Artificial intelligence in medical imaging: implications for patient radiation safety.

Published on Jun 23, 2021in British Journal of Radiology2.196
· DOI :10.1259/BJR.20210406
Jarrel Seah , Jarrel Seah + 1 AuthorsMeng Law53
Estimated H-index: 53
(Monash University)
Sources
Abstract
Artificial Intelligence (AI), including deep learning, is currently revolutionising the field of medical imaging, with far reaching implications for almost every facet of diagnostic imaging, including patient radiation safety. This paper introduces basic concepts in deep learning and provides an overview of its recent history and its application in tomographic reconstruction as well as other applications in medical imaging to reduce patient radiation dose, as well as a brief description of previous tomographic reconstruction techniques. This review also describes the commonly used deep learning techniques as applied to tomographic reconstruction and draws parallels to current reconstruction techniques. Finally, this paper reviews some of the estimated dose reductions in computed tomography (CT) and positron emission tomography (PET) in the recent literature enabled by deep learning, as well as some of the potential problems that may be encountered such as the obscuration of pathology, and highlights the need for additional clinical reader studies from the imaging community.
📖 Papers frequently viewed together
2021
46 Citations
2 Citations
1984
1 Author (Melvyn Greberman)
3 Citations
References30
Newest
#1Michele AvanzoH-Index: 14
#2Annalisa TrianniH-Index: 1
Last. Mauro IoriH-Index: 14
view all 6 authors...
: Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application...
5 CitationsSource
#1Taylor R. Moen (Mayo Clinic)H-Index: 1
#2Baiyu Chen (NYU: New York University)H-Index: 13
Last. Cynthia H. McCollough (Mayo Clinic)H-Index: 76
view all 9 authors...
Purpose To describe a large, publicly available dataset comprising computed tomography (CT) projection data from patient exams, both at routine clinical doses and simulated lower doses. Acquisition and validation methods The library was developed under local ethics committee approval. Projection and image data from 299 clinically performed patient CT exams were archived for three types of clinical exams: noncontrast head CT scans acquired for acute cognitive or motor deficit, low-dose noncontras...
3 CitationsSource
#1Samuel L. BradyH-Index: 15
#2Andrew T. TroutH-Index: 26
Last. Jonathan R. DillmanH-Index: 40
view all 6 authors...
Background CT deep learning reconstruction (DLR) algorithms have been developed to remove image noise. How the DLR affects image quality and radiation dose reduction has yet to be fully investigated. Purpose To investigate a DLR algorithm's dose reduction and image quality improvement for pediatric CT. Materials and Methods DLR was compared with filtered back projection (FBP), statistical-based iterative reconstruction (SBIR), and model-based iterative reconstruction (MBIR) in a retrospective st...
5 CitationsSource
#1Ge WangH-Index: 80
#2Jong Chul YeH-Index: 52
Last. Bruno De ManH-Index: 19
view all 3 authors...
Deep-learning-based tomographic imaging is an important application of artificial intelligence and a new frontier of machine learning. Deep learning has been widely used in computer vision and image analysis, which deal with existing images, improve these images, and produce features from them. Since 2016, deep learning techniques have been actively researched for tomographic imaging, especially in the context of biomedicine, with impressive results and great potential. Tomographic reconstructio...
28 CitationsSource
#1Vegard Antun (University of Oslo)H-Index: 4
#2Francesco Renna (University of Porto)H-Index: 13
Last. Anders C. Hansen (University of Oslo)H-Index: 21
view all 5 authors...
Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this paper we demonstrate a crucial phenomenon: deep learning typically yields unstablemethods for image reconstruction. The instabilities usually occur in several forms: (1) tiny, almost undetectable perturbations, both in the image and sampling domain, may result in severe artefacts in the reconstruction, (2) a small str...
148 CitationsSource
OBJECTIVES: Exposure to ionizing radiation remains a hazard for patients and healthcare providers. We evaluated the utility of an artificial intelligence (AI)-enabled fluoroscopy system to minimize radiation exposure during image-guided endoscopic procedures. METHODS: We conducted a prospective study of 100 consecutive patients who underwent fluoroscopy-guided endoscopic procedures. Patients underwent interventions using either conventional or AI-equipped fluoroscopy system that uses ultrafast c...
5 CitationsSource
#1Ramandeep Singh (Harvard University)H-Index: 10
#2Subba R. Digumarthy (Harvard University)H-Index: 38
Last. Mannudeep K. Kalra (Harvard University)H-Index: 64
view all 11 authors...
OBJECTIVE. The objective of this study was to compare image quality and clinically significant lesion detection on deep learning reconstruction (DLR) and iterative reconstruction (IR) images of sub...
41 CitationsSource
#1Kaplan Sydney (Philips)H-Index: 1
#1Sydney Kaplan (WashU: Washington University in St. Louis)H-Index: 1
Last. Yang-Ming Zhu (Siemens)H-Index: 12
view all 2 authors...
Positron emission tomography (PET) imaging is an effective tool used in determining disease stage and lesion malignancy; however, radiation exposure to patients and technicians during PET scans continues to draw concern. One way to minimize radiation exposure is to reduce the dose of radioactive tracer administered in order to obtain the scan. Yet, low-dose images are inherently noisy and have poor image quality making them difficult to read. This paper proposes the use of a deep learning model ...
49 CitationsSource
#1Martin J. Willemink (Stanford University)H-Index: 23
#2Peter B. Noël (TUM: Technische Universität München)H-Index: 30
The first CT scanners in the early 1970s already used iterative reconstruction algorithms; however, lack of computational power prevented their clinical use. In fact, it took until 2009 for the first iterative reconstruction algorithms to come commercially available and replace conventional filtered back projection. Since then, this technique has caused a true hype in the field of radiology. Within a few years, all major CT vendors introduced iterative reconstruction algorithms for clinical rout...
122 CitationsSource
#1Mohamed El-Kaddoury (Mohammed V University)H-Index: 1
#2Abdelhak Mahmoudi ('ENS Paris': École Normale Supérieure)H-Index: 5
Last. Mohammed Majid Himmi (Mohammed V University)H-Index: 5
view all 3 authors...
Deep Learning models can achieve impressive performance in supervised learning but not for unsupervised one. In image generation problem for example, we have no concrete target vector. Generative models have been proven useful for solving this kind of issues. In this paper, we will compare two types of generative models: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). We apply those methods to different data sets to point out their differences and see their capabiliti...
4 CitationsSource
Cited By0
Newest