Medical Image Analysis in CT Imaging

A special issue of Tomography (ISSN 2379-139X).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 675

Special Issue Editor


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Guest Editor
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

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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 (1 paper)

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Research

11 pages, 1626 KB  
Article
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
Viewed by 321
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) [...] Read more.
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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