Observer studies of image quality of denoising reduced-count cardiac single photon emission computed tomography myocardial perfusion imaging by three-dimensional Gaussian post-reconstruction filtering and deep learning.

BACKGROUND: The aim of this research was to asses perfusion-defect detection-accuracy by human observers as a function of reduced-counts for 3D Gaussian post-reconstruction filtering vs deep learning (DL) denoising to determine if there was improved performance with DL. METHODS: SPECT projection data of 156 normally interpreted patients were used for these studies. Half were altered to include hybrid perfusion defects with defect presence and location known. Ordered-subset expectation-maximization (OSEM) reconstruction was employed with the optional correction of attenuation (AC) and scatter (SC) in addition to distance-dependent resolution (RC). Count levels varied from full-counts (100%) to 6.25% of full-counts. The denoising strategies were previously optimized for defect detection using total perfusion deficit (TPD). Four medical physicist (PhD) and six physician (MD) observers rated the slices using a graphical user interface. Observer ratings were analyzed using the LABMRMC multi-reader, multi-case receiver-operating-characteristic (ROC) software to calculate and compare statistically the area-under-the-ROC-curves (AUCs). RESULTS: For the same count-level no statistically significant increase in AUCs for DL over Gaussian denoising was determined when counts were reduced to either the 25% or 12.5% of full-counts. The average AUC for full-count OSEM with solely RC and Gaussian filtering was lower than for the strategies with AC and SC, except for a reduction to 6.25% of full-counts, thus verifying the utility of employing AC and SC with RC. CONCLUSION: We did not find any indication that at the dose levels investigated and with the DL network employed, that DL denoising was superior in AUC to optimized 3D post-reconstruction Gaussian filtering.

Measurement of neurodegeneration using a multivariate early frame amyloid PET classifier.

Introduction: Amyloid measurement provides important confirmation of pathology for Alzheimer's disease (AD) clinical trials. However, many amyloid positive (Am+) early-stage subjects do not worsen clinically during a clinical trial, and a neurodegenerative measure predictive of decline could provide critical information. Studies have shown correspondence between perfusion measured by early amyloid frames post-tracer injection and fluorodeoxyglucose (FDG) positron emission tomography (PET), but with limitations in sensitivity. Multivariate machine learning approaches may offer a more sensitive means for detection of disease related changes as we have demonstrated with FDG. Methods: Using summed dynamic florbetapir image frames acquired during the first 6 minutes post-injection for 107 Alzheimer's Disease Neuroimaging Initiative subjects, we applied optimized machine learning to develop and test image classifiers aimed at measuring AD progression. Early frame amyloid (EFA) classification was compared to that of an independently developed FDG PET AD progression classifier by scoring the FDG scans of the same subjects at the same time point. Score distributions and correlation with clinical endpoints were compared to those obtained from FDG. Region of interest measures were compared between EFA and FDG to further understand discrimination performance. Results: The EFA classifier produced a primary pattern similar to that of the FDG classifier whose expression correlated highly with the FDG pattern (R-squared 0.71), discriminated cognitively normal (NL) amyloid negative (Am-) subjects from all Am+ groups, and that correlated in Am+ subjects with Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Assessment Scale-13-item Cognitive subscale (R = 0.59, 0.63, 0.73) and with subsequent 24-month changes in these measures (R = 0.67, 0.73, 0.50). Discussion: Our results support the ability to use EFA with a multivariate machine learning-derived classifier to obtain a sensitive measure of AD-related loss in neuronal function that correlates with FDG PET in preclinical and early prodromal stages as well as in late mild cognitive impairment and dementia. Highlights: The summed initial post-injection minutes of florbetapir positron emission tomography  correlate with fluorodeoxyglucose.A machine learning classifier enabled sensitive detection of early prodromal Alzheimer's disease.Early frame amyloid (EFA) classifier scores correlate with subsequent change in Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Assessment Scale-13-item Cognitive subscale.EFA classifier effect sizes and clinical prediction outperformed region of interest standardized uptake value ratio.EFA classification may aid in stratifying patients to assess treatment effect.

