Motion-Tracking Hardware and Advanced Applications in PET and PET/CT

Quantitative analysis of patient motion in walk-through PET scanner and standard axial field of view pet scanner using infrared-based tracking

BACKGROUND

Long-axial field-of-view (LAFOV) Positron Emission Tomography (PET) scanners provide high sensitivity, but throughput is limited because of time-consuming patient positioning. To enhance throughput, a novel Walk-Through PET (WT-PET) scanner has been developed, allowing patients to stand upright, supported by an adjustable headrest and hand supports. This study evaluates the degree of motion in the WT-PET system and compares it with the standard PET-CT.

METHODS

Three studies were conducted with healthy volunteers to estimate motion. The first two studies assessed motion in the WT-PET's Design I (Study 1) and Design II (Study 2), while the third study compared motion on a standard PET-CT scanner bed (Study 3). Infrared markers placed on the head, shoulders, chest, and abdomen were tracked and processed using image-processing techniques involving thresholding and connected component analysis. Videos were recorded for normal breathing and breath-holding conditions, and 2D centroids were transformed into 3D coordinates using depth information.

RESULTS

The results shows a significant reduction in motion during breath-holding, especially for the abdomen. Mean motion distances decreased from 2.63 mm to 2.18 mm in Study 1 and from 2.42 mm to 1.67 mm in Study 2. Statistical analysis revealed notable differences in motion between the WT-PET and mCT scanners. The Shapiro-Wilk test indicated non-normal motion distributions in the head, right shoulder, and abdomen for both systems, leading to the use of the Wilcoxon signed-rank test for all markers. Significant differences were found in the right shoulder (p = 0.0266), left shoulder (p = 0.0004) and chest (p < 0.0001) but no significant differences were observed in the head (p = 0.1327) and abdomen (p = 0.8404).

CONCLUSION

This study provides a comprehensive analysis of patient motion in a WT-PET scanner with respect to the standard PET. The findings highlight a significant increase in shoulder and chest motion, while the head and abdomen regions showed more stability.

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).

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 estimation and correction in SPECT, PET and CT

Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.

Consensus Recommendations on the Use of 18F-FDG PET/CT in Lung Disease

PET with 18F-FDG has been increasingly applied, predominantly in the research setting, to study drug effects and pulmonary biology and to monitor disease progression and treatment outcomes in lung diseases that interfere with gas exchange through alterations of the pulmonary parenchyma, airways, or vasculature. To date, however, there are no widely accepted standard acquisition protocols or imaging data analysis methods for pulmonary 18F-FDG PET/CT in these diseases, resulting in disparate approaches. Hence, comparison of data across the literature is challenging. To help harmonize the acquisition and analysis and promote reproducibility, we collated details of acquisition protocols and analysis methods from 7 PET centers. From this information and our discussions, we reached the consensus recommendations given here on patient preparation, choice of dynamic versus static imaging, image reconstruction, and image analysis reporting.

COVID-19 Pandemic Spurs Medical Telerobotic Systems: A Survey of Applications Requiring Physiological Organ Motion Compensation

The coronavirus disease 2019 (COVID-19) pandemic has resulted in public health interventions such as physical distancing restrictions to limit the spread and transmission of the novel coronavirus, causing significant effects on the delivery of physical healthcare procedures worldwide. The unprecedented pandemic spurs strong demand for intelligent robotic systems in healthcare. In particular, medical telerobotic systems can play a positive role in the provision of telemedicine to both COVID-19 and non-COVID-19 patients. Different from typical studies on medical teleoperation that consider problems such as time delay and information loss in long-distance communication, this survey addresses the consequences of physiological organ motion when using teleoperation systems to create physical distancing between clinicians and patients in the COVID-19 era. We focus on the control-theoretic approaches that have been developed to address inherent robot control issues associated with organ motion. The state-of-the-art telerobotic systems and their applications in COVID-19 healthcare delivery are reviewed, and possible future directions are outlined.

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.

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.

