List Mode–Driven Cardiac and Respiratory Gating in PET

Optical surface information-based respiratory phase-sorting and motion-incorporated reconstruction for SPECT imaging

BACKGROUND

Respiratory motion during the single photon emission computed tomography (SPECT) acquisition can cause blurring artifacts in the reconstructed images, leading to inaccurate estimates for activity and absorbed doses.

PURPOSE

To address the impact of respiratory motion, we utilized a new optical surface imaging (OSI) system to extract the respiratory signals for phase sorting and verified its effectiveness through simulation and patient data. Additionally, we implemented GPU-accelerated motion-incorporated reconstruction algorithms for the SPECT projections, integrating motion information to produce motion-free images from all acquired data.

METHODS

We used the 4D XCAT Phantom to generate attenuation maps and activity images across different respiratory phases, with activity distributions based on patient images. SPECT projections were simulated using the SIMIND Monte Carlo program with Poisson noise. The OSI system was modeled by introducing Gaussian noise into the point clouds on the body surface within the attenuation map. The body surface images were registered across phases using a Gaussian mixture model combined with principal component analysis. The extracted respiratory signals were compared to the center-of-light (COL) approach, with or without filtering and kidney masking. The OSI method was further validated by comparing respiratory signals derived from a real patient using OSI to simultaneous cone-beam CT (CBCT) projections. Two motion-incorporated techniques, namely, 4D reconstruction (4D-Recon) and post-reconstruction registration and summation (post-Recon), were compared with non-motion-corrected images (non-MC) and single-phase gating (Gating). The quantitative evaluation of image quality utilized recovery coefficients (RC), contrast recovery coefficients (CRC), and uncertainty estimation.

RESULTS

In simulation, the correlation between the ground-truth and OSI-based signals remained high and stable (0.99 ± 0.004, p-value < $<$ 0.001 vs. COL-filter with kidney masking). While the kidney mask improved performance (0.87 ± 0.07 without filtering and 0.90 ± 0.06 with filtering, p-value < $<$ 0.001), it was less effective and more uncertain than the OSI method. Validation with patient data showed high consistency in breathing frequencies and phase alignment between CBCT-based and OSI-based signals. For reconstruction, both 4D-Recon and post-Recon significantly enhanced RC and CRC compared to non-MC, with less uncertainty than Gating. In addition, 4D-Recon outperformed post-Recon in certain aspects.

CONCLUSIONS

Our novel respiratory signal extraction approach based on OSI demonstrated superior accuracy and reliability compared to a data-driven method. Applying motion-incorporated SPECT reconstruction using these accurate breathing signals has the potential to enhance image quality and improve absorbed dose quantification in radiopharmaceutical therapy. The relevant reconstruction algorithms are also made available for public use in the open-source library PyTomography.

Long Axial Field of View PET/CT: Technical Aspects in Cardiovascular Diseases

Positron emission tomography / computed tomography (PET/CT) plays a pivotal role in the assessment of cardiovascular diseases (CVD), particularly in the context of ischemic heart disease. Nevertheless, its application in other forms of CVD, such as infiltrative, infectious, or inflammatory conditions, remains limited. Recently, PET/CT systems with an extended axial field of view (LAFOV) have been developed, offering greater anatomical coverage and significantly enhanced PET sensitivity. These advancements enable head-to-pelvis imaging with a single bed position, and in systems with an axial field of view (FOV) of approximately 2 meters, even total body (TB) imaging is feasible in a single scan session. The application of LAFOV PET/CT in CVD presents a promising opportunity to improve systemic cardiovascular assessments and address the limitations inherent to conventional short axial field of view (SAFOV) devices. However, several technical challenges, including procedural considerations for LAFOV systems in CVD, complexities in data processing, arterial input function extraction, and artefact management, have not been fully explored. This review aims to discuss the technical aspects of LAFOV PET/CT in relation to CVD by highlighting key opportunities and challenges and examining the impact of these factors on the evaluation of most relevant CVD.

Deep generative denoising networks enhance quality and accuracy of gated cardiac PET data

BACKGROUND

Cardiac positron emission tomography (PET) can visualize and quantify the molecular and physiological pathways of cardiac function. However, cardiac and respiratory motion can introduce blurring that reduces PET image quality and quantitative accuracy. Dual cardiac- and respiratory-gated PET reconstruction can mitigate motion artifacts but increases noise as only a subset of data are used for each time frame of the cardiac cycle.

AIM

The objective of this study is to create a zero-shot image denoising framework using a conditional generative adversarial networks (cGANs) for improving image quality and quantitative accuracy in non-gated and dual-gated cardiac PET images.

METHODS

Our study included retrospective list-mode data from 40 patients who underwent an 18F-fluorodeoxyglucose (18F-FDG) cardiac PET study. We initially trained and evaluated a 3D cGAN-known as Pix2Pix-on simulated non-gated low-count PET data paired with corresponding full-count target data, and then deployed the model on an unseen test set acquired on the same PET/CT system including both non-gated and dual-gated PET data.