C-Nitrosothioformamide: A Donor Template for Dual Release of HNO and H2 S.

C-Nitrosothioformamide was demonstrated to be a donor template for dual release of HNO and COS triggered by a retro-Diels-Alder reaction. COS is an H2 S precursor in the presence of carbonic anhydrase. This process produces HNO and H2 S in a slow but steady manner. As such, the direct reaction between HNO and H2 S under this situation appears to be minor. This may provide a useful tool for studying the synergistic effects of HNO and H2 S.

The role of the sonographer - An investigation into the scope of practice for the sonographer internationally.

INTRODUCTION: There is global variance in the role of a practicing sonographer. Literature examining global sonographic roles and scope of practice is limited, despite the international applicability of ultrasound imaging. This study aimed to examine the common and divergent features of a practicing sonographer internationally, and their impact upon the development of a global standard of practice. METHODS: An ethically approved mixed-methods online survey was conducted. The purposive sample included all 75 current elected council members of the ISRRT [International Society of Radiographers and Radiological Technologists], an international professional organisation. RESULTS: Thirty-six individuals from at least 32 different countries responded, reflecting the sonography profession in all four ISRRT regions. The results suggest that sonographer education requirements differ widely, from on the job training (16%, 6/36) to undergraduate or postgraduate schooling (44%, 16/36). Registration and accreditation bodies were present in the jurisdiction of 41% (14/34) and 35% (12/33) of respondents respectively, though many were voluntary, physician-focused or non-specific to sonographers. Five of 11 (45%) respondents suggested that the sonographer-radiologist relationship is individual-dependent, and not primarily positive or negative. Ten of 28 (36%) suggested that other professionals do not know the role of the sonographer. CONCLUSION: The majority of ISRRT council member respondents believe that an international scope of practice could benefit and be implemented in their jurisdiction (26/28, 93% and 22/33, 67%). The key advantages noted were standardisation of education and improved professional mobility. However, lack of sonographer education and radiologist acceptance are important potential barriers. IMPLICATIONS FOR PRACTICE: An international scope of practice could be beneficial and implementable in most ISRRT jurisdictions. Professional mobility and education standardization are the primary advantages. Insufficient sonographer education and physician acceptance are the key potential obstacles.

Improving detection accuracy of perfusion defect in standard dose SPECT-myocardial perfusion imaging by deep-learning denoising.

BACKGROUND: We previously developed a deep-learning (DL) network for image denoising in SPECT-myocardial perfusion imaging (MPI). Here we investigate whether this DL network can be utilized for improving detection of perfusion defects in standard-dose clinical acquisitions. METHODS: To quantify perfusion-defect detection accuracy, we conducted a receiver-operating characteristic (ROC) analysis on reconstructed images with and without processing by the DL network using a set of clinical SPECT-MPI data from 190 subjects. For perfusion-defect detection hybrid studies were used as ground truth, which were created from clinically normal studies with simulated realistic lesions inserted. We considered ordered-subset expectation-maximization (OSEM) reconstruction with corrections for attenuation, resolution, and scatter and with 3D Gaussian post-filtering. Total perfusion deficit (TPD) scores, computed by Quantitative Perfusion SPECT (QPS) software, were used to evaluate the reconstructed images. RESULTS: Compared to reconstruction with optimal Gaussian post-filtering (sigma = 1.2 voxels), further DL denoising increased the area under the ROC curve (AUC) from 0.80 to 0.88 (P-value < 10-4). For reconstruction with less Gaussian post-filtering (sigma = 0.8 voxels), thus better spatial resolution, DL denoising increased the AUC value from 0.78 to 0.86 (P-value < 10-4) and achieved better spatial resolution in reconstruction. CONCLUSIONS: DL denoising can effectively improve the detection of abnormal defects in standard-dose SPECT-MPI images over conventional reconstruction.

