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Intelligent MRI Sensing: Novel Acquisition and AI-Powered Diagnosis
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Intelligent MRI Sensing: Novel Acquisition and AI-Powered Diagnosis

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Sensing and Imaging".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2228

Editor

Dr. Yashwant Kurmi

E-Mail Website
Guest Editor
Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
Interests: MRI; AI &ML

Special Issue Information

Dear Colleagues,

Recent advances in magnetic resonance imaging (MRI) have opened up new opportunities for improving image acquisition, reconstruction, enhancement, and diagnostic accuracy. With the integration of intelligent sensing, molecular imaging, and artificial intelligence, MRI now enables more precise visualization, faster acquisition, and enhanced analysis for both clinical and research applications. Challenges such as low spatial and through-plane resolution, noise, anisotropic sampling, and long scan times are increasingly being addressed through AI-powered methods, super-resolution techniques, and physics-informed reconstruction frameworks.

This Special Issue will therefore bring together original research and review articles on recent developments, novel technologies, practical solutions, applications, and emerging challenges in intelligent MRI sensing, AI-driven diagnosis, and advanced reconstruction techniques.

We welcome original research articles, review papers, and methodological studies that advance the state of the art in intelligent MRI sensing and AI-powered diagnosis, supporting the development of next-generation MRI technologies.

Dr. Yashwant Kurmi
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • intelligent and AI-assisted MRI acquisition and sensing
  • molecular and functional MRI techniques and imaging biomarkers
  • MRI reconstruction, slice profile correction, and reformatting methods
  • through-plane and isotropic resolution enhancement
  • super-resolution MRI using conventional, learning-based, and GAN-based methods
  • MRI denoising using classical, variational, and deep learning approaches
  • CNN-, transformer-, diffusion-, and hybrid AI models for MRI
  • transfer learning, domain adaptation, and robustness across scanners and protocols
  • AI-powered MRI visualization, interpretation, and diagnosis
  • clinical validation and translational MRI applications

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Published Papers (2 papers)

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Research

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21 pages, 4006 KB  
Article
Interpretable 2D Deep Learning for Alzheimer’s Detection from sMRI: A Lightweight Residual CNN Approach with Comprehensive Preprocessing and Stratified Data Partitioning
by Vyshnavi Ramineni, Jun-Hyung Kim and Goo-Rak Kwon
Sensors 2026, 26(13), 4100; https://doi.org/10.3390/s26134100 - 27 Jun 2026
Viewed by 860
Abstract
Neuroimaging is a promising modality for early AD detection, facilitating timely clinical intervention. This study proposes an enhanced deep learning framework that extracts critical AD biomarkers from structural MRI (sMRI) data acquired from the ADNI. Our novel CNN architecture integrates conventional convolutional layers [...] Read more.
Neuroimaging is a promising modality for early AD detection, facilitating timely clinical intervention. This study proposes an enhanced deep learning framework that extracts critical AD biomarkers from structural MRI (sMRI) data acquired from the ADNI. Our novel CNN architecture integrates conventional convolutional layers with residual and skip connections for efficient feature extraction, achieving substantially lower computational cost than standard deep architectures such as VGG-16 (138 M), while remaining more parameter-intensive than highly compact architectures such as MobileNet and EfficientNet, which are designed explicitly for resource-constrained deployment. A comprehensive preprocessing pipeline converts 3D MRI scans into 2D slices through quality control (discarding slices with mean intensity < 5% of the maximum), bilinear resizing to 96 × 96 pixels, normalization using training-set statistics, and data augmentation. Stratified, subject-level data partitioning combined with robust statistical validation via bootstrapping demonstrates superior multiclass classification performance across AD, early and late MCI, and cognitively normal groups compared to state-of-the-art methods. Additionally, Grad-CAM-based interpretability maps were generated to highlight disease-relevant brain regions, confirming consistent activation around the hippocampus and temporal lobe. Full article
(This article belongs to the Special Issue Intelligent MRI Sensing: Novel Acquisition and AI-Powered Diagnosis)
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Review

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29 pages, 2894 KB  
Review
Enhancing Cerebral Blood Flow Quantification: A Comprehensive Review of Denoising, Artifact Correction, and Simulation in Arterial Spin Labeling MRI
by Shyna A, Jini Raju, Ansamma John, Chandrasekharan Kesavadas, Aditya Ajith, Manu J. Pillai, Shameem Ansar and Ginu Rajan
Sensors 2026, 26(16), 5202; https://doi.org/10.3390/s26165202 - 17 Aug 2026
Viewed by 332
Abstract
Arterial Spin Labeling (ASL) Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique used to quantify cerebral blood flow (CBF) by using magnetically labeled arterial blood water as an endogenous tracer. Although ASL eliminates the need for exogenous contrast agents, its widespread clinical [...] Read more.
Arterial Spin Labeling (ASL) Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique used to quantify cerebral blood flow (CBF) by using magnetically labeled arterial blood water as an endogenous tracer. Although ASL eliminates the need for exogenous contrast agents, its widespread clinical use is limited by several challenges, including low Signal-to-Noise Ratio (SNR), susceptibility to motion, and various imaging artifacts. To address these limitations, both traditional denoising techniques and Machine Learning (ML)/Deep Learning (DL)-based approaches have been developed to improve the reliability of ASL by reducing noise, correcting artifacts, and enhancing image quality. In addition, the generation of simulated ASL datasets has become an important strategy for training and validating novel methods when sufficient clinical data are unavailable. This review examines conventional image-processing techniques together with modern machine learning and deep learning approaches developed to improve ASL image quality through denoising and enhancement. It also discusses the major artifacts that affect ASL acquisition and summarizes the simulation methodologies used for the development and evaluation of new algorithms. Full article
(This article belongs to the Special Issue Intelligent MRI Sensing: Novel Acquisition and AI-Powered Diagnosis)
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