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Several genetic pathogenic variants increase the risk of Parkinson’s disease (PD) with pathogenic variants in the leucine-rich repeat kinase 2 (LRRK2) gene being among the most common. A joint pattern analysis based on multi-set canonical correlation analysis (MCCA) was utilized to extract PD and LRRK2 pathogenic variant-specific spatial patterns in relation to healthy controls (HCs) from multi-tracer Positron Emission Tomography (PET) data. Spatial patterns were extracted for individual subject cohorts, as well as for pooled subject cohorts, to explore whether complementary spatial patterns of dopaminergic denervation are different in the asymptomatic and symptomatic stages of PD. The MCCA results are also compared to the traditional univariate analysis, which serves as a reference.We identified PD-induced spatial distribution alterations common to DAT and VMAT2 in both asymptomatic LRRK2 pathogenic variant carriers and PD subjects. The inclusion of HCs in the analysis demonstrated that the dominant common PD-induced pattern is related to an overall dopaminergic terminal density denervation, followed by asymmetry and rostro-caudal gradient with deficits in the less affected side still being the best marker of disease progression.The analysis was able to capture a trend towards PD-related patterns in the LRRK2 pathogenic variant carrier cohort with increasing age in line with the known increased risk of this patient cohort to develop PD as they age. The advantage of this method thus resides in its ability to identify not only regional differences in tracer binding between groups, but also common disease-related alterations in the spatial distribution patterns of tracer binding, thus potentially capturing more complex aspects of disease induced alterations.

Abstract Background Positron emission tomography (PET) with prostate specific membrane antigen (PSMA) have shown superior performance in detecting metastatic prostate cancers. Relative to [18F]fluorodeoxyglucose ([18F]FDG) PET images, PSMA PET images tend to visualize significantly higher-contrast focal lesions. We aim to evaluate segmentation and reconstruction algorithms in this emerging context. Specifically, Bayesian or maximum a posteriori (MAP) image reconstruction, compared to standard ordered subsets expectation maximization (OSEM) reconstruction, has received significant interest for its potential to reach convergence with minimal noise amplifications. However, few phantom studies have evaluated the quantitative accuracy of such reconstructions for high contrast, small lesions (sub-10 mm) that are typically observed in PSMA images. In this study, we cast 3 mm–16-mm spheres using epoxy resin infused with a long half-life positron emitter (sodium-22; 22Na) to simulate prostate cancer metastasis. The anthropomorphic Probe-IQ phantom, which features a liver, bladder, lungs, and ureters, was used to model relevant anatomy. Dynamic PET acquisitions were acquired and images were reconstructed with OSEM (varying subsets and iterations) and BSREM (varying β parameters), and the effects on lesion quantitation were evaluated. Results The 22Na lesions were scanned against an aqueous solution containing fluorine-18 (18F) as the background. Regions-of-interest were drawn with MIM Software using 40% fixed threshold (40% FT) and a gradient segmentation algorithm (MIM’s PET Edge+). Recovery coefficients (RCs) (max, mean, peak, and newly defined “apex”), metabolic tumour volume (MTV), and total tumour uptake (TTU) were calculated for each sphere. SUVpeak and SUVapex had the most consistent RCs for different lesion-to-background ratios and reconstruction parameters. The gradient-based segmentation algorithm was more accurate than 40% FT for determining MTV and TTU, particularly for lesions $$\le$$ ≤ 6 mm in diameter (R 2 = 0.979–0.996 vs. R 2 = 0.115–0.527, respectively). Conclusion An anthropomorphic phantom was used to evaluate quantitation for PSMA PET imaging of metastatic prostate cancer lesions. BSREM with β = 200–400 and OSEM with 2–5 iterations resulted in the most accurate and robust measurements of SUVmean, MTV, and TTU for imaging conditions in 18F-PSMA PET/CT images. SUVapex, a hybrid metric of SUVmax and SUVpeak, was proposed for robust, accurate, and segmentation-free quantitation of lesions for PSMA PET.

