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Diagnostics
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Spectral CT Techniques and Functional Applications in Disease Diagnosis
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Spectral CT Techniques and Functional Applications in Disease Diagnosis

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

Deadline for manuscript submissions: closed (16 December 2022) | Viewed by 10168

Special Issue Editor

Prof. Dr. Nico Buls

E-Mail Website
Guest Editor
Department of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), Vrije Universiteit Brussel (VUB), Brussels, Belgium
Interests: diagnostic imaging; radiology; computed tomography (CT); medical physics; quantitative imaging; radiation dosimetry; advanced imaging technologies; biomedical engineering

Special Issue Information

Dear Colleagues, 

X-ray imaging, in particular with Computed Tomography (CT), remains one of the cornerstones for the diagnosis and follow-up assessment of many diseases. The advent of spectral CT allows the transition from conventional CT imaging to material-specific imaging and holds great promise to add quantitative and functional information.    With spectral CT techniques, multiple X-ray acquisitions at two or more X-ray energies are performed in the same region, revealing energy-dependent X-ray absorption properties of the materials inside the body. These spectral data provide additional information on their elemental composition, allowing tissue classification and differentiation. Commercial dual-energy CT systems were clinically introduced more than 10 years ago. Meanwhile, great progress has been accomplished in the development of energy-resolving CT detectors, which provide spectral attenuation information at three or more X-ray energy bins. Currently, the use of photon-counting detectors is being evaluated on prototype research scanners. The clinical use of spectral CT mainly includes material and energy-selective imaging for a vast—and still increasing—number of applications in all radiology subspecialties. Amongst many others, one example is the creation of parametric maps of iodine concentration in contrast-enhanced CT, which provide information of perfused blood volume in tissues and organs such as in lung parenchyma or in the myocardial tissue. Additionally, material differentiation has proved successful for many applications such as for the detection and characterization of atherosclerotic plaques, for lesions in oncology imaging, and for the differentiation of urinary stones, to name only a few.  Spectral CT enables CT imaging to advance beyond the mere detection of pathology, expanding its role in the evaluation and management of disease. This Special Issue promotes research papers that address spectral CT techniques to make quantitative statements on disease. Next to clinical studies, we are also interested in the implementation of new scan protocols in experimental and preclinical studies that investigate the quantitative performance of spectral CT, including technology development papers that describe novel technologies. 

Prof. Dr. Nico Buls
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

  • medical imaging
  • computed tomography (CT)
  • dual-energy computed tomography (CT)
  • spectral CT
  • CT detector development
  • quantitative imaging
  • material decomposition
  • material selective imaging
  • energy selective imaging
  • image reconstruction and processing

Published Papers (4 papers)

