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 lmaging)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 23.9 days after submission; acceptance to publication is undertaken in 3.4 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually in the journal.
Impact Factor:
2.2 (2023);
5-Year Impact Factor:
2.3 (2023)
Latest Articles
Diagnostic Value of Contrast-Enhanced Dual-Energy Computed Tomography in the Pancreatic Parenchymal and Delayed Phases for Pancreatic Cancer
Tomography 2024, 10(10), 1591-1604; https://doi.org/10.3390/tomography10100117 - 7 Oct 2024
Abstract
Background/Objectives: The usefulness of dual-energy computed tomography (DECT) for low absorption in the parenchymal phase and contrast effects in the delayed phase for pancreatic cancer is not clear. Therefore, the diagnostic capability of low-KeV images obtained using DECT for pancreatic cancer in
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Background/Objectives: The usefulness of dual-energy computed tomography (DECT) for low absorption in the parenchymal phase and contrast effects in the delayed phase for pancreatic cancer is not clear. Therefore, the diagnostic capability of low-KeV images obtained using DECT for pancreatic cancer in the pancreatic parenchymal and delayed phases was evaluated quantitatively and qualitatively. Methods: Twenty-five patients with pancreatic cancer who underwent contrast-enhanced DECT were included. A total of 50 and 70 KeV CT images, classified as low-keV and conventional CT-equivalent images, were produced, respectively. The tumor-to-pancreas contrast (Hounsfield units [HU]) in the pancreatic parenchymal and delayed phases was calculated by subtracting the CT value of the pancreatic tumor from that of normal parenchyma. Results: The median tumor-to-pancreas contrast on 50 KeV CT in the pancreatic parenchymal phase (133 HU) was higher than that on conventional CT (68 HU) (p < 0.001). The median tumor-to-pancreas contrast in the delayed phase was −28 HU for 50 KeV CT and −9 HU for conventional CT (p = 0.545). For tumors < 20 mm, the tumor-to-pancreas contrast of 50 KeV CT (−39 HU) had a significantly clearer contrast effect than that of conventional CT (−16.5 HU), even in the delayed phase (p = 0.034). Conclusions: These 50 KeV CT images may clarify the low-absorption areas of pancreatic cancer in the pancreatic parenchymal phase. A good contrast effect was observed in small pancreatic cancers on 50 KeV delayed-phase images, suggesting that DECT is useful for the visualization of early pancreatic cancer with a small tumor diameter.
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(This article belongs to the Section Abdominal Imaging)
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Open AccessArticle
Lightweight MRI Brain Tumor Segmentation Enhanced by Hierarchical Feature Fusion
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Lei Zhang, Rong Zhang, Zhongjie Zhu, Pei Li, Yongqiang Bai and Ming Wang
Tomography 2024, 10(10), 1577-1590; https://doi.org/10.3390/tomography10100116 - 1 Oct 2024
Abstract
Background: Existing methods for MRI brain tumor segmentation often suffer from excessive model parameters and suboptimal performance in delineating tumor boundaries. Methods: For this issue, a lightweight MRI brain tumor segmentation method, enhanced by hierarchical feature fusion (EHFF), is proposed. This method reduces
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Background: Existing methods for MRI brain tumor segmentation often suffer from excessive model parameters and suboptimal performance in delineating tumor boundaries. Methods: For this issue, a lightweight MRI brain tumor segmentation method, enhanced by hierarchical feature fusion (EHFF), is proposed. This method reduces model parameters while improving segmentation performance by integrating hierarchical features. Initially, a fine-grained feature adjustment network is crafted and guided by global contextual information, leading to the establishment of an adaptive feature learning (AFL) module. This module captures the global features of MRI brain tumor images through macro perception and micro focus, adjusting spatial granularity to enhance feature details and reduce computational complexity. Subsequently, a hierarchical feature weighting (HFW) module is constructed. This module extracts multi-scale refined features through multi-level weighting, enhancing the detailed features of spatial positions and alleviating the lack of attention to local position details in macro perception. Finally, a hierarchical feature retention (HFR) module is designed as a supplementary decoder. This module retains, up-samples, and fuses feature maps from each layer, thereby achieving better detail preservation and reconstruction. Results: Experimental results on the BraTS 2021 dataset demonstrate that the proposed method surpasses existing methods. Dice similarity coefficients (DSC) for the three semantic categories ET, TC, and WT are 88.57%, 91.53%, and 93.09%, respectively.
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(This article belongs to the Topic AI in Medical Imaging and Image Processing)
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Open AccessArticle
Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and Selection Operator (LASSO) Method
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Xingfeng Li and Yuan Zhang
Tomography 2024, 10(10), 1564-1576; https://doi.org/10.3390/tomography10100115 - 30 Sep 2024
Abstract
Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude
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Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input–output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.
