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Photon-Counting Micro-CT for Bone Morphometry in Murine Models -
Prediction of Microsatellite Instability in Colorectal Cancer Using Two Internally Validated Radiomic Models -
Virtual Non Contrast Photon Counting CT for Aortic Valve Calcium Scoring -
Comparison of Virtual Dose Simulator and K-Factor Methods for Effective Dose Assessment in Thoracic CT -
Development of Cardiac Computed Tomography for Evaluation of Aortic Valve Stenosis
Journal Description
Tomography
Tomography
is an international, peer-reviewed open access journal on imaging technologies published monthly online by MDPI (from Volume 7, Issue 1 - 2021).
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, and other databases.
- Journal Rank: JCR - Q2 (Radiology, Nuclear Medicine and Medical Imaging)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 26.3 days after submission; acceptance to publication is undertaken in 4.1 days (median values for papers published in this journal in the second half of 2025).
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually in the journal.
Impact Factor:
2.2 (2024);
5-Year Impact Factor:
2.2 (2024)
Latest Articles
Rehabilitative Ultrasound Imaging as Visual Biofeedback in Pelvic Floor Dysfunction: A Narrative Review
Tomography 2026, 12(1), 10; https://doi.org/10.3390/tomography12010010 - 15 Jan 2026
Abstract
Background: Pelvic floor dysfunction, more prevalent in women but affecting both genders, impairs sphincter control and sexual health, and causes pelvic pain. Pelvic floor muscle (PFM) training is the first-line treatment for urinary incontinence, supported by robust evidence. Rehabilitative ultrasound imaging (RUSI)
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Background: Pelvic floor dysfunction, more prevalent in women but affecting both genders, impairs sphincter control and sexual health, and causes pelvic pain. Pelvic floor muscle (PFM) training is the first-line treatment for urinary incontinence, supported by robust evidence. Rehabilitative ultrasound imaging (RUSI) serves as a visual biofeedback tool, providing real-time imaging to enhance PFM training, motor learning, and treatment adherence. Aim: This narrative review evaluates the role and efficacy of RUSI in pelvic floor rehabilitation. Method: A comprehensive search of PubMed, Cochrane, and MEDLINE was conducted using keywords related to pelvic floor rehabilitation, ultrasound, and biofeedback, limited to English-language publications up to July 2025. Systematic reviews, meta-analyses, and clinical trials were prioritized. Results: Transperineal and transabdominal ultrasound improve PFM function across diverse populations. In post-prostatectomy men, transperineal ultrasound-guided training enhanced PFM contraction and reduced urinary leakage. In postpartum women with pelvic girdle pain, transabdominal ultrasound-guided biofeedback combined with exercises decreased pain and improved function. Ultrasound-guided pelvic floor muscle contraction demonstrated superior performance compared to verbal instruction. Notably, 57% of participants who were unable to contract the pelvic floor muscles with verbal cues achieved a correct contraction with ultrasound biofeedback, and this approach also resulted in more sustained improvements in PFM strength. Compared to other biofeedback modalities, RUSI demonstrated outcomes that are comparable to or superior to those of alternative methods. However, evidence is limited by a lack of standardized protocols and randomized controlled trials comparing RUSI with other modalities. Conclusions: RUSI is an effective visual biofeedback tool that enhances outcomes of PFM training in pelvic floor rehabilitation. It supports clinical decision-making and patient engagement, particularly in cases where traditional assessments are challenging. Further research, including the development of standardized protocols and comparative trials, is necessary to optimize the clinical integration of this method and confirm its superiority over other biofeedback methods.
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Open AccessArticle
Anatomical Evaluation of the Pterygomaxillary Complex Using Cone Beam Computed Tomography
by
Ömer Demir and Kamil Serkan Ağaçayak
Tomography 2026, 12(1), 9; https://doi.org/10.3390/tomography12010009 - 9 Jan 2026
Abstract
Background: The pterygomaxillary region is a complex anatomical area formed by the junction of the maxillary, palatine, and sphenoid bones and contains critical neurovascular structures. Accurate assessment of this region during Le Fort I osteotomy is essential, particularly to prevent hemorrhage and nerve
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Background: The pterygomaxillary region is a complex anatomical area formed by the junction of the maxillary, palatine, and sphenoid bones and contains critical neurovascular structures. Accurate assessment of this region during Le Fort I osteotomy is essential, particularly to prevent hemorrhage and nerve injury that may occur during the pterygomaxillary separation phase. This study aims to investigate the morphometric characteristics of the pterygomaxillary region using cone-beam computed tomography (CBCT) and to evaluate the effects of age, sex, and laterality on these anatomical parameters. Materials and Methods: In this retrospective study, CBCT scans of 200 individuals (100 males and 100 females) aged 20–80 years were analyzed. Axial measurements included distances between the piriform rim, the descending palatine artery, the pterygomaxillary osteotomy line, and the pterygomaxillary fissure. Additionally, the thickness and width of the pterygomaxillary region and pterygoid process, lengths of the medial and lateral pterygoid laminae, and the distance between the greater palatine canal and the medial pterygoid lamina apex were recorded. Measurements were statistically evaluated by sex, age group, and laterality. Results: The following parameters demonstrated statistically significant differences based on the conducted measurements: The distance between the piriform rim and the descending palatine artery was significantly greater on the left side (p < 0.001). The length of the lateral pterygoid lamina increased with advancing age (p = 0.048). The thickness of the pterygomaxillary region was significantly greater in females (p = 0.014). Additionally, the distance between the greater palatine canal and the terminal point of the medial pterygoid lamina was significantly higher in males (p < 0.001). Conclusions: The pterygomaxillary region exhibits anatomical variations that may lead to serious complications during Le Fort I osteotomy. Detailed preoperative evaluation of this area using CBCT can guide surgical planning and help prevent potential vascular and neural complications.
