Bootstrap-Optimised Regularised Image Reconstruction for Emission Tomography

In emission tomography, iterative image reconstruction from noisy measured data usually results in noisy images, and so regularisation is often used to compensate for noise. However, in practice, an appropriate, automatic and precise specification of the strength of regularisation for image reconstruction from a given noisy measured dataset remains unresolved. Existing approaches are either empirical approximations with no guarantee of generalisation, or else are computationally intensive cross-validation methods requiring full reconstructions for a limited set of preselected regularisation strengths. In contrast, we propose a novel methodology embedded within iterative image reconstruction, using one or more bootstrapped replicates of the measured data for precise optimisation of the regularisation. The approach uses a conventional unregularised iterative update of a current image estimate from the noisy measured data, and then also uses the bootstrap replicate to obtain a bootstrap update of the current image estimate. The method then seeks the regularisation hyperparameters which, when applied to the bootstrap update of the image, lead to a best fit of the regularised bootstrap update to the conventional measured data update. This corresponds to estimating the degree of regularisation needed in order to map the noisy update to a model of the mean of an ensemble of noisy updates. For a given regularised objective function (e.g. penalised likelihood), no hyperparameter selection or tuning is required. The method is demonstrated for positron emission tomography (PET) data at different noise levels, and delivers near-optimal reconstructions (in terms of reconstruction error) without any knowledge of the ground truth, nor any form of training data

Assessment of the impact of modeling axial compression on PET image reconstruction

Purpose: To comprehensively evaluate both the acceleration and image-quality impacts of axial compression and its degree of modeling in fully 3D PET image reconstruction. Method: Despite being used since the very dawn of 3D PET reconstruction, there are still no extensive studies on the impact of axial compression and its degree of modeling during reconstruction on the end-point reconstructed image quality. In this work, an evaluation of the impact of axial compression on the image quality is performed by extensively simulating data with span values from 1 to 121. In addition, two methods for modeling the axial compression in the reconstruction were evaluated. The first method models the axial compression in the system matrix, while the second method uses an unmatched projector/backprojector, where the axial compression is modeled only in the forward projector. The different system matrices were analyzed by computing their singular values and the point response functions for small subregions of the FOV. The two methods were evaluated with simulated and real data for the Biograph mMR scanner. Results: For the simulated data, the axial compression with span values lower than 7 did not show a decrease in the contrast of the reconstructed images. For span 11, the standard sinogram size of the mMR scanner, losses of contrast in the range of 5-10 percentage points were observed when measured for a hot lesion. For higher span values, the spatial resolution was degraded considerably. However, impressively, for all span values of 21 and lower, modeling the axial compression in the system matrix compensated for the spatial resolution degradation and obtained similar contrast values as the span 1 reconstructions. Such approaches have the same processing times as span 1 reconstructions, but they permit significant reduction in storage requirements for the fully 3D sinograms. For higher span values, the system has a large condition number and it is therefore difficult to recover accurately the higher frequencies. Modeling the axial compression also achieved a lower coefficient of variation but with an increase of intervoxel correlations. The unmatched projector/backprojector achieved similar contrast values to the matched version at considerably lower reconstruction times, but at the cost of noisier images. For a line source scan, the reconstructions with modeling of the axial compression achieved similar resolution to the span 1 reconstructions. Conclusions: Axial compression applied to PET sinograms was found to have a negligible impact for span values lower than 7. For span values up to 21, the spatial resolution degradation due to the axial compression can be almost completely compensated for by modeling this effect in the system matrix at the expense of considerably larger processing times and higher intervoxel correlations, while retaining the storage benefit of compressed data. For even higher span values, the resolution loss cannot be completely compensated possibly due to an effective null space in the system. The use of an unmatched projector/backprojector proved to be a practical solution to compensate for the spatial resolution degradation at a reasonable computational cost but can lead to noisier images.</p