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(This article belongs to the Special Issue New Insights into Functional Magnetic Resonance Imaging (fMRI))
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Open AccessReview
Nailfold Video-Capillaroscopy in Sarcoidosis: New Perspectives and Challenges
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Maria Chianese, Gianluca Screm, Paola Confalonieri, Francesco Salton, Liliana Trotta, Beatrice Da Re, Antonio Romallo, Alessandra Galantino, Mario D’Oria, Michael Hughes, Giulia Bandini, Marco Confalonieri, Elisa Baratella, Lucrezia Mondini and Barbara Ruaro
Tomography 2024, 10(10), 1547-1563; https://doi.org/10.3390/tomography10100114 - 25 Sep 2024
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Introduction: Nailfold video-capillaroscopy (NVC) is a non-invasive cost-effective technique involving the microscopic examination of small blood vessels of the distal nailfold with a magnification device. It provides valuable information regarding the microcirculation including anomalies such as tortuous or dilated capillaries, hemorrhages, and avascular
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Introduction: Nailfold video-capillaroscopy (NVC) is a non-invasive cost-effective technique involving the microscopic examination of small blood vessels of the distal nailfold with a magnification device. It provides valuable information regarding the microcirculation including anomalies such as tortuous or dilated capillaries, hemorrhages, and avascular areas, which can characterize connective tissue diseases. The utility of NVC in the diagnosis and monitoring of systemic sclerosis (SSc) has been investigated in numerous studies allowing the distinction of the specific microvascular pattern of scleroderma from different conditions other than scleroderma (non-scleroderma pattern). Sarcoidosis (SA) is a systemic inflammatory disease that can affect various organs, including the lungs, skin, and lymph nodes. The purpose of our review was to evaluate the current state of the art in the use of NVC in the diagnosis of SA, to understand the indications for its use and any consequent advantages in the management of the disease in different settings in terms of benefits for patients. Materials and Methods: We searched for the key terms “sarcoidosis” and “video-capillaroscopy” in a computerized search of Pub-Med, extending the search back in time without setting limits. We provided a critical overview of the literature, based on a precise evaluation. After our analysis, we examined the six yielded works looking for answers to our questions. Results: Few studies have evaluated that microcirculation is often compromised in SA, with alterations in blood flow and consequent tissue damage. Discussion: Basing on highlighted findings, NVC appears to be a useful tool in the initial evaluation of sarcoidosis patients. Furthermore, capillaroscopy is useful in the evaluation of the coexistence of sarcoidosis and scleroderma spectrum disorder or overlap syndromes. Conclusions: In conclusions, no specific pattern has been described for sarcoidosis, and further re-search is needed to fully understand the implications of nailfold capillaroscopy find-ings in this disease and to establish standardized guidelines for its use in clinical practice.
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Open AccessArticle
Comparison of Traumatic Brain Injury in Adult Patients with and without Facial Fractures
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Iulia Tatiana Lupascu, Sorin Hostiuc, Costin Aurelian Minoiu, Mihaela Hostiuc and Bogdan Valeriu Popa
Tomography 2024, 10(10), 1534-1546; https://doi.org/10.3390/tomography10100113 - 24 Sep 2024
Abstract
Objectives: Facial fractures and associated traumatic brain injuries represent a worldwide public health concern. Therefore, we aimed to determine the pattern of brain injury accompanying facial fractures by comparing adult patients with and without facial fractures in terms of demographic, clinical, and imaging
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Objectives: Facial fractures and associated traumatic brain injuries represent a worldwide public health concern. Therefore, we aimed to determine the pattern of brain injury accompanying facial fractures by comparing adult patients with and without facial fractures in terms of demographic, clinical, and imaging features. Methods: This single-center, retrospective study included 492 polytrauma patients presenting at our emergency department from January 2019 to July 2023, which were divided in two groups: with facial fractures (FF) and without facial fractures (non-FF). The following data were collected: age, sex, mechanism of trauma (road traffic accident, fall, and other causes), Glasgow Coma Scale (GCS), the evolution of the patient (admitted to a medical ward or intensive care unit, neurosurgery performed, death), and imaging features of the injury. Data were analyzed using descriptive tests, Chi-square tests, and regression analyses. A p-value less than 0.05 was considered statistically significant. Results: In the FF group, there were 79% (n = 102) men and 21% (n = 27) women, with a mean age of 45 ± 17 years, while in the non-FF group, there were 70% (n = 253) men and 30% (n = 110) women, with a mean age 46 ± 17 years. There was a significant association between brain injuries and facial fractures (p < 0.001, AOR 1.7). The most frequent facial fracture affected the zygoma bone in 28.1% (n = 67) cases. The most frequent brain injury associated with FF was subdural hematoma 23.4% (n = 44), and in the non-FF group, the most common head injury was intraparenchymal hematoma 29% (n = 73); Conclusions: Both groups shared similarities regarding gender, age, cause of traumatic event, and outcome but had significant differences in association with brain injuries, ICU admission, and clinical status.