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(This article belongs to the Topic Human Anatomy and Pathophysiology, 3rd Edition)
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Open AccessSystematic Review
The Correlation of Computed Tomography (CT)-Based Body Composition and Survival in Pancreatic Cancer Patients: A Systematic Review
by
Lena Supe and Stefania Rizzo
Tomography 2026, 12(1), 8; https://doi.org/10.3390/tomography12010008 - 8 Jan 2026
Abstract
Background/Objectives: Pancreatic cancer is among the most aggressive malignancies, with poor survival rates. Emerging evidence suggests that body composition, including skeletal muscle mass and adiposity distribution, plays a crucial role in predicting patient outcomes. However, its impact on survival in pancreatic cancer
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Background/Objectives: Pancreatic cancer is among the most aggressive malignancies, with poor survival rates. Emerging evidence suggests that body composition, including skeletal muscle mass and adiposity distribution, plays a crucial role in predicting patient outcomes. However, its impact on survival in pancreatic cancer remains incompletely understood. The aim of this systematic review was to assess the correlation between body composition parameters and survival outcomes in pancreatic cancer patients, focusing on overall survival. Methods: A comprehensive literature search was conducted, including three main components: pancreatic cancer, body composition, and survival outcomes. Results: 23 studies were included in this review. The findings indicate that body composition can serve as a predictor of survival in pancreatic cancer patients, with 21 studies reporting a significant correlation. The most frequently observed predictor, with 11 studies reporting, was not a baseline parameter but rather changes in parameters over time during treatment. However, discrepancies remain regarding the extent of predictive power and the relative importance of individual components. Conclusions: Specific body composition parameters hold potential as prognostic indicators of survival in pancreatic cancer patients. However, further research is necessary to establish consistent patterns and to clarify which parameters are most predictive and under what conditions.
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(This article belongs to the Section Abdominal Imaging)
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Open AccessArticle
Super-Resolution Deep Learning Reconstruction Improves Image Quality of Dynamic Myocardial Computed Tomography Perfusion Imaging
by
Yusuke Kobayashi, Yuki Tanabe, Tomoro Morikawa, Kazuki Yoshida, Kentaro Ohara, Takaaki Hosokawa, Takanori Kouchi, Shota Nakano, Osamu Yamaguchi and Teruhito Kido
Tomography 2026, 12(1), 7; https://doi.org/10.3390/tomography12010007 - 7 Jan 2026
Abstract
Background/Objectives: Super-resolution deep-learning reconstruction (SR-DLR) is an advanced image reconstruction technique, but its effect on dynamic myocardial computed tomography perfusion (CTP) imaging has not been evaluated. This study aimed to examine the impact of SR-DLR on image quality and perfusion parameters in
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Background/Objectives: Super-resolution deep-learning reconstruction (SR-DLR) is an advanced image reconstruction technique, but its effect on dynamic myocardial computed tomography perfusion (CTP) imaging has not been evaluated. This study aimed to examine the impact of SR-DLR on image quality and perfusion parameters in dynamic myocardial CTP. Methods: Thirty-five patients who underwent dynamic myocardial CTP for coronary artery disease assessment were retrospectively analyzed. Two CTP datasets were reconstructed using hybrid iterative reconstruction (HIR) and SR-DLR. Image quality was compared qualitatively and quantitatively, including image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge rise slope (ERS). Equivalence of CT-derived myocardial blood flow (CT-MBF) between two reconstructions was tested using a previously reported 15% equivalence margin. Intra-patient variability of CT-MBF was evaluated using the robust coefficient of variation (rCV). Results: In the qualitative assessment, SR-DLR had significantly higher scores in contrast (4.0 vs. 2.0) and sharpness (4.5 vs. 2.5) compared with HIR (p < 0.001), while contrast scores were similar. In the quantitative assessment, SR-DLR demonstrated significantly lower image noise (19.4 vs. 29.4 HU), and improved SNR (6.1 vs. 4.1), CNR (13.7 vs. 10.9), and ERS (171.0 vs. 135.1 HU/mm) (all p < 0.001). Mean global CT-MBF was comparable (3.15 ± 0.91 mL/g/min for HIR vs. 3.18 ± 0.97 mL/g/min for SR-DLR) and equivalence was confirmed (p = 0.022). SR-DLR significantly reduced rCV compared with HIR (36.0% vs. 41.0%, p < 0.001). Conclusions: SR-DLR enhances image quality in dynamic myocardial CTP while maintaining mean global CT-MBF and reducing intra-patient variability.
