PURPOSE: Irregular breathing causes motion blurring artifacts in 4D PET images. Audiovisual (AV) biofeedback has been demonstrated to improve breathing regularity. To investigate the hypothesis that, compared with free breathing, motion blurring artifacts are reduced with AV biofeedback, the authors performed the first experimental phantom-based quantification of the impact of AV biofeedback on 4D PET image quality. METHODS: The authors acquired 4D PET dynamic phantom images with AV biofeedback and free breathing by moving a phantom programmed with AV biofeedback trained and free breathing respiratory traces of ten healthy subjects. The authors also acquired stationary phantom images for reference. The phantom was cylindrical with six hollow sphere targets (10, 13, 17, 22, 28, and 37 mm in diameter). The authors quantified motion blurring using the target diameter, Dice coefficient and recovery coefficient (RC) metrics to estimate the effect of motion. RESULTS: The average increase in target diameter for AV biofeedback was 0.6±1.6mm (4.7±13%), which was significantly (p<0.001) smaller than for free breathing 1.3±2.2mm (9.1±19%). The average Dice coefficient for AV biofeedback was 0.90±0.07, which was significantly (p<0.001) larger than for free breathing (0.88±0.10). The RCs for AV biofeedback were consistently higher than those for free breathing and comparable to those for stationary targets. However, for RCs the impact of target sizes was more dominant than that of motion. In addition, the authors observed large variations in the results with respect to target sizes, subject traces and respiratory bins due to partial volume effects and respiratory motion irregularity. CONCLUSIONS: The results indicate that AV biofeedback can significantly reduce motion blurring artifacts and may facilitate improved identification and localization of lung tumors in 4D PET images. The results justify proceeding with clinical studies to quantify the impact of AV biofeedback on 4D PET image quality and tumor detectability.
PURPOSE: Irregular breathing causes motion blurring artifacts in 4D PET images. Audiovisual (AV) biofeedback has been demonstrated to improve breathing regularity. To investigate the hypothesis that, compared with free breathing, motion blurring artifacts are reduced with AV biofeedback, the authors performed the first experimental phantom-based quantification of the impact of AV biofeedback on 4D PET image quality. METHODS: The authors acquired 4D PET dynamic phantom images with AV biofeedback and free breathing by moving a phantom programmed with AV biofeedback trained and free breathing respiratory traces of ten healthy subjects. The authors also acquired stationary phantom images for reference. The phantom was cylindrical with six hollow sphere targets (10, 13, 17, 22, 28, and 37 mm in diameter). The authors quantified motion blurring using the target diameter, Dice coefficient and recovery coefficient (RC) metrics to estimate the effect of motion. RESULTS: The average increase in target diameter for AV biofeedback was 0.6±1.6mm (4.7±13%), which was significantly (p<0.001) smaller than for free breathing 1.3±2.2mm (9.1±19%). The average Dice coefficient for AV biofeedback was 0.90±0.07, which was significantly (p<0.001) larger than for free breathing (0.88±0.10). The RCs for AV biofeedback were consistently higher than those for free breathing and comparable to those for stationary targets. However, for RCs the impact of target sizes was more dominant than that of motion. In addition, the authors observed large variations in the results with respect to target sizes, subject traces and respiratory bins due to partial volume effects and respiratory motion irregularity. CONCLUSIONS: The results indicate that AV biofeedback can significantly reduce motion blurring artifacts and may facilitate improved identification and localization of lung tumors in 4D PET images. The results justify proceeding with clinical studies to quantify the impact of AV biofeedback on 4D PET image quality and tumor detectability.
Authors: Sadek A Nehmeh; Yusuf E Erdi; Kenneth E Rosenzweig; Heiko Schoder; Steve M Larson; Olivia D Squire; John L Humm Journal: J Nucl Med Date: 2003-10 Impact factor: 10.057
Authors: S A Nehmeh; Y E Erdi; T Pan; E Yorke; G S Mageras; K E Rosenzweig; H Schoder; H Mostafavi; O Squire; A Pevsner; S M Larson; J L Humm Journal: Med Phys Date: 2004-06 Impact factor: 4.071
Authors: Yusuf E Erdi; Sadek A Nehmeh; Tinsu Pan; Alexander Pevsner; Kenneth E Rosenzweig; Gikas Mageras; Ellen D Yorke; Heiko Schoder; Wendy Hsiao; Olivia D Squire; Phil Vernon; Jonathan B Ashman; Hassan Mostafavi; Steven M Larson; John L Humm Journal: J Nucl Med Date: 2004-08 Impact factor: 10.057
Authors: J A Koutcher; A A Alfieri; M L Devitt; J G Rhee; A B Kornblith; U Mahmood; T E Merchant; D Cowburn Journal: Cancer Res Date: 1992-09-01 Impact factor: 12.701
Authors: Sean Pollock; Ricky O'Brien; Kuldeep Makhija; Fiona Hegi-Johnson; Jane Ludbrook; Angela Rezo; Regina Tse; Thomas Eade; Roland Yeghiaian-Alvandi; Val Gebski; Paul J Keall Journal: BMC Cancer Date: 2015-07-18 Impact factor: 4.430
Authors: David T To; Joshua P Kim; Ryan G Price; Indrin J Chetty; Carri K Glide-Hurst Journal: J Appl Clin Med Phys Date: 2016-05-08 Impact factor: 2.102
Authors: Taeho Kim; Benjamin C Lewis; Alex Price; Thomas Mazur; H Michael Gach; Justin C Park; Bin Cai; Erin Wittland; Lauren Henke; Hyun Kim; Sasa Mutic; Olga Green Journal: J Appl Clin Med Phys Date: 2020-09-15 Impact factor: 2.102