RESULTS

Quantitative analysis demonstrated that the 3D Pix2Pix network architecture achieved significantly (p value<0.05) enhanced image quality and accuracy in both non-gated and gated cardiac PET images. At 5%, 10%, and 15% preserved count statistics, the model increased peak signal-to-noise ratio (PSNR) by 33.7%, 21.2%, and 15.5%, structural similarity index (SSIM) by 7.1%, 3.3%, and 2.2%, and reduced mean absolute error (MAE) by 61.4%, 54.3%, and 49.7%, respectively. When tested on dual-gated PET data, the model consistently reduced noise, irrespective of cardiac/respiratory motion phases, while maintaining image resolution and accuracy. Significant improvements were observed across all gates, including a 34.7% increase in PSNR, a 7.8% improvement in SSIM, and a 60.3% reduction in MAE.

CONCLUSION

The findings of this study indicate that dual-gated cardiac PET images, which often have post-reconstruction artifacts potentially affecting diagnostic performance, can be effectively improved using a generative pre-trained denoising network.

Automatic Quantitative Assessment for Diagnostic and Therapeutic Response in Rodent Myocardial Infarct Model

The purpose of this study was to investigate the most appropriate methodological approach for the automatic measurement of rodent myocardial infarct polar map using histogram-based thresholding and unsupervised deep learning (DL)-based segmentation. A rat myocardial infarction model was induced by ligation of the left coronary artery. Positron emission tomography (PET) was performed 60 min after the administration of 18F-fluoro-deoxy-glucose (18F-FDG), and PET was performed after injecting 64Cu-pyruvaldehyde-bis(N4-methylthiosemicarbazone). Single photon emission computed tomography was performed 60 min after injection of 99mTc-hexakis-2-methoxyisobutylisonitrile and 201Tl. Delayed contrast-enhanced magnetic resonance imaging was performed after injecting Gd-DTPA-BMA. Three types of thresholding methods (naive thresholding, Otsu's algorithm, and multi-Gaussian mixture model (MGMM)) were used. DL segmentation methods were based on a convolution neural network and trained with constraints on feature similarity and spatial continuity of the response map extracted from images by the network. The relative infarct sizes measured by histology and estimated R2 for 18F-FDG were 0.8477, 0.7084, 0.8353, and 0.9024 for naïve thresholding, Otsu's algorithm, MGMM, and DL segmentation, respectively. DL-based method improved the accuracy of MI size assessment.

Unsupervised deep learning framework for data-driven gating in positron emission tomography

BACKGROUND

Physiological motion, such as respiratory motion, has become a limiting factor in the spatial resolution of positron emission tomography (PET) imaging as the resolution of PET detectors continue to improve. Motion-induced misregistration between PET and CT images can also cause attenuation correction artifacts. Respiratory gating can be used to freeze the motion and to reduce motion induced artifacts.

PURPOSE

In this study, we propose a robust data-driven approach using an unsupervised deep clustering network that employs an autoencoder (AE) to extract latent features for respiratory gating.

METHODS

We first divide list-mode PET data into short-time frames. The short-time frame images are reconstructed without attenuation, scatter, or randoms correction to avoid attenuation mismatch artifacts and to reduce image reconstruction time. The deep AE is then trained using reconstructed short-time frame images to extract latent features for respiratory gating. No additional data are required for the AE training. K-means clustering is subsequently used to perform respiratory gating based on the latent features extracted by the deep AE. The effectiveness of our proposed Deep Clustering method was evaluated using physical phantom and real patient datasets. The performance was compared against phase gating based on an external signal (External) and image based principal component analysis (PCA) with K-means clustering (Image PCA).

RESULTS

The proposed method produced gated images with higher contrast and sharper myocardium boundaries than those obtained using the External gating method and Image PCA. Quantitatively, the gated images generated by the proposed Deep Clustering method showed larger center of mass (COM) displacement and higher lesion contrast than those obtained using the other two methods.

CONCLUSIONS

The effectiveness of our proposed method was validated using physical phantom and real patient data. The results showed our proposed framework could provide superior gating than the conventional External method and Image PCA.

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.

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.

The prevalence of image degradation due to motion in rest-stress rubidium-82 imaging on a SiPM PET-CT system

BACKGROUND

Motion of the heart is known to affect image quality in cardiac PET. The prevalence of motion blurring in routine cardiac PET is not fully appreciated due to challenges identifying subtle motion artefacts. This study utilizes a recent prototype Data-Driven Motion Correction (DDMC) algorithm to generate corrected images that are compared with non-corrected images to identify visual differences in relative rubidium-82 perfusion images due to motion.

METHODS

300 stress and 300 rest static images were reconstructed with DDMC and without correction (NMC). The 600 DDMC/NMC image pairs were assigned Visual Difference Score (VDS). The number of non-diagnostic images were noted. A "Dwell Fraction" (DF) was derived from the data to quantify motion and predict image degradation.

RESULTS

Motion degradation (VDS = 1 or 2) was evident in 58% of stress images and 33% of rest images. Seven NMC images were non-diagnostic-these originated from six studies giving a 2% rate of non-diagnostic studies due to motion. The DF metric was able to effectively predict image degradation. The DDMC heart identification and tracking was successful in all images.

