Event-by-event non-rigid data-driven PET respiratory motion correction methods: comparison of principal component analysis and centroid of distribution

Respiratory motion is a major cause of degradation of PET image quality. Respiratory gating and motion correction can be performed to reduce the effects of respiratory motion; these methods require motion information, typically obtained from external tracking systems. Various groups have studied data-driven (DD) motion estimation methods. Recently, a data-driven respiratory motion estimation method was established by calculating the centroid of distribution (COD) of listmode events, which was then used with event-by-event respiratory motion correction (EBE-MC), and showed results comparable to those with an external motion tracking device. The EBE-MC method only corrected for rigid motion, so that non-rigid components still contributed to motion-induced blurring. A nonrigid respiratory motion correction (NRMC) was later developed to overcome this problem, but was only evaluated using signal from an external monitor. Thus, it is desirable to further develop data-driven to achieve the best respiratory motion correction results. 
 We evaluated 2 data-driven respiratory motion detection methods, COD and Principal Component Analysis (PCA), by comparing the extracted motion trace to that acquired by the Anzai system in dynamic studies with two tracers. PCA was chosen as a preliminary study indicated that it produced stable results than other DD methods. We then developed and performed DD-EBE-NRMC using either COD- or PCA-derived respiratory motion information. Data-driven correction results were compared with Anzai-based results. For all tested studies, both COD and PCA showed good-to-excellent match with Anzai signals, with PCA showing a higher correlation with Anzai signals. The DD-EBE-NRMC results showed that both COD and PCA provide comparable image quality improvement as the Anzai-based correction. Although COD showed a lower correlation with Anzai than PCA, COD-based NRMC results are comparable to those of PCA, both of which showed great reduction in motion-induced blurring

Density variation during respiration affects PET quantitation in the lung

PET quantitation depends on the accuracy of the CT-derived attenuation correction map. In the lung, respiration leads to both positional and density mismatches, causing PET quantitation errors at lung borders but also within the whole lung. The aim of this work is to determine the extent of the associated errors on the measured time activity curves (TACs) and the corresponding kinetic parameter estimates. 5 patients with idiopathic pulmonary fibrosis underwent dynamic 18 F-FDG PET and cine-CT imaging as part of an ongoing study. The cine-CT was amplitude gated using PCA techniques to produce end expiration (EXP), end inspiration (INS) and mid-breathing cycle (MID) gates representative of a short clinical CT acquisition. The ungated PET data were reconstructed with each CT gate and the TACs and kinetic parameters compared. Patient representative XCAT simulations with varying lung density, both with and without motion, were also produced to represent the above study allowing comparison of true to measured results. In all cases, the obtained PET TACs differed with each CT gate. For ROIs internal to the lung, the effect was dominated by changes in density, as opposed to motion. The errors in the TACs varied with time, providing evidence that errors due to attenuation mismatch depend on activity distribution. In the simulations, some kinetic parameters were over- and under-estimated by a factor of 2 in the INS and EXP gates respectively. For the patients, the maximum variation in kinetic parameters was 20%. Our results show that whole lung density changes during the respiratory cycle have a significant impact on PET quantitation. This is especially true of the kinetic parameter estimates as the extent of the error is dependent on tracer distribution which varies with time. It is therefore vital to use matched PET/CT for attenuation correction

Sign determination methods for the respiratory signal in data-driven PET gating

Patient respiratory motion during PET image acquisition leads to blurring in the reconstructed images and may cause significant artifacts, resulting in decreased lesion detectability, inaccurate standard uptake value calculation and incorrect treatment planning in radiation therapy. To reduce these effects data can be regrouped into (nearly) 'motion-free' gates prior to reconstruction by selecting the events with respect to the breathing phase. This gating procedure therefore needs a respiratory signal: on current scanners it is obtained from an external device, whereas with data driven (DD) methods it can be directly obtained from the raw PET data. DD methods thus eliminate the use of external equipment, which is often expensive, needs prior setup and can cause patient discomfort, and they could also potentially provide increased fidelity to the internal movement. DD methods have been recently applied on PET data showing promising results. However, many methods provide signals whose direction with respect to the physical motion is uncertain (i.e. their sign is arbitrary), therefore a maximum in the signal could refer either to the end-inspiration or end-expiration phase, possibly causing inaccurate motion correction. In this work we propose two novel methods, CorrWeights and CorrSino, to detect the correct direction of the motion represented by the DD signal, that is obtained by applying principal component analysis (PCA) on the acquired data. They only require the PET raw data, and they rely on the assumption that one of the major causes of change in the acquired data related to the chest is respiratory motion in the axial direction, that generates a cranio-caudal motion of the internal organs. We also implemented two versions of a published registration-based method, that require image reconstruction. The methods were first applied on XCAT simulations, and later evaluated on cancer patient datasets monitored by the Varian Real-time Position ManagementTM (RPM) device, selecting the lower chest bed positions. For each patient different time intervals were evaluated ranging from 50 to 300 s in duration. The novel methods proved to be generally more accurate than the registration-based ones in detecting the correct sign of the respiratory signal, and their failure rates are lower than 3% when the DD signal is highly correlated with the RPM. They also have the advantage of faster computation time, avoiding reconstruction. Moreover, CorrWeights is not specifically related to PCA and considering its simple implementation, it could easily be applied together with any DD method in clinical practice

