Grace Gang

PhD

Johns Hopkins University

Assistant Research Professor

NLST (Learn more about this study)

NLST-728

Nov 17, 2020

Nonlinear performance analysis and prediction for robust low dose lung CT

The proliferation of nonlinear algorithms in CT has posed significant challenges to image quality assessment and optimization. Many of these algorithms defy traditional signal and noise tradeoff and are highly dependent on the data they operate on. This project aims to develop an understanding of important data features pertaining to CT imaging of the lung and apply these features in the systematic assessment and optimization of nonlinear algorithms.\\


To this end, we aim to acquire chest CT volume images from various sources to create a large lung lesion library. We will analyze the morphology and texture of lung nodules in order to quantify underlying diagnostic features. We will explore both traditional analytic decomposition methods and emerging data-driven feature discovery. These features will be used as inputs to characterize the performance of particular nonlinear algorithms including model-based and deep learning data processing. Furthermore, we will develop methods to increase the diversity of the lesion library with synthetic lesions which may be used in virtual clinical trials, data augmentation of machine learning algorithms, as well as system design and optimization.

Aim 1: Create large lung nodule library from various clinical lung datasets

Aim 2: Shape and texture analysis of various classes of lung nodules

Aim 3: Application of the aim 2 analysis in nodule synthesis and assessment of nonlinear, data-driven imaging algorithms