Generation of 4-Dimensional CT Images Based on 4-Dimensional PET–Derived Motion Fields

Abstract

Respiratory motion can potentially reduce accuracy in anatomic and functional image fusion from multimodality systems. It can blur the uptake of small lesions and lead to significant activity underestimation. Solutions presented to date include respiration-synchronized anatomic and functional acquisitions. To increase the signal-to-noise ratio of the synchronized PET images, methods using nonrigid transformations during the reconstruction process have been proposed. In most of these methods, 4-dimensional (4D) CT images were used to derive the required deformation matrices. However, variations between acquired 4D PET and corresponding CT image series due to differences in respiratory conditions during PET and CT acquisitions have been reported. In addition, the radiation dose burden resulting from a 4D CT acquisition may not be justifiable for every patient. Methods: In this paper, we present a method for the generation of dynamic CT images from the combination of one reference CT image and deformation matrices obtained from the elastic registration of 4D PET images not corrected for attenuation. On the one hand, our approach eliminates the need for the acquisition of dynamic CT. On the other hand, it also ensures a good match between CT and PET images, allowing accurate attenuation correction to be performed for respirationsynchronized PET acquisitions. Results: The proposed method was first validated on Monte Carlo-simulated datasets, and then on patient datasets (n 5 4) by comparing generated 4D CT images with the corresponding acquired original CT images. Different levels of PET image statistical quality were considered in order to investigate the impact of image noise in the derivation of the 4D CT series. Conclusion: Our results suggest that clinically relevant PET acquisition times can be used for the implementation of such an approach, making this an even more attractive solution considering the absence of the extra dose given by a standard 4D CT acquisition. Finally, this approach may be applicable to other multimodality devices such as PET/MR.

Reconstruction-Incorporated Respiratory Motion Correction in Clinical Simultaneous PET/MR Imaging for Oncology Applications

“…Such mismatches are resulting from differences in the conditions of respirationsynchronized PET and CT acquisitions. In turn, such differences result in errors associated with the derivation of DMs from 4D CT frames for PET motion correction (12,13). In the case of PET/MR systems, these 2 issues associated with 4D CT acquisitions are irrelevant, given the nonionizing nature of MR acquisitions, their good tissue contrast even in the lungs as demonstrated using newly developed algorithms (14), and the capability of simultaneous PET and MR acquisitions.…”

Optimized PET Imaging for 4D Treatment Planning in Radiotherapy: the Virtual 4D PET Strategy

“…Although the robustness of the virtual 4D PET against breathing irregularities was not specifically investigated (26), the influence of breathing pattern changes is intrinsically limited in this strategy. A future evaluation of the increased robustness of the virtual 4D PET against breathing irregularities would require to consider 4D attenuation and 4D scatter corrections, since the PET activity distribution (i.e., the 4D emission map) is modified according to information coming from the CT image (i.e., the 4D attenuation map) (27). The issue of 4D CT motion model inaccuracies due to breathing irregularities is expected to be more critical for conventional motion compensated 4D PET.…”

Attenuation correction in emission tomography using the emission data—A review

“…7.C.3, a static µ-map available, one may assume simultaneous motion of attenuation and activity, estimate a motion-field from NAC PET images, and deform the static µ-map accordingly. 195 More in line with the topic of this review, in an integrated approach, others have formulated the likelihood as a function of warped activity and attenuation and deformation (maximum likelihood joint image reconstruction/motion estimation, ML-JRM); one then notes that only an arbitrarily warped µ-map is required to solve for λ and µ in all gates. 196 Using a similar algorithm (minimal MRI prior joint image reconstruction/motion estimation, MP-JRM), incomplete dynamic MRI information can also be integrated.…”