Data-driven voluntary body motion detection and non-rigid event-by-event correction for static and dynamic PET

Abstract

PET has the potential to perform absolute in vivo radiotracer quantitation. This potential can be compromised by voluntary body motion (BM), which degrades image resolution, alters apparent tracer uptakes, introduces CT-based attenuation correction mismatch artifacts and causes inaccurate parameter estimates in dynamic studies. Existing body motion correction (BMC) methods include frame-based image-registration (FIR) approaches and real-time motion tracking using external measurement devices. FIR does not correct for motion occurring within a pre-defined frame and the device-based method is generally not practical in routine clinical use, since it requires attaching a tracking device to the patient and additional device set up time. In this paper, we proposed a data-driven algorithm, centroid of distribution (COD), to detect BM. In this algorithm, the central coordinate of the time-of-flight (TOF) bin, which can be used as a reasonable surrogate for the annihilation point, is calculated for every event, and averaged over a certain time interval to generate a COD trace. We hypothesized that abrupt changes on the COD trace in lateral direction represent BMs. After detection, BM is estimated using non-rigid image registrations and corrected through list-mode reconstruction. The COD-based BMC approach was validated using a monkey study and was evaluated against FIR using four human and one dog studies with multiple tracers. The proposed approach successfully detected BMs and yielded superior correction results over conventional FIR approaches.

Data-Driven Patient Motion Compensation in Cardiac Positron Emission Tomography

“…116,117 Lastly, investigators at the Yale PET Center have developed centroid-ofdistribution tracking methods to estimate respiratory and whole-body (or bulk) patient motion signals. 118,119 In this approach, the geometric centroid of a set of LORs, either associated with full scanner FOV or a region-of-interest, is tracked over short time intervals to derive a 3D motion signal of the patient.…”

“…These consist of the positron emission tracking (PeTrack) algorithm 167 and the centroid of distribution (COD) method. 119 Both methods use only list-mode data from the PET scanner to generate a motion tracking signal. They differ, however, in that the former tracks the motion of low-activity point sources placed on the patient while the latter uses only emission data from the radiotracer.…”

“…The COD algorithm described in the work of Lu and colleagues 119 was implemented for this study with only minor modification. This data-driven approach to tracking patient motion is based on calculating the mean position of the most likely annihilation (MLA) points (calculated from TOF) from a set of LORs belonging to a specific time interval.…”

“…Non-attenuation corrected images were used to avoid the propagation of potential PET/CT mis-alignment artifacts into registration errors. 119 Given that the activity distribution is not fixed for dynamic…”

“…Several studies have been performed to assess the impact of patient motion 82,83,[141][142][143][144][145] and many others have been performed with the aim of establishing methods to compensate or correct for it. 81,[146][147][148][149] information directly from the raw PET data 119,150 to help address intra-frame motion blurring. These approaches have only been applied to PET studies that make use of longlived, 18 F-based tracers.…”