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Bioengineering
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Novel MRI Techniques and Biomedical Image Processing: Second Edition
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Novel MRI Techniques and Biomedical Image Processing: Second Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1011

Special Issue Editor

Dr. Dilbag Singh

E-Mail
Guest Editor
1. Department of Radiology, Center for Biomedical Imaging, New York University, New York, NY, USA
2. Grossman School of Medicine, New York University, New York, NY, USA
Interests: deep learning; fast MRI reconstruction; quantitative MRI; T1ρ mapping; medical image analysis; biomedical image processing; computer‑vision in radiology; deep transfer learning; vision transformers

Special Issue Information

Dear Colleagues,

Since the first picture of Magnetic Resonance Imaging published in 1973 by Lauterbur, 50 years have passed witnessing numerous important technical breakthroughs made possible by pioneers and researchers in this field. MRI has been widely used in various research and clinical applications, and it is continuing to advance owing to the efforts of researchers and clinicians. At the same time, more advanced analytic tools have become available and been adopted by research communities to help us better understand and interpret the ever-increasing quantity of image data.

This Special Issue on novel MRI techniques and biomedical image processing welcomes original research papers and comprehensive reviews with a focus on two important aspects in biomedical imaging: 1) MR image generation and 2) image processing. The image generation category includes, but is not limited to, novel MRI contrast mechanisms and acquisition and reconstruction methods, while the image processing category includes processing and understanding image data obtained from a wide range of imaging modalities, such as CT, nuclear medicine, and optical imaging, among others. One particular area of interest is the application of machine (deep) learning-based methods in MR image generation and biomedical image processing more broadly.

Dr. Dilbag Singh
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • MRI
  • image generation
  • image processing
  • image acquisition
  • image reconstruction
  • machine learning
  • deep learning

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Published Papers (1 paper)

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Research

23 pages, 3629 KB  
Article
An Explainable Plane-Wise ConvNet Approach for Detecting Femoral Head Osteonecrosis from Magnetic Resonance Images
by Şükrü Demir, Mehmet Vural, Buğra Can, Fatih Demir and Abdulkadir Sengur
Bioengineering 2026, 13(5), 529; https://doi.org/10.3390/bioengineering13050529 - 30 Apr 2026
Viewed by 627
Abstract
Background/Objectives: Osteonecrosis of the femoral head (ONFH) is difficult to diagnose, particularly in the early stages, because radiological findings may be subtle. Delayed or inaccurate staging may increase the risk of femoral head collapse and functional loss. Although magnetic resonance imaging is highly [...] Read more.
Background/Objectives: Osteonecrosis of the femoral head (ONFH) is difficult to diagnose, particularly in the early stages, because radiological findings may be subtle. Delayed or inaccurate staging may increase the risk of femoral head collapse and functional loss. Although magnetic resonance imaging is highly sensitive for early-stage lesion detection, interpretation may vary depending on observer experience. Therefore, reliable and explainable automated decision support approaches are needed. Methods: In this study, a deep learning-based approach was proposed to classify ONFH into early and late stages according to the Ficat–Arlet staging system. Stage I–II cases were defined as early-stage, whereas Stage III–IV cases were defined as late-stage. Axial and coronal MR images were evaluated separately to investigate plane-dependent classification performance. The images were converted into a three-channel format, resized to a common spatial resolution, normalized, and augmented during training. Feature extraction was performed using transfer learning with modern convolutional neural network architectures. ConvNeXt Tiny was used as the main classification backbone. Weighted loss was applied to reduce the effect of class imbalance, and the decision threshold was optimized on validation data to reduce missed clinically critical late-stage cases. Results: A dataset collected from the Orthopedics and Traumatology Department of Firat University Hospital was used in the experimental evaluation. The dataset was divided into training and test sets using an 80:20 split, and 10-fold cross-validation was additionally performed to assess model stability. In the hold-out test, the axial plane model achieved 94.51% accuracy, 96.80% sensitivity, 93.49% specificity, 0.9162 F1-score, and 0.981 AUC. In the coronal plane model, 92.84% accuracy, 96.13% sensitivity, 90.96% specificity, 0.9072 F1-score, and 0.988 AUC were obtained. The 10-fold cross-validation results provided a more conservative estimate of generalization performance. Conclusions: The findings indicate that deep learning-based plane-wise analysis of MR images can distinguish early- and late-stage ONFH with high performance. Grad-CAM-based visual explanations showed that the model focused mainly on clinically relevant subchondral and weight-bearing regions of the femoral head. The proposed approach may serve as an explainable decision support tool for reducing observer-dependent variability in clinical staging. Future studies should validate the method using external, multicenter datasets and paired patient-level axial–coronal images. Full article
(This article belongs to the Special Issue Novel MRI Techniques and Biomedical Image Processing: Second Edition)
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