Literature DB >> 30299999

Simulated Dose Reduction for Abdominal CT With Filtered Back Projection Technique: Effect on Liver Lesion Detection and Characterization.

Tobias Fält1, Marcus Söderberg2, Lisa Hörberg1, Christina Christoffersen1, Kristina Lång1, Kasim Abul-Kasim1, Peter Leander1.   

Abstract

OBJECTIVE: Previous studies have shown the possibility to reduce radiation dose in abdominal CT by 25-50% without negatively affecting detection of liver lesions. How radiation dose reduction affects characterization of liver metastases is not as well known. The objective of this study was to investigate how different levels of simulated dose reduction affect the detection and characterization of liver lesions, primarily hypovascular metastases. A secondary objective was to analyze the relationship between the lesion size and contrast-to-noise ratio (CNR) and the detection rate.
MATERIALS AND METHODS: Thirty-nine patients (19 with metastases and 20 without) were retrospectively selected. The following radiation dose levels (DLs) were simulated: 100% (reference level), 75%, 50%, and 25%. Five readers were asked to mark liver lesions and rate the probability of malignancy on a 5-grade Likert scale. Noninferiority analysis using the jackknife free-response ROC (JAFROC) method was performed as well as direct comparison of detection rates and grades.
RESULTS: JAFROC analysis showed noninferior detection and characterization of metastases at DL75 as compared with DL100. However, the number of benign lesions and false-positive localizations rated as "suspected malignancy" was significantly higher at DL75.
CONCLUSION: Radiation dose can be reduced by 25% without negatively affecting diagnosis of hypovascular liver metastases. Characterization of benign lesions, however, is impaired at DL75, which may lead to unnecessary follow-up examinations. Finally, increased image noise seems to affect the detection of small lesions to a degree that cannot be explained solely by the reduction in CNR.

Entities:  

Keywords:  CT; characterization; image quality; liver metastases; radiation dose

Mesh:

Substances:

Year:  2018        PMID: 30299999     DOI: 10.2214/AJR.17.19441

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  4 in total

1.  The influence of a deep learning image reconstruction algorithm on the image quality and auto-analysis of pulmonary nodules at ultra-low dose chest CT: a phantom study.

Authors:  Xiaohui Li; Lei Deng; Yue Yao; Baobin Guo; Jianying Li; Quanxin Yang
Journal:  Quant Imaging Med Surg       Date:  2022-05

2.  Reduced-Dose Deep Learning Reconstruction for Abdominal CT of Liver Metastases.

Authors:  Corey T Jensen; Shiva Gupta; Mohammed M Saleh; Xinming Liu; Vincenzo K Wong; Usama Salem; Wei Qiao; Ehsan Samei; Nicolaus A Wagner-Bartak
Journal:  Radiology       Date:  2022-01-11       Impact factor: 29.146

3.  Assessment of low-dose paranasal sinus CT imaging using a new deep learning image reconstruction technique in children compared to adaptive statistical iterative reconstruction V (ASiR-V).

Authors:  Yang Li; Xia Liu; Xun-Hui Zhuang; Ming-Jun Wang; Xiu-Feng Song
Journal:  BMC Med Imaging       Date:  2022-06-03       Impact factor: 2.795

4.  Impact of novel deep learning image reconstruction algorithm on diagnosis of contrast-enhanced liver computed tomography imaging: Comparing to adaptive statistical iterative reconstruction algorithm.

Authors:  Shuo Yang; Yifan Bie; Guodong Pang; Xingchao Li; Kun Zhao; Changlei Zhang; Hai Zhong
Journal:  J Xray Sci Technol       Date:  2021       Impact factor: 1.535

  4 in total

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