Radiolucent 4D Ultrasound Imaging: System Design and Application to Radiotherapy Guidance

Four-dimensional (4D) ultrasound (US) is an attractive modality for image guidance due to its real-time, non-ionizing, volumetric imaging capability with high soft tissue contrast. However, existing 4D US imaging systems contain large volumes of metal which interfere with diagnostic and therapeutic ionizing radiation in procedures such as CT imaging and radiation therapy. This study aimed to design and characterize a novel 4D Radiolucent Remotely-Actuated UltraSound Scanning (RRUSS) device that overcomes this limitation. In a phantom, we evaluated the imaging performance of the RRUSS device including frame rate, resolution, spatial integrity, and motion tracking accuracy. To evaluate compatibility with radiation therapy workflow, we evaluated device-induced CT imaging artifacts, US tracking performance during beam delivery, and device compatibility with commercial radiotherapy planning software. The RRUSS device produced 4D volumes at 0.1-3.0 Hz with 60° lateral field of view (FOV), 50° maximum elevational FOV, and 200 mm maximum depth. Imaging resolution (-3 dB point spread width) was 1.2-7.9 mm at depths up to 100 mm and motion tracking accuracy was ≤ 0.3±0.5 mm. No significant effect of the RRUSS device on CT image integrity was found, and RRUSS device performance was not affected by radiotherapy beam exposure. Agreement within ±3.0% / 2.0 mm was achieved between computed and measured radiotherapy dose delivered directly through the RRUSS device at 6 MV and 15 MV. In vivo liver, kidney, and prostate images were successfully acquired. Our investigations suggest that a RRUSS device can offer non-interfering 4D guidance for radiation therapy and other diagnostic and therapeutic procedures.

Simultaneous reconstruction of attenuation and activity in cardiac PET can remove CT misalignment artifacts

BACKGROUND

Misalignment between positron emission tomography (PET) and computed tomography (CT) data is known to generate artifactual defects in cardiac PET images due to imprecise attenuation correction (AC). In this work, the use of a maximum likelihood attenuation and activity (MLAA) algorithm is proposed to avoid such artifacts in time-of-flight (TOF) PET.

METHODS

MLAA was implemented and tested using a thorax/heart phantom and retrospectively on fourteen (13)N-ammonia PET/CT perfusion studies. Global and local misalignments between PET and CT data were generated by shifting matched CT images or using CT data representative of the end-inspiration phase. PET images were reconstructed with MLAA and a 3D-ordered-subsets-expectation-maximization (OSEM)-TOF algorithm. Images obtained with 3D-OSEM-TOF and matched CT were used as references. These images were compared (qualitatively and semi-quantitatively) with those reconstructed with 3D-OSEM-TOF and MLAA for which a misaligned CT was used, respectively, for AC and initialization.

RESULTS

Phantom experiment proved the capability of MLAA to converge toward the correct emission and attenuation distributions using, as input, only PET emission data, but convergence was very slow. Initializing MLAA with phantom CT images markedly improved convergence speed. In patient studies, when shifted or end-inspiration CT images were used for AC, 3D-OSEM-TOF reconstructions showed artifacts of increasing severity, size, and frequency with increasing mismatch. Such artifacts were absent in the corresponding MLAA images.

CONCLUSION

The proposed implementation of the MLAA algorithm is a feasible and robust technique to avoid AC mismatch artifacts in cardiac PET studies provided that a CT of the source is available, even if poorly aligned.

Interactive monitoring system for visual respiratory biofeedback

In almost any medical procedure respiratory motion is an issue and may result in image degradation. Most currently available devices and systems, which are intended to reduce respiratory influences do not come into operation however in clinics due to their high cost and complex operation. In our paper we evaluated an interactive breath hold control system that helps flat breathing to subsequently reduce respiratory motion during signal acquisitions or procedure treatments. With that the human subjects are enabled to regulate their own breath by following visual feedback via a specially designed display. That display shows biofeedback information about the respiratory excursion through air pressure deviations measured inside an air bellows belt. The system was assessed quantitatively in a laboratory setup and qualitatively by applications in real clinical procedures. The obtained results are very promising and can be further improved with additional developments to provide an easy to use and relatively inexpensive solution for respiratory motion related imaging problems.