PURPOSE: X-ray based tomographic blood perfusion imaging requires recovery of contrast time-attenuation-curves from dynamic projection data. When using slowly rotating imaging systems, this task is challenging due to nonsimultaneous projection acquisition. A dynamic reconstruction method is proposed that aims at compensating the lack of simultaneously acquired information by incorporating prior knowledge about the expected temporal contrast dynamics. METHODS: A decomposition model using temporal basis functions to approximate time-attenuation-curves is integrated into an iterative tomographic reconstruction method. The computationally efficient implementation of the proposed approach makes use of standard forward-projections and backprojections, as well as scalar products in image space. The critical issue of projection noise propagation is tackled by the application of regularization which is realized by the early stopping of iteration cycles and by the proper selection of smooth temporal basis functions. The performance of the proposed dynamic reconstruction approach is evaluated in a simulation study concerning various aspects: Noise propagation and regularization, specification of the temporal model, and type of acquisition mode. RESULTS: The evaluation based on dynamic phantom data indicates that tomographic recovery of contrast time-attenuation-curves in tissue can be achieved with an average range of accuracy of approximately 2% (with respect to dynamic peak attenuation) under ideal noise-free conditions. The relative estimation error for arterial time-attenuation-curves is in the range of 8%, which is due to faster contrast dynamics in the artery. In general, performance depends on the level of acquired information contained in the projection data, which is mainly influenced by the type of rotational acquisition mode; restrictions in angular range and speed can lead to limited accuracy. The analysis of propagated projection noise in a statistical bias-variance framework reveals relative noise levels in estimated time-attenuation-curves of 3%-4% in tissue regions and below 1% in vessels when using optimized settings for regularization. Here, the effect of noise suppression depends on the interrelation between the number of iteration cycles and the constraints imposed by the temporal decomposition model. CONCLUSIONS: For usage with slowly rotating imaging systems, the presented model-based iterative dynamic reconstruction method is capable of recovering contrast time-attenuation-curves related to tissue perfusion. The proposed regularization framework is an effective means to limit the impact of projection noise, which is a factor dominating estimation accuracy in tissue regions.
PURPOSE: X-ray based tomographic blood perfusion imaging requires recovery of contrast time-attenuation-curves from dynamic projection data. When using slowly rotating imaging systems, this task is challenging due to nonsimultaneous projection acquisition. A dynamic reconstruction method is proposed that aims at compensating the lack of simultaneously acquired information by incorporating prior knowledge about the expected temporal contrast dynamics. METHODS: A decomposition model using temporal basis functions to approximate time-attenuation-curves is integrated into an iterative tomographic reconstruction method. The computationally efficient implementation of the proposed approach makes use of standard forward-projections and backprojections, as well as scalar products in image space. The critical issue of projection noise propagation is tackled by the application of regularization which is realized by the early stopping of iteration cycles and by the proper selection of smooth temporal basis functions. The performance of the proposed dynamic reconstruction approach is evaluated in a simulation study concerning various aspects: Noise propagation and regularization, specification of the temporal model, and type of acquisition mode. RESULTS: The evaluation based on dynamic phantom data indicates that tomographic recovery of contrast time-attenuation-curves in tissue can be achieved with an average range of accuracy of approximately 2% (with respect to dynamic peak attenuation) under ideal noise-free conditions. The relative estimation error for arterial time-attenuation-curves is in the range of 8%, which is due to faster contrast dynamics in the artery. In general, performance depends on the level of acquired information contained in the projection data, which is mainly influenced by the type of rotational acquisition mode; restrictions in angular range and speed can lead to limited accuracy. The analysis of propagated projection noise in a statistical bias-variance framework reveals relative noise levels in estimated time-attenuation-curves of 3%-4% in tissue regions and below 1% in vessels when using optimized settings for regularization. Here, the effect of noise suppression depends on the interrelation between the number of iteration cycles and the constraints imposed by the temporal decomposition model. CONCLUSIONS: For usage with slowly rotating imaging systems, the presented model-based iterative dynamic reconstruction method is capable of recovering contrast time-attenuation-curves related to tissue perfusion. The proposed regularization framework is an effective means to limit the impact of projection noise, which is a factor dominating estimation accuracy in tissue regions.
Authors: Lennart Husvogt; Stefan B Ploner; Siyu Chen; Daniel Stromer; Julia Schottenhamml; A Yasin Alibhai; Eric Moult; Nadia K Waheed; James G Fujimoto; Andreas Maier Journal: Biomed Opt Express Date: 2020-12-07 Impact factor: 3.732
Authors: Katsuyuki Taguchi; Thomas J Sauer; W Paul Segars; Eric C Frey; Jingyan Xu; Eleni Liapi; J Webster Stayman; Kelvin Hong; Ferdinand K Hui; Mathias Unberath; Yong Du Journal: Med Phys Date: 2020-10-22 Impact factor: 4.071
Authors: Johan Nuyts; Bruno De Man; Jeffrey A Fessler; Wojciech Zbijewski; Freek J Beekman Journal: Phys Med Biol Date: 2013-06-05 Impact factor: 3.609