CONCLUSION

Motion degradation is present in almost half of all relative perfusion images. The DDMC algorithm is a robust tool for predicting, assessing and correcting image degradation.

A preliminary evaluation of a high temporal resolution data-driven motion correction algorithm for rubidium-82 on a SiPM PET-CT system

BACKGROUND

In myocardial perfusion PET, images are acquired during vasodilator stress, increasing the likelihood of intra-frame motion blurring of the heart in reconstructed static images to assess relative perfusion. This work evaluated a prototype data-driven motion correction (DDMC) algorithm designed specifically for cardiac PET.

METHODS

A cardiac torso phantom, with a solid defect, was scanned stationary and being manually pulled to-and-fro in the axial direction with a random motion. Non-motion-corrected (NMC) and DDMC images were reconstructed. Total perfusion deficit was measured in the defect and profiles through the cardiac insert were defined. In addition, 46 static perfusion images from 36 rubidium-82 MPI patients were selected based upon a perception of motion blurring in the images. NMC and DDMC images were reconstructed, blinded, and scored on image quality and perceived motion.

RESULTS

Phantom data demonstrated near-perfect recovery of myocardial wall visualization and defect quantification with DDMC compared with the stationary phantom. Quality of clinical images was NMC: 10 non-diagnostic, 31 adequate, and 5 good; DDMC images: 0 non-diagnostic, 6 adequate, and 40 good.

CONCLUSION

The DDMC algorithm shows great promise in rubidium MPI PET with substantial improvements in image quality and the potential to salvage images considered non-diagnostic due to significant motion blurring.

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.

Clinical evaluation of data-driven respiratory gating for PET/CT in an oncological cohort of 149 patients: impact on image quality and patient management

OBJECTIVES

To evaluate the impact of fully automatic motion correction by data-driven respiratory gating (DDG) on positron emission tomography (PET) image quality, lesion detection and patient management.

MATERIALS AND METHODS

A total of 149 patients undergoing PET/CT for cancer (re-)staging were retrospectively included. Patients underwent a PET/CT on a digital detector scanner and for every patient a PET data set where DDG was enabled (PET_DDG) and as well as where DDG was not enabled (PET_nonDDG) was reconstructed. All PET data sets were evaluated by two readers which rated the general image quality, motion effects and organ contours. Further, both readers reviewed all scans on a case-by-case basis and evaluated the impact of PET_DDG on additional apparent lesion, change of report, and change of management.

RESULTS

In 85% (n = 126) of the patients, at least one bed position was acquired using DDG, resulting in mean scan time increase of 4:37 min per patient in the whole study cohort (n = 149). General image quality was not rated differently for PET_nonDDG and PET_DDG images (p = 1.000) while motion effects (i.e. indicating general blurring) was rated significantly lower in PET_DDG images and organ contours, including liver and spleen, were rated significantly sharper using PET_DDG as compared to PET_nonDDG (all p < 0.001). In 27% of patients, PET_DDG resulted in a change of the report and in a total of 12 cases (8%), PET_DDG resulted in a change of further clinical management.

CONCLUSION

Deviceless DDG provided reliable fully automatic motion correction in clinical routine and increased lesion detectability and changed management in a considerable number of patients.

ADVANCES IN KNOWLEDGE

DDG enables PET/CT with respiratory gating to be used routinely in clinical practice without external gating equipment needed.

Atherosclerosis imaging using PET: Insights and applications

PET imaging is able to harness biological processes to characterise high-risk features of atherosclerotic plaque prone to rupture. Current radiotracers are able to track inflammation, microcalcification, hypoxia, and neoangiogenesis within vulnerable plaque. 18 F-fluorodeoxyglucose (18 F-FDG) is the most commonly used radiotracer in vascular studies and is employed as a surrogate marker of plaque inflammation. Increasingly, 18 F-FDG and other PET tracers are also being used to provide imaging endpoints in cardiovascular interventional trials. The evolution of novel PET radiotracers, imaging protocols, and hybrid scanners are likely to enable more efficient and accurate characterisation of high-risk plaque. This review explores the role of PET imaging in atherosclerosis with a focus on PET tracers utilised in clinical research and the applications of PET imaging to cardiovascular drug development.

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.

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.

Respiratory Motion Detection and Correction for MR Using the Pilot Tone: Applications for MR and Simultaneous PET/MR Examinations

OBJECTIVES

The aim of this study was to develop a method for tracking respiratory motion throughout full MR or PET/MR studies that requires only minimal additional hardware and no modifications to the sequences.

MATERIALS AND METHODS

Patient motion that is caused by respiration affects the quality of the signal of the individual radiofrequency receive coil elements. This effect can be detected as a modulation of a monofrequent signal that is emitted by a small portable transmitter placed inside the bore (Pilot Tone). The frequency is selected such that it is located outside of the frequency band of the actual MR readout experiment but well within the bandwidth of the radiofrequency receiver, that is, the oversampling area. Temporal variations of the detected signal indicate motion. After extraction of the signal from the raw data, principal component analysis was used to identify respiratory motion. The approach and potential applications during MR and PET/MR examinations that rely on a continuous respiratory signal were validated with an anthropomorphic, PET/MR-compatible motion phantom as well as in a volunteer study.

