A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods

Sedation-free pediatric [18F]FDG imaging on totalbody PET/CT with the assistance of artificial intelligence

PURPOSE

While sedation is routinely used in pediatric PET examinations to preserve diagnostic quality, it may result in side effects and may affect the radiotracer's biodistribution. This study aims to investigate the feasibility of sedation-free pediatric PET imaging using ultra-fast total-body (TB) PET scanners and deep learning (DL)-based attenuation and scatter correction (ASC).

METHODS

This retrospective study included TB PET (uExplorer) imaging of 35 sedated pediatric patients under four years old to determine the minimum effective scanning time. A DL-based ASC method was applied to enhance PET quantification. Both quantitative and qualitative assessments were conducted to evaluate the image quality of ultra-fast DL-ASC PET. Five non-sedated pediatric patients were subsequently used to validate the proposed approach.

RESULTS

Comparisons between standard 300-second and ultra-fast 15-second imaging, CT-ASC and DL-ASC ultra-fast 15-second images, as well as DL-ASC ultra-fast 15-second images in non-sedated and sedated patients, showed no significant differences in qualitative scoring, lesion detectability, and quantitative Standard Uptake Value (SUV) (P = ns).

CONCLUSIONS

This study demonstrates that pediatric PET imaging can be effectively performed without sedation by combining ultra-fast imaging techniques with a DL-based ASC. This advancement in sedation-free ultra-fast PET imaging holds potential for broader clinical adoption.

Device-Less Data-Driven Cardiac and Respiratory Gating Using LAFOV PET Histo Images

Background: The outstanding capabilities of modern Positron Emission Tomography (PET) to highlight small tumor lesions and provide pathological function assessment are at peril from image quality degradation caused by respiratory and cardiac motion. However, the advent of the long axial field-of-view (LAFOV) scanners with increased sensitivity, alongside the precise time-of-flight (TOF) of modern PET systems, enables the acquisition of ultrafast time resolution images, which can be used for estimating and correcting the cyclic motion. Methods: 0.25 s so-called [18F]FDG PET histo image series were generated in the scope of for detecting respiratory and cardiac frequency estimates applicable for performing device-less data-driven gated image reconstructions. The frequencies of the cardiac and respiratory motion were estimated for 18 patients using Short Time Fourier Transform (STFT) with 20 s and 30 s window segments, respectively. Results: The Fourier analysis provided time points usable as input to the gated reconstruction based on eight equally spaced time gates. The cardiac investigations showed estimates in accordance with the measured pulse oximeter references (p = 0.97) and a mean absolute difference of 0.4 ± 0.3 beats per minute (bpm). The respiratory frequencies were within the expected range of 10-20 respirations per minute (rpm) in 16 out of 18 patients. Using this setup, the analysis of three patients with visible lung tumors showed an increase in tumor SUVmax and a decrease in tumor volume compared to the non-gated reconstructed image. Conclusions: The method can provide signals that were applicable for gated reconstruction of both cardiac and respiratory motion, providing a potential increased diagnostic accuracy.

Data driven surrogate signal extraction for dynamic PET using selective PCA: time windows versus the combination of components

Objective.Respiratory motion correction is beneficial in positron emission tomography (PET), as it can reduce artefacts caused by motion and improve quantitative accuracy. Methods of motion correction are commonly based on a respiratory trace obtained through an external device (like the real time position management system) or a data driven method, such as those based on dimensionality reduction techniques (for instance principal component analysis (PCA)). PCA itself being a linear transformation to the axis of greatest variation. Data driven methods have the advantage of being non-invasive, and can be performed post-acquisition. However, their main downside being that they are adversely affected by the tracer kinetics of the dynamic PET acquisition. Therefore, they are mostly limited to static PET acquisitions. This work seeks to extend on existing PCA-based data-driven motion correction methods, to allow for their applicability to dynamic PET imaging.Approach.The methods explored in this work include; a moving window approach (similar to the Kinetic Respiratory Gating method from Schleyeret al(2014)), extrapolation of the principal component from later time points to earlier time points, and a method to score, select, and combine multiple respiratory components. The resulting respiratory traces were evaluated on 22 data sets from a dynamic [18F]-FDG study on patients with idiopathic pulmonary fibrosis. This was achieved by calculating their correlation with a surrogate signal acquired using a real time position management system.Main results.The results indicate that all methods produce better surrogate signals than when applying conventional PCA to dynamic data (for instance, a higher correlation with a gold standard respiratory trace). Extrapolating a late time point principal component produced more promising results than using a moving window. Scoring, selecting, and combining components held benefits over all other methods.Significance.This work allows for the extraction of a surrogate signal from dynamic PET data earlier in the acquisition and with a greater accuracy than previous work. This potentially allows for numerous other methods (for instance, respiratory motion correction) to be applied to this data (when they otherwise could not be previously used).

Intelligent ultrafast total-body PET for sedation-free pediatric [18F]FDG imaging

PURPOSE

This study aims to develop deep learning techniques on total-body PET to bolster the feasibility of sedation-free pediatric PET imaging.

METHODS

A deformable 3D U-Net was developed based on 245 adult subjects with standard total-body PET imaging for the quality enhancement of simulated rapid imaging. The developed method was first tested on 16 children receiving total-body [18F]FDG PET scans with standard 300-s acquisition time with sedation. Sixteen rapid scans (acquisition time about 3 s, 6 s, 15 s, 30 s, and 75 s) were retrospectively simulated by selecting the reconstruction time window. In the end, the developed methodology was prospectively tested on five children without sedation to prove the routine feasibility.

RESULTS

The approach significantly improved the subjective image quality and lesion conspicuity in abdominal and pelvic regions of the generated 6-s data. In the first test set, the proposed method enhanced the objective image quality metrics of 6-s data, such as PSNR (from 29.13 to 37.09, p < 0.01) and SSIM (from 0.906 to 0.921, p < 0.01). Furthermore, the errors of mean standardized uptake values (SUVmean) for lesions between 300-s data and 6-s data were reduced from 12.9 to 4.1% (p < 0.01), and the errors of max SUV (SUVmax) were reduced from 17.4 to 6.2% (p < 0.01). In the prospective test, radiologists reached a high degree of consistency on the clinical feasibility of the enhanced PET images.

CONCLUSION

The proposed method can effectively enhance the image quality of total-body PET scanning with ultrafast acquisition time, leading to meeting clinical diagnostic requirements of lesion detectability and quantification in abdominal and pelvic regions. It has much potential to solve the dilemma of the use of sedation and long acquisition time that influence the health of pediatric patients.

Data-driven gated positron emission tomography/computed tomography for radiotherapy

Purpose

Software-based data-driven gated (DDG) positron emission tomography/computed tomography (PET/CT) has replaced hardware-based 4D PET/CT. The purpose of this article was to review DDG PET/CT, which could improve the accuracy of treatment response assessment, tumor motion evaluation, and target tumor contouring with whole-body (WB) PET/CT for radiotherapy (RT).

Material and methods

This review covered the topics of 4D PET/CT with hardware gating, advancements in PET instrumentation, DDG PET, DDG CT, and DDG PET/CT based on a systematic literature review. It included a discussion of the large axial field-of-view (AFOV) PET detector and a review of the clinical results of DDG PET and DDG PET/CT.

Results

DDG PET matched or outperformed 4D PET with hardware gating. DDG CT was more compatible with DDG PET than 4D CT, which required hardware gating. DDG CT could replace 4D CT for RT. DDG PET and DDG CT for DDG PET/CT can be incorporated in a WB PET/CT of less than 15 min scan time on a PET/CT scanner of at least 25 cm AFOV PET detector.