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(This article belongs to the Section Neuroimaging)
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Open AccessArticle
Reading Times of Common Musculoskeletal MRI Examinations: A Survey Study
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Robert M. Kwee, Asaad A. H. Amasha and Thomas C. Kwee
Tomography 2024, 10(9), 1527-1533; https://doi.org/10.3390/tomography10090112 - 20 Sep 2024
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Background: The workload of musculoskeletal radiologists has come under pressure. Our objective was to estimate the reading times of common musculoskeletal MRI examinations. Methods: A total of 144 radiologists were asked to estimate reading times (including interpretation and reporting) for MRI of the
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Background: The workload of musculoskeletal radiologists has come under pressure. Our objective was to estimate the reading times of common musculoskeletal MRI examinations. Methods: A total of 144 radiologists were asked to estimate reading times (including interpretation and reporting) for MRI of the shoulder, elbow, wrist, hip, knee, and ankle. Multivariate linear regression analyses were performed. Results: Reported median reading times with interquartile range (IQR) for the shoulder, elbow, wrist, hip, knee, and ankle were 10 (IQR 6–14), 10 (IQR 6–14), 11 (IQR 7.5–14.5), 10 (IQR 6.6–13.4), 8 (IQR 4.6–11.4), and 10 (IQR 6.5–13.5) min, respectively. Radiologists aged 35–44 years reported shorter reading times for the shoulder (β coefficient [β] = B-3.412, p = 0.041), hip (β = −3.596, p = 0.023), and knee (β = −3.541, p = 0.013) than radiologists aged 45–54 years. Radiologists not working in an academic/teaching hospital reported shorter reading times for the hip (β = −3.611, p = 0.025) and knee (β = −3.038, p = 0.035). Female radiologists indicated longer reading times for all joints (β of 2.592 to 5.186, p ≤ 0.034). Radiologists without musculoskeletal fellowship training indicated longer reading times for the shoulder (β = 4.604, p = 0.005), elbow (β = 3.989, p = 0.038), wrist (β = 4.543, p = 0.014), and hip (β = 2.380, p = 0.119). Radiologists with <5 years of post-residency experience indicated longer reading times for all joints (β of 5.355 to 6.984, p ≤ 0.045), and radiologists with 5–10 years of post-residency experience reported longer reading time for the knee (β = 3.660, p = 0.045) than those with >10 years of post-residency experience. Conclusions: There is substantial variation among radiologists in reported reading times for common musculoskeletal MRI examinations. Several radiologist-related determinants appear to be associated with reading speed, including age, gender, hospital type, training, and experience.
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Open AccessArticle
Skeletal Muscle Segmentation at the Level of the Third Lumbar Vertebra (L3) in Low-Dose Computed Tomography: A Lightweight Algorithm
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Xuzhi Zhao, Yi Du and Haizhen Yue
Tomography 2024, 10(9), 1513-1526; https://doi.org/10.3390/tomography10090111 - 13 Sep 2024
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Background: The cross-sectional area of skeletal muscles at the level of the third lumbar vertebra (L3) measured from computed tomography (CT) images is an established imaging biomarker used to assess patients’ nutritional status. With the increasing prevalence of low-dose CT scans in clinical
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Background: The cross-sectional area of skeletal muscles at the level of the third lumbar vertebra (L3) measured from computed tomography (CT) images is an established imaging biomarker used to assess patients’ nutritional status. With the increasing prevalence of low-dose CT scans in clinical practice, accurate and automated skeletal muscle segmentation at the L3 level in low-dose CT images has become an issue to address. This study proposed a lightweight algorithm for automated segmentation of skeletal muscles at the L3 level in low-dose CT images. Methods: This study included 57 patients with rectal cancer, with both low-dose plain and contrast-enhanced pelvic CT image series acquired using a radiotherapy CT scanner. A training set of 30 randomly selected patients was used to develop a lightweight segmentation algorithm, and the other 27 patients were used as the test set. A radiologist selected the most representative axial CT image at the L3 level for both the image series for all the patients, and three groups of observers manually annotated the skeletal muscles in the 54 CT images of the test set as the gold standard. The performance of the proposed algorithm was evaluated in terms of the Dice similarity coefficient (DSC), precision, recall, 95th percentile of the Hausdorff distance (HD95), and average surface distance (ASD). The running time of the proposed algorithm was recorded. An open source deep learning-based AutoMATICA algorithm was compared with the proposed algorithm. The inter-observer variations were also used as the reference. Results: The DSC, precision, recall, HD95, ASD, and running time were 93.2 ± 1.9% (mean ± standard deviation), 96.7 ± 2.9%, 90.0 ± 2.9%, 4.8 ± 1.3 mm, 0.8 ± 0.2 mm, and 303 ± 43 ms (on CPU) for the proposed algorithm, and 94.1 ± 4.1%, 92.7 ± 5.5%, 95.7 ± 4.0%, 7.4 ± 5.7 mm, 0.9 ± 0.6 mm, and 448 ± 40 ms (on GPU) for AutoMATICA, respectively. The differences between the proposed algorithm and the inter-observer reference were 4.7%, 1.2%, 7.9%, 3.2 mm, and 0.6 mm, respectively, for the averaged DSC, precision, recall, HD95, and ASD. Conclusion: The proposed algorithm can be used to segment skeletal muscles at the L3 level in either the plain or enhanced low-dose CT images.