RESULTS

Respiratory motion detection and correction were presented for MR and PET data in phantom and volunteer studies. The Pilot Tone successfully recovered the ground-truth respiratory signal provided by the phantom.

CONCLUSIONS

The presented method provides reliable respiratory motion tracking during arbitrary imaging sequences throughout a full PET/MR study. All results can directly be transferred to MR-only applications as well.

Estimation of optimal number of gates in dual gated 18F-FDG cardiac PET

Gating of positron emission tomography images has been shown to reduce the motion effects, especially when imaging small targets, such as coronary plaques. However, the selection of optimal number of gates for gating remains a challenge. Selecting too high number of gates results in a loss of signal-to-noise ratio, while too low number of gates does remove only part of the motion. Here, we introduce a respiratory-cardiac motion model to determine the optimal number of respiratory and cardiac gates. We evaluate the model using a realistic heart phantom and data from 12 cardiac patients (47–77 years, 64.5 on average). To demonstrate the benefits of our model, we compared it with an existing respiratory model. Based on our study, the optimal number of gates was determined to be five respiratory and four cardiac gates in the phantom and patient studies. In the phantom study, the diameter of the most active hot spot was reduced by 24% in the dual gated images compared to non-gated images. In the patient study, the thickness of myocardium wall was reduced on average by 21%. In conclusion, the motion model can be used for estimating the optimal number of respiratory and cardiac gates for dual gating.

Positron emission tomography/MRI for cardiac diseases assessment

Functional imaging tools have emerged in the last few decades and are increasingly used to assess the function of the human heart in vivo. Positron emission tomography (PET) is used to evaluate myocardial metabolism and blood flow. Magnetic resonance imaging (MRI) is an essential tool for morphological and functional evaluation of the heart. In cardiology, PET is successfully combined with CT for hybrid cardiac imaging. The effective integration of two imaging modalities allows simultaneous data acquisition combining functional, structural and molecular imaging. After PET/CT has been successfully accepted for clinical practices, hybrid PET/MRI is launched. This review elaborates the current evidence of PET/MRI in cardiovascular imaging and its expected clinical applications for a comprehensive assessment of cardiovascular diseases while highlighting the advantages and limitations of this hybrid imaging approach.

Motion correction of respiratory-gated PET images using deep learning based image registration framework

Artifacts caused by patient breathing and movement during PET data acquisition affect image quality. Respiratory gating is commonly used to gate the list-mode PET data into multiple bins over a respiratory cycle. Non-rigid registration of respiratory-gated PET images can reduce motion artifacts and preserve count statistics, but it is time consuming. In this work, we propose an unsupervised non-rigid image registration framework using deep learning for motion correction. Our network uses a differentiable spatial transformer layer to warp the moving image to the fixed image and uses a stacked structure for deformation field refinement. Estimated deformation fields were incorporated into an iterative image reconstruction algorithm to perform motion compensated PET image reconstruction. We validated the proposed method using simulation and clinical data and implemented an iterative image registration approach for comparison. Motion compensated reconstructions were compared with ungated images. Our simulation study showed that the motion compensated methods can generate images with sharp boundaries and reveal more details in the heart region compared with the ungated image. The resulting normalized root mean square error (NRMS) was 24.3 ± 1.7% for the deep learning based motion correction, 31.1 ± 1.4% for the iterative registration based motion correction, and 41.9 ± 2.0% for ungated reconstruction. The proposed deep learning based motion correction reduced the bias compared with the ungated image without increasing the noise level and outperformed the iterative registration based method. In the real data study, both motion compensated images provided higher lesion contrast and sharper liver boundaries than the ungated image and had lower noise than the reference gate image. The contrast of the proposed method based on the deep neural network was higher than the ungated image and iterative registration method at any matched noise level.

Motion tracking of low-activity fiducial markers using adaptive region of interest with list-mode positron emission tomography

PURPOSE

Motion compensated positron emission tomography (PET) imaging requires detecting and monitoring of patient body motion. We developed a semiautomatic list-mode method to track the three-dimensional (3D) motion of fiducial positron-emitting markers during PET imaging.

METHODS

A previously developed motion tracking method using positron-emitting markers (PeTrack) was enhanced to work with PET imaging. A novel combination of filtering methods was developed to reject physiological tracer background, which would drown out the events from the marker if unfiltered. The most critical filter rejects events whose line-of-response (LOR) is outside an adaptive region of interest (ADROI). The size of ROI was optimized by exploiting the distinct differences between the distributions of events from background and marker. The ADROI PeTrack method was evaluated with Monte Carlo and phantom studies. A 92.5-kBq 22 Na marker moving sinusoidally in 3D was simulated with Monte Carlo methods. The simulated events were combined with list-mode data from cardiac PET imaging patients to evaluate the performance of the tracking. In phantom studies, three 22 Na markers were placed on a dynamic torso phantom with an initial activity of 680 MBq of 82 Rb in its cardiac insert. The motion of the markers was tracked while the phantom simulated various types of patient motion. Motion correction on an event-by-event basis of the list-mode data was then applied and images were reconstructed.