Simple Summary: Radiomics analysis of positron emission tomography (PET) images can provide objective measurements of tumor heterogeneity and spatial patterns. However, the relatively low resolution, high noise, and limited longitudinal data availability make it difficult to systematically investigate the relationship between the microscopic tumor phenotypes and corresponding PET radiomics signatures. To address this challenge, we use a multiscale, stochastic mathematical model of tumor growth to generate cross-sections of tumors in vascularized normal tissue on a microscopic level. By varying the biological parameters of the model, distinct tumor phenotypes are obtained, and their corresponding PET images are generated. The simulated data are then used to find the optimal combination of PET radiomics features that can reliably distinguish visually similar tumor phenotypes. In addition, we study the longitudinal changes in the discriminative power of radiomics features with tumor growth from a single cell to approximately one million cells. Cancers can manifest large variations in tumor phenotypes due to genetic and microenvironmental factors, which has motivated the development of quantitative radiomics-based image analysis with the aim to robustly classify tumor phenotypes in vivo. Positron emission tomography (PET) imaging can be particularly helpful in elucidating the metabolic profiles of tumors. However, the relatively low resolution, high noise, and limited PET data availability make it difficult to study the relationship between the microenvironment properties and metabolic tumor phenotype as seen on the images. Most of previously proposed digital PET phantoms of tumors are static, have an over-simplified morphology, and lack the link to cellular biology that ultimately governs the tumor evolution. In this work, we propose a novel method to investigate the relationship between microscopic tumor parameters and PET image characteristics based on the computational simulation of tumor growth. We use a hybrid, multiscale, stochastic mathematical model of cellular metabolism and proliferation to generate simulated cross-sections of tumors in vascularized normal tissue on a microscopic level. The generated longitudinal tumor growth sequences are converted to PET images with realistic resolution and noise. By changing the biological parameters of the model, such as the blood vessel density and conditions for necrosis, distinct tumor phenotypes can be obtained. The simulated cellular maps were compared to real histology slides of SiHa and WiDr xenografts imaged with Hoechst 33342 and pimonidazole. As an example application of the proposed method, we simulated six tumor phenotypes that contain various amounts of hypoxic and necrotic regions induced by a lack of oxygen and glucose, including phenotypes that are distinct on the microscopic level but visually similar in PET images. We computed 22 standardized Haralick texture features for each phenotype, and identified the features that could best discriminate the phenotypes with varying image noise levels. We demonstrated that "cluster shade" and "difference entropy" are the most effective and noise-resilient features for microscopic phenotype discrimination. Longitudinal analysis of the simulated tumor growth showed that radiomics analysis can be beneficial even in small lesions with a diameter of 3.5–4 resolution units, corresponding to 8.7–10.0 mm in modern PET scanners. Certain radiomics features were shown to change non-monotonically with tumor growth, which has implications for feature selection for tracking disease progression and therapy response. [ABSTRACT FROM AUTHOR]

Alterations in different aspects of dopamine processing may exhibit different progressive behaviours throughout the course of Parkinson’s disease. We used a novel data-driven multivariate approach to quantify and compare spatiotemporal patterns related to different aspects of dopamine processing from cross-sectional Parkinson’s subjects obtained with: 1) 69 [11C]±dihydrotetrabenazine (DTBZ) scans, most closely related to dopaminergic denervation; 2) 73 [11C]d-threo-methylphenidate (MP) scans, marker of dopamine transporter density; 3) 50 6-[18F]fluoro-l-DOPA (FD) scans, marker of dopamine synthesis and storage. The anterior-posterior gradient in the putamen was identified as the most salient feature associated with disease progression, however the temporal progression of the spatial gradient was different for the three tracers. The expression of the anterior-posterior gradient was the highest for FD at disease onset compared to that of DTBZ and MP (P = 0.018 and P = 0.047 respectively), but decreased faster (P = 0.006) compared to that of DTBZ. The gradient expression for MP was initially similar but decreased faster (P = 0.015) compared to that for DTBZ. These results reflected unique temporal behaviours of regulatory mechanisms related to dopamine synthesis (FD) and reuptake (MP). While the relative early disease upregulation of dopamine synthesis in the anterior putamen prevalent likely extends to approximately 10 years after symptom onset, the presumed downregulation of dopamine transporter density may play a compensatory role in the prodromal/earliest disease stages only.

The dispersion composition of polymer suspensions and molecular weights of polymers obtained by suspension polymerization of MMA in the presence of polymer surfactants – methylmethacrylate and methacrylic acid copolymers – were studied. It is shown that a highly dispersed fraction of particles with diameters of 0.02–2.0 µm and a fraction of particles with large diameters (up to 1000 µm) are always present in the polymer suspension. After fractionation of polymer suspensions 3 fractions of particles with different diameters were obtained. For each particle fraction the molecular masses of polymers were determined by viscometry. A significant difference in the values of the molecular masses of polymers obtained as particles of small and large diameters – 105 and 106 Da, respectively – is shown. The presence of a highly dispersed fraction of particles in which a polymer of high molecular weight is formed has a noticeable effect on the average molecular weight of the polymer. In particles of small diameter polymerization takes place according to a mechanism close to the emulsion, due to the fact that the volume of such particles contains a small amount of radicals. The high rate of polymerization leads to the formation of a polymer of high molecular weight, the appearance of a gel effect and a decrease in the termination constant. In most particles, polymerization proceeds by a mechanism close to the solution polymerization, and polymers of low molecular weight are formed. This makes it possible to synthesize polymers of a given molecular weight in drops of certain dispersity.

