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The image quality of respiratory sorted four-dimensional (4D) cone-beam (CB) computed tomography (CT) is often limited by streak artifacts due to insufficient projections. A motion weighted reconstruction (MWR) method is proposed to decrease streak artifacts and improve image quality. Firstly, respiratory correlated CBCT projections were interpolated by directional sinogram interpolation (DSI) to generate additional CB projections for each phase and subsequently reconstructed. Secondly, local motion was estimated by deformable image registration of the interpolated 4D CBCT. Thirdly, a regular 3D FDK CBCT was reconstructed from the non-interpolated projections. Finally, weights were assigned to each voxel, based on the local motion, and then were used to combine the 3D FDK CBCT and interpolated 4D CBCT to generate the final 4D image. MWR method was compared with regular 4D CBCT scans as well as McKinnon and Bates (MKB) based reconstructions. Comparisons were made in terms of (1) comparing the steepness of an extracted profile from the boundary of the region-of-interest (ROI), (2) contrast-to-noise ratio (CNR) inside certain ROIs, and (3) the root-mean-square-error (RMSE) between the planning CT and CBCT inside a homogeneous moving region. Comparisons were made for both a phantom and four patient scans. In a 4D phantom, RMSE were reduced by 24.7% and 38.7% for MKB and MWR respectively, compared to conventional 4D CBCT. Meanwhile, interpolation induced blur was minimal in static regions for MWR based reconstructions. In regions with considerable respiratory motion, image blur using MWR is less than the MKB and 3D Feldkamp (FDK) methods. In the lung cancer patients, average CNRs of MKB, DSI and MWR improved by a factor 1.7, 2.8 and 3.5 respectively relative to 4D FDK. MWR effectively reduces RMSE in 4D cone-beam CT and improves the image quality in both the static and respiratory moving regions compared to 4D FDK and MKB methods.

Purpose Four-dimensional positron emission tomography (4D PET) imaging of the thorax produces sharper images with reduced motion artifacts. Current radiation therapy planning systems, however, do not facilitate 4D plan optimization. When images are acquired in a 2-minute time slot, the signal-to-noise ratio of each 4D frame is low, compromising image quality. The purpose of this study was to implement and evaluate the construction of mid-position 3D PET scans, with motion compensated using a 4D computed tomography (CT)-derived motion model. Methods and Materials All voxels of 4D PET were registered to the time-averaged position by using a motion model derived from the 4D CT frames. After the registration the scans were summed, resulting in a motion-compensated 3D mid-position PET scan. The method was tested with a phantom dataset as well as data from 27 lung cancer patients. Results PET motion compensation using a CT-based motion model improved image quality of both phantoms and patients in terms of increased maximum SUV (SUV max ) values and decreased apparent volumes. In homogenous phantom data, a strong relationship was found between the amplitude-to-diameter ratio and the effects of the method. In heterogeneous patient data, the effect correlated better with the motion amplitude. In case of large amplitudes, motion compensation may increase SUV max up to 25% and reduce the diameter of the 50% SUV max volume by 10%. Conclusions 4D CT-based motion-compensated mid-position PET scans provide improved quantitative data in terms of uptake values and volumes at the time-averaged position, thereby facilitating more accurate radiation therapy treatment planning of pulmonary lesions.