RESULTS

Simulation results show that the background rejection methods can significantly suppress the tracer background and increase the fraction of marker events by a factor of up to 2500. A 92.5-kBq marker can be tracked in 3D at a frequency of 2.0 Hz with an accuracy of 0.8 mm and a precision of 0.3 mm. The phantom study experimentally confirms that the algorithm can track various types of motion. The relative accuracy of the experimental tracking is 0.26 ± 0.14 mm. Motion-corrected images from the phantom study show reduced blurring.

CONCLUSIONS

An algorithm and background rejection methods were developed that can track the 3D motion of low-activity positron-emitting markers during PET imaging. The motion information may be used for motion-compensated PET imaging.

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.

Partial volume and motion correction in cardiac PET: First results from an in vs ex vivo comparison using animal datasets

BACKGROUND

In a previous study on ex vivo, static cardiac datasets, we investigated the benefits of performing partial volume correction (PVC) in cardiac 18F-Fluorodeoxyglucose(FDG) PET datasets. In the present study, we extend the analysis to in vivo cardiac datasets, with the aim of defining which reconstruction technique maximizes quantitative accuracy and, ultimately, makes PET a better diagnostic tool for cardiac pathologies.

METHODS

In vivo sheep datasets were acquired and reconstructed with/without motion correction and using several reconstruction algorithms (with/without resolution modeling, with/without non-anatomical priors). Corresponding ex vivo scans of the excised sheep hearts were performed on a small-animal PET scanner (Siemens Focus 220, microPET) to provide high-resolution reference data unaffected by respiratory and cardiac motion. A comparison between the in vivo cardiac reconstructions and the corresponding ex vivo ground truth was performed.

RESULTS

The use of an edge-preserving prior (Total Variation (TV) prior in this work) in combination with motion correction reduces the bias in absolute quantification when compared to the standard clinical reconstructions (- 0.83 vs - 3.74 SUV units), when the end-systolic gate is considered. At end-diastole, motion correction improves absolute quantification but the PVC with priors does not improve the similarity to the ground truth more than a regular iterative reconstruction with motion correction and without priors. Relative quantification was not influenced much by the chosen reconstruction algorithm.

CONCLUSIONS

The relative ranking of the algorithms suggests superiority of the PVC reconstructions with dual gating in terms of overall absolute quantification and noise properties. A well-tuned edge-preserving prior, such as TV, enhances the noise properties of the resulting images of the heart. The end-systolic gate yields the most accurate quantification of cardiac datasets.

Body motion detection and correction in cardiac PET: Phantom and human studies

PURPOSE

Patient body motion during a cardiac positron emission tomography (PET) scan can severely degrade image quality. We propose and evaluate a novel method to detect, estimate, and correct body motion in cardiac PET.

METHODS

Our method consists of three key components: motion detection, motion estimation, and motion-compensated image reconstruction. For motion detection, we first divide PET list-mode data into 1-s bins and compute the center of mass (COM) of the coincidences' distribution in each bin. We then compute the covariance matrix within a 25-s sliding window over the COM signals inside the window. The sum of the eigenvalues of the covariance matrix is used to separate the list-mode data into "static" (i.e., body motion free) and "moving" (i.e. contaminated by body motion) frames. Each moving frame is further divided into a number of evenly spaced sub-frames (referred to as "sub-moving" frames), in which motion is assumed to be negligible. For motion estimation, we first reconstruct the data in each static and sub-moving frame using a rapid back-projection technique. We then select the longest static frame as the reference frame and estimate elastic motion transformations to the reference frame from all other static and sub-moving frames using nonrigid registration. For motion-compensated image reconstruction, we reconstruct all the list-mode data into a single image volume in the reference frame by incorporating the estimated motion transformations in the PET system matrix. We evaluated the performance of our approach in both phantom and human studies.

RESULTS

Visually, the motion-corrected (MC) PET images obtained using the proposed method have better quality and fewer motion artifacts than the images reconstructed without motion correction (NMC). Quantitative analysis indicates that MC yields higher myocardium to blood pool concentration ratios. MC also yields sharper myocardium than NMC.

CONCLUSIONS

The proposed body motion correction method improves image quality of cardiac PET.

