Lung cancer continues to be the leading cause of cancer deaths in the world. In spite of decades of research on diagnosis and therapy, survival rates remain poor. Computed tomography (CT) scans can readily detect small lung nodules, but requires significantly higher radiation dose. Digital chest tomosynthesis (DCT) creates CT-like 3D images by scanning an x-ray tube in front of the patient to obtain multiple views over a limited angular range. It uses significantly lower imaging dose and costs less compared to CT. Clinical studies have shown that DCT can achieve sensitivities of over 90% as compared to conventional CT in detecting small lung nodules when respiratory motion is at a minimum. The scanning time of the current DCT systems is considerably longer than a respiratory cycle. The motion blurring of the patient and the source degrade image quality even with the patient holding their breath. When motion artifacts are present, the detection sensitivity is reduced to the level of conventional 2D chest radiography (CR). The goal of this study is to increase tomosynthesis image quality of DCT. Utilizing the spatially distributed carbon nanotube (CNT) x-ray source array technology we propose to develop a stationary DCT (s- DCT) system. The system generates the multiple projection images by electronically activating the individual x- ray sources in the source array without any mechanical motion. Our goal is to achieve a full chest tomosynthesis scan in 2s or less, 1/3 - 1/5 of the current commercial DCT scanners, which is expected to reduce image blurring due to patient respiratory motion. The CNT x-ray source array technology further enables a wider angular coverage that reduces the out-of-plane artifact and novel imaging geometries that may provide a more isotropic in-plane resolution. The hypothesis, supported by prior clinical studies, is that the improved image quality will lead to an improved sensitivity and specificity for lung cancer to the level approaching that of CT. Beyond lung cancer screening, the low-dose and highly sensitive 3D DCT lung imaging modality will likely to find applications in areas such as monitoring pediatric cystic fibrosis patients where reduction of imaging dose is critical. The proposed study is a close collaboration between two physical scientists (Zhou, PhD, and Lu, PhD) and a translational radiologist (Lee, MD/PhD) at UNC. The UNC cardiothoracic surgery simulation lab will be utilized for the physiological phantom studies. We have worked together productively for over a decade and have a demonstrated record of taking new technologies from basic research to clinical trials and commercial products. Successful examples include the development of the CNT x-ray source array technology;the dynamic micro-computed tomography scanner now installed at UNC in the small animal core as a user facility;and stationary digital breast tomosynthesis currently undergoing a pilot clinical trial.

Public Health Relevance

Early detection is the key for increasing the survival rate of lung cancer, which continues to be the leading cause of cancer deaths in the world. The aim of this proposal is to develop a stationary digital chest tomosynthesis (s-DCT) system with increased spatial resolution and enhanced sensitivity and specificity for detection of small lung nodules, utilizing a unique carbon nanotube based x-ray source array technology invented by our team. The project has the potential of leading to a high-sensitivity, high-specificity, low-dos and low-cost lung cancer screening modality for the general population.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (ZCA1-SRLB-J (J3))
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Zhang, Yantian
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University of North Carolina Chapel Hill
Schools of Arts and Sciences
Chapel Hill
United States
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Gunnell, Elias Taylor; Franceschi, Dora K; Inscoe, Christina R et al. (2018) Initial clinical evaluation of stationary digital chest tomosynthesis in adult patients with cystic fibrosis. Eur Radiol :
Wu, Gongting; Inscoe, Christina R; Calliste, Jabari et al. (2017) Estimating scatter from sparsely measured primary signal. J Med Imaging (Bellingham) 4:013508