Characterization and reduction of noise in dynamic PET data using masked volumewise principal component analysis

Masked volumewise principal component (PC) analysis (PCA) is used in PET to distinguish structures that display different kinetic behaviors after administration of a tracer. When masked volumewise PCA was introduced, one article proposed noise prenormalization because of temporal and spatial variations of the noise between slices. However, the noise prenormalization proposed in that article was applicable only to datasets reconstructed using filtered backprojection (FBP). The study presented in this article aimed at developing a new noise prenormalization that is applicable to datasets regardless of whether they were reconstructed with FBP or an iterative reconstruction algorithm, such as ordered-subset expectation maximization (OSEM).

METHODS

A phantom study was performed to investigate differences in the expectation values and SDs of datasets reconstructed with FBP and OSEM. A novel method, higher-order PC noise prenormalization, was suggested and evaluated against other prenormalization methods on clinical datasets.

RESULTS

Masked volumewise PCA of data reconstructed with FBP was much more dependent on an appropriate prenormalization than was analysis of data reconstructed with OSEM. Higher-order PC noise prenormalization showed an overall good performance with both FBP and OSEM reconstructions, whereas the other prenormalization methods performed well with only 1 of the 2 methods.

CONCLUSION

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.

Signal extraction and separation in in vivo animal PET studies with masked volumewise principal-component analysis

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.

Evaluation of the Hsp90 inhibitor NVP-AUY922 in multicellular tumour spheroids with respect to effects on growth and PET tracer uptake

BACKGROUND

Molecular targeting has become a prominent concept in cancer treatment and heat shock protein 90 (Hsp90) inhibitors are suggested as promising anticancer drugs. The Hsp90 complex is one of the chaperones that facilitate the refolding of unfolded or misfolded proteins and plays a role for key oncogenic proteins such as Her2, Raf-1, Akt/PKB, and mutant p53. NVP-AUY922 is a novel low-molecular Hsp90 inhibitor, currently under clinical development as an anticancer drug. Disruption of the Hsp90-client protein complexes leads to proteasome-mediated degradation of client proteins and cell death. The aim of the current study was to use a combination of the multicellular tumour spheroid (MTS) model and positron emission tomography (PET) to investigate the effects of NVP-AUY922 on tumour growth and its relation to PET tracer uptake for the selection of appropriate PET tracer. A further aim was to evaluate the concentration and time dependence in the relation between growth inhibition and PET tracer uptake as part of translational imaging activities.

METHODS

MTS of two breast cancer cell lines (MCF-7 and BT474), one glioblastoma cell line (U87MG) and one colon carcinoma cell line (HCT116) were prepared. Initially, we investigated MTS growth pattern and (3)H-thymidine incorporation in MTS after continuous exposure to NVP-AUY922 in order to determine dose response. Then the short-term effect of the drug on the four PET tracers 2-[(18)F] fluoro-2-deoxyglucose (FDG), 3'-deoxy-3'-fluorothymidine (FLT), methionine and choline was correlated to the long-term effect (changes in growth pattern) to determine the adequate PET tracer with high predictability. Next, the growth inhibitory effect of different dose schedules was evaluated to determine the optimal dose and time. Finally, the effect of a 2-h exposure to the drug on growth pattern and FDG/FLT uptake was evaluated.

RESULTS

A dose-dependent inhibition of growth and decrease of (3)H-thymidine uptake was observed with 100% growth cessation in the dose range 7-52 nM and 50% (3)H-thymidine reduction in the range of 10-23 nM, with the most pronounced effect on BT474 cells. The effect of the drug was best detected by FLT. The results suggested that a complete cessation of growth of the viable cell volume was achieved with about 50% inhibition of FLT uptake 3 days after continuous treatment. Significant growth inhibition was observed at all doses and all exposure time spans. Two-hour exposure to NVP-AUY922 generated a growth inhibition which persisted dose dependently up to 10 days. The uptake of FDG per viable tumour volume was reduced by just 25% with 300 nM treatment of the drug, whereas the FLT uptake decreased up to 75% in correlation with the growth inhibition and recovery.

CONCLUSIONS

Our results indicate a prolonged action of NVP-AUY922 in this cell culture, FLT is a suitable tracer for the monitoring of the effect and a FLT PET study within 3 days after treatment can predict the treatment outcome in this model. If relevant in vivo, this information can be used for efficient planning of animal PET studies and later human PET trial.