MR-based cardiac and respiratory motion correction of PET: application to static and dynamic cardiac 18F-FDG imaging

Motion of the myocardium deteriorates the quality and quantitative accuracy of cardiac PET images. We present a method for MR-based cardiac and respiratory motion correction of cardiac PET data and evaluate its impact on estimation of activity and kinetic parameters in human subjects. Three healthy subjects underwent simultaneous dynamic 18F-FDG PET and MRI on a hybrid PET/MR scanner. A cardiorespiratory motion field was determined for each subject using navigator, tagging and golden-angle radial MR acquisitions. Acquired coincidence events were binned into cardiac and respiratory phases using electrocardiogram and list mode-driven signals, respectively. Dynamic PET images were reconstructed with MR-based motion correction (MC) and without motion correction (NMC). Parametric images of 18F-FDG consumption rates (Ki) were estimated using Patlak's method for both MC and NMC images. MC alleviated motion artifacts in PET images, resulting in improved spatial resolution, improved recovery of activity in the myocardium wall and reduced spillover from the myocardium to the left ventricle cavity. Significantly higher myocardium contrast-to-noise ratio and lower apparent wall thickness were obtained in MC versus NMC images. Likewise, parametric images of Ki calculated with MC data had improved spatial resolution as compared to those obtained with NMC. Consistent with an increase in reconstructed activity concentration in the frames used during kinetic analyses, MC led to the estimation of higher Ki values almost everywhere in the myocardium, with up to 18% increase (mean across subjects) in the septum as compared to NMC. This study shows that MR-based motion correction of cardiac PET results in improved image quality that can benefit both static and dynamic studies.

A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating

Dual cardiac and respiratory gating is a well-known technique for motion compensation in nuclear medicine imaging. In this study, we present a new data fusion framework for dual cardiac and respiratory gating based on multidimensional microelectromechanical (MEMS) motion sensors. Our approach aims at robust estimation of the chest vibrations, that is, high-frequency precordial vibrations and low-frequency respiratory movements for prospective gating in positron emission tomography (PET), computed tomography (CT), and radiotherapy. Our sensing modality in the context of this paper is a single dual sensor unit, including accelerometer and gyroscope sensors to measure chest movements in three different orientations. Since accelerometer- and gyroscope-derived respiration signals represent the inclination of the chest, they are similar in morphology and have the same units. Therefore, we use principal component analysis (PCA) to combine them into a single signal. In contrast to this, the accelerometer- and gyroscope-derived cardiac signals correspond to the translational and rotational motions of the chest, and have different waveform characteristics and units. To combine these signals, we use independent component analysis (ICA) in order to obtain the underlying cardiac motion. From this cardiac motion signal, we obtain the systolic and diastolic phases of cardiac cycles by using an adaptive multi-scale peak detector and a short-time autocorrelation function. Three groups of subjects, including healthy controls (n = 7), healthy volunteers (n = 12), and patients with a history of coronary artery disease (n = 19) were studied to establish a quantitative framework for assessing the performance of the presented work in prospective imaging applications. The results of this investigation showed a fairly strong positive correlation (average r = 0.73 to 0.87) between the MEMS-derived (including corresponding PCA fusion) respiration curves and the reference optical camera and respiration belt sensors. Additionally, the mean time offset of MEMS-driven triggers from camera-driven triggers was 0.23 to 0.3 ± 0.15 to 0.17 s. For each cardiac cycle, the feature of the MEMS signals indicating a systolic time interval was identified, and its relation to the total cardiac cycle length was also reported. The findings of this study suggest that the combination of chest angular velocity and accelerations using ICA and PCA can help to develop a robust dual cardiac and respiratory gating solution using only MEMS sensors. Therefore, the methods presented in this paper should help improve predictions of the cardiac and respiratory quiescent phases, particularly with the clinical patients. This study lays the groundwork for future research into clinical PET/CT imaging based on dual inertial sensors.

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.

Perspectives on Small Animal Radionuclide Imaging; Considerations and Advances in Atherosclerosis

This review addresses nuclear SPECT and PET imaging in small animals in relation to the atherosclerotic disease process, one of our research topics of interest. Imaging of atherosclerosis in small animal models is challenging, as it operates at the limits of current imaging possibilities regarding sensitivity, and spatial resolution. Several topics are discussed, including technical considerations that apply to image acquisition, reconstruction, and analysis. Moreover, molecules developed for or applied in these small animal nuclear imaging studies are listed, including target-directed molecules, useful for imaging organs or tissues that have elevated expression of the target compared to other tissues, and molecules that serve as substrates for metabolic processes. Differences between animal models and human pathophysiology that should be taken into account during translation from animal to patient as well as differences in tracer behavior in animal vs. man are also described. Finally, we give a future outlook on small animal radionuclide imaging in atherosclerosis, followed by recommendations. The challenges and solutions described might be applicable to other research fields of health and disease as well.

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

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

The continual innovation of commercial PET/CT solutions in nuclear cardiology: Siemens Healthineers

Cardiac PET/CT is an evolving, non-invasive imaging modality that impacts patient management in many clinical scenarios. Beyond offering the capability to assess myocardial perfusion, inflammatory cardiac pathologies, and myocardial viability, cardiac PET/CT also allows for the non-invasive quantitative assessment of myocardial blood flow (MBF) and myocardial flow reserve (MFR). Recognizing the need for an enhanced comprehension of coronary physiology, Siemens Healthineers implemented a sophisticated solution for the calculation of MBF and MFR in 2009. As a result, each aspect of their innovative scanner and image-processing technology seamlessly integrates into an efficient, easy-to-use workflow for everyday clinical use that maximizes the number of patients who potentially benefit from this imaging modality.

Imaging of chemokine receptor CXCR4 expression in culprit and nonculprit coronary atherosclerotic plaque using motion-corrected [68Ga]pentixafor PET/CT

Purpose

The chemokine receptor CXCR4 is a promising target for molecular imaging of CXCR4⁺ cell types, e.g. inflammatory cells, in cardiovascular diseases. We speculated that a specific CXCR4 ligand, [⁶⁸Ga]pentixafor, along with novel techniques for motion correction, would facilitate the in vivo characterization of CXCR4 expression in small culprit and nonculprit coronary atherosclerotic lesions after acute myocardial infarction by motion-corrected targeted PET/CT.

Methods

CXCR4 expression was analysed ex vivo in separately obtained arterial wall specimens. [⁶⁸Ga]Pentixafor PET/CT was performed in 37 patients after stent-based reperfusion for a first acute ST-segment elevation myocardial infarction. List-mode PET data were reconstructed to five different datasets using cardiac and/or respiratory gating. Guided by CT for localization, the PET signals of culprit and various groups of nonculprit coronary lesions were analysed and compared.

Results

Ex vivo, CXCR4 was upregulated in atherosclerotic lesions, and mainly colocalized with CD68⁺ inflammatory cells. In vivo, elevated CXCR4 expression was detected in culprit and nonculprit lesions, and the strongest CXCR4 PET signal (median SUV_max 1.96; interquartile range, IQR, 1.55–2.31) was observed in culprit coronary artery lesions. Stented nonculprit lesions (median SUV_max 1.45, IQR 1.23–1.88; P = 0.048) and hot spots in naive remote coronary segments (median SUV_max 1.34, IQR 1.23–1.74; P = 0.0005) showed significantly lower levels of CXCR4 expression. Dual cardiac/respiratory gating provided the strongest CXCR4 PET signal and the highest lesion detectability.

Conclusion

We demonstrated the basic feasibility of motion-corrected targeted PET/CT imaging of CXCR4 expression in coronary artery lesions, which was triggered by vessel wall inflammation but also by stent-induced injury. This novel methodology may serve as a platform for future diagnostic and therapeutic clinical studies targeting the biology of coronary atherosclerotic plaque.

Electronic supplementary material

The online version of this article (10.1007/s00259-018-4076-2) contains supplementary material, which is available to authorized users.

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.

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.

Motion Correction and Its Impact on Absolute Myocardial Blood Flow Measures with PET

PURPOSE OF REVIEW

Motion artifacts, due to cardiac and respiratory cycles, myocardial cardiac creep, or gross patient movements, have been extensively investigated in the context of relative myocardial perfusion imaging with SPECT and PET. These movements have been identified as a major source of errors in image quantification and diagnosis. Recently, as dynamic PET quantification for myocardial blood flow assessment has entered clinical practice, similar questions have arisen on the impact of motion on final blood flow values.

RECENT FINDINGS

While preliminary investigations have underlined the potential impact of these motions on MBF quantification, their correction on dynamic acquisition remains challenging and limited to research studies. Gross patient's body movements occur in a consistent number of cases, particularly during stress acquisition, typically involving a limited number of image frames. If undetected, these movements can lead to great differences in flow values and consequently misdiagnosis. Quality control routines can be applied to automatically inspect the shape of time activity curves and to help identify motion artifacts. Cyclic cardiac and respiratory motion may have a considerable impact on final flow values. Correction of gross body motion represents a priority in the context of optimizing absolute flow clinical routine utilization and protocol standardization.

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.

Data-driven respiratory gating for ventilation/perfusion lung scan

BACKGROUND

Ventilation/perfusion lung scan is subject to blur due to respiratory motion whether with planar acquisition or single photon emission computed tomography (SPECT). We propose a data-driven gating method for extracting different respiratory phases from lung scan list-mode or dynamic data.

METHODS

The algorithm derives a surrogate respiratory signal from an automatically detected diaphragmatic region of interest. The time activity curve generated is then filtered using a Savitzky-Golay filter. We tested this method on an oscillating phantom in order to evaluate motion blur decrease and on one lung SPECT.

RESULTS

Our algorithm reduced motion blur on phantom acquisition: mean full width at half maximum 8.1 pixels on non-gated acquisition versus 5.3 pixels on gated acquisition and 4.1 pixels on reference image. Automated detection of the diaphragmatic region and time-activity curves generation were successful on patient acquisition.

CONCLUSIONS

This algorithm is compatible with a clinical use considering its runtime. Further studies will be needed in order to validate this method.

