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J. Imaging
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Translational Preclinical Imaging: Techniques, Applications and Perspectives
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Translational Preclinical Imaging: Techniques, Applications and Perspectives

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 7026

Special Issue Editors

Dr. Sara Gargiulo

E-Mail Website
Guest Editor
1. Institute of Clinical Physiology, National Research Council, Via Fiorentina 1, 53100 Siena, Italy
2. Core Research Laboratory (CRL), Istituto per lo Studio la Prevenzione e la Rete Oncologica (ISPRO), 53100 Siena, Italy
Interests: preclinical imaging; murine models of human diseases; anesthesia and animal care related to murine models and preclinical imaging; 3Rs
Dr. Sandra Albanese

E-Mail Website
Guest Editor
Institute of Biostructures and Bioimaging, National Research Council, 80145 Naples, Italy
Interests: preclinical imaging; laboratory animal welfare and manipulations; diagnostic imaging

Special Issue Information

Dear Colleagues,

Following several requests from prospective authors, the deadline of this Special Issue, “Translational Preclinical Imaging: Techniques, Applications and Perspectives”, launched by MDPI’s Journal of Imaging, has been extended to 31 March 2026.

The Guest Editors of this Special Issue, Sara Gargiulo from the Institute of Clinical Physiology and Sandra Albanese from the Institute of Biostructures and Bioimaging, the National Council of Research (Italy), would like to invite potential authors to submit their contributions.

This Special Issue aims to compile original research papers and/or systematic and narrative reviews that provide updates on current applications and translational issues in preclinical imaging research.

 It seeks to bring together a diverse and complementary set of articles highlighting the contribution of preclinical imaging in multidisciplinary translational research. The topics covered will include methodological improvement or innovation in animal studies using single- or multi-modality imaging.

Do not miss the opportunity to contribute to this Special Issue!

Preclinical imaging in animal models of human disease is progressing rapidly due to advances in instrumentation and technology capabilities and a better understanding of comparative medicine and the complex interplay between animal welfare and the quality of science. Preclinical imaging techniques comparable to clinical ones can improve the translation of findings from animal models to humans in line with the 3Rs. The validation of non-invasive preclinical imaging methodologies allows us to gain insights into the pathogenesis and progression of disease, as well as therapeutic responses, enhancing the quantity and statistical validity of information and reducing the number of animals used and experimental variability. Optimizing preclinical imaging methods and avoiding experimental biases related to animal care and use are crucial for obtaining reliable and reproducible results, thus improving the translation of preclinical imaging results to clinical application.

Do you want to contribute to our 2026 Special Issue?

We are looking forward to receiving your contributions!

Dr. Sara Gargiulo
Dr. Sandra Albanese
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 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. Journal of Imaging 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 1800 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

  • preclinical imaging
  • methodological refinement
  • multidisciplinary research
  • translational research