Masked volume wise Principal Component Analysis of small adrenocortical tumours in dynamic [11C]-metomidate Positron Emission Tomography

BACKGROUND

In previous clinical Positron Emission Tomography (PET) studies novel approaches for application of Principal Component Analysis (PCA) on dynamic PET images such as Masked Volume Wise PCA (MVW-PCA) have been introduced. MVW-PCA was shown to be a feasible multivariate analysis technique, which, without modeling assumptions, could extract and separate organs and tissues with different kinetic behaviors into different principal components (MVW-PCs) and improve the image quality.

METHODS

In this study, MVW-PCA was applied to 14 dynamic 11C-metomidate-PET (MTO-PET) examinations of 7 patients with small adrenocortical tumours. MTO-PET was performed before and 3 days after starting per oral cortisone treatment. The whole dataset, reconstructed by filtered back projection (FBP) 0-45 minutes after the tracer injection, was used to study the tracer pharmacokinetics.

RESULTS

Early, intermediate and late pharmacokinetic phases could be isolated in this manner. The MVW-PC1 images correlated well to the conventionally summed image data (15-45 minutes) but the image noise in the former was considerably lower. PET measurements performed by defining "hot spot" regions of interest (ROIs) comprising 4 contiguous pixels with the highest radioactivity concentration showed a trend towards higher SUVs when the ROIs were outlined in the MVW-PC1 component than in the summed images. Time activity curves derived from "50% cut-off" ROIs based on an isocontour function whereby the pixels with SUVs between 50 to 100% of the highest radioactivity concentration were delineated, showed a significant decrease of the SUVs in normal adrenal glands and in adrenocortical adenomas after cortisone treatment.

CONCLUSION

In addition to the clear decrease in image noise and the improved contrast between different structures with MVW-PCA, the results indicate that the definition of ROIs may be more accurate and precise in MVW-PC1 images than in conventional summed images. This might improve the precision of PET measurements, for instance in therapy monitoring as well as for delineation of the tumour in radiation therapy planning.

Performance of principal component analysis and independent component analysis with respect to signal extraction from noisy positron emission tomography data - a study on computer simulated images

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.

Masked-Volume-Wise PCA and "reference Logan" illustrate similar regional differences in kinetic behavior in human brain PET study using [11C]-PIB

Background

Kinetic modeling using reference Logan is commonly used to analyze data obtained from dynamic Positron Emission Tomography (PET) studies on patients with Alzheimer's disease (AD) and healthy volunteers (HVs) using amyloid imaging agent N-methyl [¹¹C]2-(4'-methylaminophenyl)-6-hydroxy-benzothiazole, [¹¹C]-PIB. The aim of the present study was to explore whether results obtained using the newly introduced method, Masked Volume Wise Principal Component Analysis, MVW-PCA, were similar to the results obtained using reference Logan.

Methods

MVW-PCA and reference Logan were performed on dynamic PET images obtained from four Alzheimer's disease (AD) patients on two occasions (baseline and follow-up) and on four healthy volunteers (HVs). Regions of interest (ROIs) of similar sizes were positioned in different parts of the brain in both AD patients and HVs where the difference between AD patients and HVs is largest. Signal-to-noise ratio (SNR) and discrimination power (DP) were calculated for images generated by the different methods and the results were compared both qualitatively and quantitatively.

Results

MVW-PCA generated images that illustrated similar regional binding patterns compared to reference Logan images and with slightly higher quality, enhanced contrast, improved SNR and DP, without being based on modeling assumptions. MVW-PCA also generated additional MVW-PC images by using the whole dataset, which illustrated regions with different and uncorrelated kinetic behaviors of the administered tracer. This additional information might improve the understanding of kinetic behavior of the administered tracer.

Conclusion

MVW-PCA is a potential multivariate method that without modeling assumptions generates high quality images, which illustrated similar regional changes compared to modeling methods such as reference Logan. In addition, MVW-PCA could be used as a new technique, applicable not only on dynamic human brain studies but also on dynamic cardiac studies when using PET.

