MR-Guided PET Reconstruction: A Potential Advancement for Patients With Epilepsy.

We report a case of a 33-year-old man with epilepsy and equivocal EEG, MRI signs of mesiotemporal sclerosis, and nondiagnostic standard FDG-PET imaging. The patient underwent repeat FDG-PET/MRI to clarify the sidedness of the epileptogenic focus and to confirm the suspected MTS. The standard PET reconstruction using block sequential regularized expectation maximization failed to provide evidence of a clear epileptogenic focus.

Retrospective Data-Driven Motion Correction of 18 F-FDG Brain PET.

A 51-year-old man with severe multifactorial neurocognitive disorders subsequent to delirium, benzodiazepine withdrawal, and preexisting psychiatric illness was referred for 18 F-FDG PET/CT brain imaging in order to rule out an underlying neurodegenerative cause of the symptoms, particularly frontotemporal lobar degeneration. Imaging was impaired by severe motion artifacts, leading to a false-positive result. However, utilizing retrospective data-driven motion correction facilitated a change in diagnosis, ruling out the presence of neurodegenerative disease.

Impact of motion correction on [F]-MK6240 tau PET imaging.

. Positron emission tomography (PET) imaging of tau deposition using [F]-MK6240 often involves long acquisitions in older subjects, many of whom exhibit dementia symptoms. The resulting unavoidable head motion can greatly degrade image quality.

Evaluation of Data-Driven Rigid Motion Correction in Clinical Brain PET Imaging.

Head motion during brain PET imaging can significantly degrade the quality of the reconstructed image, leading to reduced diagnostic value and inaccurate quantitation. A fully data-driven motion correction approach was recently demonstrated to produce highly accurate motion estimates (<1 mm) with high temporal resolution (≥1 Hz), which can then be used for a motion-corrected reconstruction. This can be applied retrospectively with no impact on the clinical image acquisition protocol.

Rigid motion tracking using moments of inertia in TOF-PET brain studies.

A data-driven method is proposed for rigid motion estimation directly from time-of-flight (TOF)-positron emission tomography (PET) emission data. Rigid motion parameters (translations and rotations) are estimated from the first and second moments of the emission data masked in a spherical volume. The accuracy of the method is analyzed on 3D analytical simulations of the PET-SORTEO brain phantom, and subsequently tested onF-FDG as well asC-PIB brain datasets acquired on a TOF-PET/CT scanner.

Optimizing the frame duration for data-driven rigid motion estimation in brain PET imaging.

Purpose: Data-driven rigid motion estimation for PET brain imaging is usually performed using data frames sampled at low temporal resolution to reduce the overall computation time and to provide adequate signal-to-noise ratio in the frames. In recent work it has been demonstrated that list-mode reconstructions of ultrashort frames are sufficient for motion estimation and can be performed very quickly. In this work we take the approach of using image-based registration of reconstructions of very short frames for data-driven motion estimation, and optimize a number of reconstruction and registration parameters (frame duration, MLEM iterations, image pixel size, post-smoothing filter, reference image creation, and registration metric) to ensure accurate registrations while maximizing temporal resolution and minimizing total computation time.

Ultra-Fast List-Mode Reconstruction of Short PET Frames and Example Applications.

Standard clinical reconstructions usually require several minutes to complete, and this time is mostly independent of the duration of the data being reconstructed. Applications such as data-driven motion estimation, which require many short frames over the duration of the scan, become unfeasible with such long reconstruction times. In this work, we present an infrastructure whereby ultra-fast list-mode reconstructions of very short frames (≤1 s) are performed.

Rigid Motion Correction for Brain PET/MR Imaging using Optical Tracking.

A significant challenge during high-resolution PET brain imaging on PET/MR scanners is patient head motion. This challenge is particularly significant for clinical patient populations who struggle to remain motionless in the scanner for long periods of time. Head motion also affects the MR scan data.

The effect of isoflurane on F-FDG uptake in the rat brain: a fully conscious dynamic PET study using motion compensation.

Background: In preclinical positron emission tomography (PET) studies an anaesthetic is used to ensure that the animal does not move during the scan. However, anaesthesia may have confounding effects on the drug or tracer kinetics under study, and the nature of these effects is usually not known.

Method: We have implemented a protocol for tracking the rigid motion of the head of a fully conscious rat during a PET scan and performing a motion compensated list-mode reconstruction of the data.

Optimising rigid motion compensation for small animal brain PET imaging.

Motion compensation (MC) in PET brain imaging of awake small animals is attracting increased attention in preclinical studies since it avoids the confounding effects of anaesthesia and enables behavioural tests during the scan. A popular MC technique is to use multiple external cameras to track the motion of the animal's head, which is assumed to be represented by the motion of a marker attached to its forehead. In this study we have explored several methods to improve the experimental setup and the reconstruction procedures of this method: optimising the camera-marker separation; improving the temporal synchronisation between the motion tracker measurements and the list-mode stream; post-acquisition smoothing and interpolation of the motion data; and list-mode reconstruction with appropriately selected subsets.