Noise Texture Augmentation Improves Pediatric Generalizability in Deep Learning CT Denoising Models
Creators: name: Nelson, Brandon
name: Kc, Prabhat
name: Zeng, Rongping
Publisher: name: Zenodo
Year: 2024

description: Abstract


Background: Deep learning (DL) denoising models have been shown to be effective at reducing noise in computed tomography (CT) images, enabling low dose acquisitions that are essential for pediatric imaging. However, these DL models are primarily trained on adult patient image data, putting their performance in pediatric patients in question due to key differences in pediatric anatomy, pathology, and acquisition protocol.

Purpose: This study aims to take key image quality features from pediatric CT protocols that are missing from adult protocols, notably coarser noise textures from sampling physically smaller fields of view, and incorporate them into denoising model training using data augmentation techniques.

Methods:  Using simulations, uniform phantoms with diameters ranging from the mean effective diameter of newborns to adolescents were repeatedly scanned and reconstructed with body-fitting fields of view (FOV)s. Taking the difference of these repeat images yields noise images with noise textures characteristic of each phantom size. Patches from these size-specific noise images were then added to the low noise target images of the adult patient training dataset to make new noise texture augmented training pairs. RED-CNN denoising models were trained with and without noise texture augmentation and performance was evaluated using objective and task-based image quality assessments for pediatric denoising generalizability.

Results: The baseline RED-CNN denoising model trained on adult patient data achieved a mean noise reduction of 78% in adult patients and 45% in newborn patients. Following noise texture augmented training on the same dataset, a mean noise reduction of 73% was achieved in adults and 65% in newborns, with the performance gap reduced from 33% to just 8% between adult and newborn patient sizes. In addition, there was an overall increase in noise reduction averaged across all investigated patient sizes, 70% with augmented training compared to 60% without. This improved pediatric generalizability for a small cost in adult performance supports noise texture augmentation as a regularization technique that reduces overfitting in adult-only trained denoising models. Furthermore, task-based performance showed similar trends with the greatest low contrast detectability improvement in infants as measured by a Channelized Hotelling model observer with an area under the receiver operator characteristic curve (AUC) of 0.84 with augmented training compared to 0.80 without.

Conclusion: Due to scarcity of pediatric radiology exams and their protected status, pediatric training data is and will likely remain scarce. However, this study has demonstrated that characterizing the causes of poor performance in pediatric patients can yield insights into improved model training. By augmenting model training with noise textures from pediatric protocol phantom scans image quality and task-based performance were improved in newborns, infants, and small children not typically available in training datasets. These findings support the use of noise texture data augmentation as a regularization technique to improve generalizability and overall performance across a wider variety of patient sizes than the model was originally trained with.