title: Deblurring of breathing motion artifacts in thoracic PET images by deconvolution methods.
abstract: In FDG-PET imaging of thoracic tumors, blurring due to breathing motion often significantly degrades the quality of the observed image, which then obscures the tumor boundary. We demonstrate a deblurring technique that combines patient-specific motion estimates of tissue trajectories with image deconvolution techniques, thereby partially eliminating breathing-motion induced artifacts. Two data sets were used to evaluate the methodology including mobile phantoms and clinical images. The clinical images consist of PET/CT co-registered images of patients diagnosed with lung cancer. A breathing motion model was used to locally estimate the location-dependent tissue location probability function (TLP) due to breathing. The deconvolution process is carried by an expectation-maximization (EM) iterative algorithm using the motion-based TLP. Several methods were used to improve the robustness of the deblurring process by mitigating noise amplification and compensating for motion estimate uncertainties. The mobile phantom study with controlled settings demonstrated significant reduction in underestimation error of concentration in high activity case without significant superiority between the different applied methods. In case of medium activity concentration (moderate noise levels), less improvement was reported (10%-15% reduction in underestimation error relative to 15%-20% reduction in high concentration). Residual denoising using wavelets offered the best performance for this case. In the clinical data case, the image spatial resolution was significantly improved, especially in the direction of greatest motion (cranio-caudal). The EM algorithm converged within 15 and 5 iterations in the large and small tumor cases, respectively. A compromise between a figure-of-merit and entropy minimization was suggested as a stopping criterion. Regularization techniques such as wavelets and Bayesian methods provided further refinement by suppressing noise amplification. Our initial results show that the proposed method provides a feasible framework for improving PET thoracic images, without the need for gated/4-D PET imaging, when 4-D CT is available to estimate tumor motion.
authors.affiliation: Department of Radiation Oncology, Washington University, School of Medicine, St. Louis, Missouri 63110, USA. elnaqa@wustl.edu
authors.forename: Issam
authors.initials: I
authors.lastname: El Naqa
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Low
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Bradley
authors.forename: Milos
authors.initials: M
authors.lastname: Vicic
authors.forename: Joseph O
authors.initials: JO
authors.lastname: Deasy
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019277:Entropy; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D009038:Motion; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D011336:Probability; D013902:Radiography, Thoracic / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D012119:Respiration; D014057:Tomography, X-Ray Computed