Conclusions

DDG PET/CT could correct the misregistration and tumor motion artifacts in a WB PET/CT and provide the quantitative PET and tumor motion information of a registered PET/CT for RT.

Reproducibility of the principal component analysis (PCA)-based data-driven respiratory gating on texture features in non-small cell lung cancer patients with 18 F-FDG PET/CT

OBJECTIVE

Texture analysis is one of the lung cancer countermeasures in the field of radiomics. Even though image quality affects texture features, the reproducibility of principal component analysis (PCA)-based data-driven respiratory gating (DDG) on texture features remains poorly understood. Hence, this study aimed to clarify the reproducibility of PCA-based DDG on texture features in non-small cell lung cancer (NSCLC) patients with ¹⁸ F-Fluorodeoxyglucose (¹⁸ F-FDG) Positron emission tomography/computed tomography (PET/CT).

METHODS

Twenty patients with NSCLC who underwent ¹⁸ F-FDG PET/CT in routine clinical practice were retrospectively analyzed. Each patient's PET data were reconstructed in two PET groups of no gating (NG-PET) and PCA-based DDG gating (DDG-PET). Forty-six image features were analyzed using LIFEx software. Reproducibility was evaluated using Lin's concordance correlation coefficient ( ρ c ${\rho _c}$ ) and percentage difference (%Diff). Non-reproducibility was defined as having unacceptable strength ( ρ c $({\rho _c}$  < 0.8) and a %Diff of >10%. NG-PET and DDG-PET were compared using the Wilcoxon signed-rank test.

RESULTS

A total of 3/46 (6.5%) image features had unacceptable strength, and 9/46 (19.6%) image features had a %Diff of >10%. Significant differences between the NG-PET and DDG-PET groups were confirmed in only 4/46 (8.7%) of the high %Diff image features.

CONCLUSION

Although the DDG application affected several texture features, most image features had adequate reproducibility. PCA-based DDG-PET can be routinely used as interchangeable images for texture feature extraction from NSCLC patients.

Anatomical validation of automatic respiratory motion correction for coronary 18F-sodium fluoride positron emission tomography by expert measurements from four-dimensional computed tomography

BACKGROUND

Respiratory motion correction is of importance in studies of coronary plaques employing ¹⁸ F-NaF; however, the validation of motion correction techniques mainly relies on indirect measures such as test-retest repeatability assessments. In this study, we aim to compare and, thus, validate the respiratory motion vector fields obtained from the positron emission tomography (PET) images directly to the respiratory motion observed during four-dimensional cine-computed tomography (CT) by an expert observer.

PURPOSE

To investigate the accuracy of the motion correction employed in a software (FusionQuant) used for evaluation of ¹⁸ F-NaF PET studies by comparing the respiratory motion of the coronary plaques observed in PET to the respiratory motion observed in 4D cine-CT images.

METHODS

This study included 23 patients who undertook thoracic PET scans for the assessment of coronary plaques using ¹⁸ F-sodium fluoride (¹⁸ F-NaF). All patients underwent a 5-s cine-CT (4D-CT), a coronary CT angiography (CTA), and ¹⁸ F-NaF PET. The 4D-CT and PET scan were reconstructed into 10 phases. Respiratory motion was estimated for the non-contrast visible coronary plaques using diffeomorphic registrations (PET) and compared to respiratory motion observed on 4D-CT. We report the PET motion vector fields obtained in the three principal axes in addition to the 3D motion. Statistical differences were examined using paired t-tests. Signal-to-noise ratios (SNR) are reported for the single-phase images (end-expiratory phase) and for the motion-corrected image-series (employing the motion vector fields extracted during the diffeomorphic registrations).

RESULTS

In total, 19 coronary plaques were identified in 16 patients. No statistical differences were observed for the maximum respiratory motion observed in x, y, and the 3D motion fields (magnitude and direction) between the CT and PET (X direction: 4D CT = 2.5 ± 1.5 mm, PET = 2.4 ± 3.2 mm; Y direction: 4D CT = 2.3 ± 1.9 mm, PET = 0.7 ± 2.9 mm, 3D motion: 4D CT = 6.6 ± 3.1 mm, PET = 5.7 ± 2.6 mm, all p ≥ 0.05). Significant differences in respiratory motion were observed in the systems' Z direction: 4D CT = 4.9 ± 3.4 mm, PET = 2.3 ± 3.2 mm, p = 0.04. Significantly improved SNR is reported for the motion corrected images compared to the end-expiratory phase images (end-expiratory phase = 6.8±4.8, motion corrected = 12.2±4.5, p = 0.001).

CONCLUSION

Similar respiratory motion was observed in two directions and 3D for coronary plaques on 4D CT as detected by automatic respiratory motion correction of coronary PET using FusionQuant. The respiratory motion correction technique significantly improved the SNR in the images.

Respiratory motion correction in F-18-FDG PET/CT impacts lymph node assessment in lung cancer patients

BACKGROUNDS

Elastic motion correction in PET has been shown to increase image quality and quantitative measurements of PET datasets affected by respiratory motion. However, little is known on the impact of respiratory motion correction on clinical image evaluation in oncologic PET. This study evaluated the impact of motion correction on expert readers' lymph node assessment of lung cancer patients.

METHODS

Forty-three patients undergoing F-18-FDG PET/CT for the staging of suspected lung cancer were included. Three different PET reconstructions were investigated: non-motion-corrected ("static"), belt gating-based motion-corrected ("BG-MC") and data-driven gating-based motion-corrected ("DDG-MC"). Assessment was conducted independently by two nuclear medicine specialists blinded to the reconstruction method on a six-point scale [Formula: see text] ranging from "certainly negative" (1) to "certainly positive" (6). Differences in [Formula: see text] between reconstruction methods, accounting for variation caused by readers, were assessed by nonparametric regression analysis of longitudinal data. From [Formula: see text], a dichotomous score for N1, N2, and N3 ("negative," "positive") and a subjective certainty score were derived. SUV and metabolic tumor volumes (MTV) were compared between reconstruction methods.

RESULTS

BG-MC resulted in higher scores for N1 compared to static (p = 0.001), whereas DDG-MC resulted in higher scores for N2 compared to static (p = 0.016). Motion correction resulted in the migration of N1 from tumor free to metastatic on the dichotomized score, consensually for both readers, in 3/43 cases and in 2 cases for N2. SUV was significantly higher for motion-corrected PET, while MTV was significantly lower (all p < 0.003). No significant differences in the certainty scores were noted.

CONCLUSIONS

PET motion correction resulted in significantly higher lymph node assessment scores of expert readers. Significant effects on quantitative PET parameters were seen; however, subjective reader certainty was not improved.

Data-driven respiratory signal estimation from temporally finely sampled projection data in conventional cardiac perfusion SPECT imaging

PURPOSE

The aim of this work was to revisit the data-driven approach of axial center-of-mass (COM) measurements to recover a surrogate respiratory signal from finely sampled (100 ms) single photon emission computed tomography (SPECT) projection data derived from list-mode acquisitions.

