Topic Editors

Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Institute of Electronics, Lodz University of Technology, Wolczanska 211/215, 90-924 Łódź, Poland
Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Cracow, Poland
Department of Radiology, Jagiellonian University Medical College, 19 Kopernika Street, 31-501 Cracow, Poland

AI in Medical Imaging and Image Processing

Abstract submission deadline
31 October 2024
Manuscript submission deadline
31 December 2024
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Topic Information

Dear Colleagues,

In modern healthcare, the importance of computer-aided diagnosis is quickly becoming obvious, with clear benefits for the medical professionals and patients. Automatization of processes traditionally maintained by human professionals is also growing in importance. The process of image analysis can be supported by the use of networks that can carry out multilayer analyses of patterns—collectively called artificial intelligence (AI). If supported by large datasets of input data, computer networks can suggest the result with low error bias. Medical imaging focused on pattern detection is typically supported by AI algorithms. AI can be used as an important aid in three major steps of decision making in the medical imaging workflow: detection (image segmentation), recognition (assignment to the class), and result description (transformation of the result to natural language). The implementation of AI algorithms may participate in the diagnostic process standardization and markedly reduces the time needed to achieve pathology detection and description of the results. With AI support, medical specialists may work more effectively, which can improve healthcare quality. As AI has been a topic of interest for a while now, there are many approaches to and techniques for the implementation of AI based on different computing methods designed to work in various systems. The aim of this Special Issue in to present the current knowledge dedicated to the AI methods used in medical systems, with their applications in different fields of diagnostic imaging. Our goal is for this collection of works to contribute to the exchange of knowledge resulting in a better understanding of AI technical aspects and applications in modern radiology.

Dr. Karolina Nurzynska
Prof. Dr. Michał Strzelecki
Prof. Dr. Adam Piorkowski
Dr. Rafał Obuchowicz
Topic Editors

Keywords

  • artificial intelligence
  • computer-aided diagnosis
  • medical imaging
  • image analysis
  • image processing

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
BioMed
biomed
- - 2021 27 Days CHF 1000 Submit
Cancers
cancers
5.2 7.4 2009 17.9 Days CHF 2900 Submit
Diagnostics
diagnostics
3.6 3.6 2011 20.7 Days CHF 2600 Submit
Journal of Clinical Medicine
jcm
3.9 5.4 2012 17.9 Days CHF 2600 Submit
Tomography
tomography
1.9 2.3 2015 24.5 Days CHF 2400 Submit

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Published Papers (8 papers)

