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J. Imaging
Special Issues
Intelligent Strategies for Medical Image Analysis
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Intelligent Strategies for Medical Image Analysis

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 37321

Special Issue Editors

Prof. Dr. Giuseppe Placidi

E-Mail Website
Guest Editor
A2VI-Lab, c/o Dept of Life, Health & Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy
Interests: medical imaging; image sampling, reconstruction, processing, and compression; artificial intelligence; inverse filtering
Prof. Dr. Mrinal Mandal

E-Mail Website
Guest Editor
Digital Image Analysis Laboratory, Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Interests: medical image analysis; computer-aided diagnosis; computer vision; machine learning; deep learning
Dr. Mustapha Bouhrara

E-Mail Website
Guest Editor
MRPAD Unit, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, 251 Bayview Blvd., Baltimore, MD 21224, USA
Interests: MR physics; neuroimaging; signal processing; image filtering; inverse problem; aging

Special Issue Information

Dear Colleagues,

Medical imaging (MI) is now an explosive field since the technologies for visualizing the body structure and functions have become increasingly various and powerful. Imaging is at the core of medical practice, as most patients are likely to undergo imaging scans during care both for diagnostic and disease monitoring purposes. Automated intelligent postprocessing analyses are fundamental for MI where images are the result of numerical processing of raw data to extract meaningful information and have wide application in all aspects, including sampling and reconstruction, filtering, compression, processing, registration, fusion, and interpretation. In recent years, in addition to classical tools, artificial intelligence is increasingly playing a decisive role in optimizing and lowering time acquisition to reduce exposure to potentially harmful radiations; reduce motion artifacts and noise; improve image quality; increase information by registering images from different modalities; identify image structures; objectively monitor disease progression; and facilitate analysis and early detection of diseases. This Special Issue aims to explore innovative advanced techniques for MI in different fields, including but not limited to MRI and fMRI, CT, EEG, US, IR, PET, SPECT, and combined modalities.

Prof. Dr. Giuseppe Placidi
Prof. Dr. Mrinal Mandal
Dr. Mustapha Bouhrara
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. 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

  • medical imaging
  • intelligent strategies
  • sampling
  • reconstruction
  • processing
  • filtering
  • compression
  • registration
  • fusion
  • interpretation
  • machine learning
  • deep learning
  • artificial intelligence

Published Papers (12 papers)

