The anti-hyperplasia effect of polysaccharide from Prunella vulgaris L. on mammary gland hyperplasia in rats through regulation of the AKT-FOXO3a signaling pathway and intestinal flora.

BACKGROUND: Prunella vulgaris L., a medicinal and edible homologous plant, is often used to treat conditions such as breast hyperplasia, thyroid enlargement and lymphatic tuberculosis. Research has demonstrated that it is particularly effective in the treatment of mammary gland hyperplasia (MGH). However, the material basis and mechanism of its efficacy are still unclear. RESULTS: Our results showed that in rats with MGH, polysaccharide from Prunella vulgaris L. (PVP) led to a reduction in the levels of estradiol, prolactin and malondialdehyde, while simultaneously increasing the concentrations of progesterone (P), superoxide dismutase (SOD), manganese superoxide dismutase (MnSOD) and catalase (CAT) in the serum. In addition, results obtained from 16S rRNA sequencing demonstrated that PVP had the capacity to increase the richness and diversity of the intestinal microbiota in MGH rats, as well as modify the structure of the microbiota. Correlation analysis revealed that the levels of P, SOD, MnSOD and CAT were positively associated with Allobaculum, Romboutsia, Faecalibaculum and Clostridium, while negatively correlated with Turicibacter. CONCLUSIONS: The mechanism of PVP in treating MGH might be through inhibiting the phosphorylation of the AKT-FOXO3a signaling pathway and then activating the expression of downstream antioxidant enzymes, such as MnSOD and CAT. At the same time, PVP could restore intestinal flora homeostasis in rats with MGH by regulating the flora changes of Allobaculum, Romboutsia, Clostridium and Faecalibaculum, thereby reducing oxidative stress in rats with MGH. © 2024 Society of Chemical Industry.

Respiratory signal estimation for cardiac perfusion SPECT using deep learning.

BACKGROUND: Respiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a respiratory signal describing the heart displacements during breathing. However, using external tracking devices to estimate respiratory signals can add cost and operational complications in a clinical setting. PURPOSE: We aim to develop a deep learning (DL) approach that uses only SPECT projection data for respiratory signal estimation. METHODS: A modified U-Net was implemented that takes temporally finely sampled SPECT sub-projection data (100 ms) as input. These sub-projections are obtained by reframing the 20-s list-mode data, resulting in 200 sub-projections, at each projection angle for each SPECT camera head. The network outputs a 200-time-point motion signal for each projection angle, which was later aggregated over all angles to give a full respiratory signal. The target signal for DL model training was from an external stereo-camera visual tracking system (VTS). In addition to comparing DL and VTS, we also included a data-driven approach based on the center-of-mass (CoM) strategy. This CoM method estimates respiratory signals by monitoring the axial changes of CoM for counts in the heart region of the sub-projections. We utilized 900 subjects with stress cardiac perfusion SPECT studies, with 302 subjects for testing and the remaining 598 subjects for training and validation. RESULTS: The Pearson's correlation coefficient between the DL respiratory signal and the reference VTS signal was 0.90, compared to 0.70 between the CoM signal and the reference. For respiratory motion correction on SPECT images, all VTS, DL, and CoM approaches partially de-blured the heart wall, resulting in a thinner wall thickness and increased recovered maximal image intensity within the wall, with VTS reducing blurring the most followed by the DL approach. Uptake quantification for the combined anterior and inferior segments of polar maps showed a mean absolute difference from the reference VTS of 1.7% for the DL method for patients with motion >12 mm, compared to 2.6% for the CoM method and 8.5% for no correction. CONCLUSION: We demonstrate the capability of a DL approach to estimate respiratory signal from SPECT projection data for cardiac perfusion imaging. Our results show that the DL based respiratory motion correction reduces artefacts and achieves similar regional quantification to that obtained using the stereo-camera VTS signals. This may enable fully automatic data-driven respiratory motion correction without relying on external motion tracking devices.

Haplotype analysis and marker development of five salt-tolerant-related genes in rice (Oryza sativa L.).

Salinity stress is a great threat to the growth and productivity of crops, and development of salt-tolerant crops is of great necessity to ensure food security. Although a few genes with natural variations that confer salt tolerance at germination and seedling stage in rice have been cloned, effective intragenic markers for these genes are awaited to be developed, which hinder the use of these genes in genetic improvement of salt tolerance in rice. In this study, we first performed haplotype analysis of five rice salt-tolerant-related genes using 38 rice accessions with reference genome and 4,726 rice germplasm accessions with imputed genotypes and classified main haplotype groups and haplotypes. Subsequently, we identified unique variations for elite haplotypes reported in previous studies and developed 11 effective intragenic makers. Finally, we conducted genotyping of 533 of the 4,726 rice accessions from worldwide and 70 approved temperate geng/japonica cultivars in China using the developed markers. These results could provide effective donors and markers of salt-tolerant-related genes and thus could be of great use in genetic improvement of salt tolerance in rice.