METHODS

For our initial evaluation, we acquired list-mode projection data from an anthropomorphic cardiac phantom mounted on a Quasar respiratory motion platform simulating 15 mm amplitude respiratory motion. We also selected 302 consecutive patients (138 males, 164 females) with list-mode acquisitions, external respiratory motion tracking, and written consent to evaluate the clinical efficacy of our data-driven approach. Linear regression, Pearson's correlation coefficient (r), and standard error of the estimates (SEE) between the respiratory signals obtained with a visual tracking system (VTS) and COM measurements were calculated for individual projection data sets and for the patient group as a whole. Both the VTS- and COM-derived respiratory signals were used to estimate and correct respiratory motion. The reconstruction for six-degree of freedom rigid-body motion estimation was done in two ways: (1) using three iterations of ordered-subsets expectation-maximization (OSEM) with four subsets (16 projection angles per subset), or 12 iterations of maximum-likelihood expectation-maximization (MLEM). Respiratory motion compensation was done employing either OSEM with 16 subsets (four projection angles per subset) and five iterations or MLEM and 80 iterations, using the two respiratory estimates, respectively. Polar map quantification was also performed, calculating the percentage count difference (%Diff) between polar maps without and with respiratory motion included. Average % Diff was calculated in 17 segments (defined according to ASNC Guidelines). Paired t-tests were used to determine significance (p-values).

RESULTS

The r-value calculated when comparing the VTS and COM respiratory signals varied widely between -0.01 and 0.96 with an average of 0.70, while the SEE varied between 0.80 and 6.48 mm with an average of 2.05 mm for our patient set, while the same values for the one anthropomorphic phantom acquisition are 0.91 and 1.11 mm, respectively. A comparison between the respiratory motion estimates for VTS and COM in the S-I direction yielded an r = 0.90 (0.94), and an SEE of 1.56 mm (1.20 mm) for OSEM (MLEM), respectively. Bland-Altman plots and calculated intraclass correlation coefficients also showed excellent agreement between the VTS and COM respiratory motion estimates. Average S-I respiratory estimates for the VTS (COM) were 9.04 (9.2 mm) and 9.01 mm (9.14 mm) for the OSEM and MLEM, respectively. The paired t-test approached significance when comparing VTS and COM estimated respiratory signals with p-values of 0.069 and 0.051 for OSEM and MLEM. The respiratory estimates from the anthropomorphic cardiac phantom experiment using the VTS (COM) were 12.62 (14.10 mm) and 12.55 mm (14.29 mm) for OSEM and MLEM, respectively. Polar map quantification yielded average % Diff consistently better when employing VTS-derived respiratory estimates to correct for respiration compared to the COM-derived estimates.

CONCLUSIONS

The results indicate that our COM method has the potential to provide an automated data-driven correction of cardiac respiratory motion without the drawbacks of our VTS methodology. However, it is not generally equivalent to the VTS method in extent of correction.

PET respiratory motion correction: quo vadis?

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

Motion in nuclear cardiology imaging: types, artifacts, detection and correction techniques

In this paper, the authors review the field of motion detection and correction in nuclear cardiology with single photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging systems. We start with a brief overview of nuclear cardiology applications and description of SPECT and PET imaging systems, then explaining the different types of motion and their related artefacts. Moreover, we classify and describe various techniques for motion detection and correction, discussing their potential advantages including reference to metrics and tasks, particularly towards improvements in image quality and diagnostic performance. In addition, we emphasize limitations encountered in different motion detection and correction methods that may challenge routine clinical applications and diagnostic performance.

Advances in PET-CT technology: An update

This article reviews the current evolution and future directions in PET-CT technology focusing on three areas: time of flight, image reconstruction, and data-driven gating. Image reconstruction is considered with advances in point spread function modelling, Bayesian penalised likelihood reconstruction, and artificial intelligence approaches. Data-driven gating is examined with reference to respiratory motion, cardiac motion, and head motion. For each of these technological advancements, theory will be briefly discussed, benefits of their use in routine practice will be detailed and potential future developments will be discussed. Representative clinical cases will be presented, demonstrating the huge opportunities given to the PET community by hardware and software advances in PET technology when it comes to lesion detection, disease characterization, accurate quantitation and quicker scans. Through this review, hospitals are encouraged to embrace, evaluate and appropriately implement the wide range of new PET technologies that are available now or in the near future, for the improvement of patient care.

Respiratory Gating and the Performance of PET/CT in Pulmonary Lesions

Background

Motion artifacts related to the patient’s breathing can be the cause of underestimation of the lesion uptake and can lead to missing of small lung lesions. The respiratory gating (RG) technology has demonstrated a significant increase in image quality.

Objective

The aim of this paper was to evaluate the advantages of RG technique on PET/CT performance in lung lesions. The impact of 4D-PET/CT on diagnosis (metabolic characterization), staging and re-staging lung cancer was also assessed, including its application for radiotherapy planning. Finally, new technologies for respiratory motion management were also discussed.

Methods

A comprehensive electronic search of the literature was performed by using Medline database (PubMed) searching “PET/CT”, “gated” and “lung”. Original articles, review articles, and editorials published in the last 10 years were selected, included and critically reviewed in order to select relevant articles.

Results

Many papers compared Standardized Uptake Value (SUV) in gated and ungated PET studies showing an increase in SUV of gated images, particularly for the small lesions located in medium and lower lung. In addition, other features as Metabolic Tumor Volume (MTV), Total Lesion Glycolysis (TLG) and textural-features presented differences when obtained from gated and ungated PET acquisitions. Besides the increase in quantification, gating techniques can determine an increase in the diagnostic accuracy of PET/CT. Gated PET/CT was evaluated for lung cancer staging, therapy response assessment and for radiation therapy planning.

Conclusion

New technologies able to track the motion of organs lesion directly from raw PET data, can reduce or definitively solve problems (i.e.: extended acquisition time, radiation exposure) currently limiting the use of gated PET/CT in clinical routine.

Prospective data-driven respiratory gating of [68Ga]Ga-DOTATOC PET/CT

Aim

The aim of this prospective study was to evaluate a data-driven gating software’s performance, in terms of identifying the respiratory signal, comparing [⁶⁸Ga]Ga-DOTATOC and [¹⁸F]FDG examinations. In addition, for the [⁶⁸Ga]Ga-DOTATOC examinations, tracer uptake quantitation and liver lesion detectability were assessed.

Methods

Twenty-four patients with confirmed or suspected neuroendocrine tumours underwent whole-body [⁶⁸Ga]Ga-DOTATOC PET/CT examinations. Prospective DDG was applied on all bed positions and respiratory motion correction was triggered automatically when the detected respiratory signal exceeded a certain threshold (R value ≥ 15), at which point the scan time for that bed position was doubled. These bed positions were reconstructed with quiescent period gating (QPG), retaining 50% of the total coincidences. A respiratory signal evaluation regarding the software’s efficacy in detecting respiratory motion for [⁶⁸Ga]Ga-DOTATOC was conducted and compared to [¹⁸F]FDG data. Measurements of SUV_max, SUV_mean, and tumour volume were performed on [⁶⁸Ga]Ga-DOTATOC PET and compared between gated and non-gated images.

Results

The threshold of R ≥ 15 was exceeded and gating triggered on mean 2.1 bed positions per examination for [⁶⁸Ga]Ga-DOTATOC as compared to 1.4 for [¹⁸F]FDG. In total, 34 tumours were evaluated in a quantitative analysis. An increase of 25.3% and 28.1%, respectively, for SUV_max (P < 0.0001) and SUV_mean (P < 0.0001), and decrease of 21.1% in tumour volume (P < 0.0001) was found when DDG was applied.

Conclusions

High respiratory signal was exclusively detected in bed positions where respiratory motion was expected, indicating reliable performance of the DDG software on [⁶⁸Ga]Ga-DOTATOC PET/CT. DDG yielded significantly higher SUV_max and SUV_mean values and smaller tumour volumes, as compared to non-gated images.

