Impact of non-uniform attenuation correction in a dynamic [18F]-FDOPA brain PET/MRI study

Composite attenuation correction method using a 68Ge-transmission multi-atlas for quantitative brain PET/MR

In positron emission tomography (PET), ⁶⁸Ge-transmission scanning is considered the gold standard in attenuation correction (AC) though not available in current dual imaging systems. In this experimental study we evaluated a novel AC method for PET/magnetic resonance (MR) imaging which is essentially based on a composite database of multiple ⁶⁸Ge-transmission maps and T1-weighted (T1w) MR image-pairs (composite transmission, CTR-AC). This proof-of-concept study used retrospectively a database with 125 pairs of co-registered ⁶⁸Ge-AC maps and T1w MR images from anatomical normal subjects and a validation dataset comprising dynamic [¹¹C]PE2I PET data from nine patients with Parkinsonism. CTR-AC maps were generated by non-rigid image registration of all database T1w MRI to each subject's T1w, applying the same transformation to every ⁶⁸Ge-AC map, and averaging the resulting ⁶⁸Ge-AC maps. [¹¹C]PE2I PET images were reconstructed using CTR-AC and a patient-specific ⁶⁸Ge-AC map as the reference standard. Standardized uptake values (SUV) and quantitative parameters of kinetic analysis were compared, i.e., relative delivery (R₁) and non-displaceable binding potential (BP_ND). CTR-AC showed high accuracy for whole-brain SUV (mean %bias ± SD: 0.5 ± 3.5%), whole-brain R₁ (-0.1 ± 3.2%), and putamen BP_ND (3.7 ± 8.1%). SUV and R₁ precision (SD of %bias) were modest and lowest in the anterior cortex, with an R₁ %bias of -1.1 ± 6.4%). The prototype CTR-AC is capable of providing accurate MRAC-maps with continuous linear attenuation coefficients though still experimental. The method's accuracy is comparable to the best MRAC methods published so far, both in SUV and as found for ZTE-AC in quantitative parameters of kinetic modelling.

Deep-learning-based attenuation correction in dynamic [15O]H2O studies using PET/MRI in healthy volunteers

Quantitative [¹⁵O]H₂O positron emission tomography (PET) is the accepted reference method for regional cerebral blood flow (rCBF) quantification. To perform reliable quantitative [¹⁵O]H₂O-PET studies in PET/MRI scanners, MRI-based attenuation-correction (MRAC) is required. Our aim was to compare two MRAC methods (RESOLUTE and DeepUTE) based on ultrashort echo-time with computed tomography-based reference standard AC (CTAC) in dynamic and static [¹⁵O]H₂O-PET. We compared rCBF from quantitative perfusion maps and activity concentration distribution from static images between AC methods in 25 resting [¹⁵O]H₂O-PET scans from 14 healthy men at whole-brain, regions of interest and voxel-wise levels. Average whole-brain CBF was 39.9 ± 6.0, 39.0 ± 5.8 and 40.0 ± 5.6 ml/100 g/min for CTAC, RESOLUTE and DeepUTE corrected studies respectively. RESOLUTE underestimated whole-brain CBF by 2.1 ± 1.50% and rCBF in all regions of interest (range -2.4%- -1%) compared to CTAC. DeepUTE showed significant rCBF overestimation only in the occipital lobe (0.6 ± 1.1%). Both MRAC methods showed excellent correlation on rCBF and activity concentration with CTAC, with slopes of linear regression lines between 0.97 and 1.01 and R² over 0.99. In conclusion, RESOLUTE and DeepUTE provide AC information comparable to CTAC in dynamic [¹⁵O]H₂O-PET but RESOLUTE is associated with a small but systematic underestimation.

Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [11C]PE2I PET-MR brain imaging

BACKGROUND

A valid photon attenuation correction (AC) method is instrumental for obtaining quantitatively correct PET images. Integrated PET/MR systems provide no direct information on attenuation, and novel methods for MR-based AC (MRAC) are still under investigation. Evaluations of various AC methods have mainly focused on static brain PET acquisitions. In this study, we determined the validity of three MRAC methods in a dynamic PET/MR study of the brain.

METHODS

Nine participants underwent dynamic brain PET/MR scanning using the dopamine transporter radioligand [11C]PE2I. Three MRAC methods were evaluated: single-atlas (Atlas), multi-atlas (MaxProb) and zero-echo-time (ZTE). The 68Ge-transmission data from a previous stand-alone PET scan was used as reference method. Parametric relative delivery (R1) images and binding potential (BPND) maps were generated using cerebellar grey matter as reference region. Evaluation was based on bias in MRAC maps, accuracy and precision of [11C]PE2I BPND and R1 estimates, and [11C]PE2I time-activity curves. BPND was examined for striatal regions and R1 in clusters of regions across the brain.

RESULTS

For BPND, ZTE-MRAC showed the highest accuracy (bias < 2%) in striatal regions. Atlas-MRAC exhibited a significant bias in caudate nucleus (- 12%) while MaxProb-MRAC revealed a substantial, non-significant bias in the putamen (9%). R1 estimates had a marginal bias for all MRAC methods (- 1.0-3.2%). MaxProb-MRAC showed the largest intersubject variability for both R1 and BPND. Standardized uptake values (SUV) of striatal regions displayed the strongest average bias for ZTE-MRAC (~ 10%), although constant over time and with the smallest intersubject variability. Atlas-MRAC had highest variation in bias over time (+10 to - 10%), followed by MaxProb-MRAC (+5 to - 5%), but MaxProb showed the lowest mean bias. For the cerebellum, MaxProb-MRAC showed the highest variability while bias was constant over time for Atlas- and ZTE-MRAC.

CONCLUSIONS

Both Maxprob- and ZTE-MRAC performed better than Atlas-MRAC when using a 68Ge transmission scan as reference method. Overall, ZTE-MRAC showed the highest precision and accuracy in outcome parameters of dynamic [11C]PE2I PET analysis with use of kinetic modelling.

Attenuation correction for human PET/MRI studies

Attenuation correction has been one of the main methodological challenges in the integrated positron emission tomography and magnetic resonance imaging (PET/MRI) field. As standard transmission or computed tomography approaches are not available in integrated PET/MRI scanners, MR-based attenuation correction approaches had to be developed. Aspects that have to be considered for implementing accurate methods include the need to account for attenuation in bone tissue, normal and pathological lung and the MR hardware present in the PET field-of-view, to reduce the impact of subject motion, to minimize truncation and susceptibility artifacts, and to address issues related to the data acquisition and processing both on the PET and MRI sides. The standard MR-based attenuation correction techniques implemented by the PET/MRI equipment manufacturers and their impact on clinical and research PET data interpretation and quantification are first discussed. Next, the more advanced methods, including the latest generation deep learning-based approaches that have been proposed for further minimizing the attenuation correction related bias are described. Finally, a future perspective focused on the needed developments in the field is given.