Dose Reduction in Abdominal Computed Tomography

2011 ◽  
Vol 46 (7) ◽  
pp. 465-470 ◽  
Author(s):  
Matthias S. May ◽  
Wolfgang Wüst ◽  
Michael Brand ◽  
Christian Stahl ◽  
Thomas Allmendinger ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Dose Reduction ◽  
Abdominal Computed Tomography

scholarly journals X-Ray Dose Reduction in Abdominal Computed Tomography Using Advanced Iterative Reconstruction Algorithms

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e92568 ◽  
Author(s):  
Peigang Ning ◽  
Shaocheng Zhu ◽  
Dapeng Shi ◽  
Ying Guo ◽  
Minghua Sun
Keyword(s):  
Computed Tomography ◽  
Dose Reduction ◽  
Iterative Reconstruction ◽  
Reconstruction Algorithms ◽  
X Ray ◽  
Abdominal Computed Tomography

scholarly journals Predicting the Presence of an Accessory Hepatic Vein Using Abdominal Computed Tomography

2017 ◽  
Vol 35 (1) ◽  
pp. 21-25 ◽  
Author(s):  
Hyung Soo Kim ◽  
Chang Hee Lee ◽  
Seong Hyun Kim ◽  
Jeong Woo Kim ◽  
Cheol Min Park ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Hepatic Vein ◽  
Abdominal Computed Tomography ◽  
Accessory Hepatic Vein

scholarly journals Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kwang-Hyun Uhm ◽  
Seung-Won Jung ◽  
Moon Hyung Choi ◽  
Hong-Kyu Shin ◽  
Jae-Ik Yoo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Kidney Cancer ◽  
Renal Tumor ◽  
Renal Tumors ◽  
Abdominal Computed Tomography ◽  
Wide Range ◽  
End To End ◽  
Multi Phase ◽  
Histologic Subtypes

AbstractIn 2020, it is estimated that 73,750 kidney cancer cases were diagnosed, and 14,830 people died from cancer in the United States. Preoperative multi-phase abdominal computed tomography (CT) is often used for detecting lesions and classifying histologic subtypes of renal tumor to avoid unnecessary biopsy or surgery. However, there exists inter-observer variability due to subtle differences in the imaging features of tumor subtypes, which makes decisions on treatment challenging. While deep learning has been recently applied to the automated diagnosis of renal tumor, classification of a wide range of subtype classes has not been sufficiently studied yet. In this paper, we propose an end-to-end deep learning model for the differential diagnosis of five major histologic subtypes of renal tumors including both benign and malignant tumors on multi-phase CT. Our model is a unified framework to simultaneously identify lesions and classify subtypes for the diagnosis without manual intervention. We trained and tested the model using CT data from 308 patients who underwent nephrectomy for renal tumors. The model achieved an area under the curve (AUC) of 0.889, and outperformed radiologists for most subtypes. We further validated the model on an independent dataset of 184 patients from The Cancer Imaging Archive (TCIA). The AUC for this dataset was 0.855, and the model performed comparably to the radiologists. These results indicate that our model can achieve similar or better diagnostic performance than radiologists in differentiating a wide range of renal tumors on multi-phase CT.

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