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Emerging MRI Techniques for Enhanced Disease Diagnosis and Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 884

Special Issue Editor


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Guest Editor
Department of Diagnostic, Molecular And Interventional Radiology, BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
Interests: magnetic resonance imaging (MRI); RF coil; MRI acquisition techniques

Special Issue Information

Dear Colleagues,

Magnetic Resonance Imaging (MRI) continues to evolve as one of the most powerful and versatile diagnostic tools in modern medicine. This Special Issue will focus on recent advances in MRI hardware and acquisition techniques that are transforming disease diagnosis and monitoring. Topics of interest include novel RF coil designs, advanced gradient systems, ultra-high-field and ultra-low-field MRI technologies, and material-based innovations that enhance imaging performance. We also welcome contributions on new pulse sequences, reconstruction algorithms, and multi-parametric imaging methods that improve sensitivity, specificity, and quantification across a range of clinical applications. By bringing together innovations in engineering, physics, and computational imaging, this issue aims to highlight interdisciplinary efforts that push the boundaries of MRI and open new pathways for early detection, longitudinal monitoring, and precision diagnosis of disease.

Submissions are encouraged from both academic and industry researchers working at the intersection of sensor development, imaging hardware, sequence design, and clinical translation.

Dr. Akbar Alipour
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 100 words) can be sent to the Editorial Office for announcement on this website.

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Keywords

  • magnetic resonance imaging (MRI)
  • RF coil
  • disease biomarkers
  • MRI hardware innovations
  • ultra-low-field MRI
  • ultra-high-field MRI
  • clinical imaging applications
  • functional and structural imaging
  • quantitative MRI
  • MRI acquisition

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Published Papers (1 paper)

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Research

26 pages, 10666 KB  
Article
FALS-YOLO: An Efficient and Lightweight Method for Automatic Brain Tumor Detection and Segmentation
by Liyan Sun, Linxuan Zheng and Yi Xin
Sensors 2025, 25(19), 5993; https://doi.org/10.3390/s25195993 - 28 Sep 2025
Viewed by 770
Abstract
Brain tumors are highly malignant diseases that severely threaten the nervous system and patients’ lives. MRI is a core technology for brain tumor diagnosis and treatment due to its high resolution and non-invasiveness. However, existing YOLO-based models face challenges in brain tumor MRI [...] Read more.
Brain tumors are highly malignant diseases that severely threaten the nervous system and patients’ lives. MRI is a core technology for brain tumor diagnosis and treatment due to its high resolution and non-invasiveness. However, existing YOLO-based models face challenges in brain tumor MRI image detection and segmentation, such as insufficient multi-scale feature extraction and high computational resource consumption. This paper proposes an improved lightweight brain tumor detection and instance segmentation model named FALS-YOLO, based on YOLOv8n-Seg and integrating three key modules: FLRDown, AdaSimAM, and LSCSHN. FLRDown enhances multi-scale tumor perception, AdaSimAM suppresses noise and improves feature fusion, and LSCSHN achieves high-precision segmentation with reduced parameters and computational burden. Experiments on the tumor-otak dataset show that FALS-YOLO achieves Precision (B) of 0.892, Recall (B) of 0.858, mAP@0.5 (B) of 0.912 in detection, and Precision (M) of 0.899, Recall (M) of 0.863, mAP@0.5 (M) of 0.917 in segmentation, outperforming YOLOv5n-Seg, YOLOv8n-Seg, YOLOv9s-Seg, YOLOv10n-Seg and YOLOv11n-Seg. Compared with YOLOv8n-Seg, FALS-YOLO reduces parameters by 31.95%, computational amount by 20.00%, and model size by 32.31%. It provides an efficient, accurate and practical solution for the automatic detection and instance segmentation of brain tumors in resource-limited environments. Full article
(This article belongs to the Special Issue Emerging MRI Techniques for Enhanced Disease Diagnosis and Monitoring)
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