Article
Radiology, Nuclear Medicine & Medical Imaging
Corrado Tagliati, Cecilia Lanza, Giovanni Pieroni, Lucia Amici, Marina Carotti, Gian Marco Giuseppetti, Andrea Giovagnoni
Summary: This study compared objective and subjective image quality and radiation dose between low-dose chest CT examinations in adult CF patients using a third-generation DSCT scanner and a 64-slices single-source CT (SSCT) scanner. The DSCT protocol showed lower radiation dose during inspiratory phase and higher radiation dose overall compared to the SSCT protocol, with improvements in subjective image quality.
Article
Radiology, Nuclear Medicine & Medical Imaging
Jiang Hsieh
Summary: This paper proposes a method to generate synthesized high-dose (SHD) images from low-dose CT scans, addressing the issues of noise texture and data availability. By performing image processing orthogonal to the imaging plane, the method effectively reduces noise. The differential signal between the original and processed image provides information about modified anatomical structures, and noise reduction is achieved through iterative noise reduction. Extensive evaluations demonstrate the effectiveness and robustness of the proposed approach in noise reduction and noise-texture preservation.
Article
Radiology, Nuclear Medicine & Medical Imaging
F. Moloney, R. G. Kavanagh, N. J. Ronan, T. M. Grey, S. Joyce, D. J. Ryan, N. Moore, O. J. O'Connor, B. J. Plant, M. M. Maher
Summary: The study evaluated the utility of a volumetric LDCTT protocol at a dose equivalent to a chest radiograph for surveillance of CF patients. It was found that LD-MBIR images were superior to LD-ASIR images and enabled acquisition of diagnostic quality CT images.
CLINICAL RADIOLOGY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Samaneh Mostafapour, Marcel Greuter, Johannes H. van Snick, Adrienne H. Brouwers, Rudi A. J. O. Dierckx, Joyce van Sluis, Adriaan A. Lammertsma, Charalampos Tsoumpas
Summary: The purpose of this study was to investigate the use of ultra-low dose CT for attenuation correction (AC) with the Sn filter and other dose reduction options. The study explores the impact of dose reduction on AC-CT and how it affects PET image quality.
Article
Respiratory System
Kevin P. Sheahan, Alexander O'Mahony, David Morrissy, Hisham Ibrahim, Claire Crowley, Michael G. Waldron, Darek Sokol-Randell, Aisling McMahon, Michael M. Maher, Owen J. O'Connor, Barry J. Plant
Summary: This study aimed to assess and quantify the cumulative effective dose (CED) in patients with cystic fibrosis (PWCF) in the context of CFTR-modulator therapy and dose reduction techniques. The results showed an increasing utilization of chest CT in PWCF and a reduction in mean annual CED, primarily due to CT dose reduction strategies.
JOURNAL OF CYSTIC FIBROSIS
(2023)
Article
Multidisciplinary Sciences
Ming Lei, Meng Zhang, Niyuan Luo, Jingzhi Ye, Fenghuan Lin, Yanxia Chen, Jun Chen, Mengqiang Xiao
Summary: The clinical performance of ultra-low-dose CT scans for shoulder joint fractures was evaluated, demonstrating their usefulness for image-based diagnosis and 3D printing surgical planning. The ultra-low-dose protocol significantly reduced radiation dose while maintaining key image quality. Although the assessment scores were slightly lower in the ultra-low-dose group compared to the standard dose group, they still reached an adequate level for clinical application.
Article
Mathematics
Muhammad Owais, Haseeb Sultan, Na Rae Baek, Young Won Lee, Muhammad Usman, Dat Tien Nguyen, Ganbayar Batchuluun, Kang Ryoung Park
Summary: In this study, we propose a novel method called DSS-Net, which considers both spatial and 3D structural features when handling volumetric CT data, for effective diagnostic decision-making. By combining a large number of positive and negative data samples for testing, DSS-Net demonstrates superior performance compared to other methods.