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Open AccessArticle
Radiomic Analysis of Treatment Effect for Patients with Radiation Necrosis Treated with Pentoxifylline and Vitamin E
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Jimmy S. Patel, Elahheh Salari, Xuxin Chen, Jeffrey Switchenko, Bree R. Eaton, Jim Zhong, Xiaofeng Yang, Hui-Kuo G. Shu and Lisa J. Sudmeier
Tomography 2024, 10(9), 1501-1512; https://doi.org/10.3390/tomography10090110 - 9 Sep 2024
Abstract
Background: The combination of oral pentoxifylline (Ptx) and vitamin E (VitE) has been used to treat radiation-induced fibrosis and soft tissue injury. Here, we review outcomes and perform a radiomic analysis of treatment effects in patients prescribed Ptx + VitE at our institution
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Background: The combination of oral pentoxifylline (Ptx) and vitamin E (VitE) has been used to treat radiation-induced fibrosis and soft tissue injury. Here, we review outcomes and perform a radiomic analysis of treatment effects in patients prescribed Ptx + VitE at our institution for the treatment of radiation necrosis (RN). Methods: A total of 48 patients treated with stereotactic radiosurgery (SRS) had evidence of RN and had MRI before and after starting Ptx + VitE. The radiation oncologist’s impression of the imaging in the electronic medical record was used to score response to treatment. Support Vector Machine (SVM) was used to train a model of radiomics features derived from radiation necrosis on pre- and 1st post-treatment T1 post-contrast MRIs that can classify the ultimate response to treatment with Ptx + VitE. Results: A total of 43.8% of patients showed evidence of improvement, 18.8% showed no change, and 25% showed worsening RN upon imaging after starting Ptx + VitE. The median time-to-response assessment was 3.17 months. Nine patients progressed significantly and required Bevacizumab, hyperbaric oxygen therapy, or surgery. Patients who had multiple lesions treated with SRS were less likely to show improvement (p = 0.037). A total of 34 patients were also prescribed dexamethasone, either before (7), with (16), or after starting (11) treatment. The use of dexamethasone was not associated with an improved response to Ptx + VitE (p = 0.471). Three patients stopped treatment due to side effects. Finally, we were able to develop a machine learning (SVM) model of radiomic features derived from pre- and 1st post-treatment MRIs that was able to predict the ultimate treatment response to Ptx + VitE with receiver operating characteristic (ROC) area under curve (AUC) of 0.69. Conclusions: Ptx + VitE appears safe for the treatment of RN, but randomized data are needed to assess efficacy and validate radiomic models, which may assist with prognostication.
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(This article belongs to the Section Cancer Imaging)
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Open AccessArticle
A Joint Classification Method for COVID-19 Lesions Based on Deep Learning and Radiomics
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Guoxiang Ma, Kai Wang, Ting Zeng, Bin Sun and Liping Yang
Tomography 2024, 10(9), 1488-1500; https://doi.org/10.3390/tomography10090109 - 5 Sep 2024
Abstract
Pneumonia caused by novel coronavirus is an acute respiratory infectious disease. Its rapid spread in a short period of time has brought great challenges for global public health. The use of deep learning and radiomics methods can effectively distinguish the subtypes of lung
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Pneumonia caused by novel coronavirus is an acute respiratory infectious disease. Its rapid spread in a short period of time has brought great challenges for global public health. The use of deep learning and radiomics methods can effectively distinguish the subtypes of lung diseases, provide better clinical prognosis accuracy, and assist clinicians, enabling them to adjust the clinical management level in time. The main goal of this study is to verify the performance of deep learning and radiomics methods in the classification of COVID-19 lesions and reveal the image characteristics of COVID-19 lung disease. An MFPN neural network model was proposed to extract the depth features of lesions, and six machine-learning methods were used to compare the classification performance of deep features, key radiomics features and combined features for COVID-19 lung lesions. The results show that in the COVID-19 image classification task, the classification method combining radiomics and deep features can achieve good classification results and has certain clinical application value.
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(This article belongs to the Section Artificial Intelligence in Medical Imaging)
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Open AccessReview
A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease
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Arshpreet Singh Badesha, Russell Frood, Marc A. Bailey, Patrick M. Coughlin and Andrew F. Scarsbrook
Tomography 2024, 10(9), 1455-1487; https://doi.org/10.3390/tomography10090108 - 3 Sep 2024
Abstract
Background: Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis in cardiovascular disease has largely focused on CT and MRI modalities.
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Background: Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis in cardiovascular disease has largely focused on CT and MRI modalities. This scoping review aims to summarise the existing literature on radiomic analysis techniques in cardiovascular disease. Methods: MEDLINE and Embase databases were searched for eligible studies evaluating radiomic techniques in living human subjects derived from CT, MRI or PET imaging investigating atherosclerotic disease. Data on study population, imaging characteristics and radiomics methodology were extracted. Results: Twenty-nine studies consisting of 5753 patients (3752 males) were identified, and 78.7% of patients were from coronary artery studies. Twenty-seven studies employed CT imaging (19 CT carotid angiography and 6 CT coronary angiography (CTCA)), and two studies studied PET/CT. Manual segmentation was most frequently undertaken. Processing techniques included voxel discretisation, voxel resampling and filtration. Various shape, first-order, second-order and higher-order radiomic features were extracted. Logistic regression was most commonly used for machine learning. Conclusion: Most published evidence was feasibility/proof of concept work. There was significant heterogeneity in image acquisition, segmentation techniques, processing and analysis between studies. There is a need for the implementation of standardised imaging acquisition protocols, adherence to published reporting guidelines and economic evaluation.
