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Repeated computed tomography (CT) scans are required in some clinicalapplications such as image-guided radiotherapy and follow-up observations overa time period. To optimize the radiation dose utility, a normal-dose (orfull-dose) CT scan is often first performed to set up reference, followed by aseries of low-dose scans. Using the previous normal-dose scan to improvefollow-up low-dose scans reconstruction has drawn great interests recently,such as the previous normal-dose induced nonlocal means (ndiNLM) regularizationmethod. However, one major concern with this method is that whether it wouldintroduce false structures or miss true structures when the previousnormal-dose image and current low-dose image have different structures (e.g., atumor could be present, grow, shrink or absent in either image). This studyaims to investigate the performance of the ndiNLM regularization method in theabove mentioned situations. A patient with lung nodule for biopsy was recruitedto this study. A normal-dose scan was acquired to set up biopsy operation,followed by a few low-dose scans during needle intervention toward the nodule.We used different slices to mimic different possible cases wherein the previousnormal-dose image and current low-dose image have different structures. Theexperimental results characterize performance of our ndiNLM regularizationmethod.

Characterization of the previous normal-dose CT scan induced nonlocal means regularization method for low-dose CT image reconstruction