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Research

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35 pages, 4625 KiB  
Article
Social Media Devices’ Influence on User Neck Pain during the COVID-19 Pandemic: Collaborating Vertebral-GLCM Extracted Features with a Decision Tree
by Bassam Al-Naami, Bashar E. A. Badr, Yahia Z. Rawash, Hamza Abu Owida, Roberto De Fazio and Paolo Visconti
J. Imaging 2023, 9(1), 14; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging9010014 - 08 Jan 2023
Cited by 3 | Viewed by 2036
Abstract
The prevalence of neck pain, a chronic musculoskeletal disease, has significantly increased due to the uncontrollable use of social media (SM) devices. The use of SM devices by younger generations increased enormously during the COVID-19 pandemic, being—in some cases—the only possibility for maintaining [...] Read more.
The prevalence of neck pain, a chronic musculoskeletal disease, has significantly increased due to the uncontrollable use of social media (SM) devices. The use of SM devices by younger generations increased enormously during the COVID-19 pandemic, being—in some cases—the only possibility for maintaining interpersonal, social, and friendship relationships. This study aimed to predict the occurrence of neck pain and its correlation with the intensive use of SM devices. It is based on nine quantitative parameters extracted from the retrospective X-ray images. The three parameters related to angle_1 (i.e., the angle between the global horizontal and the vector pointing from C7 vertebra to the occipito-cervical joint), angle_2 (i.e., the angle between the global horizontal and the vector pointing from C1 vertebra to the occipito-cervical joint), and the area between them were measured from the shape of the neck vertebrae, while the rest of the parameters were extracted from the images using the gray-level co-occurrence matrix (GLCM). In addition, the users’ ages and the duration of the SM usage (H.mean) were also considered. The decision tree (DT) machine-learning algorithm was employed to predict the abnormal cases (painful subjects) against the normal ones (no pain). The results showed that angle_1, area, and the image contrast significantly increased statistically with the time of SM-device usage, precisely in the range of 2 to 9 h. The DT showed a promising result demonstrated by classification accuracy and F1-scores of 94% and 0.95, respectively. Our findings confirmed that the objectively detected parameters, which elucidate the negative impacts of SM-device usage on neck pain, can be predicted by DT machine learning. Full article
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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10 pages, 1018 KiB  
Article
Segmentation of Pancreatic Subregions in Computed Tomography Images
by Sehrish Javed, Touseef Ahmad Qureshi, Zengtian Deng, Ashley Wachsman, Yaniv Raphael, Srinivas Gaddam, Yibin Xie, Stephen Jacob Pandol and Debiao Li
J. Imaging 2022, 8(7), 195; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8070195 - 12 Jul 2022
Cited by 1 | Viewed by 1891
Abstract
The accurate segmentation of pancreatic subregions (head, body, and tail) in CT images provides an opportunity to examine the local morphological and textural changes in the pancreas. Quantifying such changes aids in understanding the spatial heterogeneity of the pancreas and assists in the [...] Read more.
The accurate segmentation of pancreatic subregions (head, body, and tail) in CT images provides an opportunity to examine the local morphological and textural changes in the pancreas. Quantifying such changes aids in understanding the spatial heterogeneity of the pancreas and assists in the diagnosis and treatment planning of pancreatic cancer. Manual outlining of pancreatic subregions is tedious, time-consuming, and prone to subjective inconsistency. This paper presents a multistage anatomy-guided framework for accurate and automatic 3D segmentation of pancreatic subregions in CT images. Using the delineated pancreas, two soft-label maps were estimated for subregional segmentation—one by training a fully supervised naïve Bayes model that considers the length and volumetric proportions of each subregional structure based on their anatomical arrangement, and the other by using the conventional deep learning U-Net architecture for 3D segmentation. The U-Net model then estimates the joint probability of the two maps and performs optimal segmentation of subregions. Model performance was assessed using three datasets of contrast-enhanced abdominal CT scans: one public NIH dataset of the healthy pancreas, and two datasets D1 and D2 (one for each of pre-cancerous and cancerous pancreas). The model demonstrated excellent performance during the multifold cross-validation using the NIH dataset, and external validation using D1 and D2. To the best of our knowledge, this is the first automated model for the segmentation of pancreatic subregions in CT images. A dataset consisting of reference anatomical labels for subregions in all images of the NIH dataset is also established. Full article
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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19 pages, 5841 KiB  
