A novel adaptive discrete cosine transform‐domain filter for gap‐inpainting of high resolution PET scanners. Issue 8 (9th July 2014)

• Record Type: Journal Article
• Authors: Shih, Cheng‐Ting; Wu, Jay; Lin, Hsin‐Hon; Chang, Shu‐Jun; Chuang, Keh‐Shih
• Is Part Of: Medical physics. Volume 41:Issue 8(2014)Part 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-07-09
• Subjects: Positron emission tomography (PET) -- Spatial resolution -- Image quality -- Integral transforms; discrete cosine transforms -- image resolution -- mean square error methods -- medical image processing -- phantoms -- positron emission tomography; positron emission tomography -- gap‐inpainting -- discrete cosine transform -- quantitative accuracy; Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Scintigraphy -- Measuring half‐life of a radioactive substance; Computed tomography -- Image reconstruction -- Positron emission tomography -- Image sensors -- Medical image noise -- Brain -- Medical image reconstruction -- Image analysis; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4886035
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Several positron emission tomography (PET) scanners with special detector block arrangements have been developed in recent years to improve the resolution of PET images. However, the discontinuous detector blocks cause gaps in the sinogram. This study proposes an adaptive discrete cosine transform‐based (aDCT) filter for gap‐inpainting. Methods: The gap‐corrupted sinogram was morphologically closed and subsequently converted to the DCT domain. A certain number of the largest coefficients in the DCT spectrum were identified to determine the low‐frequency preservation region. The weighting factors for the remaining coefficients were determined by an exponential weighting function. The aDCT filter was constructed and applied to two digital phantoms and a simulated phantom introduced with various levels of noise. Results: For the Shepp‐Logan head phantom, the aDCT filter filled the gaps effectively. For the Jaszczak phantom, no secondary artifacts were induced after aDCT filtering. The percent mean square error and mean structure similarity of the aDCT filter were superior to those of the DCT2 filter at all noise levels. For the simulated striatal dopamine innervation study, the aDCT filter recovered the shape of the striatum and restored the striatum to reference activity ratios to the ideal value. Conclusions: The proposed aDCT filter can recover the missing gap data in the sinogram and improve the image quality and quantitative accuracy of PET images.