Penalised Maximum Likelihood Simultaneous Longitudinal PET Image Reconstruction with Difference‐Image Priors

PurposeMany clinical contexts require the acquisition of multiple positron emission tomography (PET) scans of a single subject, for example to observe and quantify changes in functional behaviour in tumours after treatment in oncology. Typically, the datasets from each of these scans are reconstructed individually, without exploiting the similarities between them. We have recently shown that sharing information between longitudinal PET datasets by penalising voxel‐wise differences during image reconstruction can improve reconstructed images by reducing background noise and increasing the contrast‐to‐noise ratio of high activity lesions. Here we present two additional novel longitudinal difference‐image priors and evaluate their performance using 2D simulation studies and a 3D real dataset case study.MethodsWe have previously proposed a simultaneous difference‐image‐based penalised maximum likelihood (PML) longitudinal image reconstruction method that encourages sparse difference images (DS‐PML), and in this work we propose two further novel prior terms. The priors are designed to encourage longitudinal images with corresponding differences which have i) low entropy (DE‐PML), and ii) high sparsity in their spatial gradients (DTV‐PML). These two new priors and the originally proposed longitudinal prior were applied to 2D simulated treatment response [18F]fluorodeoxyglucose (FDG) brain tumour datasets and compared to standard maximum likelihood expectation‐maximisation (MLEM) reconstructions. These 2D simulation studies explored the effects of penalty strengths, tumour behaviour, and inter‐scan coupling on reconstructed images. Finally, a real two‐scan longitudinal data series acquired from a head and neck cancer patient was reconstructed with the proposed methods and the results compared to standard reconstruction methods.ResultsUsing any of the three priors with an appropriate penalty strength produced images with noise levels equivalent to those seen when using standard reconstructions with increased counts levels. In tumour regions each method produces subtly different results in terms of preservation of tumour quantification and reconstruction root mean‐squared error (RMSE). In particular, in the two‐scan simulations, the DE‐PML method produced tumour means in close agreement with MLEM reconstructions, while the DTV‐PML method produced the lowest errors due to noise reduction within the tumour. Across a range of tumour responses and different numbers of scans, similar results were observed, with DTV‐PML producing the lowest errors of the three priors and DE‐PML producing the lowest bias. Similar improvements were observed in the reconstructions of the real longitudinal datasets, although imperfect alignment of the two PET images resulted in additional changes in the difference image that affected the performance of the proposed methods.ConclusionReconstruction of longitudinal datasets by penalising difference images between pairs of scans from a data series allows for noise reduction in all reconstructed images. An appropriate choice of penalty term and penalty strength allows for this noise reduction to be achieved while maintaining reconstruction performance in regions of change, either in terms of quantification of mean intensity via DE‐PML, or in terms of tumour RMSE via DTV‐PML. Overall, improving the image quality of longitudinal datasets via simultaneous reconstruction has the potential to improve upon currently used methods, allow dose reduction, or reduce scan time while maintaining image quality at current levels

Patch-based image reconstruction for PET using prior-image derived dictionaries

In PET image reconstruction, regularization is often needed to reduce the noise in the resulting images. Patch-based image processing techniques have recently been successfully used for regularization in medical image reconstruction through a penalized likelihood framework. Re-parameterization within reconstruction is another powerful regularization technique in which the object in the scanner is re-parameterized using coefficients for spatially-extensive basis vectors. In this work, a method for extracting patch-based basis vectors from the subject's MR image is proposed. The coefficients for these basis vectors are then estimated using the conventional MLEM algorithm. Furthermore, using the alternating direction method of multipliers, an algorithm for optimizing the Poisson log-likelihood while imposing sparsity on the parameters is also proposed. This novel method is then utilized to find sparse coefficients for the patch-based basis vectors extracted from the MR image. The results indicate the superiority of the proposed methods to patch-based regularization using the penalized likelihood framework.<br/

Time-invariant component-based normalization for a simultaneous PET-MR scanner

Component-based normalization is a method used to compensate for the sensitivity of each of the lines of response acquired in positron emission tomography. This method consists of modelling the sensitivity of each line of response as a product of multiple factors, which can be classified as time-invariant, time-variant and acquisition-dependent components. Typical time-variant factors are the intrinsic crystal efficiencies, which are needed to be updated by a regular normalization scan. Failure to do so would in principle generate artifacts in the reconstructed images due to the use of out of date time-variant factors. For this reason, an assessment of the variability and the impact of the crystal efficiencies in the reconstructed images is important to determine the frequency needed for the normalization scans, as well as to estimate the error obtained when an inappropriate normalization is used. Furthermore, if the fluctuations of these components are low enough, they could be neglected and nearly artifact-free reconstructions become achievable without performing a regular normalization scan. In this work, we analyse the impact of the time-variant factors in the component-based normalization used in the Biograph mMR scanner, but the work is applicable to other PET scanners. These factors are the intrinsic crystal efficiencies and the axial factors. For the latter, we propose a new method to obtain fixed axial factors that was validated with simulated data. Regarding the crystal efficiencies, we assessed their fluctuations during a period of 230 d and we found that they had good stability and low dispersion. We studied the impact of not including the intrinsic crystal efficiencies in the normalization when reconstructing simulated and real data. Based on this assessment and using the fixed axial factors, we propose the use of a time-invariant normalization that is able to achieve comparable results to the standard, daily updated, normalization factors used in this scanner. Moreover, to extend the analysis to other scanners, we generated distributions of crystal efficiencies with greater fluctuations than those found in the Biograph mMR scanner and evaluated their impact in simulations with a wide variety of noise levels. An important finding of this work is that a regular normalization scan is not needed in scanners with photodetectors with relatively low dispersion in their efficiencies

