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Proceedings Paper

The effects of slice thickness and radiation dose level variations on computer-aided diagnosis (CAD) nodule detection performance in pediatric chest CT scans
Author(s): Nastaran Emaminejad; Pechin Lo; Shahnaz Ghahremani; Grace H. Kim; Matthew S. Brown; Michael F. McNitt-Gray
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Paper Abstract

For pediatric oncology patients, CT scans are performed to assess treatment response and disease progression. CAD may be used to detect lung nodules which would reflect metastatic disease. The purpose of this study was to investigate the effects of reducing radiation dose and varying slice thickness on CAD performance in the detection of solid lung nodules in pediatric patients. The dataset consisted of CT scans of 58 pediatric chest cases, from which 7 cases had lung nodules detected by radiologist, and a total of 28 nodules were marked. For each case, the original raw data (sinogram data) was collected and a noise addition model was used to simulate reduced-dose scans of 50%, 25% and 10% of the original dose. In addition, the original and reduced-dose raw data were reconstructed at slice thicknesses of 1.5 and 3 mm using a medium sharp (B45) kernel; the result was eight datasets (4 dose levels x 2 thicknesses) for each case An in-house CAD tool was applied on all reconstructed scans, and results were compared with the radiologist’s markings. Patient level mean sensitivities at 3mm thickness were 24%, 26%, 25%, 27%, and at 1.5 mm thickness were 23%, 29%, 35%, 36% for 10%, 25%, 50%, and 100% dose level, respectively. Mean FP numbers were 1.5, 0.9, 0.8, 0.7 at 3 mm and 11.4, 3.5, 2.8, 2.8 at 1.5 mm thickness for 10%, 25%, 50%, and 100% dose level respectively. CAD sensitivity did not change with dose level for 3mm thickness, but did change with dose for 1.5 mm. False Positives increased at low dose levels where noise values were high.

Paper Details

Date Published: 27 March 2017
PDF: 9 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340B (27 March 2017); doi: 10.1117/12.2255000
Show Author Affiliations
Nastaran Emaminejad, Univ. of California, Los Angeles (United States)
Pechin Lo, Intuitive Surgical, Inc. (United States)
Shahnaz Ghahremani, Univ. of California, Los Angeles (United States)
Grace H. Kim, Univ. of California, Los Angeles (United States)
Matthew S. Brown, Univ. of California, Los Angeles (United States)
Michael F. McNitt-Gray, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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