A data-driven respiratory motion estimation approach for PET based on time-of-flight weighted positron emission particle tracking

Purpose

Respiratory motion of patients during positron emission tomography (PET)/computed tomography (CT) imaging affects both image quality and quantitative accuracy. Hardware‐based motion estimation, which is the current clinical standard, requires initial setup, maintenance, and calibration of the equipment, and can be associated with patient discomfort. Data‐driven techniques are an active area of research with limited exploration into lesion‐specific motion estimation. This paper introduces a time‐of‐flight (TOF)‐weighted positron emission particle tracking (PEPT) algorithm that facilitates lesion‐specific respiratory motion estimation from raw listmode PET data.

Methods

The TOF‐PEPT algorithm was implemented and investigated under different scenarios: (a) a phantom study with a point source and an Anzai band for respiratory motion tracking; (b) a phantom study with a point source only, no Anzai band; (c) two clinical studies with point sources and the Anzai band; (d) two clinical studies with point sources only, no Anzai band; and (e) two clinical studies using lesions/internal regions instead of point sources and no Anzai band. For studies with radioactive point sources, they were placed on patients during PET/CT imaging. The motion tracking was performed using a preselected region of interest (ROI), manually drawn around point sources or lesions on reconstructed images. The extracted motion signals were compared with the Anzai band when applicable. For the purposes of additional comparison, a center‐of‐mass (COM) algorithm was implemented both with and without the use of TOF information. Using the motion estimate from each method, amplitude‐based gating was applied, and gated images were reconstructed.

Results

The TOF‐PEPT algorithm is shown to successfully determine the respiratory motion for both phantom and clinical studies. The derived motion signals correlated well with the Anzai band; correlation coefficients of 0.99 and 0.94‐0.97 were obtained for the phantom study and the clinical studies, respectively. TOF‐PEPT was found to be 13–38% better correlated with the Anzai results than the COM methods. Maximum Standardized Uptake Values (SUVs) were used to quantitatively compare the reconstructed‐gated images. In comparison with the ungated image, a 14–39% increase in the max SUV across several lesion areas and an 8.7% increase in the max SUV on the tracked lesion area were observed in the gated images based on TOF‐PEPT. The distinct presence of lesions with reduced blurring effect and generally sharper images were readily apparent in all clinical studies. In addition, max SUVs were found to be 4–10% higher in the TOF‐PEPT‐based gated images than in those based on Anzai and COM methods.

Conclusion

A PEPT‐ based algorithm has been presented for determining movement due to respiratory motion during PET/CT imaging. Gating based on the motion estimate is shown to quantifiably improve the image quality in both a controlled point source phantom study and in clinical data patient studies. The algorithm has the potential to facilitate true motion correction where the reconstruction algorithm can use all data available.

Data-driven, projection-based respiratory motion compensation of PET data for cardiac PET/CT and PET/MR imaging

BACKGROUND

Respiratory patient motion causes blurring of the PET images that may impact accurate quantification of perfusion and infarction extents in PET myocardial viability studies. In this study, we investigate the feasibility of correcting for respiratory motion directly in the PET-listmode data prior to image reconstruction using a data-driven, projection-based, respiratory motion compensation (DPR-MoCo) technique.

METHODS

The DPR-MoCo method was validated using simulations of a XCAT phantom (Biograph mMR PET/MR) as well as experimental phantom acquisitions (Biograph mCT PET/CT). Seven patient studies following a dual-tracer (18F-FDG/13N-NH3) imaging-protocol using a PET/MR-system were also evaluated. The performance of the DPR-MoCo method was compared against reconstructions of the acquired data (No-MoCo), a reference gate method (gated) and an image-based MoCo method using the standard reconstruction-transform-average (RTA-MoCo) approach. The target-to-background ratio (TBRLV) in the myocardium and the noise in the liver (CoVliver) were evaluated for all acquisitions. For all patients, the clinical effect of the DPR-MoCo was assessed based on the end-systolic (ESV), the end-diastolic volumes (EDV) and the left ventricular ejection fraction (EF) which were compared to functional values obtained from the cardiac MR.

RESULTS

The DPR-MoCo and the No-MoCo images presented with similar noise-properties (CoV) (P = .12), while the RTA-MoCo and reference-gate images showed increased noise levels (P = .05). TBRLV values increased for the motion limited reconstructions when compared to the No-MoCo reconstructions (P > .05). DPR-MoCo results showed higher correlation with the functional values obtained from the cardiac MR than the No-MoCo results, though non-significant (P > .05).

CONCLUSION

The projection-based DPR-MoCo method helps to improve PET image quality of the myocardium without the need for external devices for motion tracking.

Data-driven respiratory gating based on localized diaphragm sensing in TOF PET

It is important to measure the respiratory cycle in positron emission tomography (PET) to enhance the contrast of the tumor as well as the accuracy of its localization in organs such as the lung and liver. Several types of data-driven respiratory gating methods, such as center of mass and principal component analysis, have been developed to directly measure the breathing cycle from PET images and listmode data. However, the breathing cycle is still hard to detect in low signal-to-noise ratio (SNR) data, particularly in low dose PET/CT scans. To address this issue, a time-of-flight (TOF) PET is currently utilized for the data-driven respiratory gating because of its higher SNR and better localization of the region of interest. To further improve the accuracy of respiratory gating with TOF information, we propose an accurate data-driven respiratory gating method, which retrospectively derives the respiratory signal using a localized sensing method based on a diaphragm mask in TOF PET data. To assess the accuracy of the proposed method, the performance is evaluated with three patient datasets, and a pressure-belt signal as the ground truth is compared. In our experiments, we validate that the respiratory signal using the proposed data-driven gating method is well matched to the pressure-belt respiratory signal with less than 5% peak time errors and over 80% trace correlations. Based on gated signals, the respiratory-gated image of the proposed method provides more clear edges of organs compared to images using conventional non-TOF methods. Therefore, we demonstrate that the proposed method can achieve improvements for the accuracy of gating signals and image quality.

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.

Clinical comparison of the positron emission tracking (PeTrack) algorithm with the real-time position management system for respiratory gating in cardiac positron emission tomography

PURPOSE

A data-driven motion tracking system was developed for respiratory gating in positron emission tomography (PET)/computed tomography (CT) studies. The positron emission tracking system (PeTrack) estimates the position of a low-activity fiducial marker placed on the patient during imaging. The aim of this study was to compare the performance of PeTrack against that of the real-time position management (RPM) system as applied to respiratory gating in cardiac PET/CT studies.

METHODS

The list-mode data of 35 patients that were referred for ⁸² Rb myocardial perfusion studies were retrospectively processed with PeTrack to generate respiratory motion signals and triggers. Fifty acquisitions from the initial cohort, conducted under physiologic rest and stress, were considered for analysis. Respiratory-gated reconstructions were performed using reconstruction software provided by the vendor. The respiratory signals and triggers of the gating systems were compared using quantitative measurements of the respiratory signal correlation, median, and interquartiles range (IQR) of observed respiratory rates and the relative frequencies of respiratory cycle outliers. Quantitative measurements of left-ventricular wall thicknesses and motion due to respiration were also compared. Real-time position management signals were also retrospectively processed using the trigger detection method of PeTrack for a third comparator ("RPMretro") that allowed direct comparison of the motion tracking quality independently of differences in the trigger detection methods. The comparison of PeTrack to the original RPM data represent a practical comparison of the two systems, whereas that of PeTrack and RPMretro represents an equal comparison of the two. Nongated images were also reconstructed to provide reference left-ventricular wall thicknesses. LV wall thickness and motion measurements were repeated for a subset of cases with motion ≥7 mm as image artifacts were expected to be more severe in these cases.