Article
Digital Watermarking as an Adversarial Attack on Medical Image Analysis with Deep Learning
by Kyriakos D. Apostolidis and George A. Papakostas
J. Imaging 2022, 8(6), 155; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8060155 - 30 May 2022
Cited by 11 | Viewed by 2558
Abstract
In the past years, Deep Neural Networks (DNNs) have become popular in many disciplines such as Computer Vision (CV), and the evolution of hardware has helped researchers to develop many powerful Deep Learning (DL) models to deal with several problems. One of the [...] Read more.
In the past years, Deep Neural Networks (DNNs) have become popular in many disciplines such as Computer Vision (CV), and the evolution of hardware has helped researchers to develop many powerful Deep Learning (DL) models to deal with several problems. One of the most important challenges in the CV area is Medical Image Analysis. However, adversarial attacks have proven to be an important threat to vision systems by significantly reducing the performance of the models. This paper brings to light a different side of digital watermarking, as a potential black-box adversarial attack. In this context, apart from proposing a new category of adversarial attacks named watermarking attacks, we highlighted a significant problem, as the massive use of watermarks, for security reasons, seems to pose significant risks to vision systems. For this purpose, a moment-based local image watermarking method is implemented on three modalities, Magnetic Resonance Images (MRI), Computed Tomography (CT-scans), and X-ray images. The introduced methodology was tested on three state-of-the art CV models, DenseNet 201, DenseNet169, and MobileNetV2. The results revealed that the proposed attack achieved over 50% degradation of the model’s performance in terms of accuracy. Additionally, MobileNetV2 was the most vulnerable model and the modality with the biggest reduction was CT-scans. Full article
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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24 pages, 10116 KiB  
Article
Explainable Multimedia Feature Fusion for Medical Applications
by Stefan Wagenpfeil, Paul Mc Kevitt, Abbas Cheddad and Matthias Hemmje
J. Imaging 2022, 8(4), 104; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8040104 - 08 Apr 2022
Cited by 3 | Viewed by 2513
Abstract
Due to the exponential growth of medical information in the form of, e.g., text, images, Electrocardiograms (ECGs), X-rays, and multimedia, the management of a patient’s data has become a huge challenge. In particular, the extraction of features from various different formats and their [...] Read more.
Due to the exponential growth of medical information in the form of, e.g., text, images, Electrocardiograms (ECGs), X-rays, and multimedia, the management of a patient’s data has become a huge challenge. In particular, the extraction of features from various different formats and their representation in a homogeneous way are areas of interest in medical applications. Multimedia Information Retrieval (MMIR) frameworks, like the Generic Multimedia Analysis Framework (GMAF), can contribute to solving this problem, when adapted to special requirements and modalities of medical applications. In this paper, we demonstrate how typical multimedia processing techniques can be extended and adapted to medical applications and how these applications benefit from employing a Multimedia Feature Graph (MMFG) and specialized, efficient indexing structures in the form of Graph Codes. These Graph Codes are transformed to feature relevant Graph Codes by employing a modified Term Frequency Inverse Document Frequency (TFIDF) algorithm, which further supports value ranges and Boolean operations required in the medical context. On this basis, various metrics for the calculation of similarity, recommendations, and automated inferencing and reasoning can be applied supporting the field of diagnostics. Finally, the presentation of these new facilities in the form of explainability is introduced and demonstrated. Thus, in this paper, we show how Graph Codes contribute new querying options for diagnosis and how Explainable Graph Codes can help to readily understand medical multimedia formats. Full article
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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14 pages, 2877 KiB  
Article
Discrete Shearlets as a Sparsifying Transform in Low-Rank Plus Sparse Decomposition for Undersampled (k, t)-Space MR Data
by Nicholas E. Protonotarios, Evangelia Tzampazidou, George A. Kastis and Nikolaos Dikaios
J. Imaging 2022, 8(2), 29; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8020029 - 29 Jan 2022
Cited by 2 | Viewed by 2321
Abstract
The discrete shearlet transformation accurately represents the discontinuities and edges occurring in magnetic resonance imaging, providing an excellent option of a sparsifying transform. In the present paper, we examine the use of discrete shearlets over other sparsifying transforms in a low-rank plus sparse [...] Read more.