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.

An overview of traditional Chinese medicine affecting gut microbiota in obesity.

Obesity, a chronic metabolic disease with a complex pathophysiology, is caused by several variables. High-fat diets lead to the disruption of the gut microbiota and impaired gut barrier function in obese people. The dysbiosis and its metabolites through the intestinal barrier lead to an imbalance in energy metabolism and inflammatory response, which eventually contributes to the development of chronic diseases such as diabetes, hypertension, and cardiovascular disease. Current medicines are therapeutic to obesity in the short term; however, they may bring significant physical and emotional problems to patients as major side effects. Therefore, it is urgent to explore new therapeutic methods that have definite efficacy, can be taken for a long time, and have mild adverse effects. Numerous studies have demonstrated that traditional Chinese medicine (TCM) can control the gut microbiota in a multi-targeted and comprehensive manner, thereby restoring flora homeostasis, repairing damaged intestinal mucosal barriers, and eventually curbing the development of obesity. The active ingredients and compounds of TCM can restore the normal physiological function of the intestinal mucosal barrier by regulating gut microbiota to regulate energy metabolism, inhibit fat accumulation, affect food appetite, and reduce intestinal mucosal inflammatory response, thereby effectively promoting weight loss and providing new strategies for obesity prevention and treatment. Although there are some studies on the regulation of gut microbiota by TCM to prevent and treat obesity, all of them have the disadvantage of being systematic and comprehensive. Therefore, this work comprehensively describes the molecular mechanism of obesity mediated by gut microbiota based on the research state of obesity, gut microbiota, and TCM. A comprehensive and systematic summary of TCM targeting the regulation of gut microbiota for the treatment of obesity should be conducted in order to provide new strategies and ideas for the treatment of obesity.

Regulation of chlorophyll biosynthesis by light-dependent acetylation of NADPH:protochlorophyll oxidoreductase A in Arabidopsis.

Chlorophylls are the major pigments that harvest light energy during photosynthesis in plants. Although reactions in chlorophyll biogenesis have been largely known, little attention has been paid to the post-translational regulation mechanism of this process. In this study, we found that four lysine sites (K128/340/350/390) of NADPH:protochlorophyllide oxidoreductase A (PORA), which catalyzes the only light-triggered step in chlorophyll biosynthesis, were acetylated after dark-grown seedlings transferred to light via acetylomics analysis. Etiolated seedlings with K390 mutation of PORA had a lower greening rate and decreased PORA acetylation after illumination. Importantly, K390 of PORA was found extremely conserved in plants and cyanobacteria via bioinformatics analysis. We further demonstrated that the acetylation level of PORA was increased by exposing the dark-grown seedlings to the histone deacetylase (HDAC) inhibitor TSA. Thus, the HDACs probably regulate the acetylation of PORA, thereby controlling this non-histone substrate to catalyze the reduction of Pchlide to produce chlorophyllide, which provides a novel regulatory mechanism by which the plant actively tunes chlorophyll biosynthesis during the conversion from skotomorphogenesis to photomorphogenesis.

The Complete Chloroplast Genome Sequence of Machilus chuanchienensis (Lauraceae): Genome Structure and Phylogenetic Analysis.

Machilus chuanchienensis is an ecological tree distributed in southwestern China. It has a significant valuation with making Hawk tea using its leaves, an ethnic traditional tea-like beverage with a long history in Chinese tea culture. The whole chloroplast (cp) genome is an ideal model for the phylogenetic study of Lauraceae because of its simple structure and highly conserved features. There have been numerous reports of complete cp genome sequences in Lauraceae, but little is known about M. chuanchienensis. Here, the next-generation sequencing (NGS) was used to sequence the M. chuanchienensis cp genome. Then, a comprehensive comparative genome analysis was performed. The results revealed that the M. chuanchienensis's cp genome measured 152,748 base pairs (bp) with a GC content of 39.15% and coded 126 genes annotated, including comprising eight ribosomal RNA (rRNA), 36 transporter RNA (tRNA), and 82 protein-coding genes. In addition, the cp genome presented a typical quadripartite structure comprising a large single-copy (LSC; 93,811) region, a small single-copy (SSC; 18,803) region, and the inverted repeats (IRs; 20,067) region and contained 92 simple sequence repeat (SSR) locus in total. Phylogenetic relationships of 37 species indicated that M. chuanchienensis was a sister to M. balansae, M. melanophylla, and M. minutiflora. Further research on this crucial species may benefit significantly from these findings.