Article
Automation & Control Systems
Mohamed Abdel-Basset, Hossam Hawash, Victor Chang
Summary: This article proposes a novel fully volumetric segmentation network called FV-Seg-Net, which effectively addresses the precise segmentation of small-size lesions in CT scans. The network utilizes a computationally efficient recalibrated anisotropic convolution module and a multilevel multiscale pyramid aggregation module to capture local and global spatial information. The introduction of stacked data augmentation further improves the generalizability of FV-Seg-Net. Experimental results show that FV-Seg-Net achieves excellent segmentation performance, outperforming current cutting-edge studies.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Aniek T. Zwart, Vitor J. Cavalheiro, Maria J. Lamers, Rudi A. J. O. Dierckx, Geertruida H. de Bock, Gyorgy B. Halmos, Anouk van der Hoorn
Summary: The study found that low-dose neck CT scans from [F-18]-FDG PET-CT can provide comparable results to diagnostic neck CT scans in head and neck cancer patients. This enables SMI analysis in patients without a diagnostic neck CT but with a [F-18]-FDG PET-CT scan.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
(2023)
Article
Chemistry, Multidisciplinary
Yuekun Bao, Zhihui Zhang, Cheng Li, Huan Ma, Pan Yin, Yinghao Wang, Guangwei Luo, Rong Lu
Summary: This study investigated the utility of geometric and volumetric measurements of orbital soft tissues on CT scans for quantitative classification of thyroid eye disease (TED). The results showed significant differences in extraocular muscle and retroorbital fat volumes, as well as their ratios to orbital volume between different motility groups. Specific angular measurements between the optic nerve and medial rectus and lateral rectus also varied significantly among different TED groups, suggesting that geometric and volumetric measurements on CT scans can aid in the quantitative classification of TED.
APPLIED SCIENCES-BASEL
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Xinhua Li, Theodore A. Marschall, Kai Yang, Bob Liu
Summary: The study evaluates the accuracy of SSDE(z) in tracking the average dose to the transverse slab of an axial image slice (D-slice), showing consistent results with D-slice in chest and abdominopelvic CT examinations. However, there may be deviations on individual image slices, indicating a need for further development to track patient dose more accurately, especially near scan range edges.
Review
Radiology, Nuclear Medicine & Medical Imaging
Kevin P. Sheahan, David Glynn, Stella Joyce, Michael M. Maher, Fiona Boland, Owen J. O'Connor
Summary: Low-dose chest CT is technically feasible, reduces dose, and renders satisfactory image quality for imaging in CF. Limited studies comparing low-dose chest CT with standard CT in CF show equivalent diagnostic capability. Low-dose chest CT with iterative reconstructive algorithms has potential for early detection of bronchiectasis and infective exacerbations, especially in asymptomatic patients.
AMERICAN JOURNAL OF ROENTGENOLOGY
(2021)
Article
Oncology
Yating Ling, Shihong Ying, Lei Xu, Zhiyi Peng, Xiongwei Mao, Zhang Chen, Jing Ni, Qian Liu, Shaolin Gong, Dexing Kong
Summary: A deep-learning based model for the diagnosis of hepatocellular carcinoma was developed, demonstrating high performance and excellent efficiency. The accuracy of the model in diagnosing HCC was significantly higher than that of radiologists, and the model was about 250 times faster in analyzing each lesion compared to the radiologists.
FRONTIERS IN ONCOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yao Sun, Boyang Pan, Qingchu Li, JiaChen Wang, Xiang Wang, Honghua Chen, Qing Cao, Hui Liu, Tao Feng, Hongbiao Sun, Yi Xiao, Nan-Jie Gong
Summary: This study successfully generated clinical ultra-high resolution computed tomography (UHRCT) images from low resolution computed tomography (LRCT) using a generative adversarial network (GAN) model. Experimental results showed that the proposed method outperformed other state-of-the-art methods and achieved good clinical performance in terms of image quality and diagnostic confidence.
Article
Radiology, Nuclear Medicine & Medical Imaging
Praneeth Reddy Amudala Puchakayala, Venkata L. Sthanam, Arie Nakhmani, Muhammad F. A. Chaudhary, Abhilash Kizhakke Puliyakote, Joseph M. Reinhardt, Chengcui Zhang, Surya P. Bhatt, Sandeep Bodduluri
Summary: This study evaluated the performance of radiomics features in the diagnosis of COPD and found that a combination of features representing parenchymal texture and lung and airway shape on inspiratory CT scans can accurately detect COPD.
Article
Genetics & Heredity
Laurike Harlaar, Pierluigi Ciet, Gijs van Tulder, Alice Pittaro, Harmke A. van Kooten, Nadine A. M. E. van der Beek, Esther Brusse, Piotr A. Wielopolski, Marleen de Bruijne, Ans T. van der Ploeg, Harm A. W. M. Tiddens, Pieter A. van Doorn
Summary: This study aimed to identify early signs of diaphragmatic weakness in Pompe patients using chest MRI. Results showed that even in early-stage Pompe disease, the motion of the diaphragm is reduced and the shape is more curved during inspiration. MRI can be used to detect early signs of diaphragmatic weakness in Pompe patients, which might help to select patients for early intervention.
