Model-Based Image Reconstruction for X-ray CT in Lung Imaging Modern X-ray computed tomography (CT) systems provide high-quality images for diagnosing numerous conditions including a variety of lung diseases. Unfortunately, technological advances in CT imaging have been accompanied by significant increases in X-ray radiation dose to patients. There is growing concern about the public health consequences of such doses. Furthermore, even with typical levels of radiation dose, current X-ray CT images have suboptimal image quality due to the limitations of the traditional image reconstruction algorithms used in clinical systems. We propose to develop, implement, analyze and evaluate model-based image reconstruction (MBIR) methods for X-ray CT to improve image quality in lung imaging and to reduce patient dose. Unlike commercially available denoising methods, the proposed MBIR methods are based on accurate models for the physics and statistics of X-ray CT systems. The methods will use edge-preserving regularization that is tailored to lung scans to control noise while improving spatial resolution. We will develop techniques for accelerating the iterative algorithms used in MBIR methods. The methods will be evaluated using computer simulations, phantom studies, and human studies. Specifically, we will focus here on lung CT applications, including morphological characterization of lung nodules and assessment of pulmonary diseases. The clinical impact of MBIR methods will be studied using automated lung image analysis tools and radiologist observer studies.
The relevance of this research to public health is that we will develop and evaluate sophisticated techniques for processing the raw data measured by X-ray CT scanners to dramatically reduce the X-ray radiation dose to patients and to further improve the image quality in lung CT imaging for more accurate diagnosis and treatment.
|Kim, Donghwan; Fessler, Jeffrey A (2016) Optimized first-order methods for smooth convex minimization. Math Program 159:81-107|
|Schmitt, Stephen; Goodsitt, Mitchell; Fessler, Jeffrey (2016) Fast Variance Prediction for Iteratively Reconstructed CT Images with Locally Quadratic Regularization. IEEE Trans Med Imaging :|
|McGaffin, Madison Gray; Fessler, Jeffrey A (2015) Edge-preserving image denoising via group coordinate descent on the GPU. IEEE Trans Image Process 24:1273-81|
|Nien, Hung; Fessler, Jeffrey A (2015) Fast X-ray CT image reconstruction using a linearized augmented Lagrangian method with ordered subsets. IEEE Trans Med Imaging 34:388-99|
|Kim, Donghwan; Ramani, Sathish; Fessler, Jeffrey A (2015) Combining ordered subsets and momentum for accelerated X-ray CT image reconstruction. IEEE Trans Med Imaging 34:167-78|
|Long, Yong; Fessler, Jeffrey A (2014) Multi-material decomposition using statistical image reconstruction for spectral CT. IEEE Trans Med Imaging 33:1614-26|
|Kim, Donghwan; Pal, Debashish; Thibault, Jean-Baptiste et al. (2013) Accelerating ordered subsets image reconstruction for X-ray CT using spatially nonuniform optimization transfer. IEEE Trans Med Imaging 32:1965-78|
|Nuyts, Johan; De Man, Bruno; Fessler, Jeffrey A et al. (2013) Modelling the physics in the iterative reconstruction for transmission computed tomography. Phys Med Biol 58:R63-96|
|Kim, Jung Kuk; Fessler, Jeffrey A; Zhang, Zhengya (2012) Forward-Projection Architecture for Fast Iterative Image Reconstruction in X-ray CT. IEEE Trans Signal Process 60:5508-5518|
|Ramani, Sathish; Fessler, Jeffrey A (2012) A splitting-based iterative algorithm for accelerated statistical X-ray CT reconstruction. IEEE Trans Med Imaging 31:677-88|
Showing the most recent 10 out of 11 publications