Most neurodegenerative disorders are characterized by progressive loss of neurons throughout the course of disease in the form of specific spatio-temporal patterns. To capture and quantify these coherent patterns across both space and time, traditionally one would either fit a pre-defined model with spatial and temporal parameters or apply analysis in the spatial and temporal domains separately. In this work, we introduce and validate the use of dynamic mode decomposition (DMD), a data-driven multivariate approach, to extract coupled spatio-temporal patterns simultaneously.We apply the method to examine progressive dopaminergic degeneration in 41 patients with Parkinson's disease (PD) using [11C](±)dihydrotetrabenazine (DTBZ) Positron Emission Tomography (PET). DMD decomposed the progressive dopaminergic changes in the putamen into two orthogonal temporal progression curves associated with distinct spatial patterns: 1) an anterior-posterior gradient, the expression of which decreased gradually with disease progression with a higher initial expression in the less affected side; 2) a dorsal-ventral gradient in the less affected side, which was present in early disease stage only. In the caudate, we found a head-tail gradient analogous to the anterior-posterior gradient seen in the putamen; as in the putamen, the expression of this gradient decreased gradually with disease progression with higher expression in the less affected side.Our results with DTBZ PET data show the applicability and relevance of the proposed method for extracting spatio-temporal patterns of neurotransmitter changes due to neurodegeneration. The method is able to decompose known PD-induced dopaminergic denervation into orthogonal (and thus loosely independent) temporal curves, which may be able to reflect and separate either different mechanisms underlying disease progression and disease initiation, or differential involvement of striatal sub-regions at different disease stages, in a completely data driven way. It is expected that this method can be easily extended to other PET tracers and neurodegenerative disorders and may help to elucidate disease mechanisms in more details compared to traditional approaches. Keywords: Novel pattern analysis, Spatio-temporal patterns, Parkinson's disease, Dopaminergic system

We used positron emission tomography imaging with [11C]-3-amino-4-(2-dimethylaminomethylphenylsulfanyl)- benzonitrile (DASB) and principal component analysis to investigate whether a specific Parkinson's disease (PD)-related spatial covariance pattern could be identified for the serotonergic system. We also explored if non-manifesting leucine-rich repeat kinase 2 (LRRK2) mutation carriers, with normal striatal dopaminergic innervation as measured with [11C]-dihydrotetrabenazine (DTBZ), exhibit a distinct spatial covariance pattern compared to healthy controls and subjects with manifest PD. 15 subjects with sporadic PD, eight subjects with LRRK2 mutation-associated PD, nine LRRK2 non-manifesting mutation carriers, and nine healthy controls participated in the study. The analysis was applied to the DASB non-displaceable binding potential values evaluated in 42 pre-defined regions of interest. PD was found to be associated with a specific spatial covariance pattern, comprising relatively decreased DASB binding in the caudate, putamen and substantia nigra and relatively preserved binding in the hypothalamus and hippocampus; the expression of this pattern in PD subjects was significantly higher than in healthy controls (P

Background: Prostate‐specific membrane antigen (PSMA) PET imaging represents a valuable source of information reflecting disease stage, response rate, and treatment optimization options, particularly with PSMA radioligand therapy. Quantification of radiopharmaceutical uptake in healthy organs from PSMA images has the potential to minimize toxicity by extrapolation of the radiation dose delivery towards personalization of therapy. However, segmentation and quantification of uptake in organs requires labor‐intensive organ delineations that are often not feasible in the clinic nor scalable for large clinical trials. Purpose: In this work we develop and test the PSMA Healthy organ segmentation network (PSMA‐Hornet), a fully‐automated deep neural net for simultaneous segmentation of 14 healthy organs representing the normal biodistribution of [18F]DCFPyL on PET/CT images. We also propose a modified U‐net architecture, a self‐supervised pre‐training method for PET/CT images, a multi‐target Dice loss, and multi‐target batch balancing to effectively train PSMA‐Hornet and similar networks. Methods: The study used manually‐segmented [18F]DCFPyL PET/CT images from 100 subjects, and 526 similar images without segmentations. The unsegmented images were used for self‐supervised model pretraining. For supervised training, Monte‐Carlo cross‐validation was used to evaluate the network performance, with 85 subjects in each trial reserved for model training, 5 for validation, and 10 for testing. Image segmentation and quantification metrics were evaluated on the test folds with respect to manual segmentations by a nuclear medicine physician, and compared to inter‐rater agreement. The model's segmentation performance was also evaluated on a separate set of 19 images with high tumor load. Results: With our best model, the lowest mean Dice coefficient on the test set was 0.826 for the sublingual gland, and the highest was 0.964 for liver. The highest mean error in tracer uptake quantification was 13.9% in the sublingual gland. Self‐supervised pretraining improved training convergence, train‐to‐test generalization, and segmentation quality. In addition, we found that a multi‐target network produced significantly higher segmentation accuracy than single‐organ networks. Conclusions: The developed network can be used to automatically obtain high‐quality organ segmentations for PSMA image analysis tasks. It can be used to reproducibly extract imaging data, and holds promise for clinical applications such as personalized radiation dosimetry and improved radioligand therapy. [ABSTRACT FROM AUTHOR]