The discrete shearlet transformation accurately represents the discontinuities and edges occurring in magnetic resonance imaging, providing an excellent option of a sparsifying transform. In the present paper, we examine the use of discrete shearlets over other sparsifying transforms in a low-rank plus sparse decomposition problem, denoted by L+S. The proposed algorithm is evaluated on simulated dynamic contrast enhanced (DCE) and small bowel data. For the small bowel, eight subjects were scanned; the sequence was run first on breath-holding and subsequently on free-breathing, without changing the anatomical position of the subject. The reconstruction performance of the proposed algorithm was evaluated against k-t FOCUSS. L+S decomposition, using discrete shearlets as sparsifying transforms, successfully separated the low-rank (background and periodic motion) from the sparse component (enhancement or bowel motility) for both DCE and small bowel data. Motion estimated from low-rank of DCE data is closer to ground truth deformations than motion estimated from L and S. Motility metrics derived from the S component of free-breathing data were not significantly different from the ones from breath-holding data up to four-fold undersampling, indicating that bowel (rapid/random) motility is isolated in S. Our work strongly supports the use of discrete shearlets as a sparsifying transform in a L+S decomposition for undersampled MR data. Full article
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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15 pages, 1618 KiB  
Article
Dataset Growth in Medical Image Analysis Research
by Nahum Kiryati and Yuval Landau
J. Imaging 2021, 7(8), 155; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7080155 - 20 Aug 2021
Cited by 11 | Viewed by 2546
Abstract
Medical image analysis research requires medical image datasets. Nevertheless, due to various impediments, researchers have been described as “data starved”. We hypothesize that implicit evolving community standards require researchers to use ever-growing datasets. In Phase I of this research, we scanned the MICCAI [...] Read more.
Medical image analysis research requires medical image datasets. Nevertheless, due to various impediments, researchers have been described as “data starved”. We hypothesize that implicit evolving community standards require researchers to use ever-growing datasets. In Phase I of this research, we scanned the MICCAI (Medical Image Computing and Computer-Assisted Intervention) conference proceedings from 2011 to 2018. We identified 907 papers involving human MRI, CT or fMRI datasets and extracted their sizes. The median dataset size had grown by 3–10 times from 2011 to 2018, depending on imaging modality. Statistical analysis revealed exponential growth of the geometric mean dataset size with an annual growth of 21% for MRI, 24% for CT and 31% for fMRI. Thereupon, we had issued a forecast for dataset sizes in MICCAI 2019 well before the conference. In Phase II of this research, we examined the MICCAI 2019 proceedings and analyzed 308 relevant papers. The MICCAI 2019 statistics compare well with the forecast. The revised annual growth rates of the geometric mean dataset size are 27% for MRI, 30% for CT and 32% for fMRI. We predict the respective dataset sizes in the MICCAI 2020 conference (that we have not yet analyzed) and the future MICCAI 2021 conference. Full article
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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13 pages, 17217 KiB  
Article
Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks
by Jonas Denck, Jens Guehring, Andreas Maier and Eva Rothgang
J. Imaging 2021, 7(8), 133; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7080133 - 04 Aug 2021
Cited by 4 | Viewed by 2390
Abstract
A magnetic resonance imaging (MRI) exam typically consists of the acquisition of multiple MR pulse sequences, which are required for a reliable diagnosis. With the rise of generative deep learning models, approaches for the synthesis of MR images are developed to either synthesize [...] Read more.
A magnetic resonance imaging (MRI) exam typically consists of the acquisition of multiple MR pulse sequences, which are required for a reliable diagnosis. With the rise of generative deep learning models, approaches for the synthesis of MR images are developed to either synthesize additional MR contrasts, generate synthetic data, or augment existing data for AI training. While current generative approaches allow only the synthesis of specific sets of MR contrasts, we developed a method to generate synthetic MR images with adjustable image contrast. Therefore, we trained a generative adversarial network (GAN) with a separate auxiliary classifier (AC) network to generate synthetic MR knee images conditioned on various acquisition parameters (repetition time, echo time, and image orientation). The AC determined the repetition time with a mean absolute error (MAE) of 239.6 ms, the echo time with an MAE of 1.6 ms, and the image orientation with an accuracy of 100%. Therefore, it can properly condition the generator network during training. Moreover, in a visual Turing test, two experts mislabeled 40.5% of real and synthetic MR images, demonstrating that the image quality of the generated synthetic and real MR images is comparable. This work can support radiologists and technologists during the parameterization of MR sequences by previewing the yielded MR contrast, can serve as a valuable tool for radiology training, and can be used for customized data generation to support AI training. Full article