Background In contemporary positron emission tomography (PET)/computed tomography (CT) scanners, PET attenuation correction is performed by means of a CT-based attenuation map. Respiratory motion can however induce offsets between the PET and CT data. Studies have demonstrated that these offsets can cause errors in quantitative PET measures. The purpose of this study is to quantify the effects of respiration-induced CT differences on the attenuation correction of pulmonary 18-fluordeoxyglucose (FDG) 3D PET/CT in a patient population and to investigate contributing factors. Methods For 32 lung cancer patients, 3D-CT, 4D-PET and 4D-CT data were acquired. The 4D FDG PET data were attenuation corrected (AC) using a free-breathing 3D-CT (3D-AC), the end-inspiration CT (EI-AC), the end-expiration CT (EE-AC) or phase-by-phase (P-AC). After reconstruction and AC, the 4D-PET data were averaged. In the 4Davg data, we measured maximum tumour standardised uptake value (SUV)max in the tumour, SUVmean in a lung volume of interest (VOI) and average SUV (SUVmean) in a muscle VOI. On the 4D-CT, we measured the lung volume differences and CT number changes between inhale and exhale in the lung VOI. Results Compared to P-AC, we found −2.3% (range −9.7% to 1.2%) lower tumour SUVmax in EI-AC and 2.0% (range −0.9% to 9.5%) higher SUVmax in EE-AC. No differences in the muscle SUV were found. The use of 3D-AC led to respiration-induced SUVmax differences up to 20% compared to the use of P-AC. SUVmean differences in the lung VOI between EI-AC and EE-AC correlated to average CT differences in this region (ρ = 0.83). SUVmax differences in the tumour correlated to the volume changes of the lungs (ρ = −0.55) and the motion amplitude of the tumour (ρ = 0.53), both as measured on the 4D-CT. Conclusions Respiration-induced CT variations in clinical data can in extreme cases lead to SUV effects larger than 10% on PET attenuation correction. These differences were case specific and correlated to differences in CT number in the lungs.

The purpose of this study was to develop a method to use amplitude binned 4D-CT (A-4D-CT) data for the construction of mid-position CT data and to compare the results with data created from phase-binned 4D-CT (P-4D-CT) data. For the latter purpose we have developed two measures which describe the regularity of the 4D data and we have tried to correlate these measures with the regularity of the external respiration signal. 4D-CT data was acquired for 27 patients on a combined PET-CT scanner. The 4D data were reconstructed twice, using phase and amplitude binning. The 4D frames of each dataset were registered using a quadrature-based optical flow method. After registration the deformation vector field was repositioned to the mid-position. Since amplitude-binned 4D data does not provide temporal information, we corrected the mid-position for the occupancy of the bins. We quantified the differences between the two mid-position datasets in terms of tumour offset and amplitude differences. Furthermore, we measured the standard deviation of the image intensity over the respiration after registration (sigma(registration)) and the regularity of the deformation vector field ((Delta vertical bar J vertical bar) over bar) to quantify the quality of the 4D-CT data. These measures were correlated to the regularity of the external respiration signal (sigma(signal)). The two irregularity measures, (Delta vertical bar J vertical bar) over bar and sigma(registration), were dependent on each other (p and sigma(registration) and large decreases led to visible quality improvements in the mid-position data. The quantity of artefact decrease was correlated to the irregularity of the external respiratory signal. The average tumour offset between the phase and amplitude binned midposition without occupancy correction was 0.42 mm in the caudal direction (10.6% of the amplitude). After correction this was reduced to 0.16 mm in caudal direction (4.1% of the amplitude). Similar relative offsets were found at the diaphragm. We have devised a method to use amplitude binned 4D-CT to construct motion model and generate a mid-position planning CT for radiotherapy treatment purposes. We have decimated the systematic offset of this midposition model with a motion model derived from P-4D-CT. We found that the A-4D-CT led to a decrease of local artefacts and that this decrease was correlated to the irregularity of the external respiration signal

Purpose and background The purpose was to validate the accuracy of motion models derived from deformable registration from four-dimensional computed tomography (4DCT) and breath-hold contrast enhanced computed tomography (BHCCT) scans for liver SBRT. Additionally, the image quality of the time averaged mid-position (MidP) CT constructed using the detected motion model was assessed. Materials and methods 4DCT and BHCCT liver scans of 11 patients were acquired with 1 or 2 fiducial markers. Using parametric sampling the markers were digitally removed. Phase-based optical flow was used to register the 4D frames and the BHCCT, and create MidP data. We compared the deformable registration of the markerless scans with the actual displacement of the markers to assess registration accuracy. The noise levels of the MidP scans were compared to those of the 4DCT and BHCCT data. Results We found an average misregistration of 1.8 mm (±0.5 mm). The constructed MidPCT scan contained around three times less noise than the original 4D scan. The residual error between the MidPCT and the BHCCT was 3.0 mm (±0.9 mm). Conclusions High precision deformable image registration of 4DCT and BHCCT liver cancer patients was achieved and used to create motion compensated MidPCT scans, with increased contrast-to-noise (CNR) levels. This improved visualisation of tumours and anatomy, facilitates radiotherapy treatment planning.