in liver specimens from 10 participants with liver metastases (n = 8), primary biliary mucinous cystic neoplasm (n = 1), and hepatic adenomatosis (n = 1). Of these, 38 lesions (67%) were < 1 cm. Overall, 52/57 (91%) of radiologically identified lesions were identified pathologically using the device. Of these, 5 lesions (10%) were not initially identified on gross examination but were confirmed histologically using MRI-guided localization. One lesion was identified grossly but not on MRI. CONCLUSIONS We successfully demonstrated the feasibility of a clinical method for image-guided co-localization and histological characterization of liver lesions using an ex vivo MRI-compatible sectioning device. KEY POINTS • The ex vivo MRI-compatible sectioning device provides a reliable method for radiologic-pathologic correlation of small (< 1 cm) liver lesions in human liver specimens. • The sectioning method can be feasibly implemented within a clinical practice setting and used in future efforts to study liver lesion characterization. • Intraoperative administration of gadoxetic acid results in enhancement in ex vivo MRI images of liver specimens hours later with excellent image quality.
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Affiliation(s)
- Victoria R Rendell
- Department of Surgery, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA
| | - Emily R Winslow
- Department of Surgery, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA
- MedStar Georgetown Transplant Institute, 3800 Reservoir Road Northwest, Pasquerilla Healthcare Center 2nd Fl, Washington, DC, 20007, USA
| | - Timothy J Colgan
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA
| | - S Krisztian Kovacs
- Department of Pathology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA
| | - Matthias R Mühler
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA
| | - Gesine Knobloch
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA
| | - Agnes G Loeffler
- Department of Pathology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA
- MetroHealth Medical Center, 2500 Metrohealth Dr, Cleveland, OH, 44109, USA
| | - Rashmi M Agni
- Department of Pathology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA
| | - Scott B Reeder
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA.
- Department of Medical Physics, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA.
- Department of Biomedical Engineering, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA.
- Department of Medicine, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA.
- Department of Emergency Medicine, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI, 53792, USA.
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Huszar IN, Pallebage-Gamarallage M, Bangerter-Christensen S, Brooks H, Fitzgibbon S, Foxley S, Hiemstra M, Howard AFD, Jbabdi S, Kor DZL, Leonte A, Mollink J, Smart A, Tendler BC, Turner MR, Ansorge O, Miller KL, Jenkinson M. Tensor image registration library: Deformable registration of stand-alone histology images to whole-brain post-mortem MRI data. Neuroimage 2023; 265:119792. [PMID: 36509214 PMCID: PMC10933796 DOI: 10.1016/j.neuroimage.2022.119792] [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: 07/27/2022] [Revised: 10/26/2022] [Accepted: 12/04/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Accurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to assist the registration of stand-alone histology sections to whole-brain MRI data. METHODS Our pipeline registers stained histology sections to whole-brain post-mortem MRI in 4 stages, with the help of two photographic intermediaries: a block face image (to undistort histology sections) and coronal brain slab photographs (to insert them into MRI space). Each registration stage is implemented as a configurable stand-alone Python script using our novel platform, Tensor Image Registration Library (TIRL), which provides flexibility for wider adaptation. We report our experience of registering 87 PLP-stained histology sections from 14 subjects and perform various experiments to assess the accuracy and robustness of each stage of the pipeline. RESULTS All 87 histology sections were successfully registered to MRI. Histology-to-block registration (Stage 1) achieved 0.2-0.4 mm accuracy, better than commonly used existing methods. Block-to-slice matching (Stage 2) showed great robustness in automatically identifying and inserting small tissue blocks into whole brain slices with 0.2 mm accuracy. Simulations demonstrated sub-voxel level accuracy (0.13 mm) of the slice-to-volume registration (Stage 3) algorithm, which was observed in over 200 actual brain slice registrations, compensating 3D slice deformations up to 6.5 mm. Stage 4 combined the previous stages and generated refined pixelwise aligned multi-modal histology-MRI stacks. CONCLUSIONS Our open-source pipeline provides robust automation tools for registering stand-alone histology sections to MRI data with sub-voxel level precision, and the underlying framework makes it readily adaptable to a diverse range of microscopy-MRI studies.
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Affiliation(s)
- Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | | | - Sarah Bangerter-Christensen
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Brigham Young University, Provo, UT, USA
| | - Hannah Brooks
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Sean Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Sean Foxley
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Marlies Hiemstra
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Anatomy, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Amy F D Howard
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Daniel Z L Kor
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Anna Leonte
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Neuroscience, University of Groningen, Groningen, the Netherlands
| | - Jeroen Mollink
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Anatomy, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Adele Smart
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Martin R Turner
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Olaf Ansorge
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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10
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Zhuang Z, Ma X, Zhang Y, Yang X, Wei M, Deng X, Wang Z. Technique to match mesorectal lymph nodes imaging findings to histopathology: node-by-node comparison. J Cancer Res Clin Oncol 2022:10.1007/s00432-022-04305-6. [PMID: 36028725 DOI: 10.1007/s00432-022-04305-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 08/15/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Lymph node status is critical for staging rectal cancer and determining neoadjuvant therapy regimens. Establishing a matching between imaging and histopathological lymph nodes is fundamental for predicting lymph node status. This study reports a technique to achieve node-by-node pairing of mesorectal lymph nodes between imaging findings and histopathology. METHODS Fifty-two patients with histopathologically verified rectal cancer underwent magnetic resonance imaging before surgery. The status of each lymph node in the surgical specimens was analyzed histopathologically and matched with preoperative imaging after the operation. RESULTS A total of 346 mesorectal lymph nodes were located on imaging evaluation, of which 313 were confirmed histopathologically, and 33 were unmatched. The total success rate of the technique was 90.5%. Node-by-node analysis revealed 280 benign and 33 malignant nodal structures. CONCLUSION The technique to match mesorectal lymph node imaging findings to histopathology was feasible and effective. It simplified the technical method and had a reasonable success matching rate, which could provide a standardized approach for obtaining a prospective correlation between imaging and histological findings, supporting all subsequent related studies at the level of mesorectal lymph nodes.
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Affiliation(s)
- Zixuan Zhuang
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan Province, China
| | - Xueqin Ma
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Zhang
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan Province, China
| | - Xuyang Yang
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan Province, China
| | - Mingtian Wei
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan Province, China
| | - Xiangbing Deng
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan Province, China
| | - Ziqiang Wang
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan Province, China.
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Post mortem brain temperature and its influence on quantitative MRI of the brain. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 35:375-387. [PMID: 34714448 PMCID: PMC9188516 DOI: 10.1007/s10334-021-00971-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 09/28/2021] [Accepted: 10/15/2021] [Indexed: 11/24/2022]
Abstract
Objective MRI temperature sensitivity presents a major issue in in situ post mortem MRI (PMMRI), as the tissue temperatures differ from living persons due to passive cooling of the deceased. This study aims at computing brain temperature effects on the MRI parameters to correct for temperature in PMMRI, laying the foundation for future projects on post mortem validation of in vivo MRI techniques. Materials and methods Brain MRI parameters were assessed in vivo and in situ post mortem using a 3 T MRI scanner. Post mortem brain temperature was measured in situ transethmoidally. The temperature effect was computed by fitting a linear model to the MRI parameters and the corresponding brain temperature. Results Linear positive temperature correlations were observed for T1, T2* and mean diffusivity in all tissue types. A significant negative correlation was observed for T2 in white matter. Fractional anisotropy revealed significant correlations in all gray matter regions except for the thalamus. Discussion The linear models will allow to correct for temperature in post mortem MRI. Comparing in vivo to post mortem conditions, the mean diffusivity, in contrast to T1 and T2, revealed additional effects besides temperature, such as cessation of perfusion and active diffusion.
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Albers J, Svetlove A, Alves J, Kraupner A, di Lillo F, Markus MA, Tromba G, Alves F, Dullin C. Elastic transformation of histological slices allows precise co-registration with microCT data sets for a refined virtual histology approach. Sci Rep 2021; 11:10846. [PMID: 34035350 PMCID: PMC8149420 DOI: 10.1038/s41598-021-89841-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/16/2021] [Indexed: 11/24/2022] Open
Abstract
Although X-ray based 3D virtual histology is an emerging tool for the analysis of biological tissue, it falls short in terms of specificity when compared to conventional histology. Thus, the aim was to establish a novel approach that combines 3D information provided by microCT with high specificity that only (immuno-)histochemistry can offer. For this purpose, we developed a software frontend, which utilises an elastic transformation technique to accurately co-register various histological and immunohistochemical stainings with free propagation phase contrast synchrotron radiation microCT. We demonstrate that the precision of the overlay of both imaging modalities is significantly improved by performing our elastic registration workflow, as evidenced by calculation of the displacement index. To illustrate the need for an elastic co-registration approach we examined specimens from a mouse model of breast cancer with injected metal-based nanoparticles. Using the elastic transformation pipeline, we were able to co-localise the nanoparticles to specifically stained cells or tissue structures into their three-dimensional anatomical context. Additionally, we performed a semi-automated tissue structure and cell classification. This workflow provides new insights on histopathological analysis by combining CT specific three-dimensional information with cell/tissue specific information provided by classical histology.
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Affiliation(s)
- Jonas Albers
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany.
| | - Angelika Svetlove
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany.,Translational Molecular Imaging, Max-Planck-Institute for Experimental Medicine, Göttingen, Germany
| | - Justus Alves
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany
| | | | | | - M Andrea Markus
- Translational Molecular Imaging, Max-Planck-Institute for Experimental Medicine, Göttingen, Germany
| | | | - Frauke Alves
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany.,Translational Molecular Imaging, Max-Planck-Institute for Experimental Medicine, Göttingen, Germany.,Clinic for Hematology and Medical Oncology, University Medical Center Göttingen, Göttingen, Germany
| | - Christian Dullin
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany
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