Atmospheric Carbon and Transport - America (ACT-America) Data Sets: Description, Management, and Delivery.

The ACT-America project is a NASA Earth Venture Suborbital-2 mission designed to study the transport and fluxes of greenhouse gases. The open and freely available ACT-America data sets provide airborne in situ measurements of atmospheric carbon dioxide, methane, trace gases, aerosols, clouds, and meteorological properties, airborne remote sensing measurements of aerosol backscatter, atmospheric boundary layer height and columnar content of atmospheric carbon dioxide, tower-based measurements, and modeled atmospheric mole fractions and regional carbon fluxes of greenhouse gases over the Central and Eastern United States. We conducted 121 research flights during five campaigns in four seasons during 2016-2019 over three regions of the US (Mid-Atlantic, Midwest and South) using two NASA research aircraft (B-200 and C-130). We performed three flight patterns (fair weather, frontal crossings, and OCO-2 underflights) and collected more than 1,140 h of airborne measurements via level-leg flights in the atmospheric boundary layer, lower, and upper free troposphere and vertical profiles spanning these altitudes. We also merged various airborne in situ measurements onto a common standard sampling interval, which brings coherence to the data, creates geolocated data products, and makes it much easier for the users to perform holistic analysis of the ACT-America data products. Here, we report on detailed information of data sets collected, the workflow for data sets including storage and processing of the quality controlled and quality assured harmonized observations, and their archival and formatting for users. Finally, we provide some important information on the dissemination of data products including metadata and highlights of applications of ACT-America data sets.

Treatment of cutaneous basal cell carcinoma with combined laser extirpation and methyl aminolevulinic acid: five-year success rates.

INTRODUCTION: Basal cell carcinoma is the most common cancer. Excisional surgery is associated with a high clearance rate, at the expense of significant functional and aesthetic morbidity, especially within the T-zone or for extensive lesions. We report five-year follow-up outcomes for carbon dioxide laser extirpation of cutaneous basal cell carcinoma, assisted by immediate methyl aminolevulinate photodynamic therapy and cost-benefit considerations. MATERIALS AND METHODS: Retrospective cohort database analysis of adult patients with biopsy-proven primary cutaneous basal cell carcinoma, completing five years of follow-up. Direct per-lesion cost was compared with conventional wide local excision. Patients with morphoeic basal cell carcinoma were excluded. RESULTS: Treated lesions were up to 1% total body surface area and up to 3.8mm (1.38 ± 0.695cm, mean ± standard deviation) in biopsy-proven depth. At the five-year follow-up mark, 93.6% of treated areas remained free of recurrence. Nodular basal cell carcinoma was the most common subtype (41.5%). A mean tumour depth greater than 2 ± 0.872mm was significantly associated with recurrence (Mann-Whitney, p = 0.0487). For a service delivered through the NHS at 2015 prices, we report a 43% saving, equating to a saving of £235 per basal cell carcinoma or a national annualised saving of £70 million by 2025 for the NHS. CONCLUSION: Our results suggest that CO2-assisted photodynamic therapy is non-inferior to excision but may offer better functional and cosmetic preservation at a fraction of the direct like for like cost of operative surgery. Investigation of this method by randomised controlled methodology is warranted.

Retrospective fractional dose reduction in Tc-99m cardiac perfusion SPECT/CT patients: A human and model observer study.

BACKGROUND: In the ongoing efforts to reduce cardiac perfusion dose (injected radioactivity) for conventional SPECT/CT systems, we performed a human observer study to confirm our clinical model observer findings that iterative reconstruction employing OSEM (ordered-subset expectation-maximization) at 25% of the full dose (quarter-dose) has a similar performance for detection of hybrid cardiac perfusion defects as FBP at full dose. METHODS: One hundred and sixty-six patients, who underwent routine rest-stress Tc-99m sestamibi cardiac perfusion SPECT/CT imaging and clinically read as normally perfused, were included in the study. Ground truth was established by the normal read and the insertion of hybrid defects. In addition to the reconstruction of the 25% of full-dose data using OSEM with attenuation (AC), scatter (SC), and spatial resolution correction (RC), FBP and OSEM (with AC, SC, and RC) both at full dose (100%) were done. Both human observer and clinical model observer confidence scores were obtained to generate receiver operating characteristics (ROC) curves in a task-based image quality assessment. RESULTS: Average human observer AUC (area under the ROC curve) values of 0.725, 0.876, and 0.890 were obtained for FBP at full dose, OSEM at 25% of full dose, and OSEM at full dose, respectively. Both OSEM strategies were significantly better than FBP with P values of 0.003 and 0.01 respectively, while no significant difference was recorded between OSEM methods (P = 0.48). The clinical model observer results were 0.791, 0.822, and 0.879, respectively, for the same patient cases and processing strategies used in the human observer study. CONCLUSIONS: Cardiac perfusion SPECT/CT using OSEM reconstruction at 25% of full dose has AUCs larger than FBP and closer to those of full-dose OSEM when read by human observers, potentially replacing the higher dose studies during clinical reading.

Deep learning with noise-to-noise training for denoising in SPECT myocardial perfusion imaging.

PURPOSE: Post-reconstruction filtering is often applied for noise suppression due to limited data counts in myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT). We study a deep learning (DL) approach for denoising in conventional SPECT-MPI acquisitions, and investigate whether it can be more effective for improving the detectability of perfusion defects compared to traditional postfiltering. METHODS: Owing to the lack of ground truth in clinical studies, we adopt a noise-to-noise (N2N) training approach for denoising in SPECT-MPI images. We consider a coupled U-Net (CU-Net) structure which is designed to improve learning efficiency through feature map reuse. For network training we employ a bootstrap procedure to generate multiple noise realizations from list-mode clinical acquisitions. In the experiments we demonstrated the proposed approach on a set of 895 clinical studies, where the iterative OSEM algorithm with three-dimensional (3D) Gaussian postfiltering was used to reconstruct the images. We investigated the detection performance of perfusion defects in the reconstructed images using the non-prewhitening matched filter (NPWMF), evaluated the uniformity of left ventricular (LV) wall in terms of image intensity, and quantified the effect of smoothing on the spatial resolution of the reconstructed LV wall by using its full-width at half-maximum (FWHM). RESULTS: Compared to OSEM with Gaussian postfiltering, the DL denoised images with CU-Net significantly improved the detection performance of perfusion defects at all contrast levels (65%, 50%, 35%, and 20%). The signal-to-noise ratio (SNRD ) in the NPWMF output was increased on average by 8% over optimal Gaussian smoothing (P < 10-4 , paired t-test), while the inter-subject variability was greatly reduced. The CU-Net also outperformed a 3D nonlocal means (NLM) filter and a convolutional autoencoder (CAE) denoising network in terms of SNRD . In addition, the FWHM of the LV wall in the reconstructed images was varied by less than 1%. Furthermore, CU-Net also improved the detection performance when the images were processed with less post-reconstruction smoothing (a trade-off of increased noise for better LV resolution), with SNRD improved on average by 23%. CONCLUSIONS: The proposed DL with N2N training approach can yield additional noise suppression in SPECT-MPI images over conventional postfiltering. For perfusion defect detection, DL with CU-Net could outperform conventional 3D Gaussian filtering with optimal setting as well as NLM and CAE.

Improving Diagnostic Accuracy in Low-Dose SPECT Myocardial Perfusion Imaging With Convolutional Denoising Networks.

