Local respiratory motion correction for PET/CT imaging: Application to lung cancer. Issue 10 (17th September 2015)

• Record Type: Journal Article
• Authors: Lamare, F.; Fayad, H.; Fernandez, P.; Visvikis, D.
• Is Part Of: Medical physics. Volume 42:Issue 10(2015)
• Page Start: 5903
• Page End: 5912
• Publication Date: 2015-09-17
• Subjects: biomechanics -- cancer -- computerised tomography -- image motion analysis -- image reconstruction -- image registration -- lung -- medical image processing -- phantoms -- positron emission tomography -- tumours; Positron emission tomography (PET) -- Computed tomography -- Movement and locomotion -- Registration -- Reconstruction -- Cancer; Computerised tomographs -- 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 -- Analysis of motion -- Scintigraphy -- Measuring half‐life of a radioactive substance; PET/CT -- local motion correction -- list‐mode -- image reconstruction -- affine and elastic registration; Medical image reconstruction -- Cancer -- Image reconstruction -- Lungs -- Biomedical modeling -- Motion compensation -- Computed tomography -- Computer modeling -- Medical image noise; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4930251
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Despite multiple methodologies already proposed to correct respiratory motion in the whole PET imaging field of view (FOV), such approaches have not found wide acceptance in clinical routine. An alternative can be the local respiratory motion correction (LRMC) of data corresponding to a given volume of interest (VOI: organ or tumor). Advantages of LRMC include the use of a simple motion model, faster execution times, and organ specific motion correction. The purpose of this study was to evaluate the performance of LMRC using various motion models for oncology (lung lesion) applications. Methods: Both simulated (NURBS based 4D cardiac‐torso phantom) and clinical studies (six patients) were used in the evaluation of the proposed LRMC approach. PET data were acquired in list‐mode and synchronized with respiration. The implemented approach consists first in defining a VOI on the reconstructed motion average image. Gated PET images of the VOI are subsequently reconstructed using only lines of response passing through the selected VOI and are used in combination with a center of gravity or an affine/elastic registration algorithm to derive the transformation maps corresponding to the respiration effects. Those are finally integrated in the reconstruction process to produce a motion free image over the lesion regions. Results: Although the center of gravity or affine algorithm achieved similar performance for individual lesion motion correction, the elastic model, applied either locally or to the whole FOV, led to an overall superior performance. The spatial tumor location was altered by 89% and 81% for the elastic model applied locally or to the whole FOV, respectively (compared to 44% and 39% for the center of gravity and affine models, respectively). This resulted in similar associated overall tumor volume changes of 84% and 80%, respectively (compared to 75% and 71% for the center of gravity and affine models, respectively). The application of the nonrigid deformation model in LRMC led to over an order of magnitude gain in computational efficiency of the correction relative to the application of the deformable model to the whole FOV. Conclusions: The results of this study support the use of LMRC as a flexible and efficient correction approach for respiratory motion effects for single lesions in the thoracic area.