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(This article belongs to the Section Cardiovascular Imaging)
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Open AccessCorrection
Correction: Honda et al. Visual Evaluation of Ultrafast MRI in the Assessment of Residual Breast Cancer After Neoadjuvant Systemic Therapy: A Preliminary Study Association with Subtype. Tomography 2022, 8, 1522–1533
by
Maya Honda, Masako Kataoka, Mami Iima, Rie Ota, Akane Ohashi, Ayami Ohno Kishimoto, Kanae Kawai Miyake, Marcel Dominik Nickel, Yosuke Yamada, Masakazu Toi and Yuji Nakamoto
Tomography 2026, 12(1), 6; https://doi.org/10.3390/tomography12010006 - 6 Jan 2026
Abstract
This correction addresses several errors identified in the original publication [...]
Full article
Open AccessArticle
Effects of Scout Direction, Off-Centering, and Scout Imaging Parameters on Radiation Dose Modulation in CT
by
Yusuke Inoue, Hiroyasu Itoh, Hirofumi Hata and Kei Kikuchi
Tomography 2026, 12(1), 5; https://doi.org/10.3390/tomography12010005 - 1 Jan 2026
Abstract
Background: In computed tomography (CT), automatic exposure control (AEC) determines the tube current and thus the radiation dose based on scout images. We investigated CT dose modulation using two versions of CARE Dose 4D, Siemens AEC software. Methods: A cylindrical phantom and an
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Background: In computed tomography (CT), automatic exposure control (AEC) determines the tube current and thus the radiation dose based on scout images. We investigated CT dose modulation using two versions of CARE Dose 4D, Siemens AEC software. Methods: A cylindrical phantom and an anthropomorphic phantom with the upper extremities raised or down were imaged. The CT tube current was determined using two versions of CARE Dose 4D and different scout directions: the posteroanterior scout image alone (PA scout), the lateral scout image alone (Lat scout), and the combination of the PA and Lat scout images (PA + Lat scout). The new version is designed to utilize the Lat image solely for off-center correction when both PA and Lat images are available. Experiments were performed at various vertical positions and with various scout imaging parameters. Results: The influence of the scout direction on CT dose was demonstrated, with variations depending on the imaging object and software version. The CT dose determined with the PA scout varied according to vertical positioning, presumably due to changes in image magnification. Such effects were small with the Lat scout or PA + Lat scout. Decreasing the tube voltage or tube current in scout imaging affected CT dose modulation with the Lat scout but not with the PA scout. With the PA + Lat scout, the effects of scout parameters were evident using the previous version but minimal using the new version. Conclusions: Off-center correction in the new version functioned appropriately. Because the behavior of an AEC system is complicated, it is recommended to examine the characteristics of each AEC system under various imaging conditions.
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(This article belongs to the Section Artificial Intelligence in Medical Imaging)
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Open AccessArticle
Detection and Classification of Alzheimer’s Disease Using Deep and Machine Learning
by
Muhammad Zaeem Khalid, Nida Iqbal, Babar Ali, Jawwad Sami Ur Rahman, Saman Iqbal, Lama Almudaimeegh, Zuhal Y. Hamd and Awadia Gareeballah
Tomography 2026, 12(1), 4; https://doi.org/10.3390/tomography12010004 - 26 Dec 2025
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Background/Objectives: Alzheimer’s disease is the leading cause of dementia, marked by progressive cognitive decline and a severe socioeconomic burden. Early and accurate diagnosis is crucial to enhancing patient outcomes, yet traditional clinical and imaging assessments are often limited in sensitivity, particularly at early
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Background/Objectives: Alzheimer’s disease is the leading cause of dementia, marked by progressive cognitive decline and a severe socioeconomic burden. Early and accurate diagnosis is crucial to enhancing patient outcomes, yet traditional clinical and imaging assessments are often limited in sensitivity, particularly at early stages. This study presents a dual-modal framework that integrates symptom-based clinical data with magnetic resonance imaging (MRI) using machine learning (ML) and deep learning (DL) models, enhanced by explainable AI (XAI). Methods: Four ML classifiers—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)—were trained on demographic and clinical features. For stage-wise classification, five DL models—CNN, EfficientNetB3, DenseNet-121, ResNet-50, and MobileNetV2—were applied to MRI scans. Interpretability was incorporated through SHAP and Grad-CAM visualizations. Results: Random Forest achieves the highest accuracy of 97% on clinical data, while CNN achieves the best overall performance of 94% in MRI-based staging. SHAP and Grad-CAM were used to find clinically relevant characteristics and brain areas, including hippocampal atrophy and ventricular enlargement. Conclusions: Integrating clinical and imaging data and interpretable AI improves the accuracy and reliability of AD staging. The proposed model offers a valid and clear diagnostic route, which can assist clinicians in making timely diagnoses and adjusting individual treatment.