Spatially-Compact MR-Guided Kernel EM for PET Image Reconstruction

Positron emission tomography (PET) is a highly sensitive functional and molecular imaging modality which can measure picomolar concentrations of an injected radionuclide. However, the physical sensitivity of PET is limited, and reducing the injected dose leads to low count data and noisy reconstructed images. A highly effective way of reducing noise is to reparameterise the reconstruction in terms of MR-derived spatial basis functions. Spatial basis functions derived using the kernel method have demonstrated excellent noise reduction properties and maintain shared PET-MR detailed structures. However, as previously shown in the literature, the MR-guided kernel method may lead to excessive smoothing of structures that are only present in the PET data. This work makes two main contributions in order to address this problem: first, we exploit the potential of the MR-guided kernel method to form more spatially-compact basis functions which are able to preserve PET-unique structures, and secondly, we consider reconstruction at the native MR resolution. The former contribution notably improves the recovery of structures which are unique to the PET data. These adaptations of the kernel method were compared to the conventional implementation of the MR-guided kernel method and also to MLEM, in terms of ability to recover PET unique structures for both simulated and real data. The spatially-compact kernel method showed clear visual and quantitative improvements in the reconstruction of the PET unique structures, relative to the conventional kernel method for all sizes of PET unique structures investigated, whilst maintaining to a large extent the impressive noise mitigating and detail preserving properties of the conventional MR-guided kernel method. We therefore conclude that a spatially-compact parameterisation of the MR-guided kernel method, should be the preferred implementation strategy in order to obviate unnecessary losses in PET-unique details

Evaluation of Strategies for PET Motion Correction-Manifold Learning vs. Deep Learning

Image quality in abdominal PET is degraded by respiratory motion. In this paper we compare existing data-driven gating methods for motion correction which are based on manifold learning, with a proposed method in which a convolutional neural network learns estimated motion fields in an end-to-end manner, and then uses those estimated motion fields to motion correct the PET frames. We find that this proposed network approach is unable to outperform manifold learning methods in the literature, in terms of the image quality of the motion corrected volumes. We investigate possible explanations for this negative result and discuss the benefits of these unsupervised approaches which remain the state of the art

Multi-modal weighted quadratic priors for robust intensity independent synergistic PET-MR reconstruction

We propose a simple and robust synergistic PET-MR reconstruction algorithm using mutually-weighted quadratic priors. Maximum a-posteriori (MAP) objective functions were used for PET and MR reconstructions, including MAP expectation maximization (MAPEM) for PET and MAP sensitivity encoding (SENSE) for MR reconstruction. For both reconstructions, mutually-weighted quadratic priors were used for reduction of noise and artifacts, with preservation of PET-MR common boundaries. The weighting coefficients are updated from the current PET and MR estimates using normalized multi-modal Gaussian similarity kernels, which are in turn derived as the product of modality-specific kernels. Hence, the resulting kernels are independent of both signal intensities and contrast orientations. The performance of the proposed method was evaluated using 3D realistic simulations and a clinical FDG PET/T1-MPRAGE/FLAIR MR dataset. For simulations, undersampled MR reconstructions with undersampling factors of 4, 6 and 8 were considered while for the clinical dataset an MR undersampling factor of 4 was used. For PET reconstructions, the proposed method was compared with maximum likelihood expectation maximization (MLEM) and fully-sampled MR guided MAPEM (as a PET benchmark). For MR reconstructions, the proposed method was compared with fully-sampled reconstruction (as an MR benchmark), total variation (TV) regularized undersampled SENSE, and PET/FLAIR guided undersampled SENSE. Results showed that the proposed method can outperform conventional reconstructions especially for highly undersampled MR data, while preserving modality-unique features. For the clinical dataset, the proposed method showed promising results especially for PET reconstruction, in spite of the substantial PET-MR intensity and contrast differences. In summary, the proposed synergistic algorithm and priors offer a robust multi-modal synergistic image reconstruction framework

Metabotropic Glutamate Receptor Type 5 (mGluR5) Cortical Abnormalities in Focal Cortical Dysplasia Identified In Vivo With [11C]ABP688 Positron-Emission Tomography (PET) Imaging

