Dataset Information

Joint Reconstruction of Activity and Attenuation in Time-of-Flight PET: A Quantitative Analysis.

ABSTRACT: Methods for joint activity reconstruction and attenuation reconstruction of time-of-flight (TOF) PET data provide an effective solution to attenuation correction when no (or incomplete or inaccurate) information on attenuation is available. One of the main barriers limiting use of these methods in clinical practice is their lack of validation in a relatively large patient database. In this contribution, we aim to validate reconstruction performed with maximum-likelihood activity reconstruction and attenuation registration (MLRR) in a whole-body patient dataset. Furthermore, a partial validation (because the scale problem of the algorithm is avoided for now) of reconstruction performed with maximum-likelihood activity and attenuation (MLAA) is also provided. We present a quantitative comparison between these 2 methods of joint reconstruction and the current clinical gold standard, maximum-likelihood expectation maximization (MLEM) with CT-based attenuation correction. Methods: The whole-body TOF PET emission data of each patient dataset were processed as a whole to reconstruct an activity volume covering all the acquired bed positions, helping reduce the problem of a scale per bed position in MLAA to a global scale for the entire activity volume. Three reconstruction algorithms were used: MLEM, MLRR, and MLAA. A maximum-likelihood scaling of the single-scatter simulation estimate to the emission data was used for scatter correction. The reconstruction results for various regions of interest were then analyzed. Results: The joint reconstructions of the whole-body patient dataset provided better quantification than the gold standard in cases of PET and CT misalignment caused by patient or organ motion. Our quantitative analysis showed a difference of -4.2% ± 2.3% between MLRR and MLEM and a difference of -7.5% ± 4.6% between MLAA and MLEM, averaged over all regions of interest. Conclusion: Joint reconstruction of activity and attenuation provides a useful means to estimate tracer distribution when CT-based-attenuation images are subject to misalignment or are not available. With an accurate estimate of the scatter contribution in the emission measurements, the joint reconstructions of TOF PET data are within clinically acceptable accuracy.

SUBMITTER: Rezaei A

PROVIDER: S-EPMC6167531 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Publications

Joint Reconstruction of Activity and Attenuation in Time-of-Flight PET: A Quantitative Analysis.

Rezaei Ahmadreza A Deroose Christophe M CM Vahle Thomas T Boada Fernando F Nuyts Johan J

Journal of nuclear medicine : official publication, Society of Nuclear Medicine 20180301 10

Methods for joint activity reconstruction and attenuation reconstruction of time-of-flight (TOF) PET data provide an effective solution to attenuation correction when no (or incomplete or inaccurate) information on attenuation is available. One of the main barriers limiting use of these methods in clinical practice is their lack of validation in a relatively large patient database. In this contribution, we aim to validate reconstruction performed with maximum-likelihood activity reconstruction a ...[more]

PMID: 29496982

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Project description:PurposeMany neurological diseases affect small structures in the brain and, as such, reliable visual evaluation and accurate quantification are required. Recent technological developments made the clinical use of hybrid positron emission tomography/magnetic resonance (PET/MR) systems possible, providing both functional and anatomical information in a single imaging session. Nevertheless, there is a trade-off between spatial resolution and image quality (contrast and noise), which is dictated mainly by the chosen acquisition and reconstruction protocols. Image reconstruction algorithms using spherical symmetric basis functions (blobs) for image representation have a number of additional parameters that impact both the qualitative and quantitative image characteristics. Hence, a detailed investigation of the blob-based reconstruction characteristics using different parameters is needed to achieve optimal reconstruction results.ProceduresThis work evaluated the impact of a range of blob parameters on image quality and quantitative accuracy of brain PET images acquired on the Ingenuity Time-of-Flight (TOF) PET/MR system. Two different phantoms were used to simulate brain imaging applications. Image contrast and noise characteristics were assessed using an image quality phantom. Quantitative performance in a clinical setting was investigated using the Hoffman 3D brain phantom at various count levels. Furthermore, the visual quality of four clinical studies was scored blindly by two experienced physicians to qualitatively evaluate the influence of different reconstruction protocols, hereby providing indications on parameters producing the best image quality.ResultsQuantitative evaluation using the image quality phantom showed that larger basis function radii result in lower contrast recovery (∼2%) and lower variance levels (∼15%). The brain phantom and clinical studies confirmed these observations since lower contrast was seen between anatomical structures. High and low count statistics gave comparable values. The qualitative evaluation of the clinical studies, based on the assessment performed by the physicians, showed a preference towards lower image variance levels with a slightly lower contrast, favoring higher radii and four iterations.ConclusionThis study systematically evaluated a number of basis function parameters and their quantitative and qualitative effect within PET image reconstruction in the context of brain imaging. A range of blob parameters can minimize error and provided optimal image quality, where the anatomical structures could be recognized but the exact delineation of these structures is simplified in scans with lower image variance levels and thus, higher radii should be preferred. With the optimization of blob parameters, the reconstructed images were found to be qualitatively improved (optimum parameters {d = 2.0375, alpha = 10.4101, radius = 3.9451}) as assessed by the physicians compared to the current clinical protocol. However, this qualitative improvement is task specific, depending on the desired image characteristics to be extracted. Finally, this work could be used as a guide for application-specific optimal parameter selection.