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Open AccessArticle
Correlation Between Radiological Features of Axillary Lymph Nodes with CD4 Count and Plasma Viral Load in Patients with HIV
by
Gulten Taskin, Muzaffer Elmali, Aydin Deveci and Irem Ceren Koc
Tomography 2026, 12(1), 3; https://doi.org/10.3390/tomography12010003 - 25 Dec 2025
Abstract
Objective: Axillary lymph node changes are frequently observed in patients with HIV, yet their radiological characteristics and clinical significance remain underexplored. This study aimed to evaluate the association between axillary lymph node computed tomography (CT) features and clinical markers of immune function, including
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Objective: Axillary lymph node changes are frequently observed in patients with HIV, yet their radiological characteristics and clinical significance remain underexplored. This study aimed to evaluate the association between axillary lymph node computed tomography (CT) features and clinical markers of immune function, including CD4 lymphocyte count and plasma viral load, in HIV-positive patients. Materials and Methods: In this retrospective study, 113 HIV-positive patients who underwent contrast-enhanced chest CT were included. Patients were stratified by CD4 count (<200, 200–500, >500 cells/μL) and plasma viral load (<100,000 or >100,000 copies/mL). Axillary lymph node parameters—including maximum and minimum diameters, cortical thickness, hilar width, and density (Hounsfield units, HU)—were measured on multiplanar reconstructed CT images. Group differences were assessed using the Kruskal–Wallis and Mann–Whitney U tests, and Spearman’s correlation was used to evaluate associations between imaging and laboratory findings. Receiver operating characteristic (ROC) curve analysis identified optimal density thresholds. Results: Lymph node diameters, cortical thickness, and hilar width did not significantly differ between CD4 groups. However, mean lymph node density was higher in patients with CD4 < 200 cells/μL (p = 0.024). A density threshold of 84.5 HU distinguished impaired from preserved immune function (sensitivity 61.1%, specificity 71.2%). Patients with viral load >100,000 copies/mL showed increased lymph node density, minimal diameter, and cortical thickness. Conclusions: Elevated axillary lymph node density correlates with immune suppression and high viral load, suggesting its potential as a non-invasive prognostic imaging biomarker in HIV infection.
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(This article belongs to the Special Issue Celebrate the 10th Anniversary of Tomography)
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Open AccessArticle
Altered Functional Connectivity of Amygdala Subregions with Large-Scale Brain Networks in Schizophrenia: A Resting-State fMRI Study
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Rasha Rudaid Alharthi, Duaa Banaja, Adnan Alahmadi, Jaber Hussain Alsalah, Arwa Baeshen, Ali H. Alghamdi, Magbool Alelyani and Njoud Aldusary
Tomography 2026, 12(1), 2; https://doi.org/10.3390/tomography12010002 - 23 Dec 2025
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Objective: This study aimed to investigate the functional connectivity (FC) of three amygdala subregions—the laterobasal amygdala (LBA), centromedial amygdala (CMA), and superficial amygdala (SFA)—with large-scale brain networks in individuals with schizophrenia (SCZ) compared to healthy controls (HC). Methodology: Resting-state functional magnetic resonance imaging
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Objective: This study aimed to investigate the functional connectivity (FC) of three amygdala subregions—the laterobasal amygdala (LBA), centromedial amygdala (CMA), and superficial amygdala (SFA)—with large-scale brain networks in individuals with schizophrenia (SCZ) compared to healthy controls (HC). Methodology: Resting-state functional magnetic resonance imaging (rs-fMRI) data were obtained from 100 participants (50 SCZ, 50 HC) with balanced age and gender distributions. FC between amygdala subregions and target functional networks was assessed using a region-of-interest (ROI)-to-ROI approach implemented in the CONN toolbox. Result: Connectivity patterns of the LBA, CMA, and SFA differed between SCZ and HC groups. After false discovery rate (FDR) correction (p < 0.05), SCZ patients exhibited significantly increased FC between the left CMA and both the default mode network (DMN) and the visual network (VN). In contrast, decreased FC was observed between the right LBA and the sensorimotor network (SMN) in SCZ compared with HC. Conclusions: These findings reveal novel FC alterations linking amygdala subregions with large-scale networks in schizophrenia. The results underscore the importance of examining the amygdala as distinct functional subregions rather than as a single structure, offering new insights into the neural mechanisms underlying SCZ.
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Open AccessEditorial
Scientific Publishing Credibility: Analysis of the Main Factors Threatening It
by
Emilio Quaia
Tomography 2026, 12(1), 1; https://doi.org/10.3390/tomography12010001 - 22 Dec 2025
Abstract
The scientific publishing crisis represents a complex problem, mainly stemming from the “publish or perish” culture that prioritizes quantity over quality, which leads to the proliferation of low-quality research manuscripts and research misconduct, including data fabrication (making up data or results), falsification (manipulating
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The scientific publishing crisis represents a complex problem, mainly stemming from the “publish or perish” culture that prioritizes quantity over quality, which leads to the proliferation of low-quality research manuscripts and research misconduct, including data fabrication (making up data or results), falsification (manipulating research materials, equipment, or processes, or changing or omitting data or results such that the research is not accurately represented in the research record), or even plagiarism (the appropriation of another person’s ideas, processes, results, or words without giving appropriate credit) [...]