Personalized MR-Informed Diffusion Models for 3D PET Image Reconstruction

Recent work has shown improved lesion detectability and flexibility to reconstruction hyperparameters (e.g. scanner geometry or dose level) when PET images are reconstructed by leveraging pre-trained diffusion models. Such methods train a diffusion model (without sinogram data) on high-quality, but still noisy, PET images. In this work, we propose a simple method for generating subject-specific PET images from a dataset of multi-subject PET-MR scans, synthesizing 'pseudo-PET"images by transforming between different patients' anatomy using image registration. The images we synthesize retain information from the subject's MR scan, leading to higher resolution and the retention of anatomical features compared to the original set of PET images. With simulated and real datasets, we show that pre-training a personalized diffusion model with subject-specific 'pseudo-PET"images improves reconstruction accuracy with low-count data. In particular, the method shows promise in combining information from a guidance MR scan without overly imposing anatomical features, demonstrating an improved trade-off between reconstructing PET-unique image features versus features present in both PET and MR. We believe this approach for generating and utilizing synthetic data has further applications to medical imaging tasks, particularly because patient-specific PET images can be generated without resorting to generative deep learning or large training datasets.</p

Multitracer Guided PET Image Reconstruction

Multitracer positron emission tomography (PET) has the potential to enhance PET imaging by providing complementary information from different physiological processes. However, one or more of the images may present high levels of noise. Guided image reconstruction methods transfer information from a guide image into the PET image reconstruction to encourage edge-preserving noise reduction. In this paper, we aim to reduce noise in poorer quality PET datasets via guidance from higher quality ones by using a weighted quadratic penalty approach. In particular, we applied this methodology to [ 18 F]fluorodeoxyglucose (FDG) and [ 11 C]methionine imaging of gliomas. 3-D simulation studies showed that guiding the reconstruction of methionine datasets using pre-existing FDG images reduced reconstruction errors across the whole-brain (−8%) and within a tumor (−36%) compared to maximum likelihood expectation-maximization (MLEM). Furthermore, guided reconstruction outperformed a comparable nonlocal means filter, indicating that regularizing during reconstruction is preferable to post-reconstruction approaches. Hyperparameters selected from the 3-D simulation study were applied to real data, where it was observed that the proposed FDG-guided methionine reconstruction allows for better edge preservation and noise reduction than standard MLEM. Overall, the results in this paper demonstrate that transferring information between datasets in multitracer PET studies improves image quality and quantification performance

The integrin αvβ6 drives pancreatic cancer through diverse mechanisms and represents an effective target for therapy

Pancreatic ductal adenocarcinoma (PDAC) has a five‐year survival rate of &lt;4% and desperately needs novel effective therapeutics. Integrin αvβ6 has been linked with poor prognosis in cancer but its potential as a target in PDAC remains unclear. We report that transcriptional expression analysis revealed high levels of β6 mRNA correlated strongly with significantly poorer survival (n=491 cases, p= 3.17x10‐8). In two separate cohorts we showed that over 80% of PDAC expressed αvβ6 protein and that paired metastases retained αvβ6 expression. In vitro, integrin αvβ6 promoted PDAC cell growth, survival, migration and invasion. Treatment of both αvβ6‐positive human PDAC xenografts and transgenic mice bearing αvβ6‐positive PDAC with the αvβ6 blocking antibody 264RAD, combined with gemcitabine, significantly reduced tumour growth (p&lt;0.0001) and increased survival (Log‐rank test, p&lt;0.05). Antibody therapy was associated with suppression of both tumour cell activity (suppression of pErk growth signals, increased apoptosis seen as activated Caspase 3) and suppression of the pro‐tumourigenic microenvironment (suppression of TGFβ signalling, fewer αSMA‐positive myofibroblasts, decreased blood vessel density). These data show that αvβ6 promotes PDAC growth through both tumour cell and tumour microenvironment mechanisms and represents a valuable target for PDAC therapy