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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11 pages, 1711 KiB  
Article
Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images
by Alessandro Stefano and Albert Comelli
J. Imaging 2021, 7(8), 131; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7080131 - 04 Aug 2021
Cited by 39 | Viewed by 2768
Abstract
Background: In the field of biomedical imaging, radiomics is a promising approach that aims to provide quantitative features from images. It is highly dependent on accurate identification and delineation of the volume of interest to avoid mistakes in the implementation of the texture-based [...] Read more.
Background: In the field of biomedical imaging, radiomics is a promising approach that aims to provide quantitative features from images. It is highly dependent on accurate identification and delineation of the volume of interest to avoid mistakes in the implementation of the texture-based prediction model. In this context, we present a customized deep learning approach aimed at addressing the real-time, and fully automated identification and segmentation of COVID-19 infected regions in computed tomography images. Methods: In a previous study, we adopted ENET, originally used for image segmentation tasks in self-driving cars, for whole parenchyma segmentation in patients with idiopathic pulmonary fibrosis which has several similarities to COVID-19 disease. To automatically identify and segment COVID-19 infected areas, a customized ENET, namely C-ENET, was implemented and its performance compared to the original ENET and some state-of-the-art deep learning architectures. Results: The experimental results demonstrate the effectiveness of our approach. Considering the performance obtained in terms of similarity of the result of the segmentation to the gold standard (dice similarity coefficient ~75%), our proposed methodology can be used for the identification and delineation of COVID-19 infected areas without any supervision of a radiologist, in order to obtain a volume of interest independent from the user. Conclusions: We demonstrated that the proposed customized deep learning model can be applied to rapidly identify, and segment COVID-19 infected regions to subsequently extract useful information for assessing disease severity through radiomics analyses. Full article
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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26 pages, 10909 KiB  
Article
Super Resolution of Magnetic Resonance Images
by Prabhjot Kaur, Anil Kumar Sao and Chirag Kamal Ahuja
J. Imaging 2021, 7(6), 101; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7060101 - 21 Jun 2021
Cited by 3 | Viewed by 2200
Abstract
In this work, novel denoising and super resolution (SR) approaches for magnetic resonance (MR) images are addressed, and are integrated in a unified framework, which do not require example low resolution (LR)/high resolution (HR)/cross-modality/noise-free images and prior information of noise–noise variance. The proposed [...] Read more.
In this work, novel denoising and super resolution (SR) approaches for magnetic resonance (MR) images are addressed, and are integrated in a unified framework, which do not require example low resolution (LR)/high resolution (HR)/cross-modality/noise-free images and prior information of noise–noise variance. The proposed method categorizes the patches as either smooth or textured and then denoises them by deploying different denoising strategies for efficient denoising. The denoising algorithm is integrated into the SR approach, which uses a gradient profile-based constraint in a sparse representation-based framework to improve the resolution of MR images with reduced smearing of image details. This constraint regularizes the estimation of HR images such that the estimated HR image has gradient profiles similar to the gradient profiles of the original HR image. For this, the gradient profile sharpness (GPS) values of an unknown HR image are estimated using an approximated piece-wise linear relation among GPS values of LR and upsampled LR images. The experiments are performed on three different publicly available datasets. The proposed SR approach outperforms the existing unsupervised SR approach addressed for real MR images that exploits low rank and total variation (LRTV) regularization, by an average peak signal to noise ratio (PSNR) of 0.73 dB and 0.38 dB for upsampling factors 2 and 3, respectively. For the super resolution of noisy real MR images (degraded with 2% noise), the proposed approach outperforms the LRTV approach by an average PSNR of 0.54 dB and 0.46 dB for upsampling factors 2 and 3, respectively. The qualitative analysis is shown for real MR images from healthy subjects and subjects with Alzheimer’s disease and structural deformity, i.e., cavernoma. Full article
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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Review