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Research

15 pages, 6735 KiB  
Article
Spectral Photon-Counting CT Imaging of Gold Nanoparticle Labelled Monocytes for Detection of Atherosclerosis: A Preclinical Study
by Mahdieh Moghiseh, Emily Searle, Devyani Dixit, Johoon Kim, Yuxi C. Dong, David P. Cormode, Anthony Butler, Steven P. Gieseg and MARS Bioimaging Ltd.
Diagnostics 2023, 13(3), 499; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics13030499 - 29 Jan 2023
Cited by 5 | Viewed by 2126
Abstract
A key process in the development of atherosclerotic plaques is the recruitment of monocytes into the artery wall. Using spectral photon-counting computed tomography we examine whether monocyte deposition within the artery wall of ApoE-/- mouse can be detected. Primary mouse monocytes were labelled [...] Read more.
A key process in the development of atherosclerotic plaques is the recruitment of monocytes into the artery wall. Using spectral photon-counting computed tomography we examine whether monocyte deposition within the artery wall of ApoE-/- mouse can be detected. Primary mouse monocytes were labelled by incubating them with 15 nm gold nanoparticles coated with 11-mercaptoundecanoic acid The monocyte uptake of the particle was confirmed by electron microscopy of the cells before injection into 6-week-old apolipoprotein E deficient (ApoE-/-) mouse that had been fed with the Western diet for 10 weeks. Four days following injection, the mouse was sacrificed and imaged using a MARS spectral photon counting computed tomography scanner with a spectral range of 7 to 120 KeV with five energy bins. Imaging analysis showed the presence of X-ray dense material within the mouse aortic arch which was consistent with the spectral characteristic of gold rather than calcium. The imaging is interpreted as showing the deposition of gold nanoparticles containing monocytes within the mouse aorta. The results of our study determined that spectral photon-counting computed tomography could provide quantitative information about gold nanoparticles labelled monocytes in voxels of 90 × 90 × 90 µm3. The imaging was consistent with previous micro-CT and electron microscopy of mice using the same nanoparticles. This study demonstrates that spectral photon-counting computed tomography, using a MARS small bore scanner, can detect a fundamental atherogenic process within mouse models of atherogenesis. The present study demonstrates the feasibility of spectral photon-counting computed tomography as an emerging molecular imaging modality to detect atherosclerotic disease. Full article
(This article belongs to the Special Issue Spectral CT Techniques and Functional Applications in Disease Diagnosis)
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18 pages, 4031 KiB  
Article
Dual-Energy Computed Tomography Applications to Reduce Metal Artifacts in Hip Prostheses: A Phantom Study
by Daniele Conti, Fabio Baruffaldi, Paolo Erani, Anna Festa, Stefano Durante and Miriam Santoro
Diagnostics 2023, 13(1), 50; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics13010050 - 23 Dec 2022
Cited by 1 | Viewed by 1560
Abstract
Metal components of hip prostheses cause severe artifacts in CT images, influencing diagnostic accuracy. Metal artifact reduction (MAR) software and virtual monoenergetic reconstructions on dual-energy CT (DECT) systems are possible solutions that should be considered. In this study, we created a customized adjustable [...] Read more.
Metal components of hip prostheses cause severe artifacts in CT images, influencing diagnostic accuracy. Metal artifact reduction (MAR) software and virtual monoenergetic reconstructions on dual-energy CT (DECT) systems are possible solutions that should be considered. In this study, we created a customized adjustable phantom to quantify the severity of artifacts on periprosthetic tissues (cortical and spongious bone, soft tissues) for hip prostheses. The severity of artifacts was classified by different thresholds of deviation from the CT numbers for reference objects not affected by artifacts. The in vitro setup was applied on four unilateral and three bilateral configurations of hip prostheses (made of titanium, cobalt, and stainless steel alloys) with a DECT system, changing the energy of virtual monoenergetic reconstructions, with and without MAR. The impact of these tools on the severity of artifacts was scored, looking for the best scan conditions for the different configurations. For titanium prostheses, the reconstruction at 110 keV, without MAR, always minimized the artifacts. For cobalt and stainless-steel prostheses, MAR should always be applied, while monoenergetic reconstruction alone did not show clear advantages. The available tools for reducing metal artifacts must therefore be applied depending on the examined prosthetic configuration. Full article
(This article belongs to the Special Issue Spectral CT Techniques and Functional Applications in Disease Diagnosis)
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12 pages, 3200 KiB  
Article
Dual-Energy CT, Virtual Non-Calcium Bone Marrow Imaging of the Spine: An AI-Assisted, Volumetric Evaluation of a Reference Cohort with 500 CT Scans
by Philipp Fervers, Florian Fervers, Mathilda Weisthoff, Miriam Rinneburger, David Zopfs, Robert Peter Reimer, Gregor Pahn, Jonathan Kottlors, David Maintz, Simon Lennartz, Thorsten Persigehl and Nils Große Hokamp
Diagnostics 2022, 12(3), 671; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12030671 - 09 Mar 2022
Cited by 2 | Viewed by 2040
Abstract
Virtual non-calcium (VNCa) images from dual-energy computed tomography (DECT) have shown high potential to diagnose bone marrow disease of the spine, which is frequently disguised by dense trabecular bone on conventional CT. In this study, we aimed to define reference values for VNCa [...] Read more.
Virtual non-calcium (VNCa) images from dual-energy computed tomography (DECT) have shown high potential to diagnose bone marrow disease of the spine, which is frequently disguised by dense trabecular bone on conventional CT. In this study, we aimed to define reference values for VNCa bone marrow images of the spine in a large-scale cohort of healthy individuals. DECT was performed after resection of a malignant skin tumor without evidence of metastatic disease. Image analysis was fully automated and did not require specific user interaction. The thoracolumbar spine was segmented by a pretrained convolutional neuronal network. Volumetric VNCa data of the spine’s bone marrow space were processed using the maximum, medium, and low calcium suppression indices. Histograms of VNCa attenuation were created for each exam and suppression setting. We included 500 exams of 168 individuals (88 female, patient age 61.0 ± 15.9). A total of 8298 vertebrae were segmented. The attenuation histograms’ overlap of two consecutive exams, as a measure for intraindividual consistency, yielded a median of 0.93 (IQR: 0.88–0.96). As our main result, we provide the age- and sex-specific bone marrow attenuation profiles of a large-scale cohort of individuals with healthy trabecular bone structure as a reference for future studies. We conclude that artificial-intelligence-supported, fully automated volumetric assessment is an intraindividually robust method to image the spine’s bone marrow using VNCa data from DECT. Full article
(This article belongs to the Special Issue Spectral CT Techniques and Functional Applications in Disease Diagnosis)
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27 pages, 13817 KiB  
Article
COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts
by Jasjit S. Suri, Sushant Agarwal, Alessandro Carriero, Alessio Paschè, Pietro S. C. Danna, Marta Columbu, Luca Saba, Klaudija Viskovic, Armin Mehmedović, Samriddhi Agarwal, Lakshya Gupta, Gavino Faa, Inder M. Singh, Monika Turk, Paramjit S. Chadha, Amer M. Johri, Narendra N. Khanna, Sophie Mavrogeni, John R. Laird, Gyan Pareek, Martin Miner, David W. Sobel, Antonella Balestrieri, Petros P. Sfikakis, George Tsoulfas, Athanasios Protogerou, Durga Prasanna Misra, Vikas Agarwal, George D. Kitas, Jagjit S. Teji, Mustafa Al-Maini, Surinder K. Dhanjil, Andrew Nicolaides, Aditya Sharma, Vijay Rathore, Mostafa Fatemi, Azra Alizad, Pudukode R. Krishnan, Ferenc Nagy, Zoltan Ruzsa, Archna Gupta, Subbaram Naidu, Kosmas I. Paraskevas and Mannudeep K. Kalraadd Show full author list remove Hide full author list
Diagnostics 2021, 11(12), 2367; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11122367 - 15 Dec 2021
Cited by 18 | Viewed by 3500
Abstract
(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 [...] Read more.
(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg. Full article
(This article belongs to the Special Issue Spectral CT Techniques and Functional Applications in Disease Diagnosis)
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