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14 pages, 2340 KiB  
Article
Classification of Osteophytes Occurring in the Lumbar Intervertebral Foramen
by Abdullah Emre Taçyıldız and Feyza İnceoğlu
Tomography 2024, 10(4), 618-631; https://doi.org/10.3390/tomography10040047 - 19 Apr 2024
Viewed by 183
Abstract
Background: Surgeons have limited knowledge of the lumbar intervertebral foramina. This study aimed to classify osteophytes in the lumbar intervertebral foramen and to determine their pathoanatomical characteristics, discuss their potential biomechanical effects, and contribute to developing surgical methods. Methods: We conducted a retrospective, [...] Read more.
Background: Surgeons have limited knowledge of the lumbar intervertebral foramina. This study aimed to classify osteophytes in the lumbar intervertebral foramen and to determine their pathoanatomical characteristics, discuss their potential biomechanical effects, and contribute to developing surgical methods. Methods: We conducted a retrospective, non-randomized, single-center study involving 1224 patients. The gender, age, and anatomical location of the osteophytes in the lumbar intervertebral foramina of the patients were recorded. Results: Two hundred and forty-nine (20.34%) patients had one or more osteophytes in their lumbar 4 and 5 foramina. Of the 4896 foramina, 337 (6.88%) contained different types of osteophytes. Moreover, four anatomical types of osteophytes were found: mixed osteophytes in 181 (3.69%) foramina, osteophytes from the lower endplate of the superior vertebrae in 91 (1.85%) foramina, osteophytes from the junction of the pedicle and lamina of the upper vertebrae in 39 foramina (0.79%), and osteophytes from the upper endplate of the lower vertebrae in 26 (0.53%) foramina. The L4 foramen contained a significantly higher number of osteophytes than the L5 foramen. Osteophyte development increased significantly with age, with no difference between males and females. Conclusions: The findings show that osteophytic extrusions, which alter the natural anatomical structure of the lumbar intervertebral foramina, are common and can narrow the foramen. Full article
(This article belongs to the Topic AI in Medical Imaging and Image Processing)
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17 pages, 9206 KiB  
Article
Contrast Agent Dynamics Determine Radiomics Profiles in Oncologic Imaging
by Martin L. Watzenboeck, Lucian Beer, Daria Kifjak, Sebastian Röhrich, Benedikt H. Heidinger, Florian Prayer, Ruxandra-Iulia Milos, Paul Apfaltrer, Georg Langs, Pascal A. T. Baltzer and Helmut Prosch
Cancers 2024, 16(8), 1519; https://doi.org/10.3390/cancers16081519 - 16 Apr 2024
Viewed by 261
Abstract
Background: The reproducibility of radiomics features extracted from CT and MRI examinations depends on several physiological and technical factors. The aim was to evaluate the impact of contrast agent timing on the stability of radiomics features using dynamic contrast-enhanced perfusion CT (dceCT) or [...] Read more.
Background: The reproducibility of radiomics features extracted from CT and MRI examinations depends on several physiological and technical factors. The aim was to evaluate the impact of contrast agent timing on the stability of radiomics features using dynamic contrast-enhanced perfusion CT (dceCT) or MRI (dceMRI) in prostate and lung cancers. Methods: Radiomics features were extracted from dceCT or dceMRI images in patients with biopsy-proven peripheral prostate cancer (pzPC) or biopsy-proven non-small cell lung cancer (NSCLC), respectively. Features that showed significant differences between contrast phases were identified using linear mixed models. An L2-penalized logistic regression classifier was used to predict class labels for pzPC and unaffected prostate regions-of-interest (ROIs). Results: Nine pzPC and 28 NSCLC patients, who were imaged with dceCT and/or dceMRI, were included in this study. After normalizing for individual enhancement patterns by defining seven individual phases based on a reference vessel, 19, 467 and 128 out of 1204 CT features showed significant temporal dynamics in healthy prostate parenchyma, prostate tumors and lung tumors, respectively. CT radiomics-based classification accuracy of healthy and tumor ROIs was highly dependent on contrast agent phase. For dceMRI, 899 and 1027 out of 1118 features were significantly dependent on time after contrast agent injection for prostate and lung tumors. Conclusions: CT and MRI radiomics features in both prostate and lung tumors are significantly affected by interindividual differences in contrast agent dynamics. Full article
(This article belongs to the Topic AI in Medical Imaging and Image Processing)
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16 pages, 2832 KiB  
