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Advances in Imaging Diagnosis and Management of Cardiovascular and Pulmonary...
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Advances in Imaging Diagnosis and Management of Cardiovascular and Pulmonary Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 5391

Special Issue Editors

Dr. Chiara Martini

E-Mail Website
Guest Editor
Department of Radiology, Parma University Hospital, Via Gramsci 14, 43125 Parma, Italy
Interests: cardiac imaging; pulmonary imaging; cardiac computed tomography; cardiac magnetic resonance; post-processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paolo Perini

E-Mail Website
Guest Editor
Vascular Surgery, University Hospital of Parma, Parma, Italy
Interests: vascular surgery; aneurysm; carotid; peripheral arterial disease; critical limb ischemia; vascular graft infection; 3D printing; vascular imaging; duplex ultrasound
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue highlights recent advancements in cardiac or pulmonary imaging. It includes selected original research articles that demonstrate the use of advanced imaging techniques for morphological and functional characterization through multimodal diagnostics. These techniques are poised to become crucial tools for radiologists, as they could potentially be integrated seamlessly into their workflows. Our research interests encompass the development of automated anatomy segmentation and measurement for diagnosis, as well as the use of 3D printing to predict optimal treatment choices and evaluate treatment responses.

Dr. Chiara Martini
Prof. Dr. Paolo Perini
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. Diagnostics 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

  • cardiovascular imaging
  • pulmonary imaging
  • AI
  • machine learning
  • vascular
  • CT

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Published Papers (6 papers)