MRI slice stacking using manifold alignment and wave kernel signatures

MRI slice stacking involves retrospective combination of 2D MRI images to form pseudo 3D volumes. It is useful because physical constraints limit the temporal/spatial resolutions with which dynamic 3D MRI volumes can be acquired and so stacking fast highresolution 2D images can yield pseudo 3D volumes with high inplane spatial and temporal resolution. However, it is important that the stacked 2D images were acquired at consistent motion states. Assessing motion state consistency between slices representing different anatomy is challenging as the image contents are not easily comparable. Manifold alignment (MA) is a technique which provides a solution to this problem by embedding the 2D images for all slices into one globally consistent low-dimensional space. One successful approach to MA involves forming graphs from each slice dataset and using graph descriptors to find correspondences between datasets. Here we propose a new graph descriptor for the slice stacking problem, inspired by work in the computer vision literature, and evaluate it with two experiments. First, using a highly realistic synthetic MRI dataset in which reconstructed volumes can be compared to a ground truth, we find our method significantly outperforms the state of the art. Second, we use in vivo MRI data and show that the volumes reconstructed by our method have a higher degree of self-consistency.</p

The significance of prey avoidance behaviour for the maintenance of a predator colour polymorphism

The existence of conspicuous colour polymorphisms in animals provides an ideal opportunity to examine the mechanisms which determine genetic and phenotypic variation in populations. It is well known that directional and negative frequency-dependent selection by predators can influence the persistence of colour polymorphisms in their prey, but much less attention has been paid to the idea that prey behaviour could generate selection on predator colour morphs. In this study, we examine the role that avoidance behaviour by honeybees might play in selection on a colour-polymorphic sit-and-wait predator, the crab spider Synema globosum. In two field experiments, we offered flowers harbouring spiders of different colour morphs to foraging honeybees. In the first, we tested for a pre-existing propensity in honeybees to avoid one spider morph over another, and whether this behaviour is influenced by the flower species on which spiders hunt. In the second, we tested the ability of bees to learn to avoid spider morphs associated with a previous simulated attack. Our results suggest that honeybees do not impose strong directional selection on spider morphs in our study population, and that avoidance behaviour is not influenced by flower species. However, we find evidence that honeybees learn to avoid spiders of a colour morph that has previously been associated with a simulated attack. These findings are the first empirical evidence for a mechanism by which prey behaviour might generate negative frequency-dependent selection on predator colour morphs, and hence potentially influence the long-term persistence of genetic and phenotypic diversity in predator populations

Deep learned triple-tracer multiplexed PET myocardial image separation

IntroductionIn multiplexed positron emission tomography (mPET) imaging, physiological and pathological information from different radiotracers can be observed simultaneously in a single dynamic PET scan. The separation of mPET signals within a single PET scan is challenging due to the fact that the PET scanner measures the sum of the PET signals of all the tracers. The conventional multi-tracer compartment modeling (MTCM) method requires staggered injections and assumes that the arterial input functions (AIFs) of each tracer are known.MethodsIn this work, we propose a deep learning-based method to separate triple-tracer PET images without explicitly knowing the AIFs. A dynamic triple-tracer noisy MLEM reconstruction was used as the network input, and dynamic single-tracer noisy MLEM reconstructions were used as training labels.ResultsA simulation study was performed to evaluate the performance of the proposed framework on triple-tracer ([ 18F]FDG+ 82Rb+[ 94mTc]sestamibi) PET myocardial imaging. The results show that the proposed methodology substantially reduced the noise level compared to the results obtained from single-tracer imaging. Additionally, it achieved lower bias and standard deviation in the separated single-tracer images compared to the MTCM-based method at both the voxel and region of interest (ROI) levels.DiscussionAs compared to MTCM separation, the proposed method uses spatiotemporal information for separation, which improves the separation performance at both the voxel and ROI levels. The simulation study also demonstrates the feasibility and potential of the proposed DL-based method for the application to pre-clinical and clinical studies