MR-based motion correction for cardiac PET parametric imaging: a simulation study

BACKGROUND

Both cardiac and respiratory motions bias the kinetic parameters measured by dynamic PET. The aim of this study was to perform a realistic positron emission tomography-magnetic resonance (PET-MR) simulation study using 4D XCAT to evaluate the impact of MR-based motion correction on the estimation of PET myocardial kinetic parameters using PET-MR. Dynamic activity distributions were obtained based on a one-tissue compartment model with realistic kinetic parameters and an arterial input function. Realistic proton density/T1/T2 values were also defined for the MRI simulation. Two types of motion patterns, cardiac motion only (CM) and both cardiac and respiratory motions (CRM), were generated. PET sinograms were obtained by the projection of the activity distributions. PET image for each time frame was obtained using static (ST), gated (GA), non-motion-corrected (NMC), and motion-corrected (MC) methods. Voxel-wise unweighted least squares fitting of the dynamic PET data was then performed to obtain K₁ values for each study. For each study, the mean and standard deviation of K₁ values were computed for four regions of interest in the myocardium across 25 noise realizations.

RESULTS

Both cardiac and respiratory motions introduce blurring in the PET parametric images if the motion is not corrected. Conventional cardiac gating is limited by high noise level on parametric images. Dual cardiac and respiratory gating further increases the noise level. In contrast to GA, the MR-based MC method reduces motion blurring in parametric images without increasing noise level. It also improves the myocardial defect delineation as compared to NMC method. Finally, the MR-based MC method yields lower bias and variance in K₁ values than NMC and GA, respectively. The reductions of K₁ bias by MR-based MC are 7.7, 5.1, 15.7, and 29.9% in four selected 0.18-mL myocardial regions of interest, respectively, as compared to NMC for CRM. MR-based MC yields 85.9, 75.3, 71.8, and 95.2% less K₁ standard deviation in the four regions, respectively, as compared to GA for CRM.

CONCLUSIONS

This simulation study suggests that the MR-based motion-correction method using PET-MR greatly reduces motion blurring on parametric images and yields less K₁ bias without increasing noise level.

A new era in gated myocardial perfusion imaging: Feasibility of data-driven cardiac contraction gating with multiple pinhole CZT SPECT

BACKGROUND

We previously validated the use of a data-driven cardiac respiratory-motion (RM) correction method (REGAT) applicable to CZT SPECT myocardial perfusion imaging (MPI). In this study, we adapted the same process used with REGAT for RM to generate data-driven cardiac contraction triggers and corresponding cardiac contraction-gated SPECT studies (GSPECT-DD). We aimed to study its feasibility and compare its performances to GSPECT studies generated with ECG monitor-based triggers (GSPECT-ECG).

METHODS

We included seven non-consecutive randomly chosen patients addressed for 1-day 99mTc-Tetrofosmin stress/rest MPI acquired with multi-pinhole CZT SPECT. We studied the degree of agreement between GSPECT-DD and GSPECT-ECG for the classification of acquired images into the 16 categories of mean cardiac cycle, and compared between the two methods the cine image quality and global LV systolic function of reconstructed studies.

RESULTS

We found almost perfect agreement between cardiac contraction triggers generated with data-driven and ECG monitor-based methods. As compared to GSPECT-ECG, GSPECT-DD provided comparable and well-correlated LV global systolic function parameters and similar cine image quality at both stress and rest.

CONCLUSIONS

Data-driven cardiac contraction gating using REGAT is feasible with low-dose and high-dose MPI CZT SPECT. It provides GSPECT-DD studies comparable to GSPECT-ECG.

Impact of motion compensation and partial volume correction for 18F-NaF PET/CT imaging of coronary plaque

Recent studies have suggested that ¹⁸F-NaF-PET enables visualization and quantification of plaque micro-calcification in the coronary tree. However, PET imaging of plaque calcification in the coronary arteries is challenging because of the respiratory and cardiac motion as well as partial volume effects. The objective of this work is to implement an image reconstruction framework, which incorporates compensation for respiratory as well as cardiac motion (MoCo) and partial volume correction (PVC), for cardiac ¹⁸F-NaF PET imaging in PET/CT. We evaluated the effect of MoCo and PVC on the quantification of vulnerable plaques in the coronary arteries. Realistic simulations (Biograph TPTV, Biograph mCT) and phantom acquisitions (Biograph mCT) were used for these evaluations. Different uptake values in the calcified plaques were evaluated in the simulations, while three 'plaque-type' lesions of 36, 31 and 18 mm³ were included in the phantom experiments. After validation, the MoCo and PVC methods were applied in four pilot NaF-PET patient studies. In all cases, the MoCo-based image reconstruction was performed using the STIR software. The PVC was obtained from a local projection (LP) method, previously evaluated in preclinical and clinical PET. The results obtained show a significant increase of the measured lesion-to-background ratios (LBR) in the MoCo  +  PVC images. These ratios were further enhanced when using directly the tissue-activities from the LP method, making this approach more suitable for the quantitative evaluation of coronary plaques. When using the LP method on the MoCo images, LBR increased between 200% and 1119% in the simulated data, between 212% and 614% in the phantom experiments and between 46% and 373% in the plaques with positive uptake observed in the pilot patients. In conclusion, we have built and validated a STIR framework incorporating MoCo and PVC for ¹⁸F-NaF PET imaging of coronary plaques. First results indicate an improved quantification of plaque-type lesions.