Article
Impact of Deep Learning Denoising Algorithm on Diffusion Tensor Imaging of the Growth Plate on Different Spatial Resolutions
by Laura Santos, Hao-Yun Hsu, Ronald R. Nelson, Jr., Brendan Sullivan, Jaemin Shin, Maggie Fung, Marc R. Lebel, Sachin Jambawalikar and Diego Jaramillo
Tomography 2024, 10(4), 504-519; https://doi.org/10.3390/tomography10040039 - 02 Apr 2024
Viewed by 286
Abstract
To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions, this IRB-approved prospective study was conducted at a tertiary pediatric center in compliance with the Health [...] Read more.
To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions, this IRB-approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. A General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) was employed to acquire two DTI (diffusion tensor imaging) sequences of the left knee on each child at 3T: an in-plane 2.0 × 2.0 mm2 with section thickness of 3.0 mm and a 2 mm3 isovolumetric voxel; neither had an intersection gap. For image acquisition, a multi-band DTI with a fat-suppressed single-shot spin-echo echo-planar sequence (20 non-collinear directions; b-values of 0 and 600 s/mm2) was utilized. The MR vendor-provided a commercially available DL model which was applied with 75% noise reduction settings to the same subject DTI sequences at different spatial resolutions. We compared DTI tract metrics from both DL-reconstructed scans and non-denoised scans for the femur and tibia at each spatial resolution. Differences were evaluated using Wilcoxon-signed ranked test and Bland–Altman plots. When comparing DL versus non-denoised diffusion metrics in femur and tibia using the 2 mm × 2 mm × 3 mm voxel dimension, there were no significant differences between tract count (p = 0.1, p = 0.14) tract volume (p = 0.1, p = 0.29) or tibial tract length (p = 0.16); femur tract length exhibited a significant difference (p < 0.01). All diffusion metrics (tract count, volume, length, and fractional anisotropy (FA)) derived from the DL-reconstructed scans, were significantly different from the non-denoised scan DTI metrics in both the femur and tibial physes using the 2 mm3 voxel size (p < 0.001). DL reconstruction resulted in a significant decrease in femorotibial FA for both voxel dimensions (p < 0.01). Leveraging denoising algorithms could address the drawbacks of lower signal-to-noise ratios (SNRs) associated with smaller voxel volumes and capitalize on their better spatial resolutions, allowing for more accurate quantification of diffusion metrics. Full article
(This article belongs to the Topic AI in Medical Imaging and Image Processing)
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17 pages, 2365 KiB  
Article
Characterization of CD34+ Cells from Patients with Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS) Using a t-Distributed Stochastic Neighbor Embedding (t-SNE) Protocol
by Cathrin Nollmann, Wiebke Moskorz, Christian Wimmenauer, Paul S. Jäger, Ron P. Cadeddu, Jörg Timm, Thomas Heinzel and Rainer Haas
Cancers 2024, 16(7), 1320; https://doi.org/10.3390/cancers16071320 - 28 Mar 2024
Viewed by 494
Abstract
Using multi-color flow cytometry analysis, we studied the immunophenotypical differences between leukemic cells from patients with AML/MDS and hematopoietic stem and progenitor cells (HSPCs) from patients in complete remission (CR) following their successful treatment. The panel of markers included CD34, CD38, CD45RA, CD123 [...] Read more.
Using multi-color flow cytometry analysis, we studied the immunophenotypical differences between leukemic cells from patients with AML/MDS and hematopoietic stem and progenitor cells (HSPCs) from patients in complete remission (CR) following their successful treatment. The panel of markers included CD34, CD38, CD45RA, CD123 as representatives for a hierarchical hematopoietic stem and progenitor cell (HSPC) classification as well as programmed death ligand 1 (PD-L1). Rather than restricting the evaluation on a 2- or 3-dimensional analysis, we applied a t-distributed stochastic neighbor embedding (t-SNE) approach to obtain deeper insight and segregation between leukemic cells and normal HPSCs. For that purpose, we created a t-SNE map, which resulted in the visualization of 27 cell clusters based on their similarity concerning the composition and intensity of antigen expression. Two of these clusters were “leukemia-related” containing a great proportion of CD34+/CD38 hematopoietic stem cells (HSCs) or CD34+ cells with a strong co-expression of CD45RA/CD123, respectively. CD34+ cells within the latter cluster were also highly positive for PD-L1 reflecting their immunosuppressive capacity. Beyond this proof of principle study, the inclusion of additional markers will be helpful to refine the differentiation between normal HSPCs and leukemic cells, particularly in the context of minimal disease detection and antigen-targeted therapeutic interventions. Furthermore, we suggest a protocol for the assignment of new cell ensembles in quantitative terms, via a numerical value, the Pearson coefficient, based on a similarity comparison of the t-SNE pattern with a reference. Full article