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Open AccessReview
Cancer-Associated Fibroblasts: Clinical Applications in Imaging and Therapy
by
Neda Nilforoushan, Ashkan Khavaran, Maierdan Palihati, Yashvi Patel, Anna O. Giarratana, Jeeban Paul Das and Kathleen M. Capaccione
Tomography 2025, 11(12), 143; https://doi.org/10.3390/tomography11120143 - 17 Dec 2025
Abstract
Cancer-associated fibroblasts (CAFs) are an abundant and diverse cell population within tumor microenvironments of solid tumors. Multiple subtypes of CAFs, defined by molecular and functional markers, have been described in the literature. CAFs contribute to tumor progression by remodeling the extracellular matrix, promoting
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Cancer-associated fibroblasts (CAFs) are an abundant and diverse cell population within tumor microenvironments of solid tumors. Multiple subtypes of CAFs, defined by molecular and functional markers, have been described in the literature. CAFs contribute to tumor progression by remodeling the extracellular matrix, promoting immune evasion, and supporting angiogenesis and metastasis. Fibroblast activation protein (FAP) is a transmembrane serine protease minimally expressed in normal adult tissues but significantly upregulated in certain subtypes of CAFs across many solid tumors. High levels of FAP have been associated with poor prognosis in various cancers. FAP has increasingly emerged as a promising target for both imaging and therapy. Multiple FAP-targeting strategies, such as small molecules, monoclonal antibodies, drug conjugates, and radiolabeled ligands, are currently being investigated in preclinical and early clinical settings. This review provides a clinically focused overview of CAFs in the tumor microenvironment, highlighting key fibroblast markers, their associations with prognosis across various tumor types, and their utility in radiologic imaging and targeted therapy. We also discuss the potential of non-FAP fibroblast targeting molecules and the clinical rationale for more selective, subtype-specific strategies. By examining fibroblast biology through a radiologist’s lens, we aim to explore the evolving role of stromal targeting in imaging and the treatment of solid tumors.
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(This article belongs to the Special Issue Celebrate the 10th Anniversary of Tomography)
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Open AccessArticle
Prediction of Breast Radiation Absorbed Dose Chest CT Examinations Using Machine Learning Techniques
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Sevgi Ünal, Remzi Gürfidan, Merve Gürsoy Bulut and Mustafa Fazıl Gelal
Tomography 2025, 11(12), 142; https://doi.org/10.3390/tomography11120142 - 16 Dec 2025
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Background/Objectives: The breast is a highly radiosensitive organ that is directly exposed to ionizing radiation during chest computed tomography (CT) examinations. Excessive radiation exposure increases the risk of radiation-induced malignancies, highlighting the importance of accurate and patient-specific dose estimation. This study aims
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Background/Objectives: The breast is a highly radiosensitive organ that is directly exposed to ionizing radiation during chest computed tomography (CT) examinations. Excessive radiation exposure increases the risk of radiation-induced malignancies, highlighting the importance of accurate and patient-specific dose estimation. This study aims to estimate the effective radiation dose absorbed by the breast during chest CT examinations using a machine learning (ML)-based personalized prediction approach. Methods: In this retrospective study, a total of 653 female patients who underwent both mammography and chest CT between 2020 and 2024 were included. A structured database was created incorporating demographic and anatomical parameters, including body weight, height, body mass index (BMI), and breast thickness (mm) obtained from mammography, along with dose length product (DLP) values from chest CT scans. Five regression-based ML algorithms—CatBoost, Gradient Boosting, Extra Trees, AdaBoost, and Random Forest—were implemented to predict breast radiation dose. Model performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R2). Results: Among the evaluated models, the CatBoost algorithm optimized with Particle Swarm Optimization (CatBoostPSO) achieved the best overall predictive performance, yielding the lowest MSE (0.3795), MAE (0.3846), and MAPE (4.37%), along with the highest R2 value (0.9875). CatBoost and Gradient Boosting models demonstrated predictions most closely aligned with ground truth values, indicating that ensemble-based and dynamically optimized models are particularly effective for breast dose estimation. Conclusions: The proposed machine learning framework enables rapid, accurate, and clinically applicable estimation of breast radiation dose during chest CT examinations. This patient-specific approach has strong potential to support personalized radiation dose monitoring and optimization strategies, contributing to improved radiation safety in clinical practice.
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Open AccessArticle
Volume and Attenuation Characteristics of Chronic Subdural Hematoma: An Annotated Patient Cohort of 257 Patients with Interrater Reliability Assessments
by
Mattias Drake, Emma Hall, Birgitta Ramgren, Björn M. Hansen and Johan Wassélius
Tomography 2025, 11(12), 141; https://doi.org/10.3390/tomography11120141 - 16 Dec 2025
Abstract
Background: Accurate volumetry and imaging characterization of chronic subdural hematoma (cSDH) are essential for prognostication and treatment planning, but manual assessment is time-consuming and therefore underutilized. Methods: We retrospectively analyzed preoperative non-contrast CT (NCCT) scans of 257 patients undergoing first-time surgery for uni-
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Background: Accurate volumetry and imaging characterization of chronic subdural hematoma (cSDH) are essential for prognostication and treatment planning, but manual assessment is time-consuming and therefore underutilized. Methods: We retrospectively analyzed preoperative non-contrast CT (NCCT) scans of 257 patients undergoing first-time surgery for uni- or bilateral cSDH. Hematoma volumes were measured manually using a semi-automated area-outlining tool on every second axial slice and compared with the volumes estimated through the ABC/2 formula. Hematoma attenuation patterns and components were categorized, and interrater reliability was assessed for volume, maximum diameter, and imaging features using intraclass correlation coefficients (ICCs) and Cohen’s κ. Results: A total of 339 hematomas were evaluated. Manual and ABC/2 volume measurements correlated strongly (R2 = 0.83, ICC [3, 1] = 0.90). The interrater agreement for manual volumetry was excellent (ICC [2, 1] = 0.96). Agreement was also excellent for maximum diameter (ICC [2, 1] > 0.9) and good for midline shift assessment (κ = 0.81). Agreement was moderate for the identification of fresh clots, trabeculations, and laminations (κ = 0.62–0.72) but poor for general attenuation patterns (κ = 0.44). Conclusions: The manual volumetry of cSDH is feasible and highly reproducible between raters of different experience levels. These results provide a robust reference standard for the validation of automated volumetry tools and support the implementation of quantitative hematoma assessment in future clinical trials and routine care.