Recent Advances in Aptamer-Based Nanopore Sensing at Single-Molecule Resolution.

Aptamers as the recognition element of the stochastic nanopore sensors have been under intense investigation. This paper reviews recent research advances in aptamer-based nanopore sensing techniques, including the classification and selection of nanopores (biological nanopores, solid-state nanopores, and nanopipettes), different strategies of aptamer-based nanopore sensing, and their values and outlook for applications in areas such as environmental analysis, precision diagnosis, pharmaceutical industry, and security. Furthermore, the single-molecule nanopore sensors have been applied to reveal the aptamer-target interactions, such as recognition orientation, binding sites, and conformational heterogeneity dissociation kinetics at the single-molecule level. In this review, recent research efforts to develop aptamer-based single-molecule nanopore sensors with high selectivity and sensitivity are highlighted, and some perspectives are drawn.

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.

Time-of-Year Variation in Intraocular Pressure.

PRÉCIS: In this study conducted in Chicago, IL, intraocular pressure (IOP) level was found to have a subtle, but measurable, annual pattern. Reasonable evidence is presented for a time-of-year variation in IOP. Adequate numbers of subjects must be studied to detect this small variation. PURPOSE: The aim was to investigate the relationship between IOP and time of year. METHODS: During a separate investigation, patients from 2011 to 2018 (dataset A, N=3041) in an urban, academic facility in Chicago, IL received an examination that included Goldmann applanation tonometry. Regression analyses assessed the relationship between time of year and IOP. Two additional datasets, 1 collected in a similar manner during 1999 and 2002 (dataset B, N=3261) and another consisting of all first visits during 2012 and 2017 (dataset C, N=69,858), were used to confirm and further investigate trends. RESULTS: For dataset A, peak mean IOP occurred in December/January (15.7±3.7/15.7±3.8 mm Hg) and lowest in September (14.5±3.1 mm Hg). The analysis suggested conventional quarterly analysis (January to March, etc.) can conceal time-of-year relationships because of inadequate statistical power and timing of IOP variation. Multiple linear regression analysis, with a November-to-October reordering, detected an annual, downward IOP trend (P<0.0001). Analysis of dataset B confirmed this trend (P<0.001). Fourier analysis on datasets A and B combined supported a 12-month IOP cycle for right/left eyes (P=0.01/P=0.005) and dataset C provided stronger evidence for an annual periodicity (P<0.0001). Harmonics analysis of dataset C showed a repeating pattern where IOP trended downward around April, and then back upward around October. CONCLUSIONS: This analysis strongly supports a demonstrable annual, cyclical IOP pattern with a trough to peak variation of ≈1 mm Hg, which has a seasonal relationship.

Development and evaluation of a high performance T1-weighted brain template for use in studies on older adults.

Τhe accuracy of template-based neuroimaging investigations depends on the template's image quality and representativeness of the individuals under study. Yet a thorough, quantitative investigation of how available standardized and study-specific T1-weighted templates perform in studies on older adults has not been conducted. The purpose of this work was to construct a high-quality standardized T1-weighted template specifically designed for the older adult brain, and systematically compare the new template to several other standardized and study-specific templates in terms of image quality, performance in spatial normalization of older adult data and detection of small inter-group morphometric differences, and representativeness of the older adult brain. The new template was constructed with state-of-the-art spatial normalization of high-quality data from 222 older adults. It was shown that the new template (a) exhibited high image sharpness, (b) provided higher inter-subject spatial normalization accuracy and (c) allowed detection of smaller inter-group morphometric differences compared to other standardized templates, (d) had similar performance to that of study-specific templates constructed with the same methodology, and (e) was highly representative of the older adult brain.

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.

Association Between Sleep Quality and Semen Parameters and Reproductive Hormones: A Cross-Sectional Study in Zhejiang, China.

BACKGROUND: The effects of sleep duration on semen quality have been documented in many epidemiological studies. However, the association between sleep quality and semen parameters and reproductive hormones is still unclear. PATIENTS ENROLLMENT AND METHODS: We conducted a cross-sectional study among 970 outpatients from the Reproductive Medicine Center in Zhejiang, China between October 2017 and July 2019. All participants delivered a semen sample, underwent a physical examination, and answered a questionnaire to provide the following information: demographics, life habits, and sleep habits. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI). We first divided the patients into two groups according to sleep quality (good sleep: PQSI < 5 and poor sleep: PSQI ≥ 5). Then, we analyzed routine sperm parameters (semen volume, sperm total motility, progressive motility, sperm concentration, total sperm number, and normal sperm morphology) and reproductive hormones (follicle-stimulating hormone, luteinizing hormone, estrogen, testosterone, and prolactin) of each group. Finally, we used multivariate linear regression analysis and Spearