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Medical Image Analysis in CT Imaging

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A Special Issue of Tomography (ISSN 2379-139X).

Deadline for manuscript submissions: 28 February 2027 | Viewed by 6068

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Dr. Martin Pichotka

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Division of Medical Physics, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University Medical Center Freiburg, University of Freiburg, 79098 Freiburg, Germany
Interests: detectors; radiation detection; gamma spectrometry; radiation physics; monte carlo simulation; computed tomography; ionizing radiation; radiation; imaging; composites

Special Issue Information

Dear Colleagues,

Medical image analysis is entering a new era driven by major advances in computed tomography (CT) hardware, reconstruction algorithms, and artificial intelligence. The rapid emergence of photon-counting detectors, spectral and phase-contrast techniques, and compact robotic CT systems is opening unprecedented opportunities for quantitative imaging at low dose and high spatial resolution. In parallel, machine learning methods are transforming how we process and interpret CT data—from model-based iterative reconstruction and material decomposition to radiomics, projection-space classification, and semantic segmentation.

This Special Issue aims to gather cutting-edge contributions on medical image analysis in CT imaging, with a focus on innovations that go beyond conventional reconstruction and deliver new diagnostic or interventional capabilities. Topics of interest include spectral and super-resolution CT, region-of-interest (ROI) imaging, Compton and molecular imaging integration, advanced denoising and artifact correction, simulation-driven optimization, and radiomics/AI-based decision support. Submissions combining hardware innovation with novel analysis algorithms and translational or clinical validation are particularly encouraged.

By bringing together contributions from physics, engineering, and clinical research, this Special Issue seeks to highlight how advanced image analysis can translate technical innovation into tangible improvements in patient care.

We look forward to receiving your contributions

Dr. Martin Pichotka
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 monthly 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 2400 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

photon-counting CT
spectral CT
phase-contrast imaging
super-resolution CT
region-of-interest imaging
compton imaging
molecular imaging
iterative reconstruction
deep learning
radiomics
artifact correction
simulation-based optimization

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Published Papers (5 papers)

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Research

18 pages, 1793 KB

Open AccessArticle

CT Radiomics and Machine Learning for Three-Class Bone Density Classification: An Exploratory Comparison Across Lumbar Vertebral Levels

by Asiye Sozeri, Halitcan Batur and Nurdan Cay

Tomography 2026, 12(10), 147; https://doi.org/10.3390/tomography12100147 - 2 Oct 2026

Viewed by 181

Abstract

Objectives: The aim of this study is to provide early diagnosis of osteoporosis, osteopenia, and normal bone mineral density (BMD) on CT scans utilizing machine learning and radiomics techniques. Methods: In this retrospective study, 874 patients who met the predefined eligibility criteria were included. BMD was categorized as normal, osteopenia, or osteoporosis according to DEXA-derived T-scores. Lumbar vertebral bodies were automatically segmented on abdominal and lumbar CT images, and radiomic features were extracted from the segmented regions. Logistic regression (LR), decision table (DT), random forest (RF), multilayer perceptron (MLP), and bagging models were developed to distinguish among the three BMD categories. Model performance was evaluated using accuracy, F-score, area under the receiver operating characteristic curve (ROC area), and area under the precision–recall curve (PRC area). Results: Notably, the best results were observed in the L3 vertebra among the 874 patients included in the study. Accordingly, the MLP and LR methods showed the greatest accuracy value of 83.5%, F-score value of 81.2%, ROC area value of 0.912, and PRC area value of 0.892 in cases with normal BMD. At the L2 vertebral level, the LR approach produced the greatest results for patients with osteoporosis (Acc: 72.5%, F-score: 72.5%, ROC area: 0.897, and PRC area: 0.797). Conclusions: In the three-class BMD assessment, the models showed the highest performance for normal BMD and the lowest performance for osteopenia. Among the individual lumbar levels, L3 demonstrated the highest radiomics-based classification performance, whereas L1 showed comparatively lower performance. These level-specific findings should be regarded as exploratory and should not be interpreted as supporting clinical BMD classification from a single vertebra. CT radiomics may have potential as an opportunistic screening tool to identify patients who may benefit from formal DEXA or further osteoporosis evaluation; however, patient-level multivertebral validation is required before clinical implementation. Full article

(This article belongs to the Special Issue Medical Image Analysis in CT Imaging)

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13 pages, 4352 KB

Open AccessArticle

The Impact of Cone Beam Computed Tomography on Surgical Decision-Making and Risk Assessment in Mandibular Third Molar Extractions: A Prospective Observational Diagnostic Study

by Fatma Hande Aktemur Gürkan and Mustafa Cenk Durmuşlar

Tomography 2026, 12(7), 97; https://doi.org/10.3390/tomography12070097 - 1 Jul 2026

