Advanced Techniques and Applications in Magnetic Resonance Imaging

Dear Colleagues,

Magnetic resonance imaging (MRI) is an established imaging modality for the multiparametric evaluation of tissues in many pathologies, ranging from degenerative and inflammatory illnesses to malignancy. Traditionally, MRI utilizes strong magnetic fields and radio waves to generate intricate images of the interior of bodily structures. Nonetheless, the analysis of these scans can be laborious and necessitates a considerable degree of knowledge. In recent years, the ongoing technical advancement of equipment has resulted in the creation of sequences that deliver both anatomical and functional metabolic information.

The manual interpretation and processing of medical images is difficult and susceptible to errors, leading to the oversight of minor lesions that can occasionally result in misdiagnosis. Developing advanced signal processing techniques for various low-level (such as image reconstruction, contrast enhancement, and image segmentation) and high-level applications (including interpretation, classification, and assessment of image findings in diagnoses, as well as the planning, monitoring, and evaluation of treatment) in medical imaging is essential for precise diagnosis and tailored treatment. These applications require innovative, sophisticated methodologies in fields including, but not limited to, computer vision, artificial intelligence, machine learning, pattern recognition, and evolutionary algorithms and optimization. Advancements in artificial intelligence (AI), particularly deep learning (DL), have enhanced the interpretation of magnetic resonance imaging (MRI) data, allowing for AI-assisted medical picture diagnostics and prognoses. Nevertheless, the majority of deep learning models are regarded as "black boxes." There exists a necessity to elucidate deep learning models so that domain experts can have confidence in these high-performance models.

AI can enhance MRI processes by automating multiple phases of the imaging process, including planning, data acquisition, reconstruction, parameter mapping, and segmentation, thus improving total MRI efficiency. Artificial intelligence can surmount difficulties typically faced in MRI, like enhancing patient motion tolerance. It can also improve the quality of MRI scans to more correctly detect disorders and inform subsequent medical interventions.

This Special Issue will publish high-quality, original research papers, in the following overlapping fields:

* Medical imaging modalities (MRI, MRSI, DTI, fMRI, MR spectroscopy, etc.)

* New algorithms, models and applications of advanced signal processing methods using the following:

    * Computer vision;

    * Artificial intelligence/machine learning/pattern recognition (e.g., Explainable AI (XAI), GAN, CNN, deep learning, perceptron, Bayesian network, support vector machine, fuzzy logic, etc.);

    * K-space optimization;

    * Brain tumor imaging;

    * Gliomas;

    * Treatment-related changes;

    * Advanced MR imaging and perfusion;

    * MR spectroscopy;

    * Motion correction;

    * MR image-denoising algorithms.

* Smart and interactive medical systems.

* Global optimization and evolutionary algorithms.

* Metaheuristics and swarm intelligence.

* Fractional signal processing.

* Medical security.

Dr. Krzysztof Malczewski
Dr. Ryszard Kozera
Guest Editors

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.

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. Applied Sciences 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 2400 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.

• MRI
• fMRI
• DTI
• magnetic resonance spectroscopy
• nuclear magnetic resonance
• medical physics
• nuclear
• AI