RESULTS

A significant correlation (P < 0.05) was observed between the RPM and PeTrack respiratory signals in 45/50 acquisitions; the mean correlation coefficient was 0.43. Similar results were found between PeTrack and RPMretro. No significant difference was observed between the RPM and PeTrack with respect to median respiratory rates and the percentage of respiratory cycles outliers. Respiratory rate variability (IQR) was significantly higher with PeTrack vs RPM (P = 0.002) and RPMretro (P = 0.04). Both PeTrack and RPM had a significant increase in the percentage of respiratory rate outliers compared to RPMretro (P < 0.001 and P = 0.001, respectively). All methods indicated significant differences in LV thickness compared to nongated images (P < 0.02). LV thickness was significantly larger for PeTrack compared to RPMretro in the highest motion subset (P = 0.009). Images gated with RPMretro showed significant increases in motion compared to both PeTrack (P < 0.001) and prospective RPM (P = 0.002). In the subset of highest motion cases, the difference between RPM and RPMretro was no longer present.

CONCLUSIONS

The data-driven PeTrack algorithm performed similarly to the well-established RPM system for respiratory gating of ⁸² Rb cardiac perfusion PET/CT studies. Real-time position management performance improved after retrospective processing and led to enhanced performance compared to both PeTrack and prospective RPM. With further development PeTrack has the potential to reduce the need for ancillary hardware systems to monitor respiratory motion.

Clinical Evaluation of a Data-Driven Respiratory Gating Algorithm for Whole-Body PET with Continuous Bed Motion

Respiratory gating is the standard to prevent respiration effects from degrading image quality in PET. Data-driven gating (DDG) using signals derived from PET raw data is a promising alternative to gating approaches requiring additional hardware (e.g., pressure-sensitive belt gating [BG]). However, continuous-bed-motion (CBM) scans require dedicated DDG approaches for axially extended PET, compared with DDG for conventional step-and-shoot scans. In this study, a CBM-capable DDG algorithm was investigated in a clinical cohort and compared with BG using optimally gated (OG) and fully motion-corrected (elastic motion correction [EMOCO]) reconstructions. Methods: Fifty-six patients with suspected malignancies in the thorax or abdomen underwent whole-body ¹⁸F-FDG CBM PET/CT using DDG and BG. Correlation analyses were performed on both gating signals. Besides static reconstructions, OG and EMOCO reconstructions were used for BG and DDG. The metabolic volume, SUV_max, and SUV_mean of lesions were compared among the reconstructions. Additionally, the quality of lesion delineation in the different PET reconstructions was independently evaluated by 3 experts. Results: The global correlation coefficient between BG and DDG signals was 0.48 ± 0.11, peaking at 0.89 ± 0.07 when scanning the kidney and liver region. In total, 196 lesions were analyzed. SUV measurements were significantly higher in BG-OG, DDG-OG, BG-EMOCO, and DDG-EMOCO than in static images (P < 0.001; median SUV_max: static, 14.3 ± 13.4; BG-EMOCO, 19.8 ± 15.7; DDG-EMOCO, 20.5 ± 15.6; BG-OG, 19.6 ± 17.1; and DDG-OG, 18.9 ± 16.6). No significant differences between BG-OG and DDG-OG or between BG-EMOCO and DDG-EMOCO were found. Visual lesion delineation was significantly better in BG-EMOCO and DDG-EMOCO than in static reconstructions (P < 0.001); no significant difference was found when comparing BG and DDG for either EMOCO or OG reconstruction. Conclusion: DDG-based motion compensation of CBM PET acquisitions outperforms static reconstructions, delivering qualities comparable to BG approaches. The new algorithm may be a valuable alternative for CBM PET systems.

Improved Alignment of PET and CT Images in Whole-Body PET/CT in Cases of Respiratory Motion During CT

Respiratory motion during the CT and PET parts of a PET/CT scan leads to imperfect alignment of anatomic features seen by the 2 modalities. In this work, we concentrate on the effects of motion during CT. We propose a novel approach for improving the alignment. Methods: Respiratory waveform data were gathered during the CT and PET parts of 28 PET/CT scans of cancer patients with 40 lesions up to 3 cm in size in the lung or upper abdomen. PET list-mode data were reconstructed by 3 reconstruction methods: PET/static (the standard method with no motion correction); PET/ex (a method that calculates a range of expiratory amplitudes from the lowest one to the highest one); and PET/matched (a novel method that uses both waveforms). The 3 methods were compared. The distance between tumor positions in PET and CT were characterized in visual interpretation by physicians as well as quantitatively. Tumor SUVs (SUV_max and SUV_peak) were determined relative to SUV based on the static method. Image noise was evaluated in the liver and compared with PET/static. Results: In visual interpretation, the rate of good alignment was 13 of 21, 13 of 23, and 18 of 21 for the PET/static, PET/ex, and PET/matched methods, respectively, and the mean PET/CT distances were 3.5, 5.1, and 2.8 mm. In visual comparison with PET/ex, the rate of good alignment was increased in 1 of 10 and 7 of 10 cases for PET/static and PET/matched, respectively. SUV_max was on average 21% higher than PET/static when either PET/ex or PET/matched was used. SUV_peak was 12% higher. Image noise in the liver was 15% higher than PET/static for the PET/ex method, and 40% higher for PET/matched; that is, noise was much lower than in gated PET. Conclusion: Acquiring respiratory waveforms both in PET (as in the current state of the art) and in CT (an unusual key step in this approach) has the potential to improve the alignment of PET and CT images. A proposed method for using this information was tested. Improved alignment was demonstrated.

Respiratory Motion Correction Using A Novel Positron Emission Particle Tracking Technique: A Framework Towards Individual Lesion-Based Motion Correction

Respiratory motion during PET/CT imaging is a matter of concern due to degraded image quality and reduced quantitative accuracy caused by motion artifacts. One class of motion correction methods relies on hardware-based respiratory motion tracking systems in order to use respiratory cycles for correcting motion artifacts. Another class of hardware-free methods extract motion information from the reconstructed images or sinograms. Hardware-based methods, however, are limited by calibration requirement, patient discomfort, lack of adaptability during scanning, presence of electronic drift during respiratory monitoring etc. Extracting motion information from reconstructed images is also limited by the fact that the original raw information requires significant processing before it can be used. Hence the motivation behind this work is to introduce a software-based approach that can be applied on raw 64-bit listmode data. The basic design of the proposed method is based on the fundamentals of Positron Emission Particle Tracking (PEPT) with additional incorporation of Time of Flight (TOF) information. Respiratory motion of patients has been extracted from the raw PET data by tracking a point source attached to the patient in areas on and near the chest. The key objective of this work is to describe a new process by which this particle tracking based motion correction system can eventually be lesion specific and correct the motion for a particular lesion within the patient. This work thus serves as a framework for lesion specific motion correction.

Gating Approaches in Cardiac PET Imaging

Cardiac PET provides high sensitivity and high negative predictive value in the diagnosis of coronary artery disease and cardiomyopathies. Cardiac, respiratory as well as bulk patient motion have detrimental effects on thoracic PET imaging, in particular on cardiovascular PET imaging where the motion can affect the PET images quantitatively as well as qualitatively. Gating can ameliorate the unfavorable impact of motion additionally enabling evaluation of left ventricular systolic function. In this article, the authors review the recent advances in gating approaches and highlight the advances in data-driven approaches, which hold promise in motion detection without the need for complex hardware setup.

