Monitoring Head Movement in a Brain PET Scanner

TL;DR

Head motion degrades brain PET image quality, especially during long acquisitions. The authors propose CrowN@22, a monitoring system using six $^{22}$Na point sources in two crown-like rings to tag triple coincidences (two 511 keV gammas plus a 1274 keV gamma), enabling precise tracking of head movement with minimal brain-background interference. Through Geant4-based simulations of a dedicated brain PET scanner, they show sub-degree angular accuracy ($<0.3^{\circ}$) and sub-millimeter spatial accuracy at a 1 Hz sampling rate, robust to crystal size, energy resolution, and axial shifts. The approach offers a practical, noninvasive fiducial for motion correction and CT-PET co-registration, potentially improving attenuation correction and image fidelity in clinical brain PET, with pathways toward prototype development.

Abstract

When acquiring PET images, body motions are unavoidable, given that the acquisition time could last 10-20 minutes or more. These motions can seriously deteriorate the quality of the final image at the level of image reconstruction and attenuation corrections. Movements can have rhythmic patterns, related to respiratory or cardiac motions, or they can be abrupt reflexive actions caused by the patient's discomfort. Many approaches, software and hardware, have been developed to mitigate this problem where each approach has its own advantages and disadvantages. In this work we present a simulation study of a head monitoring device, named CrowN@22, intended to be used in conjunction with a dedicated brain PET scanner. The CrowN@22 device consists of six point sources of non-pure positron emitter isotopes, such as 22Na or 44Sc, mounted in crown-like rings around the head of the patient. The relative positions of the point sources are predefined and their actual position, once mounted, can be reconstructed by tagging the extra 1274 keV photon (in the case of 22Na). These two factors contribute to a superb signal-to-noise ratio, distinguishing between the signal from the 22Na monitor point sources and the background signal from the FDG in the brain. Hence, even with a low activity for the monitor point sources, as low as 10 kBq per point source, in the presence of 75 MBq activity of 18F in the brain, one can detect brain movements with a precision of less than 0.3 degrees, or 0.5 mm, which is of the order of the PET spatial resolution, at a sampling rate of 1 Hz.

Figures (9)

• Figure 1: 3D depiction of the PET scintillator scanner geometry used in the simulations for this article. Left and centre: the entire scanner. Right: one block of $10 \times 10$ scintillation LYSO crystals.
• Figure 2: Depiction of standard coincidences and triple coincidences in one of the two monitor rings around the head. Left: a standard coincidence of two 511 keV gammas, coming from the positron emission of the $^{18}$F radiotracer in the brain. Right: a triple coincidence of two 511 keV gammas plus an additional 1274 keV of the $^{22}$Na radiotracer in the monitor point sources.
• Figure 3: Depiction of the $^{22}$Na monitor point sources in two rings. Left: Front view of the $^{22}$Na point sources arranged in two rings and the PET scanner (grey), the simulated brain (light blue) and cranium (turquoise). The front ring (green) with 3 $^{22}$Na point sources (dark blue) can be clearly seen. Centre and right: Depiction of the location of the 3 front and the 3 back point sources.
• Figure 4: Illustration of the position of the head inside the coordinate system.
• Figure 5: Left: energy distribution of the single hits in the scanner. Right: logarithmic scale of the same energy distribution to show the additional 1274 keV gamma contribution.