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31 pages, 603 KiB  
Review
Microwave Imaging for Early Breast Cancer Detection: Current State, Challenges, and Future Directions
by Nour AlSawaftah, Salma El-Abed, Salam Dhou and Amer Zakaria
J. Imaging 2022, 8(5), 123; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8050123 - 23 Apr 2022
Cited by 44 | Viewed by 6873
Abstract
Breast cancer is the most commonly diagnosed cancer type and is the leading cause of cancer-related death among females worldwide. Breast screening and early detection are currently the most successful approaches for the management and treatment of this disease. Several imaging modalities are [...] Read more.
Breast cancer is the most commonly diagnosed cancer type and is the leading cause of cancer-related death among females worldwide. Breast screening and early detection are currently the most successful approaches for the management and treatment of this disease. Several imaging modalities are currently utilized for detecting breast cancer, of which microwave imaging (MWI) is gaining quite a lot of attention as a promising diagnostic tool for early breast cancer detection. MWI is a noninvasive, relatively inexpensive, fast, convenient, and safe screening tool. The purpose of this paper is to provide an up-to-date survey of the principles, developments, and current research status of MWI for breast cancer detection. This paper is structured into two sections; the first is an overview of current MWI techniques used for detecting breast cancer, followed by an explanation of the working principle behind MWI and its various types, namely, microwave tomography and radar-based imaging. In the second section, a review of the initial experiments along with more recent studies on the use of MWI for breast cancer detection is presented. Furthermore, the paper summarizes the challenges facing MWI as a breast cancer detection tool and provides future research directions. On the whole, MWI has proven its potential as a screening tool for breast cancer detection, both as a standalone or complementary technique. However, there are a few challenges that need to be addressed to unlock the full potential of this imaging modality and translate it to clinical settings. Full article
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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12 pages, 850 KiB  
Review
Spectral Photon-Counting Computed Tomography: A Review on Technical Principles and Clinical Applications
by Mario Tortora, Laura Gemini, Imma D’Iglio, Lorenzo Ugga, Gaia Spadarella and Renato Cuocolo
J. Imaging 2022, 8(4), 112; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8040112 - 15 Apr 2022
Cited by 36 | Viewed by 5062
Abstract
Photon-counting computed tomography (CT) is a technology that has attracted increasing interest in recent years since, thanks to new-generation detectors, it holds the promise to radically change the clinical use of CT imaging. Photon-counting detectors overcome the major limitations of conventional CT detectors [...] Read more.
Photon-counting computed tomography (CT) is a technology that has attracted increasing interest in recent years since, thanks to new-generation detectors, it holds the promise to radically change the clinical use of CT imaging. Photon-counting detectors overcome the major limitations of conventional CT detectors by providing very high spatial resolution without electronic noise, providing a higher contrast-to-noise ratio, and optimizing spectral images. Additionally, photon-counting CT can lead to reduced radiation exposure, reconstruction of higher spatial resolution images, reduction of image artifacts, optimization of the use of contrast agents, and create new opportunities for quantitative imaging. The aim of this review is to briefly explain the technical principles of photon-counting CT and, more extensively, the potential clinical applications of this technology. Full article
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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10 pages, 1030 KiB  
Review
Cardiac Magnetic Resonance Imaging in Immune Check-Point Inhibitor Myocarditis: A Systematic Review
by Luca Arcari, Giacomo Tini, Giovanni Camastra, Federica Ciolina, Domenico De Santis, Domitilla Russo, Damiano Caruso, Massimiliano Danti and Luca Cacciotti
J. Imaging 2022, 8(4), 99; https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging8040099 - 05 Apr 2022
Cited by 5 | Viewed by 2674
Abstract
Immune checkpoint inhibitors (ICIs) are a family of anticancer drugs in which the immune response elicited against the tumor may involve other organs, including the heart. Cardiac magnetic resonance (CMR) imaging is increasingly used in the diagnostic work-up of myocardial inflammation; recently, several [...] Read more.
Immune checkpoint inhibitors (ICIs) are a family of anticancer drugs in which the immune response elicited against the tumor may involve other organs, including the heart. Cardiac magnetic resonance (CMR) imaging is increasingly used in the diagnostic work-up of myocardial inflammation; recently, several studies investigated the use of CMR in patients with ICI-myocarditis (ICI-M). The aim of the present systematic review is to summarize the available evidence on CMR findings in ICI-M. We searched electronic databases for relevant publications; after screening, six studies were selected, including 166 patients from five cohorts, and further 86 patients from a sub-analysis that were targeted for a tissue mapping assessment. CMR revealed mostly preserved left ventricular ejection fraction; edema prevalence ranged from 9% to 60%; late gadolinium enhancement (LGE) prevalence ranged from 23% to 83%. T1 and T2 mapping assessment were performed in 108 and 104 patients, respectively. When available, the comparison of CMR with endomyocardial biopsy revealed partial agreement between techniques and was higher for native T1 mapping amongst imaging biomarkers. The prognostic assessment was inconsistently assessed; CMR variables independently associated with the outcome included decreasing LVEF and increasing native T1. In conclusion, CMR findings in ICI-M include myocardial dysfunction, edema and fibrosis, though less evident than in more classic forms of myocarditis; native T1 mapping retained the higher concordance with EMB and significant prognostic value. Full article
(This article belongs to the Special Issue Intelligent Strategies for Medical Image Analysis)
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