Joint solution for PET image segmentation, denoising, and partial volume correction

• Published in: Medical image analysis Vol. 46; pp. 229 - 243
• Main Authors: Xu, Ziyue, Gao, Mingchen, Papadakis, Georgios Z., Luna, Brian, Jain, Sanjay, Mollura, Daniel J., Bagci, Ulas
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.05.2018; Elsevier BV
• Subjects: Adaptive algorithms; Affinity propagation; Algorithms; Animals; Clustering; Denoising; Humans; Image Enhancement - methods; Image processing; Image reconstruction; Image segmentation; Magnetic Resonance Imaging; Medical imaging; Neoplasms - diagnostic imaging; Noise reduction; Normal distribution; Partial volume correction; Phantoms, Imaging; Polyvinyl chloride; Positron emission; Positron Emission Tomography Computed Tomography - methods; Positron-Emission Tomography - methods; Qualitative analysis; Qualitative research; Quantitative analysis; Rabbits; Radiographic Image Interpretation, Computer-Assisted - methods; Radiopharmaceuticals; Regional means denoising; Reproducibility of Results; Segmentation; Sensitivity and Specificity; Signal-To-Noise Ratio; Smoothness; Tomography; Tuberculosis, Pulmonary - diagnostic imaging; Regional means denoising; Partial volume correction; Segmentation; Affinity propagation; Denoising
• ISSN: 1361-8415; 1361-8423; 1361-8431; 1361-8423
• DOI: 10.1016/j.media.2018.03.007

•Interactions among segmentation, denoising, and partial volume corrections have been utilized to improve solution of each of these problems for PET images.•Noise in PET imaging is modeled as mixed Poisson–Gaussian, as it is more realistic than the current standards.•Partial volume correction is shown to improve when segmentation and noise information are incorporated into the proposed joint solution model.•Segmentation process gets benefit from denoising and partial volume correction step, leading to improved boundary definitions of lesions in PET images.•Extensive set of experiments (phantom, pre-clinical, and clinical) with PET, PET/CT, and PET/MRI validate the proposed algorithm’s performance. [Display omitted] Segmentation, denoising, and partial volume correction (PVC) are three major processes in the quantification of uptake regions in post-reconstruction PET images. These problems are conventionally addressed by independent steps. In this study, we hypothesize that these three processes are dependent; therefore, jointly solving them can provide optimal support for quantification of the PET images. To achieve this, we utilize interactions among these processes when designing solutions for each challenge. We also demonstrate that segmentation can help in denoising and PVC by locally constraining the smoothness and correction criteria. For denoising, we adapt generalized Anscombe transformation to Gaussianize the multiplicative noise followed by a new adaptive smoothing algorithm called regional mean denoising. For PVC, we propose a volume consistency-based iterative voxel-based correction algorithm in which denoised and delineated PET images guide the correction process during each iteration precisely. For PET image segmentation, we use affinity propagation (AP)-based iterative clustering method that helps the integration of PVC and denoising algorithms into the delineation process. Qualitative and quantitative results, obtained from phantoms, clinical, and pre-clinical data, show that the proposed framework provides an improved and joint solution for segmentation, denoising, and partial volume correction.