Development and validation of a generic image-based noise addition software for simulating reduced dose computed tomography images using synthetic projections

Alsaihati, Njood; Solomon, Justin; Samei, Ehsan

doi:10.1117/12.2612666

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…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

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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.

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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

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Influence of CT dose reduction on AI-driven malignancy estimation of incidental pulmonary nodules

Peters,

Solomon,

von Stackelberg

et al. 2023

Eur Radiol

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Objectives The purpose of this study was to determine the influence of dose reduction on a commercially available lung cancer prediction convolutional neuronal network (LCP-CNN). Methods CT scans from a cohort provided by the local lung cancer center (n = 218) with confirmed pulmonary malignancies and their corresponding reduced dose simulations (25% and 5% dose) were subjected to the LCP-CNN. The resulting LCP scores (scale 1–10, increasing malignancy risk) and the proportion of correctly classified nodules were compared. The cohort was divided into a low-, medium-, and high-risk group based on the respective LCP scores; shifts between the groups were studied to evaluate the potential impact on nodule management. Two different malignancy risk score thresholds were analyzed: a higher threshold of ≥ 9 (“rule-in” approach) and a lower threshold of > 4 (“rule-out” approach). Results In total, 169 patients with 196 nodules could be included (mean age ± SD, 64.5 ± 9.2 year; 49% females). Mean LCP scores for original, 25% and 5% dose levels were 8.5 ± 1.7, 8.4 ± 1.7 (p > 0.05 vs. original dose) and 8.2 ± 1.9 (p < 0.05 vs. original dose), respectively. The proportion of correctly classified nodules with the “rule-in” approach decreased with simulated dose reduction from 58.2 to 56.1% (p = 0.34) and to 52.0% for the respective dose levels (p = 0.01). For the “rule-out” approach the respective values were 95.9%, 96.4%, and 94.4% (p = 0.12). When reducing the original dose to 25%/5%, eight/twenty-two nodules shifted to a lower, five/seven nodules to a higher malignancy risk group. Conclusion CT dose reduction may affect the analyzed LCP-CNN regarding the classification of pulmonary malignancies and potentially alter pulmonary nodule management. Clinical relevance statement Utilization of a “rule-out” approach with a lower malignancy risk threshold prevents underestimation of the nodule malignancy risk for the analyzed software, especially in high-risk cohorts. Key Points • LCP-CNN may be affected by CT image parameters such as noise resulting from low-dose CT acquisitions. • CT dose reduction can alter pulmonary nodule management recommendations by affecting the outcome of the LCP-CNN. • Utilization of a lower malignancy risk threshold prevents underestimation of pulmonary malignancies in high-risk cohorts.

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Development and validation of a generic image-based noise addition software for simulating reduced dose computed tomography images using synthetic projections

Cited by 2 publications

References 6 publications

Deep Learning–Based Image Noise Quantification Framework for Computed Tomography

Deep Learning–Based Image Noise Quantification Framework for Computed Tomography

Influence of CT dose reduction on AI-driven malignancy estimation of incidental pulmonary nodules

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