Improvement of the Sign Determination Method for Data-Driven respiratory signal in TOF-PET

Respiratory gating and motion correction can increase resolution in PET chest imaging, but require a respiratory signal. Data-Driven (DD) methods aim to produce a respiratory signal from PET data, avoiding the use of external devices. Principal Component Analysis (PCA) is an easy to implement DD algorithm whose signals, however, are determined up to an arbitrary factor. The direction of the motion represented by its signal has to be determined. In this work we present the extension to TOF data of a previously presented sign-determination method. Furthermore, we propose the application of a selection process in sinogram space, to automatically select the areas of the data mostly affected by respiratory motion. The performance of the updated sign-determination method is evaluated on patient data, and the effect of TOF information and masking process is investigated also in terms of quality of the PCA respiratory signal

Data Driven Respiratory Signal Detection in PET Taking Advantage of Time-of-Flight Data

Respiratory gating is a powerful tool for tackling motion-related issues in chest PET imaging. On current scanners the respiratory signal is obtained from external devices, whereas with Data-Driven methods it can be extracted directly from the data. The aim of this work is to show the increased potential of the application of Principal Component Analysis (PCA) on TOF data. We propose a methodology that retains the TOF information and compare it to the non-TOF method. We tested the method on 16 FDG oncology patients, monitored with an RPM camera. To further investigate the benefit of TOF, PCA was selectively applied to sets of TOF bins equidistant from the center. The correlation with the RPM, the level of noise and the respiratory-likeness were analysed for all the obtained respiratory signals. The results of our analysis showed that retaining the TOF information into the sinograms considerably increased the quality of the extracted respiratory signals

Implementation of Image Reconstruction for GE SIGNA PET/MR PET Data in the STIR Library

Software for Tomographic Image Reconstruction (STIR: http://stir.sf.net) is an open source C++ library available for reconstruction of emission tomography data. This work aims at the incorporation of the GE SIGNA PET/MR scanner in STIR and enables PET image reconstruction with data corrections. The data extracted from the scanner after an acquisition includes a list of raw data files (emission, normalisation, geometric and well counter calibration (wcc) factors), magnetic resonance attenuation correction (MRAC) images and the scanner-based reconstructions. The listmode (LM) file stores a list of 'prompt' events and the singles per crystal per second. MRAC images from the scanner are used for attenuation correction. The modifications to STIR that allow accurate histogramming of this LM data in the same sinogram organisation as the scanner are also described. This allows reconstruction of acquisition data with all data corrections using STIR, and independent of any software supplied by the manufacturer. The implementations were validated by comparing the histogrammed data, data corrections and final reconstruction using the ordered subset expectation maximisation (OSEM) algorithm with the equivalents from the GE-toolbox, supplied by the manufacturer for the scanner. There is no difference in the histogrammed counts whereas an overall relative difference of 6.7 × 10 -8 % and from 0.01% to 0.86% is seen in the normalisation and randoms correction sinograms respectively. The STIR reconstructed images have similar resolution and quantification but have some residual differences due to wcc factors, decay and deadtime corrections, as well as the offset between PET and MR gantries that will be addressed in future work. This work will enable the use of all current and future STIR algorithms, including penalized image reconstruction, motion correction and direct parametric image estimation, on data from GE SIGNA PET/MR scanners

Detection of Lung Density Variations With Principal Component Analysis in PET

Respiratory motion generates lung volume changes during the breathing cycle. These affect the lung tissue density and therefore influence both the attenuation effect and the radiotracer concentration in PET imaging. To detect and correct for these effects could improve the quantitative accuracy of lung PET imaging. In this work we propose the use of Principal Component Analysis (PCA) to detect respiratory-induced lung density changes in the upper lung, where motion is expected to be minimal. The method is firstly applied to simulation data, specifically generated to simulate density changes only and no motion. Secondly, it is applied on the upper lung bed position of 15 lung cancer patients datasets. The total number of counts in time is also evaluated. The results show that the PCA signal is highly correlated to the respiratory trace obtained from an external device, and also to the variation of total counts in time. As the bed positions taken into account do not include moving organs, the results suggest that PCA is successful in detecting respiratory-induced density changes in the upper lung

Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare

Towards nationally curated data archives for clinical radiology image analysis at scale: learnings from national data collection in response to a pandemic

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare