Evaluation of Data-Driven Respiration Gating in Continuous Bed Motion in Lung Lesions

Respiration gating is used in PET to prevent image quality degradation due to respiratory effects. In this study, we evaluated a type of data-driven respiration gating for continuous bed motion, OncoFreeze AI, which was implemented to improve image quality and the accuracy of semiquantitative uptake values affected by respiratory motion. Methods: ¹⁸F-FDG PET/CT was performed on 32 patients with lung lesions. Two types of respiration-gated images (OncoFreeze AI with data-driven respiration gating, device-based amplitude-based OncoFreeze with elastic motion compensation) and ungated images (static) were reconstructed. For each image, we calculated SUV and metabolic tumor volume (MTV). The improvement rate (IR) from respiration gating and the contrast-to-noise ratio (CNR), which indicates the improvement in image noise, were also calculated for these indices. IR was also calculated for the upper and lower lobes of the lung. As OncoFreeze AI assumes the presence of respiratory motion, we examined quantitative accuracy in regions where respiratory motion was not present using a ⁶⁸Ge cylinder phantom with known quantitative accuracy. Results: OncoFreeze and OncoFreeze AI showed similar values, with a significant increase in SUV and decrease in MTV compared with static reconstruction. OncoFreeze and OncoFreeze AI also showed similar values for IR and CNR. OncoFreeze AI increased SUV_max by an average of 18% and decreased MTV by an average of 25% compared with static reconstruction. From the IR results, both OncoFreeze and OncoFreeze AI showed a greater IR from static reconstruction in the lower lobe than in the upper lobe. OncoFreeze and OncoFreeze AI increased CNR by 17.9% and 18.0%, respectively, compared with static reconstruction. The quantitative accuracy of the ⁶⁸Ge phantom, assuming a region of no respiratory motion, was almost equal for the static reconstruction and OncoFreeze AI. Conclusion: OncoFreeze AI improved the influence of respiratory motion in the assessment of lung lesion uptake to a level comparable to that of the previously launched OncoFreeze. OncoFreeze AI provides more accurate imaging with significantly larger SUVs and smaller MTVs than static reconstruction.

Impact of acquisition time and misregistration with CT on data-driven gated PET

Objective. Data-driven gating (DDG) can address patient motion issues and enhance PET quantification but suffers from increased image noise from utilization of <100% of PET data. Misregistration between DDG-PET and CT may also occur, altering the potential benefits of gating. Here, the effects of PET acquisition time and CT misregistration were assessed with a combined DDG-PET/DDG-CT technique.Approach. In the primary PET bed with lesions of interest and likely respiratory motion effects, PET acquisition time was extended to 12 min and a low-dose cine CT was acquired to enable DDG-CT. Retrospective reconstructions were created for both non-gated (NG) and DDG-PET using 30 s to 12 min of PET data. Both the standard helical CT and DDG-CT were used for attenuation correction of DDG-PET data. SUVmax, SUVpeak, and CNR were compared for 45 lesions in the liver and lung from 27 cases.Main results. For both NG-PET (p= 0.0041) and DDG-PET (p= 0.0028), only the 30 s acquisition time showed clear SUVmaxbias relative to the 3 min clinical standard. SUVpeakshowed no bias at any change in acquisition time. DDG-PET alone increased SUVmaxby 15 ± 20% (p< 0.0001), then was increased further by an additional 15 ± 29% (p= 0.0007) with DDG-PET/CT. Both 3 min and 6 min DDG-PET had lesion CNR statistically equivalent to 3 min NG-PET, but then increased at 12 min by 28 ± 48% (p= 0.0022). DDG-PET/CT at 6 min had comparable counts to 3 min NG-PET, but significantly increased CNR by 39 ± 46% (p< 0.0001).Significance. 50% counts DDG-PET did not lead to inaccurate or biased SUV-increased SUV resulted from gating. Improved registration from DDG-CT was equally as important as motion correction with DDG-PET for increasing SUV in DDG-PET/CT. Lesion detectability could be significantly improved when DDG-PET used equivalent counts to NG-PET, but only when combined with DDG-CT in DDG-PET/CT.