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(This article belongs to the Section Neuroimaging)
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Open AccessArticle
Pilot Evaluation of a Deep Learning Model for Nasogastric Tube Verification on Chest Radiographs: A Single-Center Retrospective Study
by
Sang Won Park, Doohee Lee, Jae Eun Song, Yoon Kim, Hyun-Soo Choi, Seung-Joon Lee, Woo Jin Kim, Kyoung Min Moon and Oh Beom Kwon
Tomography 2025, 11(12), 140; https://doi.org/10.3390/tomography11120140 - 15 Dec 2025
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Background: Accurate confirmation of nasogastric (NG) tubes is essential for patient safety, but delays and variability in interpretation remain common in clinical practice. Deep learning (DL) models have shown potential for assisting in this task, but real-world performance, particularly in detecting malpositioned tubes,
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Background: Accurate confirmation of nasogastric (NG) tubes is essential for patient safety, but delays and variability in interpretation remain common in clinical practice. Deep learning (DL) models have shown potential for assisting in this task, but real-world performance, particularly in detecting malpositioned tubes, remains insufficiently characterized. Methods: We conducted a pilot evaluation of a previously developed DL model using 135 chest radiographs from Kangwon National University Hospital. Expert physicians established the reference standard. Model performance was assessed and receiver operating characteristic (ROC) curve and precision recall curve (PRC) analyses were performed. Differences between correctly classified and misclassified cases were examined using Wilcoxon rank-sum and Fisher’s exact tests to explore potential clinical or radiographic contributors to model failure. Results: The model correctly classified 129 of 135 cases. The sensitivity was 96.1% (95% confidence interval (CI): 92.2–98.9%), specificity was 85.7% (95% CI: 42.2–97.7%), positive predictive value (PPV) was 99.2% (95% CI: 96.1–99.9%), negative predictive value (NPV) was 54.5% (95% CI: 25.4–80.8%), balanced accuracy was 90.8%, and F1-score was 0.976. The area under the ROC curve was 0.970 (95% CI: 0.929–1.000) and that under the PRC was 0.727 (95% CI: 0.289–1.000), reflecting substantial uncertainty related to the very small number of incomplete cases (n = 6). No statistically significant differences in clinical or radiographic characteristics were observed between correctly classified and misclassified cases. Conclusions: The DL model performed well in identifying correctly positioned NG tubes but demonstrated limited and unstable performance for detecting incomplete placements. Given the safety implications of misclassification, the model should be used only as an assistive tool with mandatory physician oversight. Larger, multi-center studies with greater representation of incomplete cases are required to obtain more reliable estimates and support safe clinical implementation.
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Open AccessArticle
Aortic Valve Calcium Scoring Using True and Virtual Non-Contrast Reconstructions on Photon-Counting CT with Differing Slice Increments: Impact on Calcium Severity Classifications
by
Mandeep Singh, Amirhossein Moaddab, Doosup Shin, Jonathan Weber, Karen Chau, Ali H. Dakroub, Roosha Parikh, Karli Pipitone, Ziad A. Ali and Omar K. Khalique
Tomography 2025, 11(12), 139; https://doi.org/10.3390/tomography11120139 - 11 Dec 2025
Abstract
Background/Objectives: Aortic valve calcification is commonly evaluated using 3.0 mm true non-contrast (TNC) computed tomography (CT) images. This study evaluates the reproducibility of virtual non-contrast (VNC) reconstructions at different slice intervals using photon-counting detector CT (PCD-CT). Methods: In this retrospective study,
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Background/Objectives: Aortic valve calcification is commonly evaluated using 3.0 mm true non-contrast (TNC) computed tomography (CT) images. This study evaluates the reproducibility of virtual non-contrast (VNC) reconstructions at different slice intervals using photon-counting detector CT (PCD-CT). Methods: In this retrospective study, we included 279 consecutive patients, who underwent PCD-CT for evaluation of native aortic valve between February 2023 and December 2023 with both TNC and VNC images at 3.0 and 1.5 mm slice intervals. Aortic valve calcium score (AVCS) and aortic valve calcium volume (AVCV) were compared between the two methods using paired t-tests. Agreement for continuous variables was assessed using inter-class coefficients (ICCs). Cohen’s Kappa (κ) was calculated to evaluate the agreement between different modalities in diagnosing severe AV calcification. Results: Compared to the standard, TNC images at 1.5 mm intervals showed higher AVCS (mean difference: −290 ± 418, p < 0.001), with high reproducibility between techniques (CS: ICC 0.969, [IQR 0.962, 0.975]). Compared with reference, VNC showed no significant differences in AVCS at either slice intervals, with excellent reproducibility (3.0 mm, ICC 0.970 [0.963, 0.976]; 1.5 mm, ICC 0.971 [0.964, 0.977]). Compared to TNC 3.0 mm, strong concordance was observed using other reconstruction techniques in assessing severe AV calcification (κ = 0.81 [95% CI: 0.74–0.88], 0.83 [95% CI: 0.76–0.90], and 0.83 [95% CI: 0.76–0.90] for TNC at 1.5 mm, VNC at 3.0 mm, and 1.5 mm, respectively), with low misclassification rates. Conclusions: Our study highlights high reproducibility in the evaluation of AVCS by VNC reconstruction at 3.0 and 1.5 mm intervals compared with reference offering a reliable alternative with an excellent diagnostic accuracy.
