Article
Radiology, Nuclear Medicine & Medical Imaging
Konrad Pieszko, Aakash D. Shanbhag, Mark Lemley, Mark Hyun, Serge Van Kriekinge, Yuka Otaki, Joanna X. Liang, Daniel S. Berman, Damini Dey, Piotr J. Slomka
Summary: In this study, we evaluated the inter-scan and inter-reader agreement of coronary calcium (CAC) scores obtained from standard CAC scans and routine ultra-low-dose, ungated CTAC scans during cardiac PET/CT imaging. The results showed good agreement between readers and between the two scan protocols, with higher agreement observed for standard CAC scans. Most discordant readings occurred for scans with low extent of calcification.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Young Joo Suh, Cherry Kim, June-Goo Lee, Hongmin Oh, Heejun Kang, Young-Hak Kim, Dong Hyun Yang
Summary: This study validated an AI-based automatic CAC scoring software on LDCT and found that it showed good to excellent reliability in CAC score and CAC severity categorization, although the reliability varied among different institutions.
EUROPEAN RADIOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Joao Otavio Bandeira Diniz, Jonnison Lima Ferreira, Omar Andres Carmona Cortes, Aristofanes Correa Silva, Anselmo Cardoso de Paiva
Summary: This paper proposes an automatic deep learning method for heart segmentation from planning CT. The method consists of four steps and achieves good segmentation results using a public database. It can serve as an ally to specialists and quickly treat patients undergoing RT treatments.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Cheng Xu, Heng Guo, Minfeng Xu, Miao Duan, Ming Wang, Peijun Liu, Xinyi Luo, Zhengyu Jin, Hui Liu, Yining Wang
Summary: This study investigates the reliability and accuracy of automatic coronary artery calcium (CAC) scoring and risk classification using a deep learning algorithm on non-gated, non-contrast chest computed tomography (CT) scans with different slice thicknesses. The results show that the deep learning algorithm performs well in both 1mm and 3mm scans, achieving excellent correlation with the gold standard and accurate risk classification.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2022)
Article
Oncology
Sanne G. M. van Velzen, Roxanne Gal, Arco J. Teske, Femke van der Leij, Desiree H. J. G. van den Bongard, Max A. Viergever, Helena M. Verkooijen, Ivana Isgum
Summary: The study aimed to investigate whether the planned dose for cardiac structures is associated with the risk of heart disease in breast cancer patients receiving radiation therapy, and whether this association is influenced by coronary artery calcification. The results showed that radiation exposure to cardiac structures is associated with an increased risk of heart disease, particularly in patients with breast cancer and coronary artery calcification.
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2022)
Article
Medicine, General & Internal
Matthias Eberhard, Victor Mergen, Kai Higashigaito, Thomas Allmendinger, Robert Manka, Thomas Flohr, Bernhard Schmidt, Andre Euler, Hatem Alkadhi
Summary: The study evaluated the accuracy of coronary artery calcium scoring on a dual-source photon-counting detector CT. Results showed that VMI reconstructions provided accurate CAC scoring at different radiation dose levels.
Article
Cardiac & Cardiovascular Systems
Omar Dzaye, Alexander C. Razavi, Zeina A. Dardari, Daniel S. Berman, Matthew J. Budoff, Michael D. Miedema, Olufunmilayo H. Obisesan, Ellen Boakye, Khurram Nasir, Alan Rozanski, John A. Rumberger, Leslee J. Shaw, Martin Bodtker Mortensen, Seamus P. Whelton, Michael J. Blaha
Summary: This study found that mean and peak calcium density may describe plaque composition differently in the early stages of atherosclerosis, and mean calcium density performs better than peak calcium density factor for predicting ASCVD mortality among individuals with Agatston CAC 1-99.
JACC-CARDIOVASCULAR IMAGING
(2022)
Article
Medicine, General & Internal
Milan Vecsey-Nagy, Zsofia Jokkel, Adam Levente Jermendy, Martin Nagy, Melinda Boussoussou, Borbala Vattay, Marton Kolossvary, Csaba Csobay-Novak, Sigal Amin-Spector, Bela Merkely, Balint Szilveszter
Summary: Contemporary reconstruction algorithms have the potential to reduce radiation exposure in coronary computed tomography angiography (CCTA) datasets by denoising them. This study assessed the reliability of coronary artery calcium score (CACS) measurements using advanced adaptive statistical iterative reconstruction (ASIR-CV) and model-based adaptive filter (MBAF2) compared to the gold-standard filtered back projection (FBP) calculations. The results showed that the concomitant use of ASIR-CV and MBAF2 reduced noise levels and yielded similar CACS values as FBP measurements.
Article
Oncology
Roxanne Gal, Sanne G. M. van Velzen, Maartje J. Hooning, Marleen J. Emaus, Femke van der Leij, Madelijn L. Gregorowitsch, Erwin L. A. Blezer, Sofie A. M. Gernaat, Nikolas Lessmann, Margriet G. A. Sattler, Tim Leiner, Pim A. de Jong, Arco J. Teske, Janneke Verloop, Joan J. Penninkhof, Ilonca Vaartjes, Hanneke Meijer, Julia J. van Tol-Geerdink, Jean-Philippe Pignol, Desiree H. J. G. van den Bongard, Ivana Isgum, Helena M. Verkooijen
Summary: This study found an association between coronary artery calcium (CAC) levels on breast cancer radiotherapy planning CT scans and cardiovascular disease, especially coronary artery disease (CAD). Automated CAC scoring may serve as a quick, cost-effective tool to identify breast cancer patients at increased risk of CVD, enabling the implementation of risk-mitigating strategies to reduce CVD burden after breast cancer.
Article
Medicine, General & Internal
Jack Dalla Via, Nina Stewart, Mary A. Kennedy, Daniel A. Cehic, Peter Purnell, Joanne Toohey, Jamie Morton, Sabashini K. Ramchand, Joshua R. Lewis, Yvonne Zissiadis
Summary: This study aims to use coronary artery calcium (CAC) scores calculated from thoracic radiotherapy planning CT scans to identify high-risk cardiac events in cancer survivors and establish a referral pathway for assessment and management in a cardio-oncology clinic. The feasibility of the study will be assessed by adherence to the recommended pathway and the impact on quality of life and anxiety measured through questionnaires.
Article
Computer Science, Artificial Intelligence
Adel Oulefki, Sos Agaian, Thaweesak Trongtirakul, Azzeddine Kassah Laouar
Summary: The study aims to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. Extensive computer simulations show better efficiency and flexibility of this end to end learning approach on CT image segmentation with image enhancement, comparing to state of the art segmentation approaches like GraphCut, Medical Image Segmentation (MIS), and Watershed.
PATTERN RECOGNITION
(2021)
Article
Engineering, Biomedical
Joao Otavio Bandeira Diniz, Jonnison Lima Ferreira, Pedro Henrique Bandeira Diniz, Aristofanes Correa Silva, Anselmo Cardoso Paiva
Summary: The study introduces a method for automatic spinal cord segmentation in planning CT for radiotherapy, which consists mainly of a template matching technique and a novel deep convolutional neural network. Applied in a CT database of 36 patients, the best model achieved an accuracy of 99.35%, a specificity of 99.57%, a sensitivity of 91.52%, and a Dice index of 85.47%.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Dan Mu, Junjie Bai, Wenping Chen, Hongming Yu, Jing Liang, Kejie Yin, Hui Li, Zhao Qing, Kelei He, Hao-Yu Yang, Jinyao Zhang, Youbing Yin, Hunter W. McLellan, U. Joseph Schoepf, Bing Zhang
Summary: This study developed a deep learning method to automatically quantify coronary artery calcium (CAC) scores from a single coronary CT angiography (CTA) scan. The results of the validation showed that the proposed method had excellent accuracy in quantifying CAC scores and risk categorization when compared to the semiautomatic Agatston scores at noncontrast CT.
Article
Radiology, Nuclear Medicine & Medical Imaging
Angela T. Li, Peter B. Noel, Nadav Shapira
Summary: The study aims to fully automate the bolus tracking procedure in contrast-enhanced abdominal CT exams using artificial intelligence algorithms to improve standardization and diagnostic accuracy. The method consists of two steps: automatic locator scan positioning on topograms and automatic region-of-interest (ROI) positioning within the aorta on locator scans. The results show that the locator scan positioning network offers improved positional consistency and reduces error compared to manual slice positionings, while the ROI segmentation network achieves high accuracy in positioning with minimal error.
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
(2023)
Article
Oncology
Jiaqi Cui, Zhengyang Jiao, Zhigong Wei, Xiaolin Hu, Yan Wang, Jianghong Xiao, Xingchen Peng
Summary: This study proposes a contour-free dose prediction method based on CT images, which utilizes a generative adversarial network (GAN) and a multi-task learning (MTL) strategy to accurately generate dose distribution maps without the need for manual contour delineation. Experimental results demonstrate the feasibility and improvements of this method, and the proposed method outperforms other mainstream methods in terms of performance.
FRONTIERS IN ONCOLOGY
(2022)
Article
Oncology
Sanne G. M. van Velzen, Steffen Bruns, Jelmer M. Wolterink, Tim Leiner, Max A. Viergever, Helena M. Verkooijen, Ivana Isgum
Summary: This study aims to develop and evaluate an automatic deep learning method for segmentation of cardiac chambers and large arteries, and localization of the 3 main coronary arteries in radiation therapy planning on computed tomography (CT). The study found that the developed method can automatically obtain accurate estimates of planned radiation dose and dosimetric parameters for the cardiac chambers, large arteries, and coronary arteries.
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2022)
Review
Radiology, Nuclear Medicine & Medical Imaging
N. J. Kleinrensink, J. N. Pouw, E. F. A. Leijten, R. A. P. Takx, P. M. J. Welsing, B. de Keizer, P. A. de Jong, W. Foppen
Summary: This study conducted a systematic review and meta-analyses to investigate the association between aortic vascular inflammation and moderate-severe psoriasis. The results showed that patients with moderate-severe psoriasis had significantly increased vascular inflammation in the entire aorta and most aortic segments. However, the effectiveness of biologic treatment on reducing aortic vascular inflammation was inconclusive.
CLINICAL AND TRANSLATIONAL IMAGING
(2022)
Article
Cardiac & Cardiovascular Systems
Carlo Lucci, Tim C. van den Beukel, Jonas W. Bartstra, Jaco Zwanenburg, Anja van der Kolk, Richard Takx, Jeroen Hendrikse, Mirjam I. Geerlings, Daniel Bos, Wilko Spiering, Pim A. de Jong
Summary: The distribution and burden of intracranial atherosclerosis were similar between PXE patients and healthy controls, suggesting that PXE and its underlying mutations do not involve increased atherogenesis in the intracranial arteries. Vascular calcification or other mechanisms may explain the increased risk of stroke in PXE.
Article
Oncology
Sanne G. M. van Velzen, Roxanne Gal, Arco J. Teske, Femke van der Leij, Desiree H. J. G. van den Bongard, Max A. Viergever, Helena M. Verkooijen, Ivana Isgum
Summary: The study aimed to investigate whether the planned dose for cardiac structures is associated with the risk of heart disease in breast cancer patients receiving radiation therapy, and whether this association is influenced by coronary artery calcification. The results showed that radiation exposure to cardiac structures is associated with an increased risk of heart disease, particularly in patients with breast cancer and coronary artery calcification.
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2022)
Article
Health Care Sciences & Services
Annelotte Vos, Aryan Vink, Remko Kockelkoren, Richard A. P. Takx, Csilla Celeng, Willem P. T. M. Mali, Ivana Isgum, Ronald L. A. W. Bleys, Pim A. de Jong
Summary: This study investigated the detection and characterization of arterial calcifications in leg arteries using radiography and computed tomography (CT) and compared them with histology. The results showed that both radiography and CT could detect the majority of calcifications, but missed some mild calcifications. There was a moderate agreement between radiography/CT and histology in determining the location of calcifications in the intima or media of the arteries.
JOURNAL OF PERSONALIZED MEDICINE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Sanne G. M. van Velzen, Bob D. de Vos, Julia M. H. Noothout, Helena M. Verkooijen, Max A. Viergever, Ivana Isgum
Summary: This study proposes a CAC quantification method that does not require a threshold for segmentation, using a generative adversarial network (GAN) for image decomposition. The method improves the interscan reproducibility of CAC scoring compared to clinical calcium scoring.