Data-driven respiratory phase-matched PET attenuation correction without CT

We propose a deep learning-based data-driven respiratory phase-matched gated-PET attenuation correction (AC) method that does not need a gated-CT. The proposed method is a multi-step process that consists of data-driven respiratory gating, gated attenuation map estimation using maximum-likelihood reconstruction of attenuation and activity (MLAA) algorithm, and enhancement of the gated attenuation maps using convolutional neural network (CNN). The gated MLAA attenuation maps enhanced by the CNN allowed for the phase-matched AC of gated-PET images. We conducted a non-rigid registration of the gated-PET images to generate motion-free PET images. We trained the CNN by conducting a 3D patch-based learning with 80 oncologic whole-body¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT scan data and applied it to seven regional PET/CT scans that cover the lower lung and upper liver. We investigated the impact of the proposed respiratory phase-matched AC of PET without utilizing CT on tumor size and standard uptake value (SUV) assessment, and PET image quality (%STD). The attenuation corrected gated and motion-free PET images generated using the proposed method yielded sharper organ boundaries and better noise characteristics than conventional gated and ungated PET images. A banana artifact observed in a phase-mismatched CT-based AC was not observed in the proposed approach. By employing the proposed method, the size of tumor was reduced by 12.3% and SUV_90%was increased by 13.3% in tumors with larger movements than 5 mm. %STD of liver uptake was reduced by 11.1%. The deep learning-based data-driven respiratory phase-matched AC method improved the PET image quality and reduced the motion artifacts.

Data-Driven Respiratory Gating Outperforms Device-Based Gating for Clinical 18F-FDG PET/CT

A data-driven method for respiratory gating in PET has recently been commercially developed. We sought to compare the performance of the algorithm with an external, device-based system for oncologic ¹⁸F-FDG PET/CT imaging. Methods: In total, 144 whole-body ¹⁸F-FDG PET/CT examinations were acquired, with a respiratory gating waveform recorded by an external, device-based respiratory gating system. In each examination, 2 of the bed positions covering the liver and lung bases were acquired with a duration of 6 min. Quiescent-period gating retaining approximately 50% of coincidences was then able to produce images with an effective duration of 3 min for these 2 bed positions, matching the other bed positions. For each examination, 4 reconstructions were performed and compared: data-driven gating (DDG) (we use the term DDG-retro to distinguish that we did not use the real-time R-threshold-based application of DDG that is available within the manufacturer's product), external device-based gating (real-time position management (RPM)-gated), no gating but using only the first 3 min of data (ungated-matched), and no gating retaining all coincidences (ungated-full). Lesions in the images were quantified and image quality scored by a radiologist who was masked to the method of data processing. Results: Compared with the other reconstruction options, DDG-retro increased the SUV_max and decreased the threshold-defined lesion volume. Compared with RPM-gated, DDG-retro gave an average increase in SUV_max of 0.66 ± 0.1 g/mL (n = 87, P < 0.0005). Although the results from the masked image evaluation were most commonly equivalent, DDG-retro was preferred over RPM-gated in 13% of examinations, whereas the opposite occurred in just 2% of examinations. This was a significant preference for DDG-retro (P = 0.008, n = 121). Liver lesions were identified in 23 examinations. Considering this subset of data, DDG-retro was ranked superior to ungated-full in 6 of 23 (26%) cases. Gated reconstruction using the external device failed in 16% of examinations, whereas DDG-retro always provided a clinically acceptable image. Conclusion: In this clinical evaluation, DDG-retro provided performance superior to that of the external device-based system. For most examinations the performance was equivalent, but DDG-retro had superior performance in 13% of examinations, leading to a significant preference overall.

