Movement degrades image quality in PET/CT. The first step in correcting for movement is to gate the data into dif ferent motion states. The gating is usually based on information from external devices, such as the chest position for respiratory movement, or an ECG signal for cardiac gating. Various groups have proposed methods to extract a gating signal out of the PET and/or CT data. Most of these methods are slow or require prior information (and associated tuning of parameters). Here we propose and evaluate a method that uses a well-known technique for data analysis called Principal Component Analysis (PCA). We test the method on clinical PET list mode data and CINE CT images to extract a gating signal. We show good correlation with the chest position as measured by the Varian RPM system. Total processing time for PET data is less than half a minute of which most is 10 related. I. IN TRODUCTIONIn many cases in medical imaging, motion is unavoid able. For example, in diagnostic PET, acquisition duration is currently roughly 2 minutes per bed position. Respiratory motion in patients during PET acquisition leads to blurring in the resulting (static) PET images. This may in turn lead to lower detectability of tumours, inaccurate SUV calculation, and incorrect tum or planning volumes in radiation therapy [1], [2], [3], [4]. The first step towards reducing the amount of motion in the images normally involves the use of respiratory gating [5] which results in a 4-D data set, in which multiple gates containing data from the various respiratory states, are usually individually reconstructed. This is then potentially followed by motion correction.In other applications, the different motion states are of interest, for instance in cardiac studies to find the ejection fraction [6] and/or wall motion [7], or in radio-therapy [8].The gating is usually based on information from exter nal devices, such as a spyrometer or the chest position for respiratory movement, or an ECG signal for cardiac gating. Due to the extra cost and patient management associated to the additional device, but also because of some evidence of hysteresis between the internal movement and external device [9], [10], [11], various groups have proposed methods to extract a gating signal out of the PET, SPECT and/or CT data. Many groups compute the centre-of-mass (COM) in an ROI and use this as an indicator of motion, mostly of respiration, for instance in PET [12], [13] and cardiac SPECT [14]. Filtering allows separation of a respiratory and cardiac signal in cardiac PET [15] and cone-beam CINECT [16]. These methods need a high contrast region that can be tracked over time.For PET data, Visvikis et at.[17] placed an ROI over edges of boundaries (using non-attenuation corrected images) and studied the Time Activity Curve (TACs). A characteristic frequency was derived via the Fourier Transform (FT) which then allowed finding amplitude and phase images. This method worked well on phantom data with periodic movement but was not evaluated for patients. Schleyer ...

The standardized uptake value is commonly used as a tool to supplement visual interpretation and to quantify the images acquired from static in vivo animal PET. The preferred approach for analyzing PET data is either to sum the images and calculate the standardized uptake value or to use kinetic modeling. The aim of this study was to investigate the performance of masked volumewise principal-component analysis (MVW-PCA) used in dynamic in vivo animal PET studies to extract and separate signals with different kinetic behaviors. Methods: PET data were acquired with a small-animal PET scanner and a fluorine tracer in a study of rats and mice. After acquisition, the data were reconstructed by use of 4 time protocols with different frame lengths. Data were analyzed by use of MVW-PCA with applied noise prenormalization and a new masking technique developed in this study. Results: The resulting principal-component images showed a clear separation of the activity in the spine into the first MVW-PCA component and the activity in the kidneys into the second MVW-PCA component. In addition, the different time protocols were shown to have little or no impact on the results obtained with MVW-PCA. Conclusion: MVW-PCA can efficiently separate different kinetic behaviors into different principal-component images. Moreover, MVW-PCA is a stable technique in the sense that the time protocol chosen has only a small impact on the resulting principal-component images. PET is a noninvasive imaging modality that is used to visualize the concentration of a molecule labeled with a radioactive isotope called a tracer, representing the physiologic interaction between the administered tracer and the target of interest, in the scanned object. PET studies are performed either dynamically or statically. Static PET studies are often used in clinical applications such as oncology and neurology with already well-known tracers, such as 18 F-FDG. Dynamic PET studies are often used in clinical applications such as neurology, cardiology, and oncology to explore the kinetic behavior of an administered tracer in the scanned object and to study the treatment effect.In contrast to static imaging, dynamic PET studies depict the same volume within the scanned object but at different time points during the study, suggesting the possibility of exploring the physiologic interactions of the administered tracer in the scanned object as a function of time. Furthermore, dynamic PET studies can be used to explore the kinetic behaviors of new tracers or existing tracers in new applications (1).However, independent of the type of study performed, PET data often have high levels of noise, which make it difficult to discern different areas in image volumes. A standard method for reducing noise and improving qualitative and quantitative estimation is to sum or average image volumes within a chosen time interval. The summation is usually performed over parts of the sequence in which the signal is proportionally higher. Furthermore, the signal often has a higher amplitude...

Higher-order PC noise prenormalization has potential for improving the results from masked volumewise PCA on dynamic PET datasets independent of the type of reconstruction algorithm.

Multivariate image analysis tools are used for analyzing dynamic or multidimensional Positron Emission Tomography, PET data with the aim of noise reduction, dimension reduction and signal separation. Principal Component Analysis is one of the most commonly used multivariate image analysis tools, applied on dynamic PET data. Independent Component Analysis is another multivariate image analysis tool used to extract and separate signals. Because of the presence of high and variable noise levels and correlation in the different PET images which may confound the multivariate analysis, it is essential to explore and investigate different types of pre-normalization (transformation) methods that need to be applied, prior to application of these tools. In this study, we explored the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) to extract signals and reduce noise, thereby increasing the Signal to Noise Ratio (SNR) in a dynamic sequence of PET images, where the features of the noise are different compared with some other medical imaging techniques. Applications on computer simulated PET images were explored and compared. Application of PCA generated relatively similar results, with some minor differences, on the images with different noise characteristics. However, clear differences were seen with respect to the type of pre-normalization. ICA on images normalized using two types of normalization methods also seemed to perform relatively well but did not reach the improvement in SNR as PCA. Furthermore ICA seems to have a tendency under some conditions to shift over information from IC1 to other independent components and to be more sensitive to the level of noise. PCA is a more stable technique than ICA and creates better results both qualitatively and quantitatively in the simulated PET images. PCA can extract the signals from the noise rather well and is not sensitive to type of noise, magnitude and correlation, when the input data are correctly handled by a proper pre-normalization. It is important to note that PCA as inherently a method to separate signal information into different components could still generate PC1 images with improved SNR as compared to mean images.