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Advances in Computer Assisted Tomography: New Technologies for Improving Biomedical Image, Sensor and Signal Processing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (5 September 2025) | Viewed by 5635

Special Issue Editors

Dr. Dangdang Shao

E-Mail Website
Guest Editor
Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
Interests: computer vision; image processing; physiological sensing; biomedical engineering; deep learning; machine learning; video processing
Special Issues, Collections and Topics in MDPI journals
Dr. Chenbin Liu

E-Mail Website
Guest Editor
Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center, Shenzhen 518071, China
Interests: medical physics; image processing; mobile health; proton therapy

Special Issue Information

Dear Colleagues,

Computer-assisted tomography includes well-established techniques that generate detailed internal images of human body such as CT and MRI scans. It is an important tool with many applications including in medical diagnosis, biological studies and therapeutic decision-making processes. Powered by recent advances in sensor technology, computer vision, machine learning, artificial intelligence and big data, the field is poised for expansive development that will significantly improve the accuracy and efficiency of recognizing, classifying and segmenting high-risk health indicators. Progress in these areas should assist physicians and radiologists in achieving more accurate medical diagnosis and better clinical outcomes for their patients. Topics of interest for this Special Issue include: functional CT and MR imaging; advanced techniques for CT and MR and artificial intelligence-powered CT / MR analysis.

Dr. Dangdang Shao
Dr. Chenbin Liu
Guest Editors

Manuscript Submission Information

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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

  • computer-assisted tomography
  • artificial intelligence
  • medical imaging
  • computer-aided diagnosis
  • computer vision
  • tomographic sensors

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Published Papers (4 papers)

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Research

13 pages, 4942 KB  
Article
Three-Station Non-Contrast MR Angiography of the Lower Extremities Using Standard and Centric Fresh Blood Imaging
by Won C. Bae, Anya Mesa, Vadim Malis, Yoshiki Kuwatsuru, Katsumi Nakamura, Ann Gaffey and Mitsue Miyazaki
Sensors 2025, 25(24), 7429; https://doi.org/10.3390/s25247429 - 6 Dec 2025
Viewed by 277
Abstract
Background: Peripheral artery disease (PAD) is a manifestation of atherosclerosis that affects the extremities, leading to reduced perfusion and functional impairment. Non-contrast magnetic resonance angiography (NC-MRA) provides a safe and quantitative approach for early detection of PAD without the risks associated with [...] Read more.
Background: Peripheral artery disease (PAD) is a manifestation of atherosclerosis that affects the extremities, leading to reduced perfusion and functional impairment. Non-contrast magnetic resonance angiography (NC-MRA) provides a safe and quantitative approach for early detection of PAD without the risks associated with contrast agents. The purpose of this study was to demonstrate the application of standard and centric ky-kz FBI techniques for rapid three-station NC-MRA of the entire lower extremity. Methods: This prospective cross-sectional study compared standard three-station fresh blood imaging (sFBI) with centric ky-kz ordered fresh blood imaging (cFBI) sequences in 10 healthy subjects and 3 patients with PAD (age range: 23–79 years; 7 females) using a 3-Tesla magnetic resonance imaging (MRI) system. Both sequences were acquired at the iliac, femoral, and tibial stations. Image quality (0–4 scale), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated. Statistical analysis was performed using repeated-measures analysis of variance (ANOVA) with significance set at α = 0.05. Results: Image quality did not differ significantly between sFBI and cFBI (p = 1.0). The iliac station exhibited lower image quality than the femoral station (p < 0.01). In a PAD patient with an iliac stent, cFBI preserved good image quality in the femoral and tibial stations, whereas sFBI was affected by N/2 aliasing artifacts. Both methods failed to visualize the stented iliac segment. Compared to sFBI, cFBI yielded significantly lower SNR (p < 0.01) and CNR (p < 0.001) but reduced total scan time by approximately 40% (468 s vs. 291 s). Conclusions: Three-station non-contrast FBI MRA of the peripheral arteries is feasible. The cFBI sequence substantially shortens scan time without compromising diagnostic image quality, offering practical advantages for clinical implementation, improved patient comfort, and reduced motion artifacts. Full article
(This article belongs to the Special Issue Advances in Computer Assisted Tomography: New Technologies for Improving Biomedical Image, Sensor and Signal Processing)
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25 pages, 2655 KB  
Article
Characterization of Breast Microcalcifications Using Dual-Energy CBCT: Impact of Detector Configuration on Imaging Performance—A Simulation Study
by Evangelia Karali, Christos Michail, George Fountos, Nektarios Kalyvas and Ioannis Valais
Sensors 2025, 25(22), 6853; https://doi.org/10.3390/s25226853 - 9 Nov 2025
Viewed by 638
Abstract
Microcalcifications (HAp, CaCO3, and CaC2O4) in breast tissue may indicate malignancy. Early-stage breast cancer diagnosis may benefit from the clinical application of dual-energy techniques. Dual-energy cone-beam computed tomography (CBCT) could strongly contribute to an accurate diagnosis, especially [...] Read more.
Microcalcifications (HAp, CaCO3, and CaC2O4) in breast tissue may indicate malignancy. Early-stage breast cancer diagnosis may benefit from the clinical application of dual-energy techniques. Dual-energy cone-beam computed tomography (CBCT) could strongly contribute to an accurate diagnosis, especially in dense breasts. This study focused on photon-counting detector alternatives to the standard cesium iodide (CsI) that CBCT currently relies on and investigated potential advantages over the employed CsI scintillators. Denser detector materials with a higher effective atomic number than CsI could improve image quality. A micro-CBCT was simulated in GATE using seven different detector configurations (CsI, bismuth germanate (BGO), lutetium oxyorthosilicate (LSO), lutetium–yttrium oxyorthosilicate (LYSO), gadolinium aluminum gallium garnet (GAGG), lanthanum bromide (LaBr3), and cadmium zinc telluride (CZT)) and four breast tissue phantoms containing microcalcifications of both type I and type II. The dual-energy methodology was applied to planar and tomographic acquisition data. Tomographic data were reconstructed using filtered backprojection (FBP) and the ordered-subsets expectation-maximization (OSEM) algorithm. Image quality was measured using contrast-to-noise ratio (CNR) values. Both monoenergetic and polyenergetic models were considered. CZT and GAGG crystals presented higher CNR values than CsI. HAp microcalcifications exhibited the highest CNR values, which, when accompanied by OSEM, could be distinguished for classification. Detector configurations based on CZT or GAGG crystals could be adequate alternatives to CsI in dual-energy CBCT. Full article
(This article belongs to the Special Issue Advances in Computer Assisted Tomography: New Technologies for Improving Biomedical Image, Sensor and Signal Processing)
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19 pages, 7657 KB  
Article
Subspace-Based Two-Step Iterative Shrinkage/Thresholding Algorithm for Microwave Tomography Breast Imaging
by Ji Wu, Fan Yang, Jinchuan Zheng, Hung T. Nguyen and Rifai Chai
Sensors 2025, 25(5), 1429; https://doi.org/10.3390/s25051429 - 26 Feb 2025
Viewed by 1045
Abstract
Microwave tomography serves as a promising non-invasive technique for breast imaging, yet accurate reconstruction in noisy environments remains challenging. We propose an adaptive subspace-based two-step iterative shrinkage/thresholding (S-TwIST) algorithm that enhances reconstruction accuracy through two key innovations: a singular value decomposition (SVD) approach [...] Read more.
Microwave tomography serves as a promising non-invasive technique for breast imaging, yet accurate reconstruction in noisy environments remains challenging. We propose an adaptive subspace-based two-step iterative shrinkage/thresholding (S-TwIST) algorithm that enhances reconstruction accuracy through two key innovations: a singular value decomposition (SVD) approach for extracting deterministic contrast sources, and an adaptive strategy for optimal singular value selection. Unlike conventional DBIM methods that rely solely on secondary incident fields, S-TwIST incorporates deterministic induced currents to achieve more accurate total field approximation. The algorithm’s performance is validated using both synthetic “Austria” profiles and 45 digital breast phantoms derived from the UWCEM repository. The results demonstrate robust reconstruction capabilities across varying noise levels (0–20 dB SNR), achieving average relative errors of 0.4847% in breast tissue reconstruction without requiring prior noise level knowledge. The algorithm successfully recovers complex tissue structures and density distributions, showing potential for clinical breast imaging applications. Full article
(This article belongs to the Special Issue Advances in Computer Assisted Tomography: New Technologies for Improving Biomedical Image, Sensor and Signal Processing)
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14 pages, 2268 KB  
Article
A Retinal Vessel Segmentation Method Based on the Sharpness-Aware Minimization Model
by Iqra Mariam, Xiaorong Xue and Kaleb Gadson
Sensors 2024, 24(13), 4267; https://doi.org/10.3390/s24134267 - 30 Jun 2024
Cited by 3 | Viewed by 2406
Abstract
Retinal vessel segmentation is crucial for diagnosing and monitoring various eye diseases such as diabetic retinopathy, glaucoma, and hypertension. In this study, we examine how sharpness-aware minimization (SAM) can improve RF-UNet’s generalization performance. RF-UNet is a novel model for retinal vessel segmentation. We [...] Read more.
Retinal vessel segmentation is crucial for diagnosing and monitoring various eye diseases such as diabetic retinopathy, glaucoma, and hypertension. In this study, we examine how sharpness-aware minimization (SAM) can improve RF-UNet’s generalization performance. RF-UNet is a novel model for retinal vessel segmentation. We focused our experiments on the digital retinal images for vessel extraction (DRIVE) dataset, which is a benchmark for retinal vessel segmentation, and our test results show that adding SAM to the training procedure leads to notable improvements. Compared to the non-SAM model (training loss of 0.45709 and validation loss of 0.40266), the SAM-trained RF-UNet model achieved a significant reduction in both training loss (0.094225) and validation loss (0.08053). Furthermore, compared to the non-SAM model (training accuracy of 0.90169 and validation accuracy of 0.93999), the SAM-trained model demonstrated higher training accuracy (0.96225) and validation accuracy (0.96821). Additionally, the model performed better in terms of sensitivity, specificity, AUC, and F1 score, indicating improved generalization to unseen data. Our results corroborate the notion that SAM facilitates the learning of flatter minima, thereby improving generalization, and are consistent with other research highlighting the advantages of advanced optimization methods. With wider implications for other medical imaging tasks, these results imply that SAM can successfully reduce overfitting and enhance the robustness of retinal vessel segmentation models. Prospective research avenues encompass verifying the model on vaster and more diverse datasets and investigating its practical implementation in real-world clinical situations. Full article
(This article belongs to the Special Issue Advances in Computer Assisted Tomography: New Technologies for Improving Biomedical Image, Sensor and Signal Processing)
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