Comparison of 81mKrypton and 99mTc-Technegas for ventilation single-photon emission computed tomography in severe chronic obstructive pulmonary disease.

INTRODUCTION: Ventilation and perfusion single-photon emission computed tomography combined with computed tomography (SPECT/CT) is a powerful tool to assess the state of the lungs in chronic obstructive pulmonary disease (COPD). 81mKrypton is a gaseous ventilation tracer and distributes similarly to air, but is not widely available and relatively expensive. 99mTc-Technegas is cheaper and has wider availability, but is an aerosol, which may deposit in hot spots as the severity of COPD increases. In this study, 81mKrypton and 99mTc-Technegas were compared quantitatively in patients with severe COPD. METHODS: The penetration ratio, the heterogeneity index (with and without band filtering for relevant clinical sizes) and hot spot appearance were assessed in eleven patients with severe COPD that underwent simultaneous dual-isotope ventilation SPECT/CT with both 99mTc-Technegas and 81mKrypton. RESULTS: Significant differences were found in the penetration ratio for the medium energy general purpose (MEGP) collimators, but not for the low energy general purpose (LEGP) collimators. The difference in the overall and the band filtered heterogeneity index was significant in most cases. All patients suffered from 99mTc-Technegas hot spots in at least one lung. Comparison of MEGP 81mKrypton and LEGP Technegas scans revealed similar results as the comparison for the MEGP collimators. CONCLUSION: Caution should be taken when replacing 81mKrypton with 99mTc-Technegas as a ventilation tracer in patients with severe COPD as there are significant differences in the distribution of the tracers over the lungs. Furthermore, this patient group is prone to 99mTc-Technegas hot spots and might need additional scanning if hot spots severely hamper image interpretation.

Clinical evaluation of respiration-induced attenuation uncertainties in pulmonary 3D PET/CT.

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.

4D CT amplitude binning for the generation of a time-averaged 3D mid-position CT scan.

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 (σregistration) and the regularity of the deformation vector field (Delta J) to quantify the quality of the 4D-CT data. These measures were correlated to the regularity of the external respiration signal (σsignal).The two irregularity measures, Delta J and σregistration, were dependent on each other (p<0.0001, R2=0.80 for P-4D-CT, R2=0.74 for A-4D-CT). For all datasets amplitude binning resulted in lower Delta J and σregistration 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 mid-position 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 mid-position 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.

Registration accuracy and image quality of time averaged mid-position CT scans for liver SBRT.

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 1.8mm (± 0.5mm). 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 3.0mm (± 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.

PET motion compensation for radiation therapy using a CT-based mid-position motion model: methodology and clinical evaluation.

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.