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(This article belongs to the Section Cardiovascular Imaging)
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Open AccessReview
Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction
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Jesutofunmi Ayo Fajemisin, Glebys Gonzalez, Stephen A. Rosenberg, Ghanim Ullah, Gage Redler, Kujtim Latifi, Eduardo G. Moros and Issam El Naqa
Tomography 2024, 10(9), 1439-1454; https://doi.org/10.3390/tomography10090107 - 2 Sep 2024
Abstract
Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and normal tissues. This development has significantly impacted the imaging and treatment of cancers. Radiomics is the process of extracting high-dimensional features from medical images. Several studies have shown
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Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and normal tissues. This development has significantly impacted the imaging and treatment of cancers. Radiomics is the process of extracting high-dimensional features from medical images. Several studies have shown that these extracted features may be used to build machine-learning models for the prediction of treatment outcomes of cancer patients. Various feature selection techniques and machine models interrogate the relevant radiomics features for predicting cancer treatment outcomes. This study aims to provide an overview of MRI radiomics features used in predicting clinical treatment outcomes with machine learning techniques. The review includes examples from different disease sites. It will also discuss the impact of magnetic field strength, sample size, and other characteristics on outcome prediction performance.
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(This article belongs to the Special Issue Feature Reviews for Tomography 2023)
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Open AccessReview
Magnetic Resonance Imaging Biomarkers of Muscle
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Usha Sinha and Shantanu Sinha
Tomography 2024, 10(9), 1411-1438; https://doi.org/10.3390/tomography10090106 - 2 Sep 2024
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This review is focused on the current status of quantitative MRI (qMRI) of skeletal muscle. The first section covers the techniques of qMRI in muscle with the focus on each quantitative parameter, the corresponding imaging sequence, discussion of the relation of the measured
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This review is focused on the current status of quantitative MRI (qMRI) of skeletal muscle. The first section covers the techniques of qMRI in muscle with the focus on each quantitative parameter, the corresponding imaging sequence, discussion of the relation of the measured parameter to underlying physiology/pathophysiology, the image processing and analysis approaches, and studies on normal subjects. We cover the more established parametric mapping from T1-weighted imaging for morphometrics including image segmentation, proton density fat fraction, T2 mapping, and diffusion tensor imaging to emerging qMRI features such as magnetization transfer including ultralow TE imaging for macromolecular fraction, and strain mapping. The second section is a summary of current clinical applications of qMRI of muscle; the intent is to demonstrate the utility of qMRI in different disease states of the muscle rather than a complete comprehensive survey.
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Open AccessArticle
Repurposing the Public BraTS Dataset for Postoperative Brain Tumour Treatment Response Monitoring
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Peter Jagd Sørensen, Claes Nøhr Ladefoged, Vibeke Andrée Larsen, Flemming Littrup Andersen, Michael Bachmann Nielsen, Hans Skovgaard Poulsen, Jonathan Frederik Carlsen and Adam Espe Hansen
Tomography 2024, 10(9), 1397-1410; https://doi.org/10.3390/tomography10090105 - 1 Sep 2024
Abstract
The Brain Tumor Segmentation (BraTS) Challenge has been a main driver of the development of deep learning (DL) algorithms and provides by far the largest publicly available expert-annotated brain tumour dataset but contains solely preoperative examinations. The aim of our study was to
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The Brain Tumor Segmentation (BraTS) Challenge has been a main driver of the development of deep learning (DL) algorithms and provides by far the largest publicly available expert-annotated brain tumour dataset but contains solely preoperative examinations. The aim of our study was to facilitate the use of the BraTS dataset for training DL brain tumour segmentation algorithms for a postoperative setting. To this end, we introduced an automatic conversion of the three-label BraTS annotation protocol to a two-label annotation protocol suitable for postoperative brain tumour segmentation. To assess the viability of the label conversion, we trained a DL algorithm using both the three-label and the two-label annotation protocols. We assessed the models pre- and postoperatively and compared the performance with a state-of-the-art DL method. The DL algorithm trained using the BraTS three-label annotation misclassified parts of 10 out of 41 fluid-filled resection cavities in 72 postoperative glioblastoma MRIs, whereas the two-label model showed no such inaccuracies. The tumour segmentation performance of the two-label model both pre- and postoperatively was comparable to that of a state-of-the-art algorithm for tumour volumes larger than 1 cm3. Our study enables using the BraTS dataset as a basis for the training of DL algorithms for postoperative tumour segmentation.