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

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18 pages, 5780 KB  
Article
A Generalized Deep Learning Pipeline for Stain-Invariant Ultrastructural Segmentation in Peripheral Nerves
by Vitalijs Borisovs and Guido Cavaletti
J. Imaging 2026, 12(6), 257; https://doi.org/10.3390/jimaging12060257 - 10 Jun 2026
Viewed by 88
Abstract
Automated analysis of peripheral nerve ultrastructure is bottlenecked by heterogeneous electron microscopy (EM) datasets, where varying staining protocols and resolutions create domain shifts that confound deep learning. To address this, we developed a generalized segmentation pipeline. Using a custom pre-processing workflow (CLAHE and [...] Read more.
Automated analysis of peripheral nerve ultrastructure is bottlenecked by heterogeneous electron microscopy (EM) datasets, where varying staining protocols and resolutions create domain shifts that confound deep learning. To address this, we developed a generalized segmentation pipeline. Using a custom pre-processing workflow (CLAHE and noise suppression) integrated into ZEISS Arivis Pro, we standardized inputs across three disparate domains: traditional osmium-based Palade, lanthanide-based “green” Uranyl-free method, and low-resolution Ellisman preparations. A U-Net trained on a highly constrained 15-image composite dataset achieved peak internal Intersection over Union (IoU) scores >0.95 for myelin and Schwann cells. Crucially, during open-world, zero-shot inference on an expanded independent testing cohort (N = 40), the model sustained robust Dice Similarity Coefficients of 0.854 for myelin and 0.597 for mitochondria. This demonstrates that integrating classical image standardization with deep learning effectively mitigates EM domain gaps, enabling comprehensive 3D multi-organelle reconstructions from challenging data. To ensure transparency and community utility, the pre-trained models and standardization scripts are provided in a public, open-access repository. Ultimately, this pipeline supports the transition to sustainable, non-toxic EM protocols and provides a robust pathway for unlocking historical clinical archives for automated organellomics. Full article
(This article belongs to the Special Issue Translational Preclinical Imaging: Techniques, Applications and Perspectives)
17 pages, 19745 KB  
Article
Feasibility of High-Frequency Ultrasound and Magnetic Resonance Imaging to Assess the In Ovo Development of Chicken Embryos
by Ylenia Ferrara, Cristina Terlizzi, Annachiara Sarnella, Luca Licenziato, Serena Monti and Marcello Mancini
J. Imaging 2026, 12(5), 217; https://doi.org/10.3390/jimaging12050217 - 20 May 2026
Viewed by 330
Abstract
Preclinical multimodal imaging is widely applied in small animal models for longitudinal studies of human diseases. Beyond murine systems, cost-effective and ethically sustainable models such as the chicken embryo and its chorioallantoic membrane are gaining increasing interest in accordance with the 3Rs principles. [...] Read more.
Preclinical multimodal imaging is widely applied in small animal models for longitudinal studies of human diseases. Beyond murine systems, cost-effective and ethically sustainable models such as the chicken embryo and its chorioallantoic membrane are gaining increasing interest in accordance with the 3Rs principles. This study evaluated the feasibility of using both high-frequency ultrasound and magnetic resonance imaging for the non-invasive longitudinal monitoring of chicken embryo development in ovo. Fifty fertilized eggs were incubated under controlled conditions and examined up to embryonic day 14. High-frequency ultrasound (15–71 MHz) enabled real-time imaging and quantitative assessment of superficial structures, including cranial biometry and limb growth, while magnetic resonance imaging (7T) provided high-resolution three-dimensional visualization of internal organs and extraembryonic compartments. Together, these modalities allowed the progressive identification of key anatomical structures from ED5 onward, with HFUS enabling earlier linear measurements and MRI facilitating detailed anatomical and volumetric evaluation. The integration of these techniques allowed the generation of a developmental imaging timeline and quantitative reference dataset of normal embryogenesis. This multimodal approach represents a promising strategy for in vivo developmental studies, offering a robust baseline to characterize structural alterations induced by experimental conditions. Moreover, the use of the chicken embryo model provides significant ethical and economic advantages, supporting its application in preclinical research and imaging-based studies. Full article
(This article belongs to the Special Issue Translational Preclinical Imaging: Techniques, Applications and Perspectives)
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14 pages, 2751 KB  
Article
Deep Learning and Atlas-Based MRI Segmentation Enable Longitudinal Characterization of Healthy Mouse Brain
by Edoardo Micotti, Liviu Soltuzu, Elisa Bianchi, Sebastiano La Ferla, Lorenzo Carnevale and Gianluigi Forloni
J. Imaging 2025, 11(11), 418; https://doi.org/10.3390/jimaging11110418 - 19 Nov 2025
Viewed by 1426
Abstract
We compared the results of brain magnetic resonance image (MRI) segmentation across a longitudinal dataset spanning mouse adulthood using an atlas-based approach and deep learning. Our results demonstrate that deep learning performs similarly yet faster than more established segmentation methods, even when computational [...] Read more.
We compared the results of brain magnetic resonance image (MRI) segmentation across a longitudinal dataset spanning mouse adulthood using an atlas-based approach and deep learning. Our results demonstrate that deep learning performs similarly yet faster than more established segmentation methods, even when computational resources are limited. Both methods enabled the large-scale analysis of a cohort of C57Bl6/J healthy mice, revealing sex-dependent morphological differences in the aging brain. These findings highlight the potential use of deep learning for high-throughput, longitudinal neuroimaging studies and underscore the importance of considering sex as a biological variable in preclinical brain research. Full article
(This article belongs to the Special Issue Translational Preclinical Imaging: Techniques, Applications and Perspectives)
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28 pages, 4277 KB  
Article
Preclinical Application of Computer-Aided High-Frequency Ultrasound (HFUS) Imaging: A Preliminary Report on the In Vivo Characterization of Hepatic Steatosis Progression in Mouse Models
by Sara Gargiulo, Matteo Gramanzini, Denise Bonente, Tiziana Tamborrino, Giovanni Inzalaco, Lisa Gherardini, Lorenzo Franci, Eugenio Bertelli, Virginia Barone and Mario Chiariello
J. Imaging 2025, 11(10), 369; https://doi.org/10.3390/jimaging11100369 - 17 Oct 2025
Cited by 2 | Viewed by 1299
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most common chronic liver disorders worldwide and can lead to inflammation, fibrosis, and liver cancer. To better understand the impact of an unbalanced hypercaloric diet on liver phenotype in impaired autophagy, the study [...] Read more.
Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most common chronic liver disorders worldwide and can lead to inflammation, fibrosis, and liver cancer. To better understand the impact of an unbalanced hypercaloric diet on liver phenotype in impaired autophagy, the study compared C57BL/6J wild type (WT) and MAPK15-ERK8 knockout (KO) male mice with C57BL/6J background fed for 17 weeks with “Western-type” (WD) or standard diet (SD). Liver features were monitored in vivo by high-frequency ultrasound (HFUS) using a semi-quantitative and parametric assessment of pathological changes in the parenchyma complemented by computer-aided diagnosis (CAD) methods. Liver histology was considered the reference standard. WD induced liver steatosis in both genotypes, although KO mice showed more pronounced dietary effects than WT mice. Overall, HFUS reliably detected steatosis-related parenchymal changes over time in the two mouse genotypes examined, consistent with histology. Furthermore, this study demonstrated the feasibility of extracting quantitative features from conventional B-mode ultrasound images of the liver in murine models at early clinical stages of MASLD using a computationally efficient and vendor-independent CAD method. This approach may contribute to the non-invasive characterization of genetically engineered mouse models of MASLD according to the principles of replacement, reduction, and refinement (3Rs), with interesting translational implications. Full article
(This article belongs to the Special Issue Translational Preclinical Imaging: Techniques, Applications and Perspectives)
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18 pages, 6011 KB  
Article
Comparative Analysis of Ultrasonography and MicroCT Imaging for Organ Size Evaluation in Mice
by Juan Jose Jimenez Catalan, Marina Ferrer Clotas and Juan Antonio Camara Serrano
J. Imaging 2025, 11(6), 200; https://doi.org/10.3390/jimaging11060200 - 18 Jun 2025
Viewed by 1927
Abstract
In this work, the authors compared microCT and in vivo ultrasonography in terms of accuracy and efficacy for measuring the volume of various organs in mice. Two quantification protocols were applied: ellipsoidal volume measuring maximum diameters in all three axes in both imaging [...] Read more.
In this work, the authors compared microCT and in vivo ultrasonography in terms of accuracy and efficacy for measuring the volume of various organs in mice. Two quantification protocols were applied: ellipsoidal volume measuring maximum diameters in all three axes in both imaging systems and manual delineation of organ borders in microCT studies. The results were compared with ex vivo volumes. In general, both imaging techniques and quantification protocols are accurate, but ultrasound is faster in both acquisition and analysis. The only accurate method for heart volume measurement is manual segmentation on microCT. For the ovary, none of the techniques and protocols had a positive correlation with ex vivo volume. The three-diameter method can be used for ellipsoid organs because of its rapidity, but for more irregular structures, manual segmentation is recommended, although it is time-consuming. Full article
(This article belongs to the Special Issue Translational Preclinical Imaging: Techniques, Applications and Perspectives)
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Review