Metabotropic glutamate receptor type 5 (mGluR5) abnormalities have been described in tissue resected from epilepsy patients with focal cortical dysplasia (FCD). To determine if these abnormalities could be identified in vivo, we investigated mGluR5 availability in 10 patients with focal epilepsy and an MRI diagnosis of FCD using positron-emission tomography (PET) and the radioligand [11C]ABP688. Partial volume corrected [11C]ABP688 binding potentials (BPND) were computed using the cerebellum as a reference region. Each patient was compared to homotopic cortical regions in 33 healthy controls using region-of-interest (ROI) and vertex-wise analyses. Reduced [11C]ABP688 BPND in the FCD was seen in 7/10 patients with combined ROI and vertex-wise analyses. Reduced FCD BPND was found in 4/5 operated patients (mean follow-up: 63 months; Engel I), of whom surgical specimens revealed FCD type IIb or IIa, with most balloon cells showing negative or weak mGluR5 immunoreactivity as compared to their respective neuropil and normal neurons at the border of resections. [11C]ABP688 PET shows for the first time in vivo evidence of reduced mGluR5 availability in FCD, indicating focal glutamatergic alterations in malformations of cortical development, which cannot be otherwise clearly demonstrated through resected tissue analyses

O Brasil na nova cartografia global da religião

Este artigo analisa as mudanças sociais, econômicas, culturais e religiosas que fizeram do Brasil um polo importante de produção do sagrado numa emergente cartografia global. Esta cartografia é policêntrica e entrecortada por uma miríade de redes transnacionais e multi-direcionais que facilitam o rápido movimento de pessoas, ideias, imagens, capitais e mercadorias. Entre os vetores que vamos examinar estão: imigrantes brasileiros que na tentativa de dar sentido ao processo deslocamento e de manter ligações transnacionais com o Brasil levam suas crenças, práticas, identidades religiosas para o estrangeiro, missionários e outros "entrepreneurs" religiosos, o turismo espiritual de estrangeiros que vão ao Brasil em busca de cura ou desenvolvimento espiritual, e as indústrias culturais, a mídia e a Internet que disseminam globalmente imagens do Brasil como uma terra exótica onde o sagrado faz parte intrínseca de sua cultura e natureza

Duration of androgen deprivation therapy with postoperative radiotherapy for prostate cancer: a comparison of long-course versus short-course androgen deprivation therapy in the RADICALS-HD randomised trial

Background Previous evidence supports androgen deprivation therapy (ADT) with primary radiotherapy as initial treatment for intermediate-risk and high-risk localised prostate cancer. However, the use and optimal duration of ADT with postoperative radiotherapy after radical prostatectomy remains uncertain. Methods RADICALS-HD was a randomised controlled trial of ADT duration within the RADICALS protocol. Here, we report on the comparison of short-course versus long-course ADT. Key eligibility criteria were indication for radiotherapy after previous radical prostatectomy for prostate cancer, prostate-specific antigen less than 5 ng/mL, absence of metastatic disease, and written consent. Participants were randomly assigned (1:1) to add 6 months of ADT (short-course ADT) or 24 months of ADT (long-course ADT) to radiotherapy, using subcutaneous gonadotrophin-releasing hormone analogue (monthly in the short-course ADT group and 3-monthly in the long-course ADT group), daily oral bicalutamide monotherapy 150 mg, or monthly subcutaneous degarelix. Randomisation was done centrally through minimisation with a random element, stratified by Gleason score, positive margins, radiotherapy timing, planned radiotherapy schedule, and planned type of ADT, in a computerised system. The allocated treatment was not masked. The primary outcome measure was metastasis-free survival, defined as metastasis arising from prostate cancer or death from any cause. The comparison had more than 80% power with two-sided α of 5% to detect an absolute increase in 10-year metastasis-free survival from 75% to 81% (hazard ratio [HR] 0·72). Standard time-to-event analyses were used. Analyses followed intention-to-treat principle. The trial is registered with the ISRCTN registry, ISRCTN40814031, and ClinicalTrials.gov , NCT00541047 . Findings Between Jan 30, 2008, and July 7, 2015, 1523 patients (median age 65 years, IQR 60–69) were randomly assigned to receive short-course ADT (n=761) or long-course ADT (n=762) in addition to postoperative radiotherapy at 138 centres in Canada, Denmark, Ireland, and the UK. With a median follow-up of 8·9 years (7·0–10·0), 313 metastasis-free survival events were reported overall (174 in the short-course ADT group and 139 in the long-course ADT group; HR 0·773 [95% CI 0·612–0·975]; p=0·029). 10-year metastasis-free survival was 71·9% (95% CI 67·6–75·7) in the short-course ADT group and 78·1% (74·2–81·5) in the long-course ADT group. Toxicity of grade 3 or higher was reported for 105 (14%) of 753 participants in the short-course ADT group and 142 (19%) of 757 participants in the long-course ADT group (p=0·025), with no treatment-related deaths. Interpretation Compared with adding 6 months of ADT, adding 24 months of ADT improved metastasis-free survival in people receiving postoperative radiotherapy. For individuals who can accept the additional duration of adverse effects, long-course ADT should be offered with postoperative radiotherapy. Funding Cancer Research UK, UK Research and Innovation (formerly Medical Research Council), and Canadian Cancer Society