ORPHANET JOURNAL OF RARE DISEASES
(2021)
Article
Multidisciplinary Sciences
Espen Jimenez-Solem, Tonny S. Petersen, Casper Hansen, Christian Hansen, Christina Lioma, Christian Igel, Wouter Boomsma, Oswin Krause, Stephan Lorenzen, Raghavendra Selvan, Janne Petersen, Martin Erik Nyeland, Mikkel Zollner Ankarfeldt, Gert Mehl Virenfeldt, Matilde Winther-Jensen, Allan Linneberg, Mostafa Mehdipour Ghazi, Nicki Detlefsen, Andreas David Lauritzen, Abraham George Smith, Marleen de Bruijne, Bulat Ibragimov, Jens Petersen, Martin Lillholm, Jon Middleton, Stine Hasling Mogensen, Hans-Christian Thorsen-Meyer, Anders Perner, Marie Helleberg, Benjamin Skov Kaas-Hansen, Mikkel Bonde, Alexander Bonde, Akshay Pai, Mads Nielsen, Martin Sillesen
Summary: The study used machine learning models to predict the risk of COVID-19 patients at different stages, and found that factors such as age, body mass index, and hypertension were related to disease severity, with different markers for ICU patients. External validation showed fair predictive performance for mortality prediction.
SCIENTIFIC REPORTS
(2021)
Article
Multidisciplinary Sciences
Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne
Summary: The study investigated the feasibility of using crowdsourcing to gather airway annotations, finding potential but highlighting the need for further development to improve efficiency and accuracy. Results showed moderate to strong correlations between crowd annotations and expert annotations, although slightly lower compared to inter-expert correlations.
Article
Clinical Neurology
Thom S. Lysen, Pinar Yilmaz, Florian Dubost, M. Arfan Ikram, Marleen de Bruijne, Meike W. Vernooij, Annemarie Luik
Summary: Higher sleep efficiency measured by actigraphy was found to be associated with increased perivascular space load in the centrum semiovale of the brain, contrary to the hypothesis. No other sleep characteristics were found to be associated with perivascular space load in other brain regions in this middle-aged and elderly population.
JOURNAL OF SLEEP RESEARCH
(2022)
Article
Geriatrics & Gerontology
Elisabeth J. Vinke, Pinar Yilmaz, Janine E. van der Toorn, Rahman Fakhry, Kate Frenzen, Florian Dubost, Silvan Licher, Marleen de Bruijne, Maryam Kavousi, M. Arfan Ikram, Meike W. Vernooij, Daniel Bos
Summary: Intracranial arteriosclerosis is increasingly recognized as a risk factor for cognitive impairment and dementia, with possible mechanisms involving structural brain changes and cerebral small vessel disease. The burden of intracranial carotid artery and vertebrobasilar artery calcification is related to accelerating structural brain changes over time, but not significantly associated with accelerated brain atrophy.
NEUROBIOLOGY OF AGING
(2021)
Article
Multidisciplinary Sciences
Antonio Garcia-Uceda, Raghavendra Selvan, Zaigham Saghir, Harm A. W. M. Tiddens, Marleen de Bruijne
Summary: This paper introduces a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. The method is simple, robust and efficient, achieving highly complete airway trees with few false positive errors across different datasets. Additionally, it shows good sensitivity in the EXACT'09 test set compared to other state-of-the-art methods.
SCIENTIFIC REPORTS
(2021)
Review
Radiology, Nuclear Medicine & Medical Imaging
Ivan Dudurych, Susan Muiser, Niall McVeigh, Huib A. M. Kerstjens, Maarten van den Berge, Marleen de Bruijne, Rozemarijn Vliegenthart
Summary: Research on computed tomography (CT) bronchial parameter measurements shows conflicting results, and there is heterogeneity in the methodology and population of the studies. Significant differences exist between populations for parameters such as wall area percentage, but there is overlap in their ranges.
EUROPEAN RADIOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Shuai Chen, Zahra Sedghi Gamechi, Florian Dubost, Gijs van Tulder, Marleen de Bruijne
Summary: Posterior-CRF is a segmentation method that incorporates CNN-learned features in a CRF for medical image segmentation, outperforming existing methods in terms of performance metrics across various tasks.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Clinical Neurology
Laurike Harlaar, Pierluigi Ciet, Gijs van Tulder, Esther Brusse, Remco G. M. Timmermans, Wim G. M. Janssen, Marleen de Bruijne, Ans T. van der Ploeg, Harm A. W. M. Tiddens, Pieter A. van Doorn, Nadine A. M. E. van der Beek
Summary: The aim of this exploratory study was to evaluate diaphragmatic function across various neuromuscular diseases using spirometry-controlled MRI. The results showed that the diaphragmatic function was impaired in patients with myopathies and motor neuron diseases, and significantly abnormal in Pompe patients. The study suggests that spirometry-controlled MRI can be used to investigate respiratory dysfunction in neuromuscular diseases.
NEUROMUSCULAR DISORDERS
(2022)
Article
Computer Science, Artificial Intelligence
Alain Lalande, Zhihao Chen, Thibaut Pommier, Thomas Decourselle, Abdul Qayyum, Michel Salomon, Dominique Ginhac, Youssef Skandarani, Arnaud Boucher, Khawla Brahim, Marleen de Bruijne, Robin Camarasa, Teresa M. Correia, Xue Feng, Kibrom B. Girum, Anja Hennemuth, Markus Huellebrand, Raabid Hussain, Matthias Ivantsits, Jun Ma, Craig Meyer, Rishabh Sharma, Jixi Shi, Nikolaos V. Tsekos, Marta Varela, Xiyue Wang, Sen Yang, Hannu Zhang, Yichi Zhang, Yuncheng Zhou, Xiahai Zhuang, Raphael Couturier, Fabrice Meriaudeau
Summary: This paper presents the results of the EMIDEC challenge, which aims to automatically assess the state of the heart after myocardial infarction (MI). The challenge focuses on distinguishing between non-infarct and pathological exams and automatically calculating the extent of myocardial infarction using deep learning methods. The results show that automatic classification of exams is achievable, and the segmentation of the myocardium is possible, although the segmentation of the diseased area needs improvement.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Clinical Neurology
Tavia E. Evans, Maria J. Knol, Petra Schwingenschuh, Katharina Wittfeld, Saima Hilal, M. Arfan Ikram, Florian Dubost, Kimberlin M. H. van Wijnen, Petra Katschnig, Pinar Yilmaz, Marleen de Bruijne, Mohamad Habes, Christopher Chen, Soenke Langer, Henry Volzke, M. Kamran Ikram, Hans J. Grabe, Reinhold Schmidt, Hieab H. H. Adams, Meike W. Vernooij
Summary: This study identified determinants of perivascular spaces (PVS) burden by pooling data from multiple cohort studies and using a uniform rating method. The results showed that PVS count increases with age, men have more PVS in the mesencephalon but less in the hippocampus, and higher blood pressure is associated with increased PVS in all regions. Furthermore, other factors such as high-density lipoprotein cholesterol levels, glucose levels, APOE genotypes, and presence of lacunes are also associated with PVS burden.
Article
Imaging Science & Photographic Technology
Soumick Chatterjee, Kartik Prabhu, Mahantesh Pattadkal, Gerda Bortsova, Chompunuch Sarasaen, Florian Dubost, Hendrik Mattern, Marleen de Bruijne, Oliver Speck, Andreas Nuernberger
Summary: The pathology of blood vessels in the brain can cause serious neurodegenerative diseases. This paper proposes a deep learning architecture to automatically segment small vessels in MRI images, improving diagnosis accuracy.
JOURNAL OF IMAGING
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Laurike Harlaar, Pierluigi Ciet, Gijs van Tulder, Harmke A. van Kooten, Nadine A. M. E. van der Beek, Esther Brusse, Marleen de Bruijne, Harm A. W. M. Tiddens, Ans T. van der Ploeg, Pieter A. van Doorn
Summary: MRI can evaluate the progression of Pompe disease and the effectiveness of treatment by assessing changes in diaphragmatic curvature. Once severe diaphragmatic weakness occurs, improvement in diaphragm muscle function seems unlikely.
EUROPEAN RADIOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Gerda Bortsova, Daniel Bos, Florian Dubost, Meike W. Vernooij, M. Kamran Ikram, Gijs van Tulder, Marleen de Bruijne
Summary: This study developed an automated deep learning method for assessing intracranial internal carotid artery calcification (ICAC) with high accuracy and reliability. The sensitivity and positive predictive value of the automated delineation of ICAC were 83.8% and 88%, respectively, showing high correlation with manual delineation. The automated quantification of ICAC volume had a strong correlation with incident stroke prediction, similar to manually measured volumes.
RADIOLOGY-ARTIFICIAL INTELLIGENCE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Ivan Dudurych, Antonio Garcia-Uceda, Zaigham Saghir, Harm A. W. M. Tiddens, Rozemarijn Vliegenthart, Marleen de Bruijne
Summary: Airways segmentation is important for research on pulmonary disease, and manual correction of segmentations significantly improves results by producing more branches, longer airways, and smaller luminal diameters compared to initial segmentations. Retrained 3D-Unet segmentations also show trends towards more branches and longer airways, especially from the 6th generation onwards.
EUROPEAN RADIOLOGY EXPERIMENTAL
(2021)