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37 pages, 7419 KB  
Review
Quantitative Preclinical Imaging as a Metrological Framework: Reproducibility, Validation, and Translational Maturity
by Nicolò Lauciello, Giorgio Russo and Alessandro Stefano
J. Imaging 2026, 12(6), 242; https://doi.org/10.3390/jimaging12060242 - 29 May 2026
Viewed by 148
Abstract
Quantitative preclinical imaging enables non-invasive characterization of physiological, molecular, and functional processes providing measurable biomarkers for longitudinal and translational studies. This review systematically analyzes 60 studies published between 2015 and 2025, covering major imaging modalities including Positron emission tomography (PET), Single-Photon Emission Computed [...] Read more.
Quantitative preclinical imaging enables non-invasive characterization of physiological, molecular, and functional processes providing measurable biomarkers for longitudinal and translational studies. This review systematically analyzes 60 studies published between 2015 and 2025, covering major imaging modalities including Positron emission tomography (PET), Single-Photon Emission Computed Tomography (SPECT), Magnetic resonance imaging (MRI), Computed Tomography (CT), optical imaging, and hybrid systems across murine and zebrafish models. We examine methodological frameworks for parameter extraction, reproducibility, and validation against biological reference standards, evaluating each modality through a cross-cutting analytical framework that distinguishes technical, biological, and computational sources of quantitative variance and identifies the current metrological maturity of harmonization infrastructure across platforms. Key comparative findings indicate that variability sources can be broadly categorized into technical (instrumentation, reconstruction, calibration) and biological (physiological heterogeneity, model-specific factors), with their interaction governing overall measurement uncertainty. Emerging computational approaches, including parametric modeling and artificial intelligence–assisted pipelines, show potential in reducing variance and improving parameter stability, although they introduce additional dependencies requiring validation. Collectively, this review frames quantitative preclinical imaging as a metrological discipline, emphasizing that reproducibility, bias control, and cross-modality harmonization are critical for generating robust and translationally relevant imaging biomarkers. Full article
(This article belongs to the Special Issue Translational Preclinical Imaging: Techniques, Applications and Perspectives)
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