Data‐driven optimal binning for respiratory motion management in PET

Abstract

Purpose: Respiratory gating has been used in PET imaging to reduce the amount of image blurring caused by patient motion. Optimal binning is an approach for using the motion-characterized data by binning it into a single, easy to understand/use, optimal bin. To date, optimal binning protocols have utilized externally driven motion characterization strategies that have been tuned with populationderived assumptions and parameters. In this work, we are proposing a new strategy with which to characterize motion directly from a patient's gated scan, and use that signal to create a patient/instance-specific optimal bin image. Methods: Two hundred and nineteen phase-gated FDG PET scans, acquired using data-driven gating as described previously, were used as the input for this study. For each scan, a phase-amplitude motion characterization was generated and normalized using principle component analysis. A patient-specific "optimal bin" window was derived using this characterization, via methods that mirror traditional optimal window binning strategies. The resulting optimal bin images were validated by correlating quantitative and qualitative measurements in the population of PET scans. Results: In 53% (n = 115) of the image population, the optimal bin was determined to include 100% of the image statistics. In the remaining images, the optimal binning windows averaged 60% of the statistics and ranged between 20% and 90%. Tuning the algorithm, through a single acceptance window parameter, allowed for adjustments of the algorithm's performance in the population toward conservation of motion or reduced noise-enabling users to incorporate their definition of optimal. In the population of images that were deemed appropriate for segregation, average lesion SUV max were 7.9, 8.5, and 9.0 for nongated images, optimal bin, and gated images, respectively. The Pearson correlation of FWHM measurements between optimal bin images and gated images were better than with nongated images, 0.89 and 0.85, respectively. Generally, optimal bin images had better resolution than the nongated images and better noise characteristics than the gated images. Discussion: We extended the concept of optimal binning to a data-driven form, updating a traditionally one-size-fits-all approach to a conformal one that supports adaptive imaging. This automated strategy was implemented easily within a large population and encapsulated motion information in an easy to use 3D image. Its simplicity and practicality may make this, or similar approaches ideal for use in clinical settings.

Evaluation of a Novel Elastic Respiratory Motion Correction Algorithm on Quantification and Image Quality in Abdominothoracic PET/CT

“…Many solutions exist to correct respiratory motion artifacts in PET/CT (4)(5)(6)(7)(8)(9)(10). However, all such methods first require the acquisition of the patient's respiratory waveform using external devices or data-driven techniques.…”

Real-time data-driven motion correction in PET

“…A real-time motion characterization signal could be integrated with acquisition protocols such that patients who may benefit from motion correction receive longer scans and/or longer selective bed position dwell times to achieve greater image statistics and improved motion correction.Prospective conformal data sorting in PET: Motion correction is commonly employed via sorting data based on a patient’s motion characterization, and the sorting windows, for example, an “optimal bin,” are usually generally defined and/or based on population-derived motion characteristic studies. On the other hand, real-time motion characterization can enable full-time motion assessment, which can be used for data-driven conformal sorting [17]. This approach implements data-driven processing after acquisition of the motion signal and has the advantage of being a more patient/scan-specific solution.Radiotherapy: Motion correction is often used during treatment planning and treatment delivery.…”

“…Prospective conformal data sorting in PET: Motion correction is commonly employed via sorting data based on a patient’s motion characterization, and the sorting windows, for example, an “optimal bin,” are usually generally defined and/or based on population-derived motion characteristic studies. On the other hand, real-time motion characterization can enable full-time motion assessment, which can be used for data-driven conformal sorting [17]. This approach implements data-driven processing after acquisition of the motion signal and has the advantage of being a more patient/scan-specific solution.…”

Cardiac motion and spillover correction for quantitative PET imaging using dynamic MRI

“…20 Reference activity concentrations were defined for each organ or region (activity in lungs = 969 Bq/cc, liver = 8515 Bq/cc, scar = 10,000 Bq/cc, myocardium = 50,131 Bq/cc, and background = 5000 Bq/cc) from actual [ 18 F]-FDG cardiac PET data. Simulated counts were histogrammed into a single static 3D sinogram (no gating) and reconstructed using the standard reconstruction protocol (with and without PSF to investigate the influence of PSF reconstruction) into a 344 9 344 9 127 matrix with a voxel size of 1.39 9 1.39 9 2.03 mm 3 Study population 1 As part of a prospective study on diabetes, ten volunteers (2 females, 8 males, mean age 49.5 AE 8.0 yr, mean weight 66.3 AE 12.7 kg) without known cardiovascular disease underwent standard glucose preparation. Subjects were fasted for a minimum of 6 h followed by 75 g of oral glucose 2 h prior to the scan.…”

“…This is particularly problematic for the quantification of small regions such as infarct tissues. One way to address respiratory or cardiac motion artefacts, is to use respiratory and/or ECG‐based gating which enables sorting collected PET events into different bins (gates) based on the respiratory and/or ECG signal and allows reconstruction of each gate individually . However, gated imaging results in higher noise in each gate as only a fraction of the total counts is used for reconstruction.…”