A new application of pre-normalized principal component analysis for improvement of image quality and clinical diagnosis in human brain PET studies—Clinical brain studies using [11C]-GR205171, [11C]-l-deuterium-deprenyl, [11C]-5-Hydroxy-l-Tryptophan, [11C]-l-DOPA and Pittsburgh Compound-B

Principal component analysis (PCA) is one of the most applied multivariate image analysis tool on dynamic Positron Emission Tomography (PET). Independent of used reconstruction methodologies, PET images contain correlation in-between pixels, correlations in-between frame and errors caused by the reconstruction algorithm including different corrections, which can affect the performance of the PCA. In this study, we have investigated a new approach of application of PCA on pre-normalized, dynamic human PET images. A range of different tracers have been used for this purpose to explore the performance of the new method as a way to improve detection and visualization of significant changes in tracer kinetics and to enhance the discrimination between pathological and healthy regions in the brain. We compare the new results with the results obtained using other methods. Images generated using the new approach contain more detailed anatomical information with higher quality, precision and visualization, compared with images generated using other methods.

Multicellular tumour spheroid as a model for evaluation of [18F]FDG as biomarker for breast cancer treatment monitoring

Background

In order to explore a pre-clinical method to evaluate if [¹⁸F]FDG is valid for monitoring early response, we investigated the uptake of FDG in Multicellular tumour spheroids (MTS) without and with treatment with five routinely used chemotherapy agents in breast cancer.

Methods

The response to each anticancer treatment was evaluated by measurement of the [¹⁸F]FDG uptake and viable volume of the MTSs after 2 and 3 days of treatment.

Results

The effect of Paclitaxel and Docetaxel on [¹⁸F]FDG uptake per viable volume was more evident in BT474 (up to 55% decrease) than in MCF-7 (up to 25% decrease).

Doxorubicin reduced the [¹⁸F]FDG uptake per viable volume more noticeable in MCF-7 (25%) than in BT474 MTSs.

Tamoxifen reduced the [¹⁸F]FDG uptake per viable volume only in MCF-7 at the highest dose of 1 μM.

No effect of Imatinib was observed.

Conclusion

MTS was shown to be appropriate to investigate the potential of FDG-PET for early breast cancer treatment monitoring; the treatment effect can be observed before any tumour size changes occur.

The combination of PET radiotracers and image analysis in MTS provides a good model to evaluate the relationship between tumour volume and the uptake of metabolic tracer before and after chemotherapy. This feature could be used for screening and selecting PET-tracers for early assessment of treatment response.

In addition, this new method gives a possibility to assess quickly, and in vitro, a good preclinical profile of existing and newly developed anti-cancer drugs.

A new, fast and semi-automated size determination method (SASDM) for studying multicellular tumor spheroids

Background

Considering the width and importance of using Multicellular Tumor Spheroids (MTS) in oncology research, size determination of MTSs by an accurate and fast method is essential. In the present study an effective, fast and semi-automated method, SASDM, was developed to determinate the size of MTSs. The method was applied and tested in MTSs of three different cell-lines. Frozen section autoradiography and Hemotoxylin Eosin (H&E) staining was used for further confirmation.

Results

SASDM was shown to be effective, user-friendly, and time efficient, and to be more precise than the traditional methods and it was applicable for MTSs of different cell-lines. Furthermore, the results of image analysis showed high correspondence to the results of autoradiography and staining.

Conclusion

The combination of assessment of metabolic condition and image analysis in MTSs provides a good model to evaluate the effect of various anti-cancer treatments.

Noise correlation in PET, CT, SPECT and PET/CT data evaluated using autocorrelation function: a phantom study on data, reconstructed using FBP and OSEM

Background

Positron Emission Tomography (PET), Computed Tomography (CT), PET/CT and Single Photon Emission Tomography (SPECT) are non-invasive imaging tools used for creating two dimensional (2D) cross section images of three dimensional (3D) objects. PET and SPECT have the potential of providing functional or biochemical information by measuring distribution and kinetics of radiolabelled molecules, whereas CT visualizes X-ray density in tissues in the body. PET/CT provides fused images representing both functional and anatomical information with better precision in localization than PET alone.

Images generated by these types of techniques are generally noisy, thereby impairing the imaging potential and affecting the precision in quantitative values derived from the images. It is crucial to explore and understand the properties of noise in these imaging techniques. Here we used autocorrelation function (ACF) specifically to describe noise correlation and its non-isotropic behaviour in experimentally generated images of PET, CT, PET/CT and SPECT.