Project description:We propose a new deep learning-based approach to provide more accurate whole-body PET/MRI attenuation correction than is possible with the Dixon-based 4-segment method. We use activity and attenuation maps estimated using the maximum-likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a convolutional neural network (CNN) to learn a CT-derived attenuation map. Methods: The whole-body 18F-FDG PET/CT scan data of 100 cancer patients (38 men and 62 women; age, 57.3 ± 14.1 y) were retrospectively used for training and testing the CNN. A modified U-net was trained to predict a CT-derived μ-map (μ-CT) from the MLAA-generated activity distribution (λ-MLAA) and μ-map (μ-MLAA). We used 1.3 million patches derived from 60 patients' data for training the CNN, data of 20 others were used as a validation set to prevent overfitting, and the data of the other 20 were used as a test set for the CNN performance analysis. The attenuation maps generated using the proposed method (μ-CNN), μ-MLAA, and 4-segment method (μ-segment) were compared with the μ-CT, a ground truth. We also compared the voxelwise correlation between the activity images reconstructed using ordered-subset expectation maximization with the μ-maps, and the SUVs of primary and metastatic bone lesions obtained by drawing regions of interest on the activity images. Results: The CNN generates less noisy attenuation maps and achieves better bone identification than MLAA. The average Dice similarity coefficient for bone regions between μ-CNN and μ-CT was 0.77, which was significantly higher than that between μ-MLAA and μ-CT (0.36). Also, the CNN result showed the best pixel-by-pixel correlation with the CT-based results and remarkably reduced differences in activity maps in comparison to CT-based attenuation correction. Conclusion: The proposed deep neural network produced a more reliable attenuation map for 511-keV photons than the 4-segment method currently used in whole-body PET/MRI studies.

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Project description:Although there have been impressive strides in detector development for time-of-flight positron emission tomography, most detectors still make use of simple signal processing methods to extract the time-of-flight information from the detector signals. In most cases, the timing pick-off for each waveform is computed using leading edge discrimination or constant fraction discrimination, as these were historically easily implemented with analog pulse processing electronics. However, now with the availability of fast waveform digitizers, there is opportunity to make use of more of the timing information contained in the coincident detector waveforms with advanced signal processing techniques. Here we describe the application of deep convolutional neural networks (CNNs), a type of machine learning, to estimate time-of-flight directly from the pair of digitized detector waveforms for a coincident event. One of the key features of this approach is the simplicity in obtaining ground-truth-labeled data needed to train the CNN: the true time-of-flight is determined from the difference in path length between the positron emission and each of the coincident detectors, which can be easily controlled experimentally. The experimental setup used here made use of two photomultiplier tube-based scintillation detectors, and a point source, stepped in 5?mm increments over a 15?cm range between the two detectors. The detector waveforms were digitized at 10 GS s-1 using a bench-top oscilloscope. The results shown here demonstrate that CNN-based time-of-flight estimation improves timing resolution by 20% compared to leading edge discrimination (231?ps versus 185?ps), and 23% compared to constant fraction discrimination (242?ps versus 185?ps). By comparing several different CNN architectures, we also showed that CNN depth (number of convolutional and fully connected layers) had the largest impact on timing resolution, while the exact network parameters, such as convolutional filter size and number of feature maps, had only a minor influence.

Project description:Positron emission tomography (PET) is an in vivo imaging method essential for studying the neurochemical pathophysiology of psychiatric and neurological disease. However, its high cost and exposure of participants to radiation make it unfeasible to employ large sample sizes. The major shortcoming of PET imaging is therefore its lack of power for studying clinically-relevant research questions. Here, we introduce a new method for performing PET quantification and analysis called SiMBA, which helps to alleviate these issues by improving the efficiency of PET analysis by exploiting similarities between both individuals and regions within individuals. In simulated [11C]WAY100635 data, SiMBA greatly improves both statistical power and the consistency of effect size estimation without affecting the false positive rate. This approach makes use of hierarchical, multifactor, multivariate Bayesian modelling to effectively borrow strength across the whole dataset to improve stability and robustness to measurement error. In so doing, parameter identifiability and estimation are improved, without sacrificing model interpretability. This comes at the cost of increased computational overhead, however this is practically negligible relative to the time taken to collect PET data. This method has the potential to make it possible to test clinically-relevant hypotheses which could never be studied before given the practical constraints. Furthermore, because this method does not require any additional information over and above that required for traditional analysis, it makes it possible to re-examine data which has already previously been collected at great expense. In the absence of dramatic advancements in PET image data quality, radiotracer development, or data sharing, PET imaging has been fundamentally limited in the scope of research hypotheses which could be studied. This method, especially combined with the recent steps taken by the PET imaging community to embrace data sharing, will make it possible to greatly improve the research possibilities and clinical relevance of PET neuroimaging.

Dataset Information

Joint Reconstruction of Activity and Attenuation in Time-of-Flight PET: A Quantitative Analysis.

Publications

Joint Reconstruction of Activity and Attenuation in Time-of-Flight PET: A Quantitative Analysis.

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