Lowering the administered dose in SPECT myocardial perfusion imaging (MPI) has become an important clinical problem. In this study we investigate the potential benefit of applying a deep learning (DL) approach for suppressing the elevated imaging noise in low-dose SPECT-MPI studies. We adopt a supervised learning approach to train a neural network by using image pairs obtained from full-dose (target) and low-dose (input) acquisitions of the same patients. In the experiments, we made use of acquisitions from 1,052 subjects and demonstrated the approach for two commonly used reconstruction methods in clinical SPECT-MPI: 1) filtered backprojection (FBP), and 2) ordered-subsets expectation-maximization (OSEM) with corrections for attenuation, scatter and resolution. We evaluated the DL output for the clinical task of perfusion-defect detection at a number of successively reduced dose levels (1/2, 1/4, 1/8, 1/16 of full dose). The results indicate that the proposed DL approach can achieve substantial noise reduction and lead to improvement in the diagnostic accuracy of low-dose data. In particular, at 1/2 dose, DL yielded an area-under-the-ROC-curve (AUC) of 0.799, which is nearly identical to the AUC = 0.801 obtained by OSEM at full-dose ( p -value = 0.73); similar results were also obtained for FBP reconstruction. Moreover, even at 1/8 dose, DL achieved AUC = 0.770 for OSEM, which is above the AUC = 0.755 obtained at full-dose by FBP. These results indicate that, compared to conventional reconstruction filtering, DL denoising can allow for additional dose reduction without sacrificing the diagnostic accuracy in SPECT-MPI.

Evaluation of the effect of reducing administered activity on assessment of function in cardiac gated SPECT.

BACKGROUND: We previously optimized several reconstruction strategies in SPECT myocardial perfusion imaging (MPI) with low dose for perfusion-defect detection. Here we investigate whether reducing the administered activity can also maintain the diagnostic accuracy in evaluating cardiac function. METHODS: We quantified the myocardial motion in cardiac-gated stress 99m-Tc-sestamibi SPECT studies from 163 subjects acquired with full dose (29.8 ± 3.6 mCi), and evaluated the agreement of the obtained motion/thickening and ejection fraction (EF) measures at various reduced dose levels (uniform reduction or personalized dose) with that at full dose. We also quantified the detectability of abnormal motion via a receiver-operating characteristics (ROC) study. For reconstruction we considered both filtered backprojection (FBP) without correction for degradations, and iterative ordered-subsets expectation-maximization (OS-EM) with resolution, attenuation and scatter corrections. RESULTS: With dose level lowered to 25% of full dose, the obtained results on motion/thickening, EF and abnormal motion detection were statistically comparable to full dose in both reconstruction strategies, with Pearson's r > 0.9 for global motion measures between low dose and full dose. CONCLUSIONS: The administered activity could be reduced to 25% of full dose without degrading the function assessment performance. Low dose reconstruction optimized for perfusion-defect detection can be reasonable for function assessment in gated SPECT.

Cardiac motion correction for improving perfusion defect detection in cardiac SPECT at standard and reduced doses of activity.

In cardiac SPECT perfusion imaging, cardiac motion can lead to motion blurring of anatomical detail and perfusion defects in the reconstructed myocardium. In this study, we investigated the potential benefit of cardiac motion correction for improving the detectability of perfusion defects. We considered a post-reconstruction motion correction (PMC) approach in which the image motion between two cardiac gates is obtained with optical flow estimation. In the experiments, we demonstrated the proposed post-reconstruction motion correction with optical flow estimation (PMC-OFE) approach on a set of clinical acquisitions from 194 subjects. We quantified the detectability of perfusion defects in the reconstructed images by using the total perfusion deficit scores, calculated by the clinical software tool QPS, and conducted a receiver-operating-characteristic (ROC) study to obtain the detection performance. Besides imaging with conventional standard dose, we also evaluated the approach for reduced dose SPECT imaging where the imaging dose was retrospectively reduced to 50%, 25%, and 12.5% of the standard dose. The proposed PMC-OFE approach achieved at each dose level higher area-under-the-ROC-curve (AUC) for perfusion defect detection than the traditional approach of using ungated data (Non-MC) (p -value < 0.05); in particular, with half dose, PMC-OFE achieved AUC = 0.813, which is comparable to Non-MC with standard dose (AUC = 0.795). Moreover, the proposed PMC-OFE approach also outperformed the 'Motion Frozen' (MF) method implemented in the clinical quantitative gated SPECT (QGS) software. In particular, at 25% and 12.5% of standard dose, the AUC values obtained by PMC-OFE are 0.788 and 0.779, respectively, compared to 0.758 and 0.731 for MF (p -value < 0.05).