Most neurodegenerative diseases are known to affect several aspects of brain function, including neurotransmitter systems, metabolic and functional connectivity. Diseases are generally characterized by common clinical characteristics across subjects, but there are also significant inter-subject variations. It is thus reasonable to expect that in terms of brain function, such clinical behaviors will be related to a general overall multi-system pattern of disease-induced alterations and additional brain system-specific abnormalities; these additional abnormalities would be indicative of a possible unique system response to disease or subject-specific propensity to a specific clinical progression.Based on the above considerations we introduce and validate the use of a joint pattern analysis approach, canonical correlation analysis and orthogonal signal correction, to analyze multi-tracer PET data to identify common (reflecting functional similarities) and unique (reflecting functional differences) information provided by each tracer/target. We apply the method to [11C]-DTBZ (VMAT2 marker) and [11C]-MP (DAT marker) data from 15 early Parkinson's disease (PD) subjects; the behavior of these two tracers/targets is well characterized providing robust reference information for the method's outcome. Highly significant common subject profiles were identified that decomposed the characteristic dopaminergic changes into three distinct orthogonal spatial patterns: 1) disease-induced asymmetry between the less and more affected dorsal striatum; 2) disease-induced gradient with caudate and ventral striatum being relatively spared compared to putamen; 3) progressive loss in the less affected striatum, which correlated significantly with disease duration (p

Spatial patterns of radiotracer binding in positron emission tomography (PET) images may convey information related to the disease topology. However, this information is not captured by the standard PET image analysis that quantifies the mean radiotracer uptake within a region of interest (ROI). On the other hand, spatial analyses that use more advanced radiomic features may be difficult to interpret. Here we propose an alternative data-driven, voxel-based approach to spatial pattern analysis in brain PET, which can be easily interpreted. We apply principal component analysis (PCA) to identify voxel covariance patterns, and optimally combine several patterns using the least absolute shrinkage and selection operator (LASSO). The resulting models predict clinical disease metrics from raw voxel values, allowing for inclusion of clinical covariates. The analysis is performed on high-resolution PET images from healthy controls and subjects affected by Parkinson's disease (PD), acquired with a pre-synaptic and a post-synaptic dopaminergic PET tracer. We demonstrate that PCA identifies robust and tracer-specific binding patterns in sub-cortical brain structures; the patterns evolve as a function of disease progression. Principal component LASSO (PC-LASSO) models of clinical disease metrics achieve higher predictive accuracy compared to the mean tracer binding ratio (BR) alone: the cross-validated test mean squared error of adjusted disease duration (motor impairment score) was 16.3 ± 0.17 years2 (9.7 ± 0.15) with mean BR, versus 14.4 ± 0.18 years2 (8.9 ± 0.16) with PC-LASSO. We interpret the best-performing PC-LASSO models in the spatial sense and discuss them with reference to the PD pathology and somatotopic organization of the striatum. PC-LASSO is thus shown to be a useful method to analyze clinically-relevant tracer binding patterns, and to construct interpretable, imaging-based predictive models of clinical metrics.

Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected Parkinsonian syndromes. We performed a cross-sectional study to investigate whether assessment of texture in DAT SPECT radiotracer uptake enables enhanced correlations with severity of motor and cognitive symptoms in Parkinson's disease (PD), with the long-term goal of enabling clinical utility of DAT SPECT imaging, beyond standard diagnostic tasks, to tracking of progression in PD. Quantitative analysis in routine DAT SPECT imaging, if performed at all, has been restricted to assessment of mean regional uptake. We applied a framework wherein textural features were extracted from the images. Notably, the framework did not require registration to a common template, and worked in the subject-native space. Image analysis included registration of SPECT images onto corresponding MRI images, automatic region-of-interest (ROI) extraction on the MRI images, followed by computation of Haralick texture features. We analyzed 141 subjects from the Parkinson's Progressive Marker Initiative (PPMI) database, including 85 PD and 56 healthy controls (HC) (baseline scans with accompanying 3 T MRI images). We performed univariate and multivariate regression analyses between the quantitative metrics and different clinical measures, namely (i) the UPDRS (part III - motor) score, disease duration as measured from (ii) time of diagnosis (DD-diag.) and (iii) time of appearance of symptoms (DD-sympt.), as well as (iv) the Montreal Cognitive Assessment (MoCA) score. For conventional mean uptake analysis in the putamen, we showed significant correlations with clinical measures only when both HC and PD were included (Pearson correlation r = −0.74, p-value