(This article belongs to the Topic AI in Medical Imaging and Image Processing)
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12 pages, 2975 KiB  
Article
Validation of Left Atrial Volume Correction for Single Plane Method on Four-Chamber Cine Cardiac MRI
by Hosamadin Assadi, Nicholas Sawh, Ciara Bailey, Gareth Matthews, Rui Li, Ciaran Grafton-Clarke, Zia Mehmood, Bahman Kasmai, Peter P. Swoboda, Andrew J. Swift, Rob J. van der Geest and Pankaj Garg
Tomography 2024, 10(4), 459-470; https://doi.org/10.3390/tomography10040035 - 25 Mar 2024
Viewed by 543
Abstract
Background: Left atrial (LA) assessment is an important marker of adverse cardiovascular outcomes. Cardiovascular magnetic resonance (CMR) accurately quantifies LA volume and function based on biplane long-axis imaging. We aimed to validate single-plane-derived LA indices against the biplane method to simplify the post-processing [...] Read more.
Background: Left atrial (LA) assessment is an important marker of adverse cardiovascular outcomes. Cardiovascular magnetic resonance (CMR) accurately quantifies LA volume and function based on biplane long-axis imaging. We aimed to validate single-plane-derived LA indices against the biplane method to simplify the post-processing of cine CMR. Methods: In this study, 100 patients from Leeds Teaching Hospitals were used as the derivation cohort. Bias correction for the single plane method was applied and subsequently validated in 79 subjects. Results: There were significant differences between the biplane and single plane mean LA maximum and minimum volumes and LA ejection fraction (EF) (all p < 0.01). After correcting for biases in the validation cohort, significant correlations in all LA indices were observed (0.89 to 0.98). The area under the curve (AUC) for the single plane to predict biplane cutoffs of LA maximum volume ≥ 112 mL was 0.97, LA minimum volume ≥ 44 mL was 0.99, LA stroke volume (SV) ≤ 21 mL was 1, and LA EF ≤ 46% was 1, (all p < 0.001). Conclusions: LA volumetric and functional assessment by the single plane method has a systematic bias compared to the biplane method. After bias correction, single plane LA volume and function are comparable to the biplane method. Full article
(This article belongs to the Topic AI in Medical Imaging and Image Processing)
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14 pages, 1931 KiB  
Article
Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning
by Jason Uwaeze, Ponnada A. Narayana, Arash Kamali, Vladimir Braverman, Michael A. Jacobs and Alireza Akhbardeh
Diagnostics 2024, 14(6), 632; https://doi.org/10.3390/diagnostics14060632 - 16 Mar 2024
Viewed by 625
Abstract
Background: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration [...] Read more.
Background: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs. Current state-of-the-art methods for identifying active lesions in brain MRI without GBCA administration utilize data-intensive deep learning methods. Objective: To implement nonlinear dimensionality reduction (NLDR) methods, locally linear embedding (LLE) and isometric feature mapping (Isomap), which are less data-intensive, for automatically identifying active lesions on brain MRI in MS patients, without the administration of contrast agents. Materials and Methods: Fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images were included in the multiparametric MRI dataset used in this study. Subtracted pre- and post-contrast T1-weighted images were labeled by experts as active lesions (ground truth). Unsupervised methods, LLE and Isomap, were used to reconstruct multiparametric brain MR images into a single embedded image. Active lesions were identified on the embedded images and compared with ground truth lesions. The performance of NLDR methods was evaluated by calculating the Dice similarity (DS) index between the observed and identified active lesions in embedded images. Results: LLE and Isomap, were applied to 40 MS patients, achieving median DS scores of 0.74 ± 0.1 and 0.78 ± 0.09, respectively, outperforming current state-of-the-art methods. Conclusions: NLDR methods, Isomap and LLE, are viable options for the identification of active MS lesions on non-contrast images, and potentially could be used as a clinical decision tool. Full article
(This article belongs to the Topic AI in Medical Imaging and Image Processing)
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13 pages, 3128 KiB  
Article
Impact of AI-Based Post-Processing on Image Quality of Non-Contrast Computed Tomography of the Chest and Abdomen
by Marcel A. Drews, Aydin Demircioğlu, Julia Neuhoff, Johannes Haubold, Sebastian Zensen, Marcel K. Opitz, Michael Forsting, Kai Nassenstein and Denise Bos
Diagnostics 2024, 14(6), 612; https://doi.org/10.3390/diagnostics14060612 - 13 Mar 2024
Viewed by 691
Abstract
Non-contrast computed tomography (CT) is commonly used for the evaluation of various pathologies including pulmonary infections or urolithiasis but, especially in low-dose protocols, image quality is reduced. To improve this, deep learning-based post-processing approaches are being developed. Therefore, we aimed to compare the [...] Read more.
Non-contrast computed tomography (CT) is commonly used for the evaluation of various pathologies including pulmonary infections or urolithiasis but, especially in low-dose protocols, image quality is reduced. To improve this, deep learning-based post-processing approaches are being developed. Therefore, we aimed to compare the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast chest and low-dose abdominal CTs. In this retrospective study, non-contrast chest CTs of patients suspected of COVID-19 pneumonia and low-dose abdominal CTs suspected of urolithiasis were analysed. All images were reconstructed using filtered back-projection (FBP) and were post-processed using an artificial intelligence (AI)-based commercial software (PixelShine (PS)). Additional iterative reconstruction (IR) was performed for abdominal CTs. Objective and subjective image quality were evaluated. AI-based post-processing led to an overall significant noise reduction independent of the protocol (chest or abdomen) while maintaining image information (max. difference in SNR 2.59 ± 2.9 and CNR 15.92 ± 8.9, p < 0.001). Post-processing of FBP-reconstructed abdominal images was even superior to IR alone (max. difference in SNR 0.76 ± 0.5, p ≤ 0.001). Subjective assessments verified these results, partly suggesting benefits, especially in soft-tissue imaging (p < 0.001). All in all, the deep learning-based denoising—which was non-inferior to IR—offers an opportunity for image quality improvement especially in institutions using older scanners without IR availability. Further studies are necessary to evaluate potential effects on dose reduction benefits. Full article
(This article belongs to the Topic AI in Medical Imaging and Image Processing)
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10 pages, 1275 KiB  
Article
Performance of ECG-Derived Digital Biomarker for Screening Coronary Occlusion in Resuscitated Out-of-Hospital Cardiac Arrest Patients: A Comparative Study between Artificial Intelligence and a Group of Experts
by Min Ji Park, Yoo Jin Choi, Moonki Shim, Youngjin Cho, Jiesuck Park, Jina Choi, Joonghee Kim, Eunkyoung Lee and Seo-Yoon Kim
J. Clin. Med. 2024, 13(5), 1354; https://doi.org/10.3390/jcm13051354 - 27 Feb 2024
Viewed by 545
Abstract
Acute coronary syndrome is a significant part of cardiac etiology contributing to out-of-hospital cardiac arrest (OHCA), and immediate coronary angiography has been proposed to improve survival. This study evaluated the effectiveness of an AI algorithm in diagnosing near-total or total occlusion of coronary [...] Read more.
Acute coronary syndrome is a significant part of cardiac etiology contributing to out-of-hospital cardiac arrest (OHCA), and immediate coronary angiography has been proposed to improve survival. This study evaluated the effectiveness of an AI algorithm in diagnosing near-total or total occlusion of coronary arteries in OHCA patients who regained spontaneous circulation. Conducted from 1 July 2019 to 30 June 2022 at a tertiary university hospital emergency department, it involved 82 OHCA patients, with 58 qualifying after exclusions. The AI used was the Quantitative ECG (QCG™) system, which provides a STEMI diagnostic score ranging from 0 to 100. The QCG score’s diagnostic performance was compared to assessments by two emergency physicians and three cardiologists. Among the patients, coronary occlusion was identified in 24. The QCG score showed a significant difference between occlusion and non-occlusion groups, with the former scoring higher. The QCG biomarker had an area under the curve (AUC) of 0.770, outperforming the expert group’s AUC of 0.676. It demonstrated 70.8% sensitivity and 79.4% specificity. These findings suggest that the AI-based ECG biomarker could predict coronary occlusion in resuscitated OHCA patients, and it was non-inferior to the consensus of the expert group. Full article
(This article belongs to the Topic AI in Medical Imaging and Image Processing)
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