Development of DICOM image-based CT low dose simulator using fan-beam transform

Horiuchi, Tetsuya; Yamamoto, Saburo; Murase, Kenya

doi:10.3233/thc-130746

Cited by 5 publications

(1 citation statement)

References 26 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Quarter dose patient examinations were synthesized using a validated projection-based noise insertion technique, which considers the effect of automatic exposure control, bow tie filter, and electronic noise. [19][20][21] Manual ROI SD measurements were performed at the aorta, liver, spleen, fat, and heart and compared directly with noise levels predicted by SILVER at the same locations. The ROI radius was set to 10 pixels (8 mm).…”

Section: Patient Image Datamentioning

confidence: 99%

Deep Learning–Based Image Noise Quantification Framework for Computed Tomography

Huber

Kim

Leng

et al. 2023

J Comput Assist Tomogr

View full text Add to dashboard Cite

Objective Noise quantification is fundamental to computed tomography (CT) image quality assessment and protocol optimization. This study proposes a deep learning–based framework, Single-scan Image Local Variance EstimatoR (SILVER), for estimating the local noise level within each region of a CT image. The local noise level will be referred to as a pixel-wise noise map. Methods The SILVER architecture resembled a U-Net convolutional neural network with mean-square-error loss. To generate training data, 100 replicate scans were acquired of 3 anthropomorphic phantoms (chest, head, and pelvis) using a sequential scan mode; 120,000 phantom images were allocated into training, validation, and testing data sets. Pixel-wise noise maps were calculated for the phantom data by taking the per-pixel SD from the 100 replicate scans. For training, the convolutional neural network inputs consisted of phantom CT image patches, and the training targets consisted of the corresponding calculated pixel-wise noise maps. Following training, SILVER noise maps were evaluated using phantom and patient images. For evaluation on patient images, SILVER noise maps were compared with manual noise measurements at the heart, aorta, liver, spleen, and fat. Results When tested on phantom images, the SILVER noise map prediction closely matched the calculated noise map target (root mean square error <8 Hounsfield units). Within 10 patient examinations, SILVER noise map had an average percent error of 5% relative to manual region-of-interest measurements. Conclusion The SILVER framework enabled accurate pixel-wise noise level estimation directly from patient images. This method is widely accessible because it operates in the image domain and requires only phantom data for training.

show abstract

Section: Patient Image Datamentioning

confidence: 99%