A review of our recent CT Denoising paper “Window-Level is a Strong Denoising Surrogate”

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In this article, I will discuss our recent work, a new self-supervised CT denoising method: SSWL-IDN, by Ayaan Haque (me), Adam Wang, and Abdullah-Al-Zubaer Imran, from Saratoga High School and Stanford University’s RSL. In this paper, we introduce SSWL-IDN, a novel self-supervised CT denoising window-level prediction surrogate task. Our method is task-relevant and related to the downstream task, yielding improved performance over recent methods. Our paper was recently accepted to MICCAI MLMI 2021 and will be presented in September. This article will cover our addressed problem, our methods, and (briefly) our results. Our paper is available on ArXiv, the code is available on Github, and our project page is available here.

What is CT Denoising and why is it important?

For those without a strong medical imaging background, CT imaging is a prominent imaging modality. CT imaging relies on radiation dose, and as a result, there is a tradeoff between image quality and radiation dose. The higher the radiation dose, the less noise the images will contain. However, high radiation doses are harmful to patients, meaning it is desirable to scan patients at lower doses. However, with increased noise in images, the diagnostic performance of CT images decreases, as noise may block certain structures from being visible. Thus, it is a critical medical imaging issue to denoise CT images to get the best of both worlds.

To perform deep-learning-based CT image denoising, a model will input a low-dose CT scan (LDCT) and predict the full-dose CT scan (FDCT). Full-dose scans are collected at routine dose, and low-dose scans are generally collected at quarter dose. However, this poses a glaring challenge. Medical data is often hard to acquire, especially for CT scans when it is hard to have both a clean reference and a low dose version of the same scan. However, with limited labeled data, deep learning performance will decrease. This means the use of learning frameworks that leverage unlabeled data is critical.

What is Self-Supervised Learning?

Acquiring reference images is challenging due to the harmful nature of radiation as well as the difficulty of performing two identical scans at different radiation doses. Thus, it is desirable to train denoising models with limited reference data. Self-Supervised Learning (SSL) has emerged as a promising alternative to…

Continue reading: https://towardsdatascience.com/sswl-idn-self-supervised-ct-denoising-208fde94583e?source=rss—-7f60cf5620c9—4

Source: towardsdatascience.com