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21 pages, 3123 KiB  
Article
DeSPPNet: A Multiscale Deep Learning Model for Cardiac Segmentation
by Elizar Elizar, Rusdha Muharar and Mohd Asyraf Zulkifley
Diagnostics 2024, 14(24), 2820; https://doi.org/10.3390/diagnostics14242820 - 14 Dec 2024
Viewed by 518
Abstract
Background: Cardiac magnetic resonance imaging (MRI) plays a crucial role in monitoring disease progression and evaluating the effectiveness of treatment interventions. Cardiac MRI allows medical practitioners to assess cardiac function accurately by providing comprehensive and quantitative information about the structure and function, hence [...] Read more.
Background: Cardiac magnetic resonance imaging (MRI) plays a crucial role in monitoring disease progression and evaluating the effectiveness of treatment interventions. Cardiac MRI allows medical practitioners to assess cardiac function accurately by providing comprehensive and quantitative information about the structure and function, hence making it an indispensable tool for monitoring the disease and treatment response. Deep learning-based segmentation enables the precise delineation of cardiac structures including the myocardium, right ventricle, and left ventricle. The accurate segmentation of these structures helps in the diagnosis of heart failure, cardiac functional response to therapies, and understanding the state of the heart functions after treatment. Objectives: The objective of this study is to develop a multiscale deep learning model to segment cardiac organs based on MRI imaging data. Good segmentation performance is difficult to achieve due to the complex nature of the cardiac structure, which includes a variety of chambers, arteries, and tissues. Furthermore, the human heart is also constantly beating, leading to motion artifacts that reduce image clarity and consistency. As a result, a multiscale method is explored to overcome various challenges in segmenting cardiac MRI images. Methods: This paper proposes DeSPPNet, a multiscale-based deep learning network. Its foundation follows encoder–decoder pair architecture that utilizes the Spatial Pyramid Pooling (SPP) layer to improve the performance of cardiac semantic segmentation. The SPP layer is designed to pool features from densely convolutional layers at different scales or sizes, which will be combined to maintain a set of spatial information. By processing features at different spatial resolutions, the multiscale densely connected layer in the form of the Pyramid Pooling Dense Module (PPDM) helps the network to capture both local and global context, preserving finer details of the cardiac structure while also capturing the broader context required to accurately segment larger cardiac structures. The PPDM is incorporated into the deeper layer of the encoder section of the deep learning network to allow it to recognize complex semantic features. Results: An analysis of multiple PPDM placement scenarios and structural variations revealed that the 3-path PPDM, positioned at the encoder layer 5, yielded optimal segmentation performance, achieving dice, intersection over union (IoU), and accuracy scores of 0.859, 0.800, and 0.993, respectively. Conclusions: Different PPDM configurations produce a different effect on the network; as such, a shallower layer placement, like encoder layer 4, retains more spatial data that need more parallel paths to gather the optimal set of multiscale features. In contrast, deeper layers contain more informative features but at a lower spatial resolution, which reduces the number of parallel paths required to provide optimal multiscale context. Full article
(This article belongs to the Special Issue Advances in Imaging Diagnosis and Management of Cardiovascular and Pulmonary Diseases)
19 pages, 2872 KiB  
Article
Channel and Spatial Attention in Chest X-Ray Radiographs: Advancing Person Identification and Verification with Self-Residual Attention Network
by Hazem Farah, Akram Bennour, Neesrin Ali Kurdi, Samir Hammami and Mohammed Al-Sarem
Diagnostics 2024, 14(23), 2655; https://doi.org/10.3390/diagnostics14232655 - 25 Nov 2024
Viewed by 470
Abstract
Background/Objectives: In contrast to traditional biometric modalities, such as facial recognition, fingerprints, and iris scans or even DNA, the research orientation towards chest X-ray recognition has been spurred by its remarkable recognition rates. Capturing the intricate anatomical nuances of an individual’s skeletal structure, [...] Read more.
Background/Objectives: In contrast to traditional biometric modalities, such as facial recognition, fingerprints, and iris scans or even DNA, the research orientation towards chest X-ray recognition has been spurred by its remarkable recognition rates. Capturing the intricate anatomical nuances of an individual’s skeletal structure, the ribcage of the chest, lungs, and heart, chest X-rays have emerged as a focal point for identification and verification, especially in the forensic field, even in scenarios where the human body damaged or disfigured. Discriminative feature embedding is essential for large-scale image verification, especially in applying chest X-ray radiographs for identity identification and verification. This study introduced a self-residual attention-based convolutional neural network (SRAN) aimed at effective feature embedding, capturing long-range dependencies and emphasizing critical spatial features in chest X-rays. This method offers a novel approach to person identification and verification through chest X-ray categorization, relevant for biometric applications and patient care, particularly when traditional biometric modalities are ineffective. Method: The SRAN architecture integrated a self-channel and self-spatial attention module to minimize channel redundancy and enhance significant spatial elements. The attention modules worked by dynamically aggregating feature maps across channel and spatial dimensions to enhance feature differentiation. For the network backbone, a self-residual attention block (SRAB) was implemented within a ResNet50 framework, forming a Siamese network trained with triplet loss to improve feature embedding for identity identification and verification. Results: By leveraging the NIH ChestX-ray14 and CheXpert datasets, our method demonstrated notable improvements in accuracy for identity verification and identification based on chest X-ray images. This approach effectively captured the detailed anatomical characteristics of individuals, including skeletal structure, ribcage, lungs, and heart, highlighting chest X-rays as a viable biometric tool even in cases of body damage or disfigurement. Conclusions: The proposed SRAN with self-residual attention provided a promising solution for biometric identification through chest X-ray imaging, showcasing its potential for accurate and reliable identity verification where traditional biometric approaches may fall short, especially in postmortem cases or forensic investigations. This methodology could play a transformative role in both biometric security and healthcare applications, offering a robust alternative modality for identity verification. Full article