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(This article belongs to the Section Cardiovascular Imaging)
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Open AccessArticle
Clinically Focused Computer-Aided Diagnosis for Breast Cancer Using SE and CBAM with Multi-Head Attention
by
Zeki Ogut, Mucahit Karaduman and Muhammed Yildirim
Tomography 2025, 11(12), 138; https://doi.org/10.3390/tomography11120138 - 10 Dec 2025
Abstract
Background/Objectives: Breast cancer is one of the most common malignancies in women worldwide. Early diagnosis and accurate classification in breast cancer detection are among the most critical factors determining treatment success and patient survival. In this study, a deep learning-based model was developed
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Background/Objectives: Breast cancer is one of the most common malignancies in women worldwide. Early diagnosis and accurate classification in breast cancer detection are among the most critical factors determining treatment success and patient survival. In this study, a deep learning-based model was developed that can classify benign, malignant, and normal breast tissues from ultrasound images with high accuracy and achieve better results than the methods commonly used in the literature. Methods: The proposed model was trained on a dataset of breast ultrasound images, and its classification performance was evaluated. The model is designed to effectively learn both local textural features and global contextual relationships by combining Squeeze-and-Excitation (SE) blocks, which emphasize channel-level feature importance, and Convolutional Block Attention Module (CBAM) attention mechanisms, which focus on spatial information, with the MHA structure. The model’s performance is compared with three commonly used convolutional neural networks (CNNs) and three Vision Transformer (ViT) architectures. Results: The developed model achieved an accuracy rate of 96.03% in experimental analyses, outperforming both the six compared models and similar studies in the literature. Additionally, the proposed model was tested on a second dataset consisting of histopathological images and achieved an average accuracy of 99.55%. The results demonstrate that the model can effectively learn meaningful spatial and contextual information from ultrasound data and distinguish different tissue types with high accuracy. Conclusions: This study demonstrates the potential of deep learning-based approaches in breast ultrasound-based computer-aided diagnostic systems, providing a reliable, fast, and accurate decision support tool for early diagnosis. The results obtained with the proposed model suggest that it can significantly contribute to patient management by improving diagnostic accuracy in clinical applications.
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(This article belongs to the Special Issue Imaging in Cancer Diagnosis)
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Open AccessArticle
Angiovolume and Peak Enhancement on Preoperative CAD-Derived MRI as Prognostic Factors in Primary Operable Triple-Negative Breast Cancer
by
Bo La Yun, Sun Mi Kim, Sung Ui Shin, Su Min Cho, Yoon Yeong Choi and Mijung Jang
Tomography 2025, 11(12), 137; https://doi.org/10.3390/tomography11120137 - 5 Dec 2025
Abstract
Background/Objectives: To identify preoperative MRI features using computer-assisted diagnosis (CAD) that are associated with invasive disease-free survival (IDFS) and distant metastasis-free survival (DDFS) in patients with primarily operable triple-negative breast cancer (TNBC). Methods: This retrospective study was approved by the institutional review board
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Background/Objectives: To identify preoperative MRI features using computer-assisted diagnosis (CAD) that are associated with invasive disease-free survival (IDFS) and distant metastasis-free survival (DDFS) in patients with primarily operable triple-negative breast cancer (TNBC). Methods: This retrospective study was approved by the institutional review board with informed consent was waived. Between January 2012 and December 2014, 74 consecutive women with primary TNBC (mean age, 51 years; range, 29–77 years) who underwent preoperative MRI were included and followed until August 2021. Dynamic contrast-enhanced and T2-weighted images were obtained using 3T scanners. Peritumoral edema and central necrosis were evaluated retrospectively. CAD was used to extract 3D diameters, angiovolume, and kinetic parameters, and kinetic heterogeneity was calculated. Cox proportional hazards models were used to assess associations between MRI features and IDFS and DDFS, adjusting for clinicopathologic factors. Results: During a median follow-up of 80.9 months, 12 patients developed invasive disease, and 8 developed distant metastasis. In multivariable analysis, peak enhancement (hazard ratio [HR], 1.40; 95% confidence interval [CI], 1.06–1.84; p = 0.019) and angiovolume (HR, 2.86; 95% CI, 1.26–6.47; p = 0.012) were independently associated with IDFS, whereas angiovolume (HR, 2.47; 95% CI: 1.28–4.78; p = 0.007) was independently associated with DDFS. Conclusions: Preoperative CAD-derived MRI features, particularly peak enhancement and angiovolume, were associated with IDFS in TNBC patients whereas angiovolume alone was associated with DDFS.