Data-driven gating in PET: Influence of respiratory signal noise on motion resolution

PURPOSE

Data-driven gating (DDG) approaches for positron emission tomography (PET) are interesting alternatives to conventional hardware-based gating methods. In DDG, the measured PET data themselves are utilized to calculate a respiratory signal, that is, subsequently used for gating purposes. The success of gating is then highly dependent on the statistical quality of the PET data. In this study, we investigate how this quality determines signal noise and thus motion resolution in clinical PET scans using a center-of-mass-based (COM) DDG approach, specifically with regard to motion management of target structures in future radiotherapy planning applications.

METHODS

PET list mode datasets acquired in one bed position of 19 different radiotherapy patients undergoing pretreatment [¹⁸ F]FDG PET/CT or [¹⁸ F]FDG PET/MRI were included into this retrospective study. All scans were performed over a region with organs (myocardium, kidneys) or tumor lesions of high tracer uptake and under free breathing. Aside from the original list mode data, datasets with progressively decreasing PET statistics were generated. From these, COM DDG signals were derived for subsequent amplitude-based gating of the original list mode file. The apparent respiratory shift d from end-expiration to end-inspiration was determined from the gated images and expressed as a function of signal-to-noise ratio SNR of the determined gating signals. This relation was tested against additional 25 [¹⁸ F]FDG PET/MRI list mode datasets where high-precision MR navigator-like respiratory signals were available as reference signal for respiratory gating of PET data, and data from a dedicated thorax phantom scan.

RESULTS

All original 19 high-quality list mode datasets demonstrated the same behavior in terms of motion resolution when reducing the amount of list mode events for DDG signal generation. Ratios and directions of respiratory shifts between end-respiratory gates and the respective nongated image were constant over all statistic levels. Motion resolution d/d_max could be modeled as d/dmax=1-e-1.52(SNR-1)0.52, with d_max as the actual respiratory shift. Determining d_max from d and SNR in the 25 test datasets and the phantom scan demonstrated no significant differences to the MR navigator-derived shift values and the predefined shift, respectively.

CONCLUSIONS

The SNR can serve as a general metric to assess the success of COM-based DDG, even in different scanners and patients. The derived formula for motion resolution can be used to estimate the actual motion extent reasonably well in cases of limited PET raw data statistics. This may be of interest for individualized radiotherapy treatment planning procedures of target structures subjected to respiratory motion.

Technical Note: Fast respiratory motion estimation using sorted singles without unlist processing: A feasibility study

PURPOSE

The study aims to demonstrate the feasibility of fast respiratory motion estimation using singles data available as a sorted format in list-mode files acquired in an integrated positron emission tomography/magnetic resonance imaging (PET/MRI) system for a proof-of-concept.

METHODS

The derivation of singles-driven respiratory motion (SDRM) is enabled by singles recorded and binned by second for each detector crystal in PET list-mode data acquired in a SIGNA PET/MR. The proposed method is to derive a SDRM trace by summing up all singles from all detectors through the PET data acquisition. To assess the feasibility of SDRM for data-driven gating (DDG), SDRM traces were derived from the list-mode data acquired in five liver-focused ⁶⁸ Ga-DOTA-TOC PET/MRI scans, and compared with the traces derived from bellows (pressure belt). Pearson's correlation coefficients and trigger time differences at peak-inhalation phases between SDRM and bellows traces were measured for quantitative evaluation.

RESULTS

The method presented the average processing time of 4.2 ± 0.42 s (range: 3.9 ~ 4.7 s) for the derivation of SDRM traces. The majority of the time was spent for reading singles data from a list-mode file (3.1 ± 0.40 s, range: 2.7 ~ 3.7s). On average, the correlation coefficient of SDRM and bellows traces was 0.69 ± 0.16 (range: 0.41 ~ 0.80) and the time offset of SDRM-driven triggers from bellows-driven triggers was 0.25 ± 0.39 s (range: -0.85 ~ 2.69 s later than bellows triggers), demonstrating the similar patterns and phases of SDRM and bellows traces.

CONCLUSIONS

We introduced PET singles-driven respiratory motion (SDRM) estimation as a proof-of-principle, using sorted singles ready for immediate processing in list-mode data. The results demonstrated the feasibility of SDRM and its potential use for gated PET with fast processing time.