Real-time data-driven motion correction in PET

PET imaging has been, and continues to be, an evolving diagnostic technology. In recent years, the modernizing digital landscape has opened new opportunities for data-driven innovation. One such facet has been data-driven motion correction (DDMC) in PET. As both research and industry propel this technology forward, we can recognize prospects and opportunities for further development. The concept of clinical practicality is supported by DDMC approaches—it is what sets them apart from traditional hardware-driven motion correction strategies that have largely not gained acceptance in routine diagnostic PET; the ease of use of DDMC may help propel acceptance of motion correction solutions in clinical practice. As we reflect on the present field, we should consider that DDMC can be made even more practical, and likely more impactful, if further developed to fit within a real-time acquisition framework. This vision for development is not new, but has been made more feasible with contemporary electronics, and has begun to be revisited in contemporary literature. The opportunities for development lie on a new forefront of innovation where medical physics integrates with engineering, data science, and modern computing capacities. Real-time DDMC is a systems integration challenge, and achieving it will require cooperation between hardware and software developers, and likely academia and industry. While challenges for development do exist, it is likely that we will see real-time DDMC come to fruition in the coming years. This effort may establish groundwork for developing similar innovations in the emerging digital innovation age.

Evaluation of data-driven respiratory gating waveforms for clinical PET imaging

BACKGROUND

We aimed to evaluate the clinical robustness of a commercially developed data-driven respiratory gating algorithm based on principal component analysis, for use in routine PET imaging.

METHODS

One hundred fifty-seven adult FDG PET examinations comprising a total of 1149 acquired bed positions were used for the assessment. These data are representative of FDG scans currently performed at our institution. Data were acquired for 4 min/bed position (3 min/bed for legs). The data-driven gating (DDG) algorithm was applied to each bed position, including those where minimal respiratory motion was expected. The algorithm provided a signal-to-noise measure of respiratory-like frequencies within the data, denoted as R. Qualitative evaluation was performed by visual examination of the waveforms, with each waveform scored on a 3-point scale by two readers and then averaged (score S of 0 = no respiratory signal, 1 = some respiratory-like signal but indeterminate, 2 = acceptable signal considered to be respiratory). Images were reconstructed using quiescent period gating and compared with non-gated images reconstructed with a matched number of coincidences. If present, the SUV_max of a well-defined lesion in the thorax or abdomen was measured and compared between the two reconstructions.

RESULTS

There was a strong (r = 0.86) and significant correlation between R and scores S. Eighty-six percent of waveforms with R ≥ 15 were scored as acceptable for respiratory gating. On average, there were 1.2 bed positions per patient examination with R ≥ 15. Waveforms with high R and S were found to originate from bed positions corresponding to the thorax and abdomen: 90% of waveforms with R ≥ 15 had bed centres in the range 5.6 cm superior to 27 cm inferior from the dome of the liver. For regions where respiratory motion was expected to be minimal, R tended to be < 6 and S tended to be 0. The use of DDG significantly increased the SUV_max of focal lesions, by an average of 11% when considering lesions in bed positions with R ≥ 15.

CONCLUSIONS

The majority of waveforms with high R corresponded to the part of the patient where respiratory motion was expected. The waveforms were deemed suitable for respiratory gating when assessed visually, and when used were found to increase SUV_max in focal lesions.

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.

Robust real-time extraction of respiratory signals from PET list-mode data

Respiratory motion, which typically cannot simply be suspended during PET image acquisition, affects lesions' detection and quantitative accuracy inside or in close vicinity to the lungs. Some motion compensation techniques address this issue via pre-sorting ('binning') of the acquired PET data into a set of temporal gates, where each gate is assumed to be minimally affected by respiratory motion. Tracking respiratory motion is typically realized using dedicated hardware (e.g. using respiratory belts and digital cameras). Extracting respiratory signals directly from the acquired PET data simplifies the clinical workflow as it avoids handling additional signal measurement equipment. We introduce a new data-driven method 'combined local motion detection' (CLMD). It uses the time-of-flight (TOF) information provided by state-of-the-art PET scanners in order to enable real-time respiratory signal extraction without additional hardware resources. CLMD applies center-of-mass detection in overlapping regions based on simple back-positioned TOF event sets acquired in short time frames. Following a signal filtering and quality-based pre-selection step, the remaining extracted individual position information over time is then combined to generate a global respiratory signal. The method is evaluated using seven measured FDG studies from single and multiple scan positions of the thorax region, and it is compared to other software-based methods regarding quantitative accuracy and statistical noise stability. Correlation coefficients around 90% between the reference and the extracted signal have been found for those PET scans where motion affected features such as tumors or hot regions were present in the PET field-of-view. For PET scans with a quarter of typically applied radiotracer doses, the CLMD method still provides similar high correlation coefficients which indicates its robustness to noise. Each CLMD processing needed less than 0.4 s in total on a standard multi-core CPU and thus provides a robust and accurate approach enabling real-time processing capabilities using standard PC hardware.

Self-Gating: An Adaptive Center-of-Mass Approach for Respiratory Gating in PET

The goal is to develop an adaptive center-of-mass (COM)-based approach for device-less respiratory gating of list-mode positron emission tomography (PET) data. Our method contains two steps. The first is to automatically extract an optimized respiratory motion signal from the list-mode data during acquisition. The respiratory motion signal was calculated by tracking the location of COM within a volume of interest (VOI). The signal prominence (SP) was calculated based on Fourier analysis of the signal. The VOI was adaptively optimized to maximize SP. The second step is to automatically correct signal-flipping effects. The sign of the signal was determined based on the assumption that the average patient spends more time during expiration than inspiration. To validate our methods, thirty-one ¹⁸F-FDG patient scans were included in this paper. An external device-based signal was used as the gold standard, and the correlation coefficient of the data-driven signal with the device-based signal was measured. Our method successfully extracted respiratory signal from 30 out of 31 datasets. The failure case was due to lack of uptake in the field of view. Moreover, our sign determination method obtained correct results for all scans excluding the failure case. Quantitatively, the proposed signal extraction approach achieved a median correlation of 0.85 with the device-based signal. Gated images using optimized data-driven signal showed improved lesion contrast over static image and were comparable to those using device-based signal. We presented a new data-driven method to automatically extract respiratory motion signal from list-mode PET data by optimizing VOI for COM calculation, as well as determine motion direction from signal asymmetry. Successful application of the proposed method on most clinical datasets and comparison with device-based signal suggests its potential of serving as an alternative to external respiratory monitors.

Retrospective data-driven respiratory gating for PET using TOF information

Traditional data-driven respiratory gating method is capable of detecting breathing cycles directly from positron emission tomography (PET) data, but usually fails at low SNR, particularly at low dose PET/CT study. Time-of-flight (TOF) PET has the potential to improve the SNR. In order for TOF information to reduce the statistical noise and boost the performance of respiratory gating, we present a robust data-driven respiratory gating method using TOF information, which retrospectively derived the respiratory signal from the acquired TOF-PET data. The PET data was acquired in list mode format and analyzed in sinogram space. The method was demonstrated with patient datasets acquired on a TOF PET/CT system. Data-driven gating methods by center of mass (COM) and principle component analysis (PCA) algorithm were successfully performed on nonTOF PET and TOF PET dataset. To assess the accuracy of the data-driven respiratory signal, a hardware-based signal was acquired for comparison. The study showed that retrospectively respiratory gating using TOF sinograms has improved the SNR, and outperforms the non-TOF gating under both COM and PCA algorithms.

Evaluation of principal component analysis-based data-driven respiratory gating for positron emission tomography

Objective:

Respiratory motion can degrade PET image quality and lead to inaccurate quantification of lesion uptake. Such motion can be mitigated via respiratory gating. Our objective was to evaluate a data-driven gating (DDG) technique that is being developed commercially for clinical PET/CT.

Methods:

A data-driven respiratory gating algorithm based on principal component analysis (PCA) was applied to phantom and FDG patient data. An anthropomorphic phantom and a NEMA IEC Body phantom were filled with ¹⁸F, placed on a respiratory motion platform, and imaged using a PET/CT scanner. Motion waveforms were measured using an infrared camera [the Real-time Position Management™ system (RPM)] and also extracted from the PET data using the DDG algorithm. The waveforms were compared via calculation of Pearson’s correlation coefficients. PET data were reconstructed using quiescent period gating (QPG) and compared via measurement of recovery percentage and background variability.

Results:

Data-driven gating had similar performance to the external gating system, with correlation coefficients in excess of 0.97. Phantom and patient images were visually clearer with improved contrast when QPG was applied as compared to no motion compensation. Recovery coefficients in the phantoms were not significantly different between DDG- and RPM-based QPG, but were significantly higher than those found for no motion compensation (p < 0.05).

Conclusion:

A PCA-based DDG algorithm was evaluated and found to provide a reliable respiratory gating signal in anthropomorphic phantom studies and in example patients.

Advances in knowledge:

The prototype commercial DDG algorithm may enable reliable respiratory gating in routine clinical PET-CT.

Respiratory-gated PET/CT for pulmonary lesion characterisation-promises and problems

2-deoxy-2-(¹⁸Fluorine)-fluoro-D-glucose (FDG) PET/CT is an integral part of lung carcinoma staging and frequently used in the assessment of solitary pulmonary nodules. However, a limitation of conventional three-dimensional PET/CT when imaging the thorax is its susceptibility to motion artefact, which blurs the signal from the lesion resulting in inaccurate representation of size and metabolic activity. Respiratory gated (four-dimensional) PET/CT aims to negate the effects of motion artefact and provide a more accurate interpretation of pulmonary nodules and lymphadenopathy. There have been recent advances in technology and a shift from traditional hardware to more streamlined software methods for respiratory gating which should allow more widespread use of respiratory-gating in the future. The purpose of this article is to review the evidence surrounding four-dimensional PET/CT in pulmonary lesion characterisation.

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

PURPOSE

The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application.

APPROACH

A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed.

FINDINGS

A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol.

CONCLUSIONS

Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET-AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET-AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members.

Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution

Data-driven respiratory gating techniques were developed to correct for respiratory motion in PET studies, without the help of external motion tracking systems. Due to the greatly increased image noise in gated reconstructions, it is desirable to develop a data-driven event-by-event respiratory motion correction method. In this study, using the Centroid-of-distribution (COD) algorithm, we established a data-driven event-by-event respiratory motion correction technique using TOF PET list-mode data, and investigated its performance by comparing with an external system-based correction method. Ten human scans with the pancreatic β-cell tracer ¹⁸F-FP-(+)-DTBZ were employed. Data-driven respiratory motions in superior-inferior (SI) and anterior-posterior (AP) directions were first determined by computing the centroid of all radioactive events during each short time frame with further processing. The Anzai belt system was employed to record respiratory motion in all studies. COD traces in both SI and AP directions were first compared with Anzai traces by computing the Pearson correlation coefficients. Then, respiratory gated reconstructions based on either COD or Anzai traces were performed to evaluate their relative performance in capturing respiratory motion. Finally, based on correlations of displacements of organ locations in all directions and COD information, continuous 3D internal organ motion in SI and AP directions was calculated based on COD traces to guide event-by-event respiratory motion correction in the MOLAR reconstruction framework. Continuous respiratory correction results based on COD were compared with that based on Anzai, and without motion correction. Data-driven COD traces showed a good correlation with Anzai in both SI and AP directions for the majority of studies, with correlation coefficients ranging from 63% to 89%. Based on the determined respiratory displacements of pancreas between end-expiration and end-inspiration from gated reconstructions, there was no significant difference between COD-based and Anzai-based methods. Finally, data-driven COD-based event-by-event respiratory motion correction yielded comparable results to that based on Anzai respiratory traces, in terms of contrast recovery and reduced motion-induced blur. Data-driven event-by-event respiratory motion correction using COD showed significant image quality improvement compared with reconstructions with no motion correction, and gave comparable results to the Anzai-based method.

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.

Data-Driven Methods for the Determination of Anterior-Posterior Motion in PET

Physiological motion combined with elongated scanning times in PET leads to image degradation and quantification errors. Correction approaches usually require 1-D signals that can be obtained with hardware-based or data-driven methods. Most of the latter are optimized or limited to capture internal motion along the superior-inferior (S-I) direction. In this work we present methods for also extracting anterior-posterior (A-P) motion from PET data and propose a set of novel weighting mechanisms that can be used to emphasize certain lines-of-response (LORs) for an increased sensitivity and better signal-to-noise ratio (SNR). The proper functioning of the methods was verified in a phantom experiment. Further, their application to clinical [18^F]-FDG-PET data of 72 patients revealed that using the weighting mechanisms leads to signals with significantly higher spectral respiratory weights, i.e. signals with higher quality. Information about multi-dimensional motion is contained in PET data and can be derived with data-driven methods. Motion models or correction techniques such as respiratory gating might benefit from the proposed methods as they allow to describe the three-dimensional movements of PET-positive structures more precisely.

Frequency based gating: An alternative, conformal, approach to 4D PET data utilization

PURPOSE

Respiratory gating is a strategy for overcoming image degradation caused by patient motion in Positron Emission Tomography (PET) imaging. Traditional methods for sorting data, namely, phase-based gating or amplitude-based gating, come with an inherent trade-off between resolution improvements and added noise present in the subjugated data. If the goal of motion correction in PET is realigned from creating 4D images that attempt to mimic nongated images, towards ideal utilization of the information available, then new paths for data management emerge. In this work, the authors examine the application of a method in a new class of frequency based data subjugation algorithms, termed gating +. This strategy utilizes data driven information to locally adapt signal to its optimal segregation, thereby creating a new approach to 4D data utilization PET.

METHODS

189 (18)F-fluorodeoxyglucose (FDG) PET scans were acquired at a single bed position centered on the thorax region. 4D gated image sets were reconstructed using data driven gating. The gating+ signal optimization algorithm, previously presented in small animal PET images and simulations, was used to segregate data in frequency space to generate optimized 4D images in the population-the first application and analysis of gating+ in human PET scans. The nongated, phase gated, and gating+ representations of the data were compared using FDG uptake analysis in the identified lesions and noise measurements from background regions.

RESULTS

Optimized processing required less than 1 min per scan on a standard PC (plus standard reconstruction time), and yielded entire 4D optimized volumes plus motion maps. Optimized scans had noise characteristics similar to nongated images, yet also contained much of the resolution and motion information found in the gated images. The average SUVmax increase in the lesion sample between gated/nongated and gating+/nongated (±SD in population) was 35.8% ± 34.6% and 28.6% ± 27.9%, respectively. The average percent standard deviation (%SD ± SD in population) in liver volumes of interest (VOIs) across the sample for the nongated, gated, and gating+ scans was 6.7% ± 2.4%, 13.6% ± 3.3%, and 7.1% ± 2.5%, respectively. In all cases, the noise in the gating+ liver VOIs was closer to the nongated measurements than to the gated.

CONCLUSIONS

The gating+ algorithm introduces the notion of conforming 4D data segregation to the local information and statistics that support it. By segregating data in frequency space, the authors are able to generate low noise motion information rich image sets, derived solely from selective use of raw data. Their work shows that the gating+ algorithm can be robustly applied in populations, and across varying qualities of motion and scans statistics, and be integrated as part of a fully automated motion correction workflow. Furthermore, the idea of smart signal utilization underpins a new concept of low risk or even risk-free motion correction application in PET.

