Deep learning image reconstruction for CT: technical principles and clinical prospects

LR Koetzier, D Mastrodicasa, TP Szczykutowicz… - Radiology, 2023‏ - pubs.rsna.org
Filtered back projection (FBP) has been the standard CT image reconstruction method for 4
decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in …

Application of artificial intelligence technology in oncology: Towards the establishment of precision medicine

R Hamamoto, K Suvarna, M Yamada, K Kobayashi… - Cancers, 2020‏ - mdpi.com
Simple Summary Artificial intelligence (AI) technology has been advancing rapidly in recent
years and is being implemented in society. The medical field is no exception, and the clinical …

Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study

J Greffier, A Hamard, F Pereira, C Barrau, H Pasquier… - European …, 2020‏ - Springer
Objectives To assess the impact on image quality and dose reduction of a new deep
learning image reconstruction (DLIR) algorithm compared with a hybrid iterative …

Artificial intelligence in CT and MR imaging for oncological applications

R Paudyal, AD Shah, O Akin, RKG Do, AS Konar… - Cancers, 2023‏ - mdpi.com
Simple Summary The two most common cross-sectional imaging modalities, computed
tomography (CT) and magnetic resonance imaging (MRI), have shown enormous utility in …

Image quality assessment of abdominal CT by use of new deep learning image reconstruction: initial experience

CT Jensen, X Liu, EP Tamm, AG Chandler… - American Journal of …, 2020‏ - ajronline.org
OBJECTIVE. The purpose of this study was to perform quantitative and qualitative evaluation
of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic …

A review of deep learning CT reconstruction: concepts, limitations, and promise in clinical practice

TP Szczykutowicz, GV Toia, A Dhanantwari… - Current Radiology …, 2022‏ - Springer
Abstract Purpose of Review Deep Learning reconstruction (DLR) is the current state-of-the-
art method for CT image formation. Comparisons to existing filter back-projection, iterative …

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy

H Arabi, H Zaidi - European Journal of Hybrid Imaging, 2020‏ - Springer
This brief review summarizes the major applications of artificial intelligence (AI), in particular
deep learning approaches, in molecular imaging and radiation therapy research. To this …

The augmented radiologist: artificial intelligence in the practice of radiology

E Sorantin, MG Grasser, A Hemmelmayr… - Pediatric …, 2022‏ - Springer
In medicine, particularly in radiology, there are great expectations in artificial intelligence
(AI), which can “see” more than human radiologists in regard to, for example, tumor size …

Reduced-dose deep learning reconstruction for abdominal CT of liver metastases

CT Jensen, S Gupta, MM Saleh, X Liu, VK Wong… - Radiology, 2022‏ - pubs.rsna.org
Background Assessment of liver lesions is constrained as CT radiation doses are lowered;
evidence suggests deep learning reconstructions mitigate such effects. Purpose To evaluate …

Validation of deep-learning image reconstruction for coronary computed tomography angiography: impact on noise, image quality and diagnostic accuracy

DC Benz, G Benetos, G Rampidis, E Von Felten… - Journal of …, 2020‏ - Elsevier
Background Advances in image reconstruction are necessary to decrease radiation
exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has …