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(This article belongs to the Topic AI in Medical Imaging and Image Processing)
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Open AccessArticle
The Combination of Presurgical Cortical Gray Matter Volumetry and Cerebral Perfusion Improves the Efficacy of Predicting Postoperative Cognitive Impairment of Elderly Patients
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Weijian Zhou, Binbin Zhu, Yifei Weng, Chunqu Chen, Jiajing Ni, Wenqi Shen, Wenting Lan and Jianhua Wang
Tomography 2024, 10(9), 1379-1396; https://doi.org/10.3390/tomography10090104 - 1 Sep 2024
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Background: Postoperative cognitive dysfunction (POCD) is a common complication of the central nervous system in elderly surgical patients. Structural MRI and arterial spin labelling (ASL) techniques found that the grey matter volume and cerebral perfusion in some specific brain areas are associated with
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Background: Postoperative cognitive dysfunction (POCD) is a common complication of the central nervous system in elderly surgical patients. Structural MRI and arterial spin labelling (ASL) techniques found that the grey matter volume and cerebral perfusion in some specific brain areas are associated with the occurrence of POCD, but the results are inconsistent, and the predictive accuracy is low. We hypothesised that the combination of cortical grey matter volumetry and cerebral blood flow yield higher accuracy than either of the methods in discriminating the elderly individuals who are susceptible to POCD after abdominal surgery. Materials and Methods: Participants underwent neuropsychological testing before and after surgery. Postoperative cognitive dysfunction (POCD) was defined as a decrease in cognitive score of at least 20%. ASL-MRI and T1-weighted imaging were performed before surgery. We compared differences in cerebral blood flow (CBF) and cortical grey matter characteristics between POCD and non-POCD patients and generated receiver operating characteristic curves. Results: Out of 51 patients, 9 (17%) were diagnosed with POCD. CBF in the inferior frontal gyrus was lower in the POCD group compared to the non-POCD group (p < 0.001), and the volume of cortical grey matter in the anterior cingulate gyrus was higher in the POCD group (p < 0.001). The highest AUC value was 0.973. Conclusions: The combination of cortical grey matter volumetry and cerebral perfusion based on ASL-MRI has improved efficacy in the early warning of POCD to elderly abdominal surgical patients.
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Open AccessFeature PaperReview
Diagnostic and Therapeutic Approach to Thoracic Outlet Syndrome
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Stefania Rizzo, Cammillo Talei Franzesi, Andrea Cara, Enrico Mario Cassina, Lidia Libretti, Emanuele Pirondini, Federico Raveglia, Antonio Tuoro, Sara Vaquer, Sara Degiovanni, Erica Michela Cavalli, Andrea Marchesi, Alberto Froio and Francesco Petrella
Tomography 2024, 10(9), 1365-1378; https://doi.org/10.3390/tomography10090103 - 1 Sep 2024
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Thoracic outlet syndrome (TOS) is a group of symptoms caused by the compression of neurovascular structures of the superior thoracic outlet. The knowledge of its clinical presentation with specific symptoms, as well as proper imaging examinations, ranging from plain radiographs to ultrasound, computed
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Thoracic outlet syndrome (TOS) is a group of symptoms caused by the compression of neurovascular structures of the superior thoracic outlet. The knowledge of its clinical presentation with specific symptoms, as well as proper imaging examinations, ranging from plain radiographs to ultrasound, computed tomography and magnetic resonance imaging, may help achieve a precise diagnosis. Once TOS is recognized, proper treatment may comprise a conservative or a surgical approach.
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Open AccessArticle
18F-Fluoroazomycin Arabinoside (FAZA) PET/MR as a Biomarker of Hypoxia in Rectal Cancer: A Pilot Study
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Ur Metser, Andres Kohan, Catherine O’Brien, Rebecca K. S. Wong, Claudia Ortega, Patrick Veit-Haibach, Brandon Driscoll, Ivan Yeung and Adam Farag
Tomography 2024, 10(9), 1354-1364; https://doi.org/10.3390/tomography10090102 - 30 Aug 2024
Abstract
Tumor hypoxia is a negative prognostic factor in many tumors and is predictive of metastatic spread and poor responsiveness to both chemotherapy and radiotherapy. Purpose: To assess the feasibility of using 18F-Fluoroazomycin arabinoside (FAZA) PET/MR to image tumor hypoxia in patients with
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Tumor hypoxia is a negative prognostic factor in many tumors and is predictive of metastatic spread and poor responsiveness to both chemotherapy and radiotherapy. Purpose: To assess the feasibility of using 18F-Fluoroazomycin arabinoside (FAZA) PET/MR to image tumor hypoxia in patients with locally advanced rectal cancer (LARC) prior to and following neoadjuvant chemoradiotherapy (nCRT). The secondary objective was to compare different reference tissues and thresholds for tumor hypoxia quantification. Patients and Methods: Eight patients with histologically proven LARC were included. All patients underwent 18F-FAZA PET/MR prior to initiation of nCRT, four of whom also had a second scan following completion of nCRT and prior to surgery. Tumors were segmented using T2-weighted MR. Each voxel within the segmented tumor was defined as hypoxic or oxic using thresholds derived from various references: ×1.0 or ×1.2 SUVmean of blood pool [BP] or left ventricle [LV] and SUVmean +3SD for gluteus maximus. Correlation coefficient (CoC) between HF and tumor SUVmax/reference SUVmean TRR for the various thresholds was calculated. Hypoxic fraction (HF), defined as the % hypoxic voxels within the tumor volume was calculated for each reference/threshold. Results: For all cases, baseline and follow-up, the CoCs for gluteus maximus and for BP and LV (×1.0) were 0.241, 0.344, and 0.499, respectively, and HFs were (median; range) 16.6% (2.4–33.8), 36.8% (0.3–72.9), and 30.7% (0.8–55.5), respectively. For a threshold of ×1.2, the CoCs for BP and LV as references were 0.611 and 0.838, respectively, and HFs were (median; range) 10.4% (0–47.6), and 4.3% (0–20.1%), respectively. The change in HF following nCRT ranged from (−18.9%) to (+54%). Conclusions: Imaging of hypoxia in LARC with 18F-FAZA PET/MR is feasible. Blood pool as measured in the LV appears to be the most reliable reference for calculating the HF. There is a wide range of HF and variable change in HF before and after nCRT.