Impact of Data-driven Respiratory Gating in Clinical PET

Purpose To study the feasibility and impact of respiratory gating in positron emission tomographic (PET) imaging in a clinical trial comparing conventional hardware-based gating with a data-driven approach and to describe the distribution of determined parameters. Materials and Methods This prospective study was approved by the ethics committee of the University Hospital of Münster (AZ 2014-217-f-N). Seventy-four patients suspected of having abdominal or thoracic fluorine 18 fluorodeoxyglucose (FDG)-positive lesions underwent clinical whole-body FDG PET/computed tomographic (CT) examinations. Respiratory gating was performed by using a pressure-sensitive belt system (belt gating [BG]) and an automatic data-driven approach (data-driven gating [DDG]). PET images were analyzed for lesion uptake, metabolic volumes, respiratory shifts of lesions, and diagnostic image quality. Results Forty-eight patients had at least one lesion in the field of view, resulting in a total of 164 lesions analyzed (range of number of lesions per patient, one to 13). Both gating methods revealed respiratory shifts of lesions (4.4 mm ± 3.1 for BG vs 4.8 mm ± 3.6 for DDG, P = .76). Increase in uptake of the lesions compared with nongated values did not differ significantly between both methods (maximum standardized uptake value [SUVmax], +7% ± 13 for BG vs +8% ± 16 for DDG, P = .76). Similarly, gating significantly decreased metabolic lesion volumes with both methods (-6% ± 26 for BG vs -7% ± 21 for DDG, P = .44) compared with nongated reconstructions. Blinded reading revealed significant improvements in diagnostic image quality when using gating, without significant differences between the methods (DDG was judged to be inferior to BG in 22 cases, equal in 12 cases, and superior in 15 cases; P = .32). Conclusion Respiratory gating increases diagnostic image quality and uptake values and decreases metabolic volumes compared with nongated acquisitions. Data-driven approaches are clinically applicable alternatives to belt-based methods and might help establishing routine respiratory gating in clinical PET/CT. (©) RSNA, 2016 Online supplemental material is available for this article.

Validation of Software Gating: A Practical Technology for Respiratory Motion Correction in PET

Purpose To assess the performance of hardware- and software-gating technologies in terms of qualitative and quantitative characteristics of respiratory motion in positron emission tomography (PET) imaging. Materials and Methods Between 2010 and 2013, 219 fluorine 18 fluorodeoxyglucose PET examinations were performed in 116 patients for assessment of pulmonary nodules. All patients provided informed consent in this institutional review board-approved study. Acquisitions were reconstructed as respiratory-gated images by using hardware-derived respiratory triggers and software-derived signal (via an automated postprocessing method). Asymmetry was evaluated in the joint distribution of reader preference, and linear mixed models were used to evaluate differences in outcomes according to gating type. Results In blind reviews of reconstructed gated images, software was selected as superior 16.9% of the time (111 of 657 image sets; 95% confidence interval [CI]: 14.0%, 19.8%), and hardware was selected as superior 6.2% of the time (41 of 657 image sets; 95% CI: 4.4%, 8.1%). Of the image sets, 76.9% (505 of 657; 95% CI: 73.6%, 80.1%) were judged as having indistinguishable motion quality. Quantitative analysis demonstrated that the two gating strategies exhibited similar performance, and the performance of both was significantly different from that of nongated images. The mean increase ± standard deviation in lesion maximum standardized uptake value was 42.2% ± 38.9 between nongated and software-gated images, and lesion full width at half maximum values decreased by 9.9% ± 9.6. Conclusion Compared with vendor-supplied respiratory-gating hardware methods, software gating performed favorably, both qualitatively and quantitatively. Fully automated gating is a feasible approach to motion correction of PET images. (©) RSNA, 2016 Online supplemental material is available for this article.

A dual-Kinect approach to determine torso surface motion for respiratory motion correction in PET

PURPOSE

Respiratory gating is commonly used to reduce blurring effects and attenuation correction artifacts in positron emission tomography (PET). Established clinically available methods that employ body-attached hardware for acquiring respiration signals rely on the assumption that external surface motion and internal organ motion are well correlated. In this paper, the authors present a markerless method comprising two Microsoft Kinects for determining the motion on the whole torso surface and aim to demonstrate its validity and usefulness-including the potential to study the external/internal correlation and to provide useful information for more advanced correction approaches.

METHODS

The data of two Kinects are used to calculate 3D representations of a patient's torso surface with high spatial coverage. Motion signals can be obtained for any position by tracking the mean distance to a virtual camera with a view perpendicular to the surrounding surface. The authors have conducted validation experiments including volunteers and a moving high-precision platform to verify the method's suitability for providing meaningful data. In addition, the authors employed it during clinical (18)F-FDG-PET scans and exemplarily analyzed the acquired data of ten cancer patients. External signals of abdominal and thoracic regions as well as data-driven signals were used for gating and compared with respect to detected displacement of present lesions. Additionally, the authors quantified signal similarities and time shifts by analyzing cross-correlation sequences.

RESULTS

The authors' results suggest a Kinect depth resolution of approximately 1 mm at 75 cm distance. Accordingly, valid signals could be obtained for surface movements with small amplitudes in the range of only few millimeters. In this small sample of ten patients, the abdominal signals were better suited for gating the PET data than the thoracic signals and the correlation of data-driven signals was found to be stronger with abdominal signals than with thoracic signals (average Pearson correlation coefficients of 0.74 ± 0.17 and 0.45 ± 0.23, respectively). In all cases, except one, the abdominal respiratory motion preceded the thoracic motion-a maximum delay of approximately 600 ms was detected.

CONCLUSIONS

The method provides motion information with sufficiently high spatial and temporal resolution. Thus, it enables meaningful analysis in the form of comparisons between amplitudes and phase shifts of signals from different regions. In combination with a large field-of-view, as given by combining the data of two Kinect cameras, it yields surface representations that might be useful in the context of motion correction and motion modeling.

Automatic Cardiac Self-Gating of Small-Animal PET Data

PURPOSE

The cardiac gating signal (CGS) in positron emission tomography (PET) studies is usually obtained from an electrocardiography (ECG) monitor. In this work, we propose a method to obtain the CGS in small-animal PET using the acquired list-mode data without using any hardware or end-user input.

PROCEDURES

The CGS was obtained from the number of coincidences over time acquired in the lines-of-response connected with the cardiac region. This region is identified in the image as its value changes with frequencies in the range of 3 to 12 Hz. The procedure was tested in a study with 29 Wistar rats and 6 mice injected with 2-deoxy-2-[(18)F]fluoro-D-glucose, which underwent a 45-min single-bed list-mode PET scan of the heart syncronized with an ECG. The estimated signals and the reconstructed images using eight-gated frames were compared with the ones obtained using the ECG signal from the monitor.

RESULTS

The differences of the PET-based CGS with respect to the ECG relative to the duration of the heartbeats were 5.6 % in rats and 11.0 % in mice. The reconstructed gated images obtained from the proposed method do not differ qualitatively with respect to the ones obtained with the ECG. The quantitative analysis of both set of images were performed measuring the volume of the left ventricle (LV) of the rats in the end-of-systole and end-of-diastole phase. The differences found in these parameters between both methods were below 12.1 % in the ESV and 9.3 % in the EDV with a 95 % confidence interval, which are comparable to the accuracy (7 %) of the method used for segmenting the LV.

CONCLUSION

The proposed method is able to provide a valid and accurate CGS in small-animal PET list-mode data.