JOURNAL OF MEDICAL IMAGING
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Julia M. H. Noothout, Nikolas Lessmann, Matthijs C. van Eede, Louis D. van Harten, Ecem Sogancioglu, Friso G. Heslinga, Mitko Veta, Bram van Ginneken, Ivana Isgum
Summary: Ensembles of convolutional neural networks (CNNs) are often more effective than single CNNs in medical image segmentation tasks, but they are computationally expensive. This study compared the performance of different ensembles and their corresponding distilled networks. The results showed that both uniform and diverse ensembles outperformed individual networks, and knowledge distillation allowed for the creation of smaller and faster single networks without compromising performance.
JOURNAL OF MEDICAL IMAGING
(2022)
Article
Medicine, General & Internal
Riaan Zoetmulder, Agnetha A. E. Bruggeman, Ivana Isgum, Efstratios Gavves, Charles B. L. M. Majoie, Ludo F. M. Beenen, Diederik W. J. Dippel, Nikkie Boodt, Sanne J. den Hartog, Pieter J. van Doormaal, Sandra A. P. Cornelissen, Yvo B. W. E. M. Roos, Josje Brouwer, Wouter J. Schonewille, Anne F. V. Pirson, Wim H. van Zwam, Christiaan van der Leij, Rutger J. B. Brans, Adriaan C. G. M. van Es, Henk A. Marquering
Summary: In this study, an automatic method for thrombus localization and segmentation on CT images in patients with posterior circulation stroke (PCS) was developed. The method achieved good results in localizing and segmenting thrombi. Restricting the volume-of-interest (VOI) to the brainstem improved the precision and recall of thrombus localization.
Article
Cardiac & Cardiovascular Systems
Mimount Bourfiss, Jorg Sander, Bob D. de Vos, Anneline S. J. M. Te Riele, Folkert W. Asselbergs, Ivana Isgum, Birgitta K. Velthuis
Summary: This study applies automatic deep learning-based segmentation for right and left ventricular CMR assessment and combines it with manual correction to accurately classify subjects suspected of ARVC according to CMR TFC.
CLINICAL RESEARCH IN CARDIOLOGY
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Annelotte Vos, Ignas B. Houben, Csilla Celeng, Richard A. P. Takx, Ivana Isgum, Willem P. T. M. Mali, Aryan Vink, Pim A. de Jong
Summary: This study aimed to validate the detectability and location of aortic calcification detected by computed tomography (CT) through histology. The results showed that CT reliably determined the presence and annularity of calcifications, which were mainly located in the intimal layer of the abdominal aorta.
EUROPEAN JOURNAL OF RADIOLOGY
(2023)
Article
Cardiac & Cardiovascular Systems
Nils Hampe, Sanne G. M. van Velzen, R. Nils Planken, Jose P. S. Henriques, Carlos Collet, Jean-Paul Aben, Michiel Voskuil, Tim Leiner, Ivana Isgum
Summary: This study proposes a deep learning method for predicting the invasively measured FFR of an artery using a CCTA scan, achieving an AUC of 0.78 and demonstrating potential to reduce unnecessary invasive measurements.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2022)
Article
Clinical Neurology
Frans Kauw, Birgitta K. Velthuis, Richard A. P. Takx, Marco Guglielmo, Maarten J. Cramer, Fasco van Ommen, Anneloes Bos, Edwin Bennink, L. Jaap Kappelle, Hugo W. A. M. de Jong, Jan W. Dankbaar
Summary: Identifying cardioembolic sources in patients with acute ischemic stroke is important for the choice of secondary prevention strategies. This study aimed to investigate the yield of admission cardiac computed tomography angiography (CTA) in detecting cardiac thrombi and determining cardioembolic sources in stroke patients. The results showed that the presence of cardiac thrombus on admission CTA was associated with more severe strokes, higher clot burden, and a higher likelihood of undergoing endovascular treatment. The use of spectral iodine maps on CTA increased the diagnostic certainty for left atrial appendage thrombus.
Proceedings Paper
Computer Science, Interdisciplinary Applications
Zhiwei Zhai, Yining Wang, Bob D. de Vos, Julia M. H. Noothout, Nils Planken, Ivana Isgum
Summary: This study presents a computer algorithm trained with synthesized data to improve the detection and segmentation of coronary artery plaques.
MEDICAL IMAGING 2022: IMAGE PROCESSING
(2022)