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(This article belongs to the Section Cancer Imaging)
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Open AccessArticle
Oxytocin: A Shield against Radiation-Induced Lung Injury in Rats
by
Ahmet Kayalı, Duygu Burcu Arda, Ejder Saylav Bora, Yiğit Uyanikgil, Özüm Atasoy and Oytun Erbaş
Tomography 2024, 10(9), 1342-1353; https://doi.org/10.3390/tomography10090101 - 29 Aug 2024
Abstract
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Background: Radiation-induced lung injury (RILI), a serious side effect of thoracic radiotherapy, can lead to acute radiation pneumonitis (RP) and chronic pulmonary fibrosis (PF). Despite various interventions, no effective protocol exists to prevent pneumonitis. Oxytocin (OT), known for its anti-inflammatory, antiapoptotic, and antioxidant
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Background: Radiation-induced lung injury (RILI), a serious side effect of thoracic radiotherapy, can lead to acute radiation pneumonitis (RP) and chronic pulmonary fibrosis (PF). Despite various interventions, no effective protocol exists to prevent pneumonitis. Oxytocin (OT), known for its anti-inflammatory, antiapoptotic, and antioxidant properties, has not been explored for its potential in mitigating RILI. Materials and Methods: This study involved 24 female Wistar albino rats, divided into three groups: control group, radiation (RAD) + saline, and RAD + OT. The RAD groups received 18 Gy of whole-thorax irradiation. The RAD + OT group was treated with OT (0.1 mg/kg/day) intraperitoneally for 16 weeks. Computerizing tomography (CT) imaging and histopathological, biochemical, and blood gas analyses were performed to assess lung tissue damage and inflammation. Results: Histopathological examination showed significant reduction in alveolar wall thickening, inflammation, and vascular changes in the RAD + OT group compared to the RAD + saline group. Biochemical analysis revealed decreased levels of TGF-beta, VEGF, and PDGF, and increased BMP-7 and prostacyclin in the RAD + oxytocin group (p < 0.05). Morphometric analysis indicated significant reductions in fibrosis, edema, and immune cell infiltration. CT imaging demonstrated near-normal lung parenchyma density in the RAD + oxytocin group (p < 0.001). Conclusion: Oxytocin administration significantly mitigates radiation-induced pneumonitis in rats, implying that is has potential as a therapeutic agent for preventing and treating RILI.
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Open AccessArticle
Study on Shoulder Joint Parameters and Available Supraspinatus Outlet Area Using Three-Dimensional Computed Tomography Reconstruction
by
Xi Chen, Tangzhao Liang, Xiaopeng Yin, Chang Liu, Jianhua Ren, Shouwen Su, Shihai Jiang and Kun Wang
Tomography 2024, 10(9), 1331-1341; https://doi.org/10.3390/tomography10090100 - 29 Aug 2024
Abstract
Studies addressing the anatomical values of the supraspinatus outlet area (SOA) and the available supraspinatus outlet area (ASOA) are insufficient. This study focused on precisely measuring the SOA and ASOA values in a sample from the Chinese population using 3D CT (computed tomography)
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Studies addressing the anatomical values of the supraspinatus outlet area (SOA) and the available supraspinatus outlet area (ASOA) are insufficient. This study focused on precisely measuring the SOA and ASOA values in a sample from the Chinese population using 3D CT (computed tomography) reconstruction. We analyzed CT imaging of 96 normal patients (59 males and 37 females) who underwent shoulder examinations in a hospital between 2011 and 2021. The SOA, ASOA, acromiohumeral distance (AHD), coracohumeral distance (CHD), coracoacromial arch radius (CAR), and humeral head radius (HHR) were estimated, and statistical correlation analyses were performed. There were significant sex differences observed in SOA (men: 957.62 ± 158.66 mm2; women: 735.87 ± 95.86 mm2) and ASOA (men: 661.35 ± 104.88 mm2; women: 511.49 ± 69.26 mm2), CHD (men: 11.22 ± 2.24 mm; women: 9.23 ± 1.35 mm), CAR (men: 37.18 ± 2.70 mm; women: 33.04 ± 3.15 mm), and HHR (men: 22.65 ± 1.44 mm; women: 20.53 ± 0.95 mm). Additionally, both SOA and ASOA showed positive and linear correlations with AHD, CHD, CAR, and HHR (R: 0.304–0.494, all p < 0.05). This study provides physiologic reference values of SOA and ASOA in the Chinese population, highlighting the sex differences and the correlations with shoulder anatomical parameters.
