Data-Driven Gross Patient Motion Detection and Compensation: Implications for Coronary 18F-NaF PET Imaging

Abstract

Patient motion degrades image quality, affecting the quantitative assessment of PET images. This problem affects studies of coronary lesions in which microcalcification processes are targeted. Coronary PET imaging protocols require scans of up to 30 min, introducing the risk of gross patient motion (GPM) during the acquisition. Here, we investigate the feasibility of an automated data-driven method for the detection of GPM during PET acquisition. Methods: Twenty-eight patients with stable coronary disease underwent a 30-min PET acquisition 1 h after the injection of ¹⁸F-sodium fluoride (¹⁸F-NaF) at 248 ± 10 MBq (mean ± SD) and then a coronary CT angiography scan. An automated data-driven GPM detection technique tracking the center of mass of the count rates for every 200 ms in the PET list-mode data was devised and evaluated. Two patient motion patterns were considered: sudden repositioning (motion of >0.5 mm within 3 s) and general repositioning (motion of >0.3 mm over 15 s or more). After the reconstruction of diastolic images, individual GPM frames with focal coronary uptake were coregistered in 3 dimensions, creating a GPM-compensated (GPMC) image series. Lesion motion was reported for all lesions with focal uptake. Relative differences in SUV_max and target-to-background ratio (TBR) between GPMC and non-GPMC (standard electrocardiogram-gated data) diastolic PET images were compared in 3 separate groups defined by the maximum motion observed in the lesion (<5, 5-10, and >10 mm). Results: A total of 35 ¹⁸F-NaF-avid lesions were identified in 28 patients. An average of 3.5 ± 1.5 GPM frames were considered for each patient, resulting in an average frame duration of 7 ± 4 (range, 3-21) min. The mean per-patient motion was: 7 ± 3 mm (maximum, 13.7 mm). GPM correction increased SUV_max and TBR in all lesions with greater than 5 mm of motion. In lesions with 5-10 mm of motion (n = 15), SUV_max and TBR increased by 4.6% ± 5.6% (P = 0.02) and 5.8% ± 6.4% (P < 0.002), respectively. In lesions with greater than 10 mm of motion (n = 15), the SUV_max and TBR increased by 5.0% ± 5.3% (P = 0.009) and 11.5% ± 10.1% (P = 0.001), respectively. GPM correction led to the diagnostic reclassification of 3 patients (11%). Conclusion: GPM during coronary ¹⁸F-NaF PET imaging is common and may affect quantitative accuracy. Automated retrospective compensation of this motion is feasible and should be considered for coronary PET imaging.

Keywords: PET/CT; cardiac PET; data-driven motion detection; motion compensation.

FIGURE 1.

GPM detection. (A) Information on patient repositioning relies on analyses of events (E₁–E_N) stored in list-mode file. (B) Patient position is obtained for every 200 ms using CoM assessment (solid dark blue line [including the blue enclosure and the dark blue arrow]) of single-slice rebinned sinogram series. New GPM frames are defined upon detection of changes in CoM baseline (>0.5 mm in 3 s or >0.3 mm over 15 s), as exemplified in A (green, red, dark blue, gold, and bright blue dashed frames). ECG = electrocardiogram.

FIGURE 2.

GPMC. Diastolic GPM frames (Fig. 1) are compared for similarity with original non-GPMC image. GPM frame most similar to original non-GPMC image (top) is chosen as reference image (most frequently the GPM frame with longest time duration). Coregistered images result in GPMC image.

FIGURE 3.

Average and maximum (3-dimensional) GPM observed in patients at 5-min intervals. (A) Average GPM observed in all lesions affected by patient repositioning. (B) Maximal motion throughout acquisition. Major repositioning events are observed in beginning and toward end of acquisition. In both A and B, box plots and whiskers show median motion (red line) and range (whiskers); blue line with circles connecting boxes shows mean motion for each time interval.

FIGURE 4.

Example of patient with sudden repositioning (SR) event during acquisition. (A) Repositioning led to total of 3 GPM frames (green, red, and blue dashed frames). At minute 6, patient shifted arms down for total of 3 min (red dashed frame), as shown on non–attenuation-corrected images (blue asterisks). Non–attenuation-corrected images were not used in analyses of patients but were used to visualize repositioning of arms. kCnts = kilocounts. (B and C) Attenuation-corrected PET images represented standard clinical reconstruction (non-GPMC) and GPMC datasets, respectively. Compensation of GPM resulted in increased SUV_max and TBR values in patient (thick blue arrows).

FIGURE 5.

Example of patient with significant motion (11.7 mm) observed during acquisition. Significant intraframe motion was observed for 3 GPM frames (top). Time in italic type represents frame duration, and time not in italic type represents scan duration. Through coregistration, lesion activity was increased for GPMC images (blue arrows), resulting in transition of lesion from being ¹⁸F-NaF–negative to being ¹⁸F-NaF–avid (bottom).

FIGURE 6.

Patient with highest increase in TBR. Both lesion SUV_max and TBR increased significantly on GPMC images (by 20.7% and 40.8%, respectively), leading to reclassification of lesion (blue arrows) from ¹⁸F-NaF–negative to ¹⁸F-NaF–avid. SUV_Background was increased for non-GPMC image because of repositioning events that shifted high-activity regions into volume of interest in right ventricle.

FIGURE 7.

TBR in lesions on non-GPMC and GPMC images. Larger increases in TBR were observed for patients with maximum translations of >10 mm.