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Diagnostics
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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: closed (31 August 2024) | Viewed by 5496

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

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Published Papers (4 papers)

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Research

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18 pages, 4051 KiB  
Article
An Image-Based Prior Knowledge-Free Approach for a Multi-Material Decomposition in Photon-Counting Computed Tomography
by Jonas Neumann, Tristan Nowak, Bernhard Schmidt and Joachim von Zanthier
Diagnostics 2024, 14(12), 1262; https://doi.org/10.3390/diagnostics14121262 - 14 Jun 2024
Cited by 1 | Viewed by 814
Abstract
Photon-counting CT systems generally allow for acquiring multiple spectral datasets and thus for decomposing CT images into multiple materials. We introduce a prior knowledge-free deterministic material decomposition approach for quantifying three material concentrations on a commercial photon-counting CT system based on a single [...] Read more.
Photon-counting CT systems generally allow for acquiring multiple spectral datasets and thus for decomposing CT images into multiple materials. We introduce a prior knowledge-free deterministic material decomposition approach for quantifying three material concentrations on a commercial photon-counting CT system based on a single CT scan. We acquired two phantom measurement series: one to calibrate and one to test the algorithm. For evaluation, we used an anthropomorphic abdominal phantom with inserts of either aqueous iodine solution, aqueous tungsten solution, or water. Material CT numbers were predicted based on a polynomial in the following parameters: Water-equivalent object diameter, object center-to-isocenter distance, voxel-to-isocenter distance, voxel-to-object center distance, and X-ray tube current. The material decomposition was performed as a generalized least-squares estimation. The algorithm provided material maps of iodine, tungsten, and water with average estimation errors of 4% in the contrast agent maps and 1% in the water map with respect to the material concentrations in the inserts. The contrast-to-noise ratio in the iodine and tungsten map was 36% and 16% compared to the noise-minimal threshold image. We were able to decompose four spectral images into iodine, tungsten, and water. Full article
(This article belongs to the Special Issue Applications of CT Scans to Quantitative Imaging and Precision Medicine)
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14 pages, 2930 KiB  
Article
Utility of CT Radiomics and Delta Radiomics for Survival Evaluation in Locally Advanced Nasopharyngeal Carcinoma with Concurrent Chemoradiotherapy
by Yen-Cho Huang, Shih-Ming Huang, Jih-Hsiang Yeh, Tung-Chieh Chang, Din-Li Tsan, Chien-Yu Lin and Shu-Ju Tu
Diagnostics 2024, 14(9), 941; https://doi.org/10.3390/diagnostics14090941 - 30 Apr 2024
Cited by 1 | Viewed by 1190
Abstract
Background: A high incidence rate of nasopharyngeal carcinoma (NPC) has been observed in Southeast Asia compared to other parts of the world. Radiomics is a computational tool to predict outcomes and may be used as a prognostic biomarker for advanced NPC treated with [...] Read more.
Background: A high incidence rate of nasopharyngeal carcinoma (NPC) has been observed in Southeast Asia compared to other parts of the world. Radiomics is a computational tool to predict outcomes and may be used as a prognostic biomarker for advanced NPC treated with concurrent chemoradiotherapy. Recently, radiomic analysis of the peripheral tumor microenvironment (TME), which is the region surrounding the gross tumor volume (GTV), has shown prognostic usefulness. In this study, not only was gross tumor volume (GTVt) analyzed but also tumor peripheral regions (GTVp) were explored in terms of the TME concept. Both radiomic features and delta radiomic features were analyzed using CT images acquired in a routine radiotherapy process. Methods: A total of 50 patients with NPC stages III, IVA, and IVB were enrolled between September 2004 and February 2014. Survival models were built using Cox regression with clinical factors (i.e., gender, age, overall stage, T stage, N stage, and treatment dose) and radiomic features. Radiomic features were extracted from GTVt and GTVp. GTVp was created surrounding GTVt for TME consideration. Furthermore, delta radiomics, which is the longitudinal change in quantitative radiomic features, was utilized for analysis. Finally, C-index values were computed using leave-one-out cross-validation (LOOCV) to evaluate the performances of all prognosis models. Results: Models were built for three different clinical outcomes, including overall survival (OS), local recurrence-free survival (LRFS), and progression-free survival (PFS). The range of the C-index in clinical factor models was (0.622, 0.729). All radiomics models, including delta radiomics models, were in the range of (0.718, 0.872). Among delta radiomics models, GTVt and GTVp were in the range of (0.833, 0.872) and (0.799, 0.834), respectively. Conclusions: Radiomic analysis on the proximal region surrounding the gross tumor volume of advanced NPC patients for survival outcome evaluation was investigated, and preliminary positive results were obtained. Radiomic models and delta radiomic models demonstrated performance that was either superior to or comparable with that of conventional clinical models. Full article
(This article belongs to the Special Issue Applications of CT Scans to Quantitative Imaging and Precision Medicine)
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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 - 3 Mar 2024
Cited by 3 | Viewed by 1012
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
Cited by 1 | Viewed by 1731
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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