Viewed by 782

Abstract

Aim: This study aimed to evaluate the impact of cone beam computed tomography (CBCT) on preoperative surgical decision-making and risk assessment for mandibular third molar (MM3) extractions in cases identified as high-risk by orthopantomography (OPG). Materials and Methods: This prospective observational diagnostic study utilized the purposive sampling method, recruiting 50 MM3s from 33 patients (21 females, 12 males; mean age 24.24 ± 6.77 years, range 16–42). Samples were categorized into five distinct radiographic groups based on the proximity of roots to the inferior alveolar nerve (IAN) on OPG. The methodology involved a comparative 3D analysis to determine neurovascular contact, spatial orientation, and the presence of a cortical border. Surgical strategies, specifically the necessity for coronectomy or the lingual split technique, were reassessed following 3D evaluation. Postoperative neurosensory outcomes were recorded. Statistical analysis was performed using the Fisher–Freeman–Halton and Kruskal–Wallis tests. Results: CBCT identified direct IAC contact in 74% of the cases. In 18% of the cases initially deemed high-risk by OPG, CBCT revealed a safe distance, thereby altering the surgical approach. Tooth angulation (p = 0.012) and Pell and Gregory classification (p = 0.024) were significant predictors of contact. Temporary neurosensory disturbances occurred in 4% (n = 2) of the sample, specifically in cases where CBCT had confirmed the loss of nerve canal cortication. Conclusions: In accordance with the study aim, CBCT provides essential 3D data that refines surgical planning in nearly one-fifth of high-risk cases. The findings justify selective CBCT use, guided by the ALADA principle, to minimize iatrogenic injury. Full article

(This article belongs to the Special Issue Medical Image Analysis in CT Imaging)

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15 pages, 10089 KB

Open AccessArticle

Virtual Bronchoscopic Pathfinder (VBP): An Open-Source Web-Based System for Airway Segmentation, Cost-Field Path Planning, and Cross-Device 3D Navigation

by Young Kim, Sunggyu Choi, Chulmin Park, Woojin Park and Doohee Lee

Tomography 2026, 12(7), 95; https://doi.org/10.3390/tomography12070095 - 29 Jun 2026

Viewed by 723

Abstract

Background/Objectives: Virtual Bronchoscopic Navigation is used to guide bronchoscopes toward peripheral pulmonary lesions, but broad clinical and research adoption remains limited by the cost of proprietary software and by segmentation failures in small distal airways that can interrupt path planning. This study presents Virtual Bronchoscopic Pathfinder, an open-source, web-based system designed to provide automated airway segmentation, robust path generation, and browser-based three-dimensional visualization. Methods: The system integrates five components: a connectivity-aware deep learning model for pulmonary airway segmentation using Connectivity-Aware Surrogate and Local-Sensitive Distance modules; TotalSegmentator for automated tumor localization; a topology-preserving three-dimensional thinning algorithm implemented in C++ for centerline extraction; a bidirectional Dijkstra algorithm operating on a three-tier anatomical cost field with centerline, airway lumen, and parenchymal costs; and a zero-footprint visualization interface built on vtk.js with synchronized axial viewing and interactive volume rendering. VBP was validated on 306 thin-section CT series from 154 subjects in the public Lung-PET-CT-Dx dataset. Results: Among the 306 CT series, 33 series (10.8%) were excluded because of scanner-specific segmentation artifacts. In the remaining 273 anatomically valid series, the system successfully generated complete end-to-end navigation paths for all cases. The overall pipeline success rate was therefore 273 of 306 series (89.2%). The web-based interface was also confirmed to operate without client-side installation across desktop, laptop, and mobile device configurations. Conclusions: Virtual Bronchoscopic Pathfinder demonstrates that a reliable and accessible virtual bronchoscopic navigation workflow can be constructed entirely from open-source components. By combining connectivity-aware segmentation, cost-field path planning, and browser-based visualization, the system provides a practical foundation for imaging informatics research and future development of intra-procedural bronchoscopic guidance. Full article

(This article belongs to the Special Issue Medical Image Analysis in CT Imaging)

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29 pages, 4755 KB

Open AccessArticle

DenseViT-OCT: A Hybrid CNN-Transformer Architecture with Multi-Scale Dense Feature Aggregation for Automated Epiretinal Membrane Severity Classification

by Elif Yusufoğlu, Salih Taha Alperen Özçelik, Orhan Atila, Numan Halit Guldemir and Abdulkadir Sengur

Tomography 2026, 12(6), 76; https://doi.org/10.3390/tomography12060076 - 22 May 2026