Inorganic hydrogen polysulfides: chemistry, chemical biology and detection.

Recent studies suggest that inorganic hydrogen polysulfides (H2 Sn , n ≥ 2) play important regulatory roles in redox biology. Modulation of their cellular levels could have potential therapeutic value. This review article focuses on our current understanding of the biosynthesis, biofunctions, fundamental physical/chemical properties, detection methods and delivery techniques of H2 Sn . LINKED ARTICLES: This article is part of a themed section on Chemical Biology of Reactive Sulfur Species. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.4/issuetoc.

Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning.

BACKGROUND: We developed machine-learning (ML) models to estimate a patient's risk of cardiac death based on adenosine myocardial perfusion SPECT (MPS) and associated clinical data, and compared their performance to baseline logistic regression (LR). We demonstrated an approach to visually convey the reasoning behind a patient's risk to provide insight to clinicians beyond that of a "black box." METHODS: We trained multiple models using 122 potential clinical predictors (features) for 8321 patients, including 551 cases of subsequent cardiac death. Accuracy was measured by area under the ROC curve (AUC), computed within a cross-validation framework. We developed a method to display the model's rationale to facilitate clinical interpretation. RESULTS: The baseline LR (AUC = 0.76; 14 features) was outperformed by all other methods. A least absolute shrinkage and selection operator (LASSO) model (AUC = 0.77; p = .045; 6 features) required the fewest features. A support vector machine (SVM) model (AUC = 0.83; p < .0001; 49 features) provided the highest accuracy. CONCLUSIONS: LASSO outperformed LR in both accuracy and simplicity (number of features), with SVM yielding best AUC for prediction of cardiac death in patients undergoing MPS. Combined with presenting the reasoning behind the risk scores, our results suggest that ML can be more effective than LR for this application.

Improving perfusion defect detection with respiratory motion correction in cardiac SPECT at standard and reduced doses.

BACKGROUND: In cardiac SPECT perfusion imaging, respiratory motion can cause non-uniform blurring in the reconstructed myocardium. We investigate the potential benefit of respiratory correction with respiratory-binned acquisitions, both at standard dose and at reduced dose, for defect detection and for left ventricular (LV) wall resolution. METHODS: We applied two reconstruction methods for respiratory motion correction: post-reconstruction motion correction (PMC) and motion-compensated reconstruction (MCR), and compared with reconstruction without motion correction (Non-MC). We quantified the presence of perfusion defects in reconstructed images by using the total perfusion deficit (TPD) scores and conducted receiver-operating-characteristic (ROC) studies using TPD. We quantified the LV spatial resolution by using the FWHM of its cross-sectional intensity profile. RESULTS: The values in the area-under-the-ROC-curve (AUC) achieved by MCR, PMC, and Non-MC at standard dose were 0.835, 0.830, and 0.798, respectively. Similar AUC improvements were also obtained by MCR and PMC over Non-MC at 50%, 25%, and 12.5% of full dose. Improvements in LV resolution were also observed with motion correction. CONCLUSIONS: Respiratory-binned acquisitions can improve perfusion-defect detection accuracy over traditional reconstruction both at standard dose and at reduced dose. Motion correction may contribute to achieving further dose reduction while maintaining the diagnostic accuracy of traditional acquisitions.

Personalized Models for Injected Activity Levels in SPECT Myocardial Perfusion Imaging.

