Topic Editors

Department of Radiology, Jagiellonian University Medical College, 19 Kopernika Street, 31-501 Cracow, Poland
Department of Radiology, Jagiellonian University Medical College, 3 Botaniczna St., 31-503 Kraków, Poland
Institute of Electronics, Lodz University of Technology, Wolczanska 211/215, 90-924 Łódź, Poland
Prof. Dr. Adam Piórkowski
Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Cracow, Poland

Advances in Musculoskeletal Imaging and Their Applications, 2nd Edition

Abstract submission deadline
30 September 2024
Manuscript submission deadline
31 December 2024
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Topic Information

Dear Colleagues,

Radiographic acquisition techniques have undergone tremendous improvements since their invention. Image resolution has greatly increased and the reduction in the dose of X-ray radiation required for its creation has been achieved. The increased amount of imaging data does not necessarily mean that more medical information is accessible to the reader. Some (but often important) information is hidden from the radiologist. This is especially true for radiographic techniques.

The purpose of advanced image-analysis systems is to extract occulted data to improve the objectivity of diagnosis for a given case. The treatment of clinical problems with information obtained using advanced image analyses has increased. In musculoskeletal radiology, proven associations exist between bone scan analyses, patient health and metabolic status. Moreover, the processes of bone maturation, bone healing, bone demineralization and deformation due to overuse can be extensively analyzed with the use of CR, CT and MRI. Advanced methods significantly improve differentiation and hence the diagnostic process of medication for different lesions including neoplasms of the bone.

Papers investigating the application of both classical image processing and artificial intelligence (AI) methods in the analysis and extraction of diagnostically useful data from medical images are welcomed in this Special Issue. Such methods assist in the investigation of the shape and geometry of, for example, bone tissue or its fragments. Other AI approaches allow for the automatic detection and segmentation of tissues or organs and the assessment of their pathologies. For this purpose, the achievements of radiomics are particularly useful, including image-texture analyses. Various machine learning methods are also useful for exploring medical imaging data and are widely used in medical diagnostic support systems. Deep learning algorithms play a particularly important role in this respect. Recently, dynamic developments have been achieved in the field of deep learning algorithms, and their effectiveness has been confirmed in numerous applications of medical image analyses of various modalities.

Prof. Dr. Rafał Obuchowicz
Dr. Monika Ostrogórska
Prof. Dr. Michał Strzelecki
Prof. Dr. Adam Piórkowski
Topic Editors

Keywords

  • bone imaging
  • musculoskeletal imaging
  • image processing
  • image analysis
  • segmentation
  • textural analysis
  • machine learning

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400 Submit
BioMed
biomed
- - 2021 27 Days CHF 1000 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
Journal of Imaging
jimaging
3.2 4.4 2015 21.7 Days CHF 1800 Submit

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Published Papers (2 papers)

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13 pages, 6554 KiB  
Article
Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image Quality
by Wojciech Kazimierczak, Kamila Kędziora, Joanna Janiszewska-Olszowska, Natalia Kazimierczak and Zbigniew Serafin
J. Clin. Med. 2024, 13(5), 1502; https://doi.org/10.3390/jcm13051502 - 05 Mar 2024
Viewed by 625
Abstract
Background: Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity. This [...] Read more.
Background: Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity. This study compares standard and deep learning-enhanced CBCT images for image quality in detecting osteoarthritis-related degeneration in TMJs (temporomandibular joints). This study analyzed CBCT images of patients with suspected temporomandibular joint degenerative joint disease (TMJ DJD). Methods: The DLM reconstructions were performed with ClariCT.AI software. Image quality was evaluated objectively via CNR in target areas and subjectively by two experts using a five-point scale. Both readers also assessed TMJ DJD lesions. The study involved 50 patients with a mean age of 28.29 years. Results: Objective analysis revealed a significantly better image quality in DLM reconstructions (CNR levels; p < 0.001). Subjective assessment showed high inter-reader agreement (κ = 0.805) but no significant difference in image quality between the reconstruction types (p = 0.055). Lesion counts were not significantly correlated with the reconstruction type (p > 0.05). Conclusions: The analyzed DLM reconstruction notably enhanced the objective image quality in TMJ CBCT images but did not significantly alter the subjective quality or DJD lesion diagnosis. However, the readers favored DLM images, indicating the potential for better TMD diagnosis with CBCT, meriting more study. Full article
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13 pages, 2360 KiB  
Article
Correlation between Subchondral Insufficiency Fracture of the Knee and Osteoarthritis Progression in Patients with Medial Meniscus Posterior Root Tear
by Bing-Kuan Chen, Yi-Cheng Lin, Yu-Hsin Liu, Pei-Wei Weng, Kuan-Hao Chen, Chang-Jung Chiang and Chin-Chean Wong
Diagnostics 2023, 13(23), 3532; https://doi.org/10.3390/diagnostics13233532 - 26 Nov 2023
Cited by 1 | Viewed by 949
Abstract
A medial meniscus posterior root tear (MMPRT) contributes to knee joint degeneration. Arthroscopic transtibial pullout repair (ATPR) may restore biomechanical integrity for load transmission. However, degeneration persists after ATPR in certain patients, particularly those with preoperative subchondral insufficiency fracture of the knee (SIFK). [...] Read more.
A medial meniscus posterior root tear (MMPRT) contributes to knee joint degeneration. Arthroscopic transtibial pullout repair (ATPR) may restore biomechanical integrity for load transmission. However, degeneration persists after ATPR in certain patients, particularly those with preoperative subchondral insufficiency fracture of the knee (SIFK). We explored the relationship between preoperative SIFK and osteoarthritis (OA) progression in retrospectively enrolled patients who were diagnosed as having an MMPRT and had received ATPR within a single institute. Based on their preoperative magnetic resonance imaging (MRI), these patients were then categorized into SIFK and non-SIFK groups. OA progression was evaluated by determining Kellgren–Lawrence (KL) grade changes and preoperative and postoperative median joint widths. SIFK characteristics were quantified using Image J (Version 1.52a). Both groups exhibited significant post-ATPR changes in medial knee joint widths. The SIFK group demonstrated significant KL grade changes (p < 0.0001). A larger SIFK size in the tibia and a greater lesion-to-tibia length ratio in the coronal view were positively correlated with more significant KL grade changes (p = 0.008 and 0.002, respectively). Thus, preoperative SIFK in patients with an MMPRT was associated with knee OA progression. Moreover, a positive correlation was observed between SIFK lesion characteristics and knee OA progression. Full article
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