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(This article belongs to the Topic Human Anatomy and Pathophysiology, 2nd Volume)
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Emergency Radiology in the First 24 h of Two Major Earthquakes on the Same Day and Radiologic Evaluation of Trauma Cases
by
Mehtap Ilgar and Nurullah Dağ
Tomography 2024, 10(8), 1320-1330; https://doi.org/10.3390/tomography10080099 - 22 Aug 2024
Abstract
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Background: On 6 February 2023, two major earthquakes occurred in Turkey on the same day. More than 50,000 people died, and more than 100,000 people were injured in these earthquakes. The aim of this study is to contribute to disaster management plans by
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Background: On 6 February 2023, two major earthquakes occurred in Turkey on the same day. More than 50,000 people died, and more than 100,000 people were injured in these earthquakes. The aim of this study is to contribute to disaster management plans by evaluating the functioning of a radiology department and the imaging examinations performed after this disaster. Methods: The functioning of the radiology clinic at Malatya Training and Research Hospital in the first 24 h after the earthquake was evaluated. The images of 596 patients who were admitted to Malatya Training and Research Hospital for earthquake-related trauma between 6 February 2023, at 4:17 a.m. and 7 February 2023, at 4:17 a.m., and who underwent radiography and computed tomography (CT) were retrospectively reviewed. Results: The mean age of the patients was 37.3 ± 20.1 years. A total of 313 (52.5%) patients were male. The most frequently performed imaging test was a CT scan. In total, 437 (73.3%) of 596 patients underwent a CT scan. At least one body part was affected in 160 patients (26.8%). The most commonly affected regions were the thorax, vertebrae, and extremities. Thoracic findings were observed in 52 patients (32.5%), vertebral findings in 52 patients (32.5%), and extremity findings in 46 patients (28.7%). Fractures were the most common finding in our study. Of the 160 patients with pathologic findings, 139 (86.9%) had evidence of fractures. Conclusions: The role of radiology in disasters is important. When disaster preparedness plans are made, radiology departments should be actively involved in these plans. This will ensure the quick and efficient functioning of radiology departments.
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Open AccessBrief Report
Magnetic Resonance Imaging and 99Tc WBC-SPECT/CT Scanning in Differential Diagnosis between Osteomyelitis and Charcot Neuroarthropathy: A Case Series
by
Sara Cecchini, Cristina Gatti, Daniela Fornarelli, Lorenzo Fantechi, Cinzia Romagnolo, Elena Tortato, Anna Rita Bonfigli, Roberta Galeazzi, Fabiola Olivieri, Giuseppe Bronte and Enrico Paci
Tomography 2024, 10(8), 1312-1319; https://doi.org/10.3390/tomography10080098 - 22 Aug 2024
Abstract
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Background: Distinguishing between Charcot Neuroarthropathy (CN), osteomyelitis (OM), and CN complicated with superimposed OM in diabetic patients is crucial for the treatment choice. Given that current diagnostic methods lack specificity, advanced techniques, e.g., magnetic resonance imaging (MRI) and 99mTc-HMPAO–WBC Single Photon Emission Computed
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Background: Distinguishing between Charcot Neuroarthropathy (CN), osteomyelitis (OM), and CN complicated with superimposed OM in diabetic patients is crucial for the treatment choice. Given that current diagnostic methods lack specificity, advanced techniques, e.g., magnetic resonance imaging (MRI) and 99mTc-HMPAO–WBC Single Photon Emission Computed Tomography (SPECT/CT), are needed. This study addresses the challenges in distinguishing OM and CN. Methods: We included diabetic patients with CN and soft tissue ulceration. MRI and 99mTc-HMPAO–WBC SPECT/CT were used for the diagnosis. The patients were classified into three probability levels for OM (i.e., Definite, Probable, and Unlikely) according to the Consensus Criteria for Diabetic Foot Osteomyelitis (CC-DFO). Results: Eight patients met the eligibility criteria. MRI, supported by SPECT-CT and CC-DFO, showed consistency with the OM diagnosis in three cases. The key diagnostic features included the location of signal abnormalities and secondary features such as skin ulcers, sinus tracts, and abscesses. Notably, cases with inconclusive MRI were clarified by SPECT/CT, emphasizing its efficacy in challenging scenarios. Conclusions: The primary objective of this study was to compare the results of MRI and 99mTc-HMPAO–WBC SPECT/CT with the CC-DFO score in the diabetic foot with CN and suspected OM. Advanced imaging offers a complementary approach to distinguish between CN and OM. This can help delineate the limits of the disease for presurgical planning. While MRI is valuable, 99mTc-HMPAO–WBC SPECT/CT provides additional clarity, especially in challenging cases or when metallic implants affect MRI accuracy.
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