Abstract
Although Low-dose computed tomography (LDCT) is the most effective way for early lung cancer screening, it's still a challenge to further reduce radiation dose on the premise of ensuring image quality. Penalized weighted least-squares (PWLS) image reconstruction with nonlocal means (NLM) prior has shown excellent performance to improve the image quality for LDCT, especially when the nonlocal weights are calculated from previous full-dose CT (FDCT) image. However, the previous FDCT image of the same patient is not readily available, and registration between the LDCT and FDCT images must be considered because of the scanning misalignment. This paper proposed a new NLM prior model to reconstruct high quality LDCT image without image registering. In order to estimate the nonlocal weights of NLM prior, a database was trained from FDCT images of different patients, from which the patch samples similar to each target patch of the LDCT were extracted. Then the nonlocal weights were determined by the patch samples, and integrated into PWLS reconstruction with the priori information of local structures from FDCT. Experiments with 10mAs LDCT data have shown its superiority in reducing noise, streaking artifacts and preserving structure detail, indicating the potential of further dose reduction in ultra-LDCT lung screening.
Highlights
Lung cancer is the leading cause of cancer mortality around the world, with an estimation of 1.59 million deaths in 2012 [1]
We proposed a new nonlocal means (NLM) prior and integrated it into the Penalized weighted least-squares (PWLS) framework for potential lung cancer screening with ultra-low-dose CT, which was based on a training database from full-dose CT (FDCT)
PWLS RECONSTRUCTION WITH NLM PRIOR BASED ON PATCHES FROM TRAINING DATABASE OF FDCTS (PWLS-NLMPATCH) Inspired by the adaptive prior features assisted (APFA) algorithm [27], we proposed a new NLM prior model incorporated into the PWLS framework, where all patches in the search window consist of samples from the training database
Summary
Lung cancer is the leading cause of cancer mortality around the world, with an estimation of 1.59 million deaths in 2012 [1]. The 5-year lung cancer survival rate is only 17.8% in the United States, and less than 18% in UK, Canada and China [1]–[3]. Screening with low-dose computed tomography (LDCT) is the most effective way for early detection of lung cancer [4]–[7]. It has been proved a 20% reduction in lung cancer mortality with LDCT versus that of chest radiography by the National Lung Screening Trial (NLST), a massive study in the United States [8].
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