Methods

Experiments were performed using phantoms with different shapes. In PET and PET/CT studies, data were acquired in 2D acquisition mode and reconstructed by both analytical filter back projection (FBP) and iterative, ordered subsets expectation maximisation (OSEM) methods. In the PET/CT studies, different magnitudes of X-ray dose in the transmission were employed by using different mA settings for the X-ray tube. In the CT studies, data were acquired using different slice thickness with and without applied dose reduction function and the images were reconstructed by FBP. SPECT studies were performed in 2D, reconstructed using FBP and OSEM, using post 3D filtering. ACF images were generated from the primary images, and profiles across the ACF images were used to describe the noise correlation in different directions. The variance of noise across the images was visualised as images and with profiles across these images.

Results

The most important finding was that the pattern of noise correlation is rotation symmetric or isotropic, independent of object shape in PET and PET/CT images reconstructed using the iterative method. This is, however, not the case in FBP images when the shape of phantom is not circular. Also CT images reconstructed using FBP show the same non-isotropic pattern independent of slice thickness and utilization of care dose function. SPECT images show an isotropic correlation of the noise independent of object shape or applied reconstruction algorithm. Noise in PET/CT images was identical independent of the applied X-ray dose in the transmission part (CT), indicating that the noise from transmission with the applied doses does not propagate into the PET images showing that the noise from the emission part is dominant. The results indicate that in human studies it is possible to utilize a low dose in transmission part while maintaining the noise behaviour and the quality of the images.

Conclusion

The combined effect of noise correlation for asymmetric objects and a varying noise variance across the image field significantly complicates the interpretation of the images when statistical methods are used, such as with statistical estimates of precision in average values, use of statistical parametric mapping methods and principal component analysis. Hence it is recommended that iterative reconstruction methods are used for such applications. However, it is possible to calculate the noise analytically in images reconstructed by FBP, while it is not possible to do the same calculation in images reconstructed by iterative methods. Therefore for performing statistical methods of analysis which depend on knowing the noise, FBP would be preferred.

Non-isotropic noise correlation in PET data reconstructed by FBP but not by OSEM demonstrated using auto-correlation function

Background

Positron emission tomography (PET) is a powerful imaging technique with the potential of obtaining functional or biochemical information by measuring distribution and kinetics of radiolabelled molecules in a biological system, both in vitro and in vivo. PET images can be used directly or after kinetic modelling to extract quantitative values of a desired physiological, biochemical or pharmacological entity. Because such images are generally noisy, it is essential to understand how noise affects the derived quantitative values. A pre-requisite for this understanding is that the properties of noise such as variance (magnitude) and texture (correlation) are known.

Methods

In this paper we explored the pattern of noise correlation in experimentally generated PET images, with emphasis on the angular dependence of correlation, using the autocorrelation function (ACF). Experimental PET data were acquired in 2D and 3D acquisition mode and reconstructed by analytical filtered back projection (FBP) and iterative ordered subsets expectation maximisation (OSEM) methods. The 3D data was rebinned to a 2D dataset using FOurier REbinning (FORE) followed by 2D reconstruction using either FBP or OSEM. In synthetic images we compared the ACF results with those from covariance matrix. The results were illustrated as 1D profiles and also visualized as 2D ACF images.

Results

We found that the autocorrelation images from PET data obtained after FBP were not fully rotationally symmetric or isotropic if the object deviated from a uniform cylindrical radioactivity distribution. In contrast, similar autocorrelation images obtained after OSEM reconstruction were isotropic even when the phantom was not circular. Simulations indicated that the noise autocorrelation is non-isotropic in images created by FBP when the level of noise in projections is angularly variable. Comparison between 1D cross profiles on autocorrelation images obtained by FBP reconstruction and covariance matrices produced almost identical results in a simulation study.

Conclusion

With asymmetric radioactivity distribution in PET, reconstruction using FBP, in contrast to OSEM, generates images in which the noise correlation is non-isotropic when the noise magnitude is angular dependent, such as in objects with asymmetric radioactivity distribution. In this respect, iterative reconstruction is superior since it creates isotropic noise correlations in the images.