Special Issue "Advances in Co-clinical Quantitative Imaging Research"

A special issue of Tomography (ISSN 2379-139X).

Deadline for manuscript submissions: 15 January 2023 | Viewed by 2371

Special Issue Editors

Dr. Dariya Malyarenko
E-Mail Website
Guest Editor
Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA
Interests: quantitative diffusion imaging; cancer imaging biomarkers; technical bias correction; clinical trial translation; quantitative diffusion models
Prof. Dr. Michael Lewis
E-Mail Website
Guest Editor
Departments of Molecular and Cellular Biology Radiology, Baylor College of Medicine, Houston, TX 77030, USA
Interests: breast cancer detection; cancer genetic regulation; genetically engineered animal models; therapeutic and preventive agents; genomic and imaging integration
Dr. Huiming Zhang
E-Mail Website
Guest Editor
Cancer Imaging Program, Division of Cancer Treatment & Diagnosis, National Cancer Institute, Bethesda, MD 20892, USA
Interests: MRI/MRSI; molecular imaging; quantitative imaging technology; co-clinical quantitative imaging; image-based biomarkers; cancer therapy response assessment
Prof. Dr. Cristian Badea
E-Mail Website
Guest Editor
Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA
Interests: computed tomography; quantitative imaging; theranostics; image reconstruction; deep learning

Special Issue Information

Dear Colleagues,

The National Cancer Institute’s Co-Clinical Imaging Research Resource Program (CIRP) promotes the development of quantitative imaging resources for therapeutic or prevention co-clinical trials that study both patients and human-in-mouse models. The program facilitates consensus on quantitative imaging methods and standard operating procedures for co-clinical applications. CIRP is committed to the development of freely accessible, comprehensive information resources to guide co-clinical imaging investigations in the context of experimental design, protocol and software development, modeling and information extraction, biological and pathological validations, multiscale data integration, and preclinical–clinical correlations. 

Dr. Dariya Malyarenko
Prof. Dr. Michael Lewis
Dr. Huiming Zhang
Prof. Dr. Cristian Badea
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Tomography is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantitative cancer imaging
  • pre-clinical animal imaging
  • co-clinical data integration
  • tumor mouse models
  • co-clinical trial informatics

Published Papers (3 papers)

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Research

Article
Dynamic Contrast-Enhanced MRI in the Abdomen of Mice with High Temporal and Spatial Resolution Using Stack-of-Stars Sampling and KWIC Reconstruction
Tomography 2022, 8(5), 2113-2128; https://doi.org/10.3390/tomography8050178 - 24 Aug 2022
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Abstract
Application of quantitative dynamic contrast-enhanced (DCE) MRI in mouse models of abdominal cancer is challenging due to the effects of RF inhomogeneity, image corruption from rapid respiratory motion and the need for high spatial and temporal resolutions. Here we demonstrate a DCE protocol [...] Read more.
Application of quantitative dynamic contrast-enhanced (DCE) MRI in mouse models of abdominal cancer is challenging due to the effects of RF inhomogeneity, image corruption from rapid respiratory motion and the need for high spatial and temporal resolutions. Here we demonstrate a DCE protocol optimized for such applications. The method consists of three acquisitions: (1) actual flip-angle B1 mapping, (2) variable flip-angle T1 mapping and (3) acquisition of the DCE series using a motion-robust radial strategy with k-space weighted image contrast (KWIC) reconstruction. All three acquisitions employ spoiled radial imaging with stack-of-stars sampling (SoS) and golden-angle increments between the views. This scheme is shown to minimize artifacts due to respiratory motion while simultaneously facilitating view-sharing image reconstruction for the dynamic series. The method is demonstrated in a genetically engineered mouse model of pancreatic ductal adenocarcinoma and yielded mean perfusion parameters of Ktrans = 0.23 ± 0.14 min−1 and ve = 0.31 ± 0.17 (n = 22) over a wide range of tumor sizes. The SoS-sampled DCE method is shown to produce artifact-free images with good SNR leading to robust estimation of DCE parameters. Full article
(This article belongs to the Special Issue Advances in Co-clinical Quantitative Imaging Research)
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Article
Web-Based Application for Biomedical Image Registry, Analysis, and Translation (BiRAT)
Tomography 2022, 8(3), 1453-1462; https://doi.org/10.3390/tomography8030117 - 30 May 2022
Viewed by 596
Abstract
Imaging has become an invaluable tool in preclinical research for its capability to non-invasively detect and monitor disease and assess treatment response. With the increased use of preclinical imaging, large volumes of image data are being generated requiring critical data management tools. Due [...] Read more.
Imaging has become an invaluable tool in preclinical research for its capability to non-invasively detect and monitor disease and assess treatment response. With the increased use of preclinical imaging, large volumes of image data are being generated requiring critical data management tools. Due to proprietary issues and continuous technology development, preclinical images, unlike DICOM-based images, are often stored in an unstructured data file in company-specific proprietary formats. This limits the available DICOM-based image management database to be effectively used for preclinical applications. A centralized image registry and management tool is essential for advances in preclinical imaging research. Specifically, such tools may have a high impact in generating large image datasets for the evolving artificial intelligence applications and performing retrospective analyses of previously acquired images. In this study, a web-based server application is developed to address some of these issues. The application is designed to reflect the actual experimentation workflow maintaining detailed records of both individual images and experimental data relevant to specific studies and/or projects. The application also includes a web-based 3D/4D image viewer to easily and quickly view and evaluate images. This paper briefly describes the initial implementation of the web-based application. Full article
(This article belongs to the Special Issue Advances in Co-clinical Quantitative Imaging Research)
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Article
Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden
Tomography 2022, 8(2), 740-753; https://doi.org/10.3390/tomography8020061 - 10 Mar 2022
Cited by 1 | Viewed by 1082
Abstract
The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on lymphocyte burden. High mutational load transplant soft tissue sarcomas were initiated in Rag2+/− and Rag2−/− mice to model [...] Read more.
The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on lymphocyte burden. High mutational load transplant soft tissue sarcomas were initiated in Rag2+/− and Rag2−/− mice to model varying lymphocyte burden. Mice received radiation therapy (20 Gy) to the tumor-bearing hind limb and were injected with a liposomal iodinated contrast agent. Five days later, animals underwent conventional micro-CT imaging using an energy integrating detector (EID) and spectral micro-CT imaging using a photon-counting detector (PCD). Tumor volumes and iodine uptakes were measured. The radiomic features (RF) were grouped into feature-spaces corresponding to EID, PCD, and spectral decomposition images. The RFs were ranked to reduce redundancy and increase relevance based on TL burden. A stratified repeated cross validation strategy was used to assess separation using a logistic regression classifier. Tumor iodine concentration was the only significantly different conventional tumor metric between Rag2+/− (TLs present) and Rag2−/− (TL-deficient) tumors. The RFs further enabled differentiation between Rag2+/− and Rag2−/− tumors. The PCD-derived RFs provided the highest accuracy (0.68) followed by decomposition-derived RFs (0.60) and the EID-derived RFs (0.58). Such non-invasive approaches could aid in tumor stratification for cancer therapy studies. Full article
(This article belongs to the Special Issue Advances in Co-clinical Quantitative Imaging Research)
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