Viewed by 649

Abstract

Background/Objectives: Epiretinal membrane (ERM) is a common vitreoretinal disorder characterized by fibrocellular proliferation on the inner retinal surface, often leading to progressive visual impairment. Accurate grading of ERM severity using optical coherence tomography (OCT) is critical for treatment planning and surgical decision-making; however, manual grading is labor-intensive and subjective. This study aims to develop an automated and reliable deep learning-based method for ERM severity classification. Methods: We propose DenseViT-OCT, a hybrid deep learning model that integrates dense convolutional neural networks (CNN) and vision transformers (ViT). The model introduces three key modules: Multi-Scale Dense Feature Aggregation (MDFA) for capturing hierarchical features across multiple spatial scales, Adaptive Feature Calibration (AFC) for enhancing feature discrimination through channel and spatial attention, and Cross-Attention Feature Fusion (CAFF) for enabling bidirectional interaction between convolutional and transformer representations. The model was trained and evaluated on 2195 OCT B-scan images obtained from 397 patients. Results: DenseViT-OCT achieved an overall accuracy of 94.76% on the internal four-class test set, outperforming 19 benchmark models, including ConvNeXt, EfficientNet, ViT, and Swin Transformers. The model demonstrated balanced performance with a macro-averaged precision of 93.76%, recall of 93.22%, F1-score of 93.47%, Cohen’s kappa of 92.62%, and macro-Area Under the Curve (AUC) of 98.95%. Ablation experiments confirmed the contribution of the proposed MDFA, AFC, CAFF, and deep supervision components, with the full model consistently outperforming reduced variants and standalone DenseNet121 and ViT-B/16 backbones. In repeated experiments across five random seeds, DenseViT-OCT also achieved the best mean accuracy (0.9399 ± 0.0052). External validation on the public multicenter OCTDL dataset, performed as binary ERM-versus-normal classification because of label availability, yielded 90.76% accuracy and 97.61% AUC, indicating promising generalization beyond the development cohort. Conclusions: DenseViT-OCT provides a robust framework for automated ERM severity classification from OCT B-scans. The combination of local CNN features, global transformer context, and dedicated fusion modules improves classification performance and yields clinically meaningful error patterns. Although further stage-wise multicenter validation, volumetric OCT analysis, and prospective clinical assessment are required, the proposed method shows promise as a research-oriented decision-support framework for B-scan-level ERM assessment. Full article

(This article belongs to the Special Issue Medical Image Analysis in CT Imaging)

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11 pages, 1626 KB

Open AccessArticle

A Question of Dose? Ultra-Low Dose Chest CT on Photon-Counting CT in People with Cystic Fibrosis

by Marcel Opitz, Matthias Welsner, Halil I. Tazeoglu, Florian Stehling, Sivagurunathan Sutharsan, Dirk Westhölter, Erik Büscher, Christian Taube, Nika Guberina, Denise Bos, Marcel Drews, Daniel Rosok, Sebastian Zensen, Johannes Haubold, Lale Umutlu, Michael Forsting and Marko Frings

Tomography 2025, 11(12), 134; https://doi.org/10.3390/tomography11120134 - 27 Nov 2025

Cited by 1 | Viewed by 2445

Abstract

Objective: Chest computed tomography (CT) is a key component of the diagnostic assessment of people with cystic fibrosis (PwCF) and is increasingly replacing chest radiography. Due to improvements in life expectancy, radiation exposure has become a growing concern in PwCF. Photon-counting CT (PCCT) has the potential to reduce the risk of radiation-induced malignancies while maintaining diagnostic accuracy. This study aimed to compare the radiation dose and image quality of low-dose high-resolution (LD-HR) and ultra-low-dose high-resolution (ULD-HR) CT protocols using PCCT in PwCF. Methods: This retrospective study included 72 PwCF, with 36 undergoing a LD-HR chest CT protocol and 36 receiving an ULD-HR protocol on a PCCT. The radiation dose and image quality were assessed by comparing the effective dose and signal-to-noise ratio (SNR). Three blinded radiologists evaluated the overall image quality, sharpness, noise, and assessability of the bronchi, bronchial wall thickening, and bronchiolitis using a five-point Likert scale. Results: The ULD-HR PCCT protocol reduced radiation exposure by approximately 65% compared with the LD-HR PCCT protocol (median effective dose: 0.19 vs. 0.55 mSv, p < 0.001). While LD-HR images were consistently rated higher than ULD-HR images (p < 0.001), both protocols maintained diagnostic significance (median image quality rating of “4-good”). The average SNR of the lung parenchyma was significantly lower with ULD-HR PCCT compared to LD-HR PCCT (p < 0.001). Conclusions: ULD-HR PCCT significantly reduced radiation exposure while maintaining good diagnostic image quality in PwCF. The effective dose of ULD-HR PCCT is only twice that of a two-plane chest X-ray, making it a viable low-radiation alternative for routine imaging in PwCF. Full article

(This article belongs to the Special Issue Medical Image Analysis in CT Imaging)

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