(This article belongs to the Special Issue Advances in Imaging Diagnosis and Management of Cardiovascular and Pulmonary Diseases)
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21 pages, 7299 KiB  
Article
RDAG U-Net: An Advanced AI Model for Efficient and Accurate CT Scan Analysis of SARS-CoV-2 Pneumonia Lesions
by Chih-Hui Lee, Cheng-Tang Pan, Ming-Chan Lee, Chih-Hsuan Wang, Chun-Yung Chang and Yow-Ling Shiue
Diagnostics 2024, 14(18), 2099; https://doi.org/10.3390/diagnostics14182099 - 23 Sep 2024
Viewed by 915
Abstract
Background/Objective: This study aims to utilize advanced artificial intelligence (AI) image recog-nition technologies to establish a robust system for identifying features in lung computed tomog-raphy (CT) scans, thereby detecting respiratory infections such as SARS-CoV-2 pneumonia. Spe-cifically, the research focuses on developing a new [...] Read more.
Background/Objective: This study aims to utilize advanced artificial intelligence (AI) image recog-nition technologies to establish a robust system for identifying features in lung computed tomog-raphy (CT) scans, thereby detecting respiratory infections such as SARS-CoV-2 pneumonia. Spe-cifically, the research focuses on developing a new model called Residual-Dense-Attention Gates U-Net (RDAG U-Net) to improve accuracy and efficiency in identification. Methods: This study employed Attention U-Net, Attention Res U-Net, and the newly developed RDAG U-Net model. RDAG U-Net extends the U-Net architecture by incorporating ResBlock and DenseBlock modules in the encoder to retain training parameters and reduce computation time. The training dataset in-cludes 3,520 CT scans from an open database, augmented to 10,560 samples through data en-hancement techniques. The research also focused on optimizing convolutional architectures, image preprocessing, interpolation methods, data management, and extensive fine-tuning of training parameters and neural network modules. Result: The RDAG U-Net model achieved an outstanding accuracy of 93.29% in identifying pulmonary lesions, with a 45% reduction in computation time compared to other models. The study demonstrated that RDAG U-Net performed stably during training and exhibited good generalization capability by evaluating loss values, model-predicted lesion annotations, and validation-epoch curves. Furthermore, using ITK-Snap to convert 2D pre-dictions into 3D lung and lesion segmentation models, the results delineated lesion contours, en-hancing interpretability. Conclusion: The RDAG U-Net model showed significant improvements in accuracy and efficiency in the analysis of CT images for SARS-CoV-2 pneumonia, achieving a 93.29% recognition accuracy and reducing computation time by 45% compared to other models. These results indicate the potential of the RDAG U-Net model in clinical applications, as it can accelerate the detection of pulmonary lesions and effectively enhance diagnostic accuracy. Additionally, the 2D and 3D visualization results allow physicians to understand lesions' morphology and distribution better, strengthening decision support capabilities and providing valuable medical diagnosis and treatment planning tools. Full article
(This article belongs to the Special Issue Advances in Imaging Diagnosis and Management of Cardiovascular and Pulmonary Diseases)
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13 pages, 2536 KiB  
Article
Evaluation of Progressive Architectural Distortion in Idiopathic Pulmonary Fibrosis Using Deformable Registration of Sequential CT Images
by Naofumi Yasuda, Tae Iwasawa, Tomohisa Baba, Toshihiro Misumi, Shihyao Cheng, Shingo Kato, Daisuke Utsunomiya and Takashi Ogura
Diagnostics 2024, 14(15), 1650; https://doi.org/10.3390/diagnostics14151650 - 31 Jul 2024
Viewed by 970
Abstract
Background: Monitoring the progression of idiopathic pulmonary fibrosis (IPF) using CT primarily focuses on assessing the extent of fibrotic lesions, without considering the distortion of lung architecture. Objectives: To evaluate three-dimensional average displacement (3D-AD) quantification of lung structures using deformable registration of serial [...] Read more.
Background: Monitoring the progression of idiopathic pulmonary fibrosis (IPF) using CT primarily focuses on assessing the extent of fibrotic lesions, without considering the distortion of lung architecture. Objectives: To evaluate three-dimensional average displacement (3D-AD) quantification of lung structures using deformable registration of serial CT images as a parameter of local lung architectural distortion and predictor of IPF prognosis. Materials and Methods: Patients with IPF evaluated between January 2016 and March 2017 who had undergone CT at least twice were retrospectively included (n = 114). The 3D-AD was obtained by deformable registration of baseline and follow-up CT images. A computer-aided quantification software measured the fibrotic lesion volume. Cox regression analysis evaluated these variables to predict mortality. Results: The 3D-AD and the fibrotic lesion volume change were significantly larger in the subpleural lung region (5.2 mm (interquartile range (IQR): 3.6–7.1 mm) and 0.70% (IQR: 0.22–1.60%), respectively) than those in the inner region (4.7 mm (IQR: 3.0–6.4 mm) and 0.21% (IQR: 0.004–1.12%), respectively). Multivariable logistic analysis revealed that subpleural region 3D-AD and fibrotic lesion volume change were independent predictors of mortality (hazard ratio: 1.12 and 1.23; 95% confidence interval: 1.02–1.22 and 1.10–1.38; p = 0.01 and p < 0.001, respectively). Conclusions: The 3D-AD quantification derived from deformable registration of serial CT images serves as a marker of lung architectural distortion and a prognostic predictor in patients with IPF. Full article
