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Biomedicines
Special Issues
Recent Advances in Molecular Neuroimaging
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Recent Advances in Molecular Neuroimaging

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1214

Special Issue Editor

Dr. Wenliang Fan

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Guest Editor
1. Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
2. Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan 430022, China
3. Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
Interests: neuroimaging techniques; neurological disorders; artificial intelligence; clinical decision support

Special Issue Information

Dear Colleagues,

The field of molecular neuroimaging has experienced significant advancements, with innovative imaging techniques playing a crucial role in understanding the nervous system and beyond. This Special Issue, “Recent Advances in Molecular Neuroimaging”, aims to showcase the latest developments in molecular imaging methods and technologies, as well as their clinical applications. We welcome submissions that explore the use of various imaging modalities, such as PET, SPECT, MRI, fMRI, and optical imaging, in the diagnosis, monitoring, and treatment of a wide range of diseases. Contributions that highlight the integration of artificial intelligence and machine learning to enhance image analysis and provide clinical decision support are particularly encouraged. Additionally, we are interested in studies that address the challenges and future directions in molecular neuroimaging, including the development of novel imaging probes and the translation of research findings into clinical practice. By covering a broad spectrum of applications, this Special Issue seeks to provide a comprehensive overview of the current state of molecular neuroimaging and its potential to transform medical diagnosis and treatment.

Dr. Wenliang Fan
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. Biomedicines 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 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

  • molecular neuroimaging
  • imaging techniques
  • clinical applications
  • imaging probes
  • personalized medicine
  • diagnostic imaging
  • therapeutic monitoring
  • clinical decision support

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Published Papers (1 paper)

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Research

11 pages, 1943 KB  
Article
Diagnostic Accuracy of DaTQUANT® Versus BasGanV2™ for 123I-Ioflupane Brain SPECT: A Machine Learning-Based Differentiation of Parkinson’s Disease and Essential Tremor
by Barbara Palumbo, Luca Filippi, Andrea Marongiu, Francesco Bianconi, Mario Luca Fravolini, Roberta Danieli, Viviana Frantellizzi, Giuseppe De Vincentis, Angela Spanu and Susanna Nuvoli
Biomedicines 2025, 13(10), 2367; https://doi.org/10.3390/biomedicines13102367 - 27 Sep 2025
Viewed by 966
Abstract
Background: Differentiating Parkinson’s disease (PD) from essential tremor (ET) is often challenging, especially in early or atypical cases. Dopamine transporter (DAT) single-photon emission computed tomography (SPECT) with 123I-Ioflupane supports diagnosis, and semi-quantitative tools such as DaTQUANT® and BasGanV2™ provide objective [...] Read more.
Background: Differentiating Parkinson’s disease (PD) from essential tremor (ET) is often challenging, especially in early or atypical cases. Dopamine transporter (DAT) single-photon emission computed tomography (SPECT) with 123I-Ioflupane supports diagnosis, and semi-quantitative tools such as DaTQUANT® and BasGanV2™ provide objective measures. This study compared their diagnostic performance when integrated with supervised machine learning. Methods: We retrospectively analysed 123I-Ioflupane SPECT scans from 169 patients (133 PD, 36 ET). Semi-quantitative analysis was performed using DaTQUANT® v2.0 and BasGanV2™ v.2. Classification tree (ClT), k-nearest neighbour (k-NN), and support vector machine (SVM) models were trained and validated with stratified shuffle split (250 iterations). Diagnostic accuracy was compared between the two software packages. Results: All classifiers reliably distinguished PD from ET. DaTQUANT® consistently achieved higher accuracy than BasGanV2™: 93.8%, 93.2%, and 94.5% for ClT, k-NN, and SVM, respectively, versus 90.9%, 91.7%, and 91.9% for BasGanV2™ (p < 0.001). Sensitivity and specificity were also consistently higher for DaTQUANT® than BasGanV2. Class imbalance (PD > ET) was addressed using Synthetic Minority Over-sampling Technique (SMOTE). Conclusions: Machine learning analysis of 123I-Ioflupane SPECT enhances differentiation between PD and ET. DaTQUANT® outperformed BasGanV2™, suggesting greater suitability for AI-driven decision support. These findings support the integration of semi-quantitative and AI-based approaches into clinical workflows and highlight the need for harmonised methodologies in movement disorder imaging. Full article
(This article belongs to the Special Issue Recent Advances in Molecular Neuroimaging)
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