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(This article belongs to the Section Cancer Imaging)
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Open AccessArticle
Quantitative Magnetic Resonance Imaging of the Forearm in Myotonic Dystrophy Type 1
by
Sydney Eierle, Tanja Taivassalo, Hyunjun Park, Korey D. Cooke, Zahra Moslemi, Sean C. Forbes, Glenn A. Walter, Krista Vandenborne, S. H. Subramony and Donovan J. Lott
Tomography 2025, 11(12), 136; https://doi.org/10.3390/tomography11120136 - 5 Dec 2025
Abstract
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Introduction: Myotonic dystrophy type 1 is the most prevalent muscular dystrophy in adults, characterized by weakness, impaired functional abilities, and myotonia. However, little is known about the relationship between quantitative MRI measures (fat fraction and T2 relaxation time) and clinical findings of
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Introduction: Myotonic dystrophy type 1 is the most prevalent muscular dystrophy in adults, characterized by weakness, impaired functional abilities, and myotonia. However, little is known about the relationship between quantitative MRI measures (fat fraction and T2 relaxation time) and clinical findings of the upper extremity. This study assessed forearm muscle structure in patients with myotonic dystrophy using quantitative MRI and correlated these measures with strength, function, and handgrip myotonia. Materials and Methods: Eighteen adults with myotonic dystrophy type 1 underwent MRI using three-point Dixon and T2 spin echo imaging of the forearm. Results: The average fat fraction and T2 relaxation time were greatest in the flexor digitorum profundus (26.7% and 55.6 ms, respectively). Correlations were found between quantitative MRI values and clinical tests of strength (r = −0.61 to −0.92, p < 0.01), function (r = −0.64 to −0.83, p < 0.01), and handgrip myotonia (r = 0.48, p < 0.05). Overall, the anterior forearm fat fraction values showed higher correlations with strength and function compared to those of the posterior forearm. Discussion: Our results support the use of quantitative MRI measures to assess forearm disease pathology and show potential to monitor the effectiveness of therapeutic treatments in patients with myotonic dystrophy type 1.
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Open AccessArticle
Evaluation of Projection Images for Visual Quality Control of Automated Left and Right Lung Segmentations on T1-Weighted MRI in Large-Scale Clinical Cohort Studies
by
Tobias Norajitra, Christopher L. Schlett, Ricarda von Krüchten, Prerana Agarwal, Ashis Ravindran, Thuy Duong Do, Lisa Kausch, Stefan Karrasch, Hans-Ulrich Kauczor, Klaus Maier-Hein and Claudius Melzig
Tomography 2025, 11(12), 135; https://doi.org/10.3390/tomography11120135 - 29 Nov 2025
Abstract
Background/Objectives: To assess diagnostic accuracy of two-dimensional (2D) projection methods for rapid visual quality control of automated volumetric (3D) lung segmentations compared with slice-based 3D review of segmentation results for application in large-scale studies. Methods: Segmentation of right and left lungs
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Background/Objectives: To assess diagnostic accuracy of two-dimensional (2D) projection methods for rapid visual quality control of automated volumetric (3D) lung segmentations compared with slice-based 3D review of segmentation results for application in large-scale studies. Methods: Segmentation of right and left lungs on T1-weighted MRI of 300 participants of the German National Cohort (NAKO) study was performed using the nnU-NET framework. Three variants of 2D projection images of segmentation masks were created: maximum intensity projection (MIP) using pseudo-chromadepth encoding with different color spectra for right and left lung (Colored_MIP) and standard deviation projection of segmentation mask outlines, encoded in black-and-white (Gray_outline) or using color-encoding (Colored_outline). The worst of two ratings by two independent raters conducting slice-based review for segmentation errors on underlying imaging data and review for mislabeling errors served as the standard of reference. All variants were evaluated by five raters each for identification of segmentation errors and the majority rating was used as index test. The time required for review was recorded and diagnostic accuracies were calculated. Results: Sensitivities of Colored_MIP, Colored_outline and Gray_outline were 88.2% [95%-CI 78.7%; 94.4%], 89.5% [80.3%; 95.3%] and 78.9% [68.1%; 87.5%]; specificities were 98.7% [96.1%; 99.7%], 96.4% [93.1%; 98.5%] and 98.7% [96.1%; 99.7%]; and F1-scores were 0.918, 0.895 and 0.863, respectively. Mean time per case and rater required for evaluation was 2.8 ± 0.9 s for Colored_outline, 1.7 ± 0.1 s for Colored_MIP, and 2.0 ± 0.4 s for Gray_outline. Conclusions: The 2D segmentation mask projection images enabled the detection of segmentation errors of automated 3D segmentations of left and right lungs based on MRI with high diagnostic accuracy, especially when using color-encoding. The method enabled evaluation within a matter of seconds per case. Segmentation mask projection images may assist in visual quality control of automated segmentations in large-scale studies.
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(This article belongs to the Special Issue Cutting-Edge Applications: Artificial Intelligence and Deep Learning Revolutionizing CT and MRI)
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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
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)
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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.
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(This article belongs to the Special Issue Medical Image Analysis in CT Imaging)
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