(This article belongs to the Special Issue Advances in Imaging Diagnosis and Management of Cardiovascular and Pulmonary Diseases)
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20 pages, 4029 KiB  
Study Protocol
Four-Dimensional Flow MRI for Cardiovascular Evaluation (4DCarE): A Prospective Non-Inferiority Study of a Rapid Cardiac MRI Exam: Study Protocol and Pilot Analysis
by Jiaxing Jason Qin, Mustafa Gok, Alireza Gholipour, Jordan LoPilato, Max Kirkby, Christopher Poole, Paul Smith, Rominder Grover and Stuart M. Grieve
Diagnostics 2024, 14(22), 2590; https://doi.org/10.3390/diagnostics14222590 - 18 Nov 2024
Viewed by 731
Abstract
Background: Accurate measurements of flow and ventricular volume and function are critical for clinical decision-making in cardiovascular medicine. Cardiac magnetic resonance (CMR) is the current gold standard for ventricular functional evaluation but is relatively expensive and time-consuming, thus limiting the scale of clinical [...] Read more.
Background: Accurate measurements of flow and ventricular volume and function are critical for clinical decision-making in cardiovascular medicine. Cardiac magnetic resonance (CMR) is the current gold standard for ventricular functional evaluation but is relatively expensive and time-consuming, thus limiting the scale of clinical applications. New volumetric acquisition techniques, such as four-dimensional flow (4D-flow) and three-dimensional volumetric cine (3D-cine) MRI, could potentially reduce acquisition time without loss in accuracy; however, this has not been formally tested on a large scale. Methods: 4DCarE (4D-flow MRI for cardiovascular evaluation) is a prospective, multi-centre study designed to test the non-inferiority of a compressed 20 min exam based on volumetric CMR compared with a conventional CMR exam (45–60 min). The compressed exam utilises 4D-flow together with a single breath-hold 3D-cine to provide a rapid, accurate quantitative assessment of the whole heart function. Outcome measures are (i) flow and chamber volume measurements and (ii) overall functional evaluation. Secondary analyses will explore clinical applications of 4D-flow-derived parameters, including wall shear stress, flow kinetic energy quantification, and vortex analysis in large-scale cohorts. A target of 1200 participants will enter the study across three sites. The analysis will be performed at a single core laboratory site. Pilot Results: We present a pilot analysis of 196 participants comparing flow measurements obtained by 4D-flow and conventional 2D phase contrast, which demonstrated moderate–good consistency in ascending aorta and main pulmonary artery flow measurements between the two techniques. Four-dimensional flow underestimated the flow compared with 2D-PC, by approximately 3 mL/beat in both vessels. Conclusions: We present the study protocol of a prospective non-inferiority study of a rapid cardiac MRI exam compared with conventional CMR. The pilot analysis supports the continuation of the study. Study Registration: This study is registered with the Australia and New Zealand Clinical Trials Registry (Registry number ACTRN12622000047796, Universal Trial Number: U1111-1270-6509, registered 17 January 2022—Retrospectively registered). Full article
(This article belongs to the Special Issue Advances in Imaging Diagnosis and Management of Cardiovascular and Pulmonary Diseases)
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15 pages, 1798 KiB  
Systematic Review
Systematic Review on the Use of 3D-Printed Models for Planning, Training and Simulation in Vascular Surgery
by Alexandra Catasta, Chiara Martini, Arianna Mersanne, Ruben Foresti, Claudio Bianchini Massoni, Antonio Freyrie and Paolo Perini
Diagnostics 2024, 14(15), 1658; https://doi.org/10.3390/diagnostics14151658 - 31 Jul 2024
Viewed by 1054
Abstract
The use of 3D-printed models in simulation-based training and planning for vascular surgery is gaining interest. This study aims to provide an overview of the current applications of 3D-printing technologies in vascular surgery. We performed a systematic review by searching four databases: PubMed, [...] Read more.
The use of 3D-printed models in simulation-based training and planning for vascular surgery is gaining interest. This study aims to provide an overview of the current applications of 3D-printing technologies in vascular surgery. We performed a systematic review by searching four databases: PubMed, Web of Science, Scopus, and Cochrane Library (last search: 1 March 2024). We included studies considering the treatment of vascular stenotic/occlusive or aneurysmal diseases. We included papers that reported the outcome of applications of 3D-printed models, excluding case reports or very limited case series (≤5 printed models or tests/simulations). Finally, 22 studies were included and analyzed. Computed tomography angiography (CTA) was the primary diagnostic method used to obtain the images serving as the basis for generating the 3D-printed models. Processing the CTA data involved the use of medical imaging software; 3DSlicer (Brigham and Women’s Hospital, Harvard University, Boston, MA), ITK-Snap, and Mimics (Materialise NV, Leuven, Belgium) were the most frequently used. Autodesk Meshmixer (San Francisco, CA, USA) and 3-matic (Materialise NV, Leuven, Belgium) were the most frequently employed mesh-editing software during the post-processing phase. PolyJet™, fused deposition modeling (FDM), and stereolithography (SLA) were the most frequently employed 3D-printing technologies. Planning and training with 3D-printed models seem to enhance physicians’ confidence and performance levels by up to 40% and lead to a reduction in the procedure time and contrast volume usage to varying extents. Full article
(This article belongs to the Special Issue Advances in Imaging Diagnosis and Management of Cardiovascular and Pulmonary Diseases)
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