We propose a patient-specific ("personalized") approach for tailoring the injected activities to individual patients in order to achieve dose reduction in SPECT-myocardial perfusion imaging (MPI). First, we develop a strategy to determine the minimum dose levels required for each patient in a large set of clinical acquisitions (857 subjects) such that the reconstructed images are sufficiently similar to that obtained at conventional clinical dose. We then apply machine learning models to predict the required dose levels on an individual basis based on a set of patient attributes which include body measurements and various clinical variables. We demonstrate the personalized dose models for two commonly used reconstruction methods in clinical SPECT-MPI: 1) conventional filtered backprojection (FBP) with post-filtering and 2) ordered-subsets expectation-maximization (OS-EM) with corrections for attenuation, scatter and resolution, and evaluate their performance in perfusion-defect detection by using the clinical Quantitative Perfusion SPECT software package. The results indicate that the achieved dose reduction can vary greatly among individuals from their conventional clinical dose and that the personalized dose models can achieve further reduction on average compared with a global (non-patient specific) dose reduction approach. In particular, the average personalized dose level can be reduced to 58% and 54% of the full clinical dose, respectively, for FBP and OS-EM reconstruction, while without deteriorating the accuracy in perfusion-defect detection. Furthermore, with the average personalized dose further reduced to only 16% of full dose, OS-EM can still achieve a detection accuracy level comparable to that of FBP with full dose.

FDG PET Parkinson's disease-related pattern as a biomarker for clinical trials in early stage disease.

Background: The development of therapeutic interventions for Parkinson disease (PD) is challenged by disease complexity and subjectivity of symptom evaluation. A Parkinson's Disease Related Pattern (PDRP) of glucose metabolism via fluorodeoxyglucose positron emission tomography (FDG-PET) has been reported to correlate with motor symptom scores and may aid the detection of disease-modifying therapeutic effects. Objectives: We sought to independently evaluate the potential utility of the PDRP as a biomarker for clinical trials of early-stage PD. Methods: Two machine learning approaches (Scaled Subprofile Model (SSM) and NPAIRS with Canonical Variates Analysis) were performed on FDG-PET scans from 17 healthy controls (HC) and 23 PD patients. The approaches were compared regarding discrimination of HC from PD and relationship to motor symptoms. Results: Both classifiers discriminated HC from PD (p < 0.01, p < 0.03), and classifier scores for age- and gender- matched HC and PD correlated with Hoehn & Yahr stage (R2 = 0.24, p < 0.015) and UPDRS (R2 = 0.23, p < 0.018). Metabolic patterns were highly similar, with hypometabolism in parieto-occipital and prefrontal regions and hypermetabolism in cerebellum, pons, thalamus, paracentral gyrus, and lentiform nucleus relative to whole brain, consistent with the PDRP. An additional classifier was developed using only PD subjects, resulting in scores that correlated with UPDRS (R2 = 0.25, p < 0.02) and Hoehn & Yahr stage (R2 = 0.16, p < 0.06). Conclusions: Two independent analyses performed in a cohort of mild PD patients replicated key features of the PDRP, confirming that FDG-PET and multivariate classification can provide an objective, sensitive biomarker of disease stage with the potential to detect treatment effects on PD progression.

Motion-compensated image reconstruction vs postreconstruction correction in respiratory-binned SPECT with standard and reduced-dose acquisitions.

