Robust real-time extraction of respiratory signals from PET list-mode data.

Respiratory motion, which typically cannot simply be suspended during PET image acquisition, affects lesions' detection and quantitative accuracy inside or in close vicinity to the lungs. Some motion compensation techniques address this issue via pre-sorting ('binning') of the acquired PET data into a set of temporal gates, where each gate is assumed to be minimally affected by respiratory motion. Tracking respiratory motion is typically realized using dedicated hardware (e.g. using respiratory belts and digital cameras). Extracting respiratory signals directly from the acquired PET data simplifies the clinical workflow as it avoids handling additional signal measurement equipment. We introduce a new data-driven method 'combined local motion detection' (CLMD). It uses the time-of-flight (TOF) information provided by state-of-the-art PET scanners in order to enable real-time respiratory signal extraction without additional hardware resources. CLMD applies center-of-mass detection in overlapping regions based on simple back-positioned TOF event sets acquired in short time frames. Following a signal filtering and quality-based pre-selection step, the remaining extracted individual position information over time is then combined to generate a global respiratory signal. The method is evaluated using seven measured FDG studies from single and multiple scan positions of the thorax region, and it is compared to other software-based methods regarding quantitative accuracy and statistical noise stability. Correlation coefficients around 90% between the reference and the extracted signal have been found for those PET scans where motion affected features such as tumors or hot regions were present in the PET field-of-view. For PET scans with a quarter of typically applied radiotracer doses, the CLMD method still provides similar high correlation coefficients which indicates its robustness to noise. Each CLMD processing needed less than 0.4 s in total on a standard multi-core CPU and thus provides a robust and accurate approach enabling real-time processing capabilities using standard PC hardware.

A Digital Preclinical PET/MRI Insert and Initial Results.

Combining Positron Emission Tomography (PET) with Magnetic Resonance Imaging (MRI) results in a promising hybrid molecular imaging modality as it unifies the high sensitivity of PET for molecular and cellular processes with the functional and anatomical information from MRI. Digital Silicon Photomultipliers (dSiPMs) are the digital evolution in scintillation light detector technology and promise high PET SNR. DSiPMs from Philips Digital Photon Counting (PDPC) were used to develop a preclinical PET/RF gantry with 1-mm scintillation crystal pitch as an insert for clinical MRI scanners. With three exchangeable RF coils, the hybrid field of view has a maximum size of 160 mm × 96.6 mm (transaxial × axial). 0.1 ppm volume-root-mean-square B 0-homogeneity is kept within a spherical diameter of 96 mm (automatic volume shimming). Depending on the coil, MRI SNR is decreased by 13% or 5% by the PET system. PET count rates, energy resolution of 12.6% FWHM, and spatial resolution of 0.73 mm (3) (isometric volume resolution at isocenter) are not affected by applied MRI sequences. PET time resolution of 565 ps (FWHM) degraded by 6 ps during an EPI sequence. Timing-optimized settings yielded 260 ps time resolution. PET and MR images of a hot-rod phantom show no visible differences when the other modality was in operation and both resolve 0.8-mm rods. Versatility of the insert is shown by successfully combining multi-nuclei MRI ((1)H/(19)F) with simultaneously measured PET ((18)F-FDG). A longitudinal study of a tumor-bearing mouse verifies the operability, stability, and in vivo capabilities of the system. Cardiac- and respiratory-gated PET/MRI motion-capturing (CINE) images of the mouse heart demonstrate the advantage of simultaneous acquisition for temporal and spatial image registration.

MRI-based attenuation correction for hybrid PET/MRI systems: a 4-class tissue segmentation technique using a combined ultrashort-echo-time/Dixon MRI sequence.

UNLABELLED: Accurate γ-photon attenuation correction (AC) is essential for quantitative PET/MRI as there is no simple relation between MR image intensity and attenuation coefficients. Attenuation maps (µ-maps) can be derived by segmenting MR images and assigning attenuation coefficients to the compartments. Ultrashort-echo-time (UTE) sequences have been used to separate cortical bone and air, and the Dixon technique has enabled differentiation between soft and adipose tissues. Unfortunately, sequential application of these sequences is time-consuming and complicates image registration. METHODS: A UTE triple-echo (UTILE) MRI sequence is proposed, combining UTE sampling for bone detection and gradient echoes for Dixon water-fat separation in a radial 3-dimensional acquisition (repetition time, 4.1 ms; echo times, 0.09/1.09/2.09 ms; field strength, 3 T). Air masks are derived mainly from the phase information of the first echo; cortical bone is segmented using a dual-echo technique. Soft-tissue and adipose-tissue decomposition is achieved using a 3-point Dixon-like decomposition. Predefined linear attenuation coefficients are assigned to classified voxels to generate MRI-based µ-maps. The results of 6 patients are obtained by comparing µ-maps, reciprocal sensitivity maps, reconstructed PET images, and brain region PET activities based on either CT AC, two 3-class MRI AC techniques, or the proposed 4-class UTILE AC. RESULTS: Using the UTILE MRI sequence, an acquisition time of 214 s was achieved for the head-and-neck region with 1.75-mm isotropic resolution, compared with 164 s for a single-echo UTE scan. MRI-based reciprocal sensitivity maps show a high correlation with those derived from CT scans (R(2) = 0.9920). The same is true for PET activities (R(2) = 0.9958). An overall voxel classification accuracy (compared with CT) of 81.1% was reached. Bone segmentation is inaccurate in complex regions such as the paranasal sinuses, but brain region activities in 48 regions across 6 patients show a high correlation after MRI-based and CT-based correction (R(2) = 0.9956), with a regression line slope of 0.960. All overall correlations are higher and brain region PET activities more accurate in terms of mean and maximum deviations for the 4-class technique than for 3-class techniques. CONCLUSION: The UTILE MRI sequence enables the generation of MRI-based 4-class µ-maps without anatomic priors, yielding results more similar to CT-based results than can be obtained with 3-class segmentation only.