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Diagnostics
Special Issues
Applications of CT Scans to Quantitative Imaging and Precision Medicine
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Applications of CT Scans to Quantitative Imaging and Precision Medicine

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1530

Special Issue Editor

Dr. Shu-Ju Tu

E-Mail Website
Guest Editor
1. Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Tao-Yuan 333, Taiwan
2. Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Tao-Yuan 333, Taiwan
3. Department of Nuclear Medicine, Linkou Chang Gung Memorial Hospital, Tao-Yuan 333, Taiwan
Interests: medical physics; medical imaging; radiation therapy; radiomics; machine learning; artificial intelligence

Special Issue Information

Dear Colleagues,

The image quality produced by CT scanning systems has been tremendously improved since the introduction of the first-generation CT system, developed in the early 1970s by Godfrey Hounsfield and Allan Cormack. This progress can be attributed to the advancement of CT core technologies such as the x-ray tube, electronic system of attenuating x-ray photon detection, and image reconstruction algorithm. CT imaging is now widely used in clinical disease diagnosis, treatment planning and patient positioning in radiation therapy, and preclinical imaging of various animal models. The new era of CT imaging has been undergoing a paradigm shift from anatomy-based methods to quantitative and prognostic diagnosis imaging. The integration of modern CT imaging with advanced computing tools such as radiomics and machine learning has made individualized medicine a potentially attainable goal. However, challenges and technical difficulties remain. These issues include access to the diverse and huge data sets from multiple healthcare institutions and independent clinical validation of predictive models. We cordially invite contributions on CT-related research from scientists and clinical physicians across diverse fields.

Dr. Shu-Ju Tu
Guest Editor

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. Diagnostics 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 2600 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 imaging
  • radiomics
  • machine learning
  • precision medicine
  • individualized treatment

Published Papers (2 papers)

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Research

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13 pages, 2097 KiB  
Article
Dynamic Perviousness Predicts Revascularization Success in Acute Ischemic Stroke
by Gergely Bertalan, Roxane Duparc, Miklos Krepuska, Daniel Toth, Jawid Madjidyar, Patrick Thurner, Tilman Schubert and Zsolt Kulcsar
Diagnostics 2024, 14(5), 535; https://doi.org/10.3390/diagnostics14050535 - 03 Mar 2024
Viewed by 542
Abstract
Background: The predictive value of thrombus perviousness in acute ischemic stroke (AIS), as measured by computed tomography (CT), has been intensively studied with conflicting results. In this study, we investigate the predictive potential of the novel concept of dynamic perviousness using three-dimensional (3D) [...] Read more.
Background: The predictive value of thrombus perviousness in acute ischemic stroke (AIS), as measured by computed tomography (CT), has been intensively studied with conflicting results. In this study, we investigate the predictive potential of the novel concept of dynamic perviousness using three-dimensional (3D) volumetric evaluation of occlusive thrombi. Methods: The full thrombus volume in 65 patients with a hyperdense artery sign on non-contrast CT (NCCT), who underwent mechanical thrombectomy (MT), was segmented. Perviousness maps were computed voxel-wise for the entire thrombus volume as thrombus attenuation increase (TAI) between NCCT and CT angiography (CTA) as well as between CTA and late venous phase CT (CTV). Perviousness was analyzed for its association with NIHSS at admission, Thrombolysis In Cerebral Infarction (TICI) score, and number of MT passes. Results: The mean late-uptake TAI of thrombi with NIHSS scores greater than 21 at admission was approximately 100% higher than for lower scored NIHSS (p between 0.05 and 0.005). Concerning revascularization results, thrombi requiring less than four MT passes had ca. 80% higher group mean late-uptake TAI than clots requiring four or more passes (p = 0.03), and thrombi with TICI score III had ca. 95% higher group mean late-uptake TAI than thrombi with TICI II (p = 0.03). Standard perviousness showed no significant correlation with MT results. Conclusion: Standard thrombus perviousness of 3D clot volume is not associated with revascularization results in AIS. In contrast, dynamic perviousness assessed with a voxel-wise characterization of 3D thrombi volume may be a better predictor of MT outcomes than standard perviousness. Full article
(This article belongs to the Special Issue Applications of CT Scans to Quantitative Imaging and Precision Medicine)
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Review

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21 pages, 3677 KiB  
Review
Updates on the Applications of Spectral Computed Tomography for Musculoskeletal Imaging
by Liesl S. Eibschutz, George Matcuk, Michael Kuo-Jiun Chiu, Max Yang Lu and Ali Gholamrezanezhad
Diagnostics 2024, 14(7), 732; https://doi.org/10.3390/diagnostics14070732 - 29 Mar 2024
Viewed by 531
Abstract
Spectral CT represents a novel imaging approach that can noninvasively visualize, quantify, and characterize many musculoskeletal pathologies. This modality has revolutionized the field of radiology by capturing CT attenuation data across multiple energy levels and offering superior tissue characterization while potentially minimizing radiation [...] Read more.
Spectral CT represents a novel imaging approach that can noninvasively visualize, quantify, and characterize many musculoskeletal pathologies. This modality has revolutionized the field of radiology by capturing CT attenuation data across multiple energy levels and offering superior tissue characterization while potentially minimizing radiation exposure compared to traditional enhanced CT scans. Despite MRI being the preferred imaging method for many musculoskeletal conditions, it is not viable for some patients. Moreover, this technique is time-consuming, costly, and has limited availability in many healthcare settings. Thus, spectral CT has a considerable role in improving the diagnosis, characterization, and treatment of gout, inflammatory arthropathies, degenerative disc disease, osteoporosis, occult fractures, malignancies, ligamentous injuries, and other bone-marrow pathologies. This comprehensive review will delve into the diverse capabilities of dual-energy CT, a subset of spectral CT, in addressing these musculoskeletal conditions and explore potential future avenues for its integration into clinical practice. Full article
(This article belongs to the Special Issue Applications of CT Scans to Quantitative Imaging and Precision Medicine)
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