PURPOSE: Cardiac perfusion images in single-photon emission computed tomography (SPECT) can suffer from respiratory motion blur. We investigated a reconstruction approach for correcting respiratory motion in respiratory-binned acquisitions and assessed the benefit of this approach in both standard dose and reduced dose. METHODS: We modeled the acquired data from different respiratory bins by a joint probability distribution which was parameterized with respect to a common reference bin. The acquired data from all the respiratory bins were then utilized simultaneously for determining the source distribution in the reference bin using maximum a posteriori (MAP) estimation. We evaluated this approach with simulated imaging data and ten sets of clinical acquisitions, and compared it with a postreconstruction motion correction approach developed previously. We quantified the accuracy of the reconstruction results both at standard dose and with imaging dose reduced by 50% and 75%, respectively. RESULTS: The proposed motion-compensated reconstruction (MCR) approach led to improved reconstruction of the myocardium in terms of both noise level and LV wall resolution. Compared to traditional acquisition (without motion correction), the proposed approach reduced the mean squared error of the image intensity in the myocardium by 27.59%, 20.59%, and 12.05% at full, half-, and quarter dose, respectively; the LV resolution, quantified by the full width at half-maximum (FWHM), was improved by 17.34%, 14.35%, and 12.95% at full, half-, and quarter dose, respectively; in addition, the proposed approach also improved the perfusion defect detectability at both full dose and reduced dose. Furthermore, with motion correction, the reconstruction results obtained at half-dose were comparable to that obtained at full dose without correction. Similar improvements were also demonstrated in the clinical acquisitions at different dose levels. CONCLUSIONS: Respiratory motion correction in perfusion SPECT can improve the reconstruction of the myocardium at both standard and reduced dose. At half-dose, the results obtained with motion correction are comparable to that of traditional reconstruction obtained at full dose. MCR can be more accurate than postreconstruction correction.

O→S Relay Deprotection: A General Approach to Controllable Donors of Reactive Sulfur Species.

Reactive sulfur species (RSS) are biologically important molecules. Among them, H2 S, hydrogen polysulfides (H2 Sn, n>1), persulfides (RSSH), and HSNO are believed to play regulatory roles in sulfur-related redox biology. However, these molecules are unstable and difficult to handle. Having access to their reliable and controllable precursors (or donors) is the prerequisite for the study of these sulfur species. Reported in this work is the preparation and evaluation of a series of O-silyl-mercaptan-based sulfur-containing molecules which undergo pH- or F- -mediated desilylation to release the corresponding H2 S, H2 Sn , RSSH, and HSNO in a controlled fashion. This O→S relay deprotection serves as a general strategy for the design of pH- or F- -triggered RSS donors. Moreover, we have demonstrated that the O-silyl groups in the donors could be changed into other protecting groups like esters. This work should allow the development of RSS donors with other activation mechanisms (such as esterase-activated donors).

High-Performance Oligomeric Catholytes for Effective Macromolecular Separation in Nonaqueous Redox Flow Batteries.

Nonaqueous redox flow batteries (NRFBs) represent an attractive technology for energy storage from intermittent renewable sources. In these batteries, electrical energy is stored in and extracted from electrolyte solutions of redox-active molecules (termed catholytes and anolytes) that are passed through an electrochemical flow cell. To avoid battery self-discharge, the anolyte and catholyte solutions must be separated by a membrane in the flow cell. This membrane prevents crossover of the redox active molecules, while simultaneously allowing facile transport of charge-balancing ions. A key unmet challenge for the field is the design of redox-active molecule/membrane pairs that enable effective electrolyte separation while maintaining optimal battery properties. Herein, we demonstrate the development of oligomeric catholytes based on tris(dialkylamino)cyclopropenium (CP) salts that are specifically tailored for pairing with size-exclusion membranes composed of polymers of intrinsic microporosity (PIMs). Systematic studies were conducted to evaluate the impact of oligomer size/structure on properties that are crucial for flow battery performance, including cycling stability, charge capacity, solubility, electron transfer kinetics, and crossover rates. These studies have led to the identification of a CP-derived tetramer in which these properties are all comparable, or significantly improved, relative to the monomeric counterpart. Finally, a proof-of-concept flow battery is demonstrated by pairing this tetrameric catholyte with a PIM membrane. After 6 days of cycling, no crossover is detected, demonstrating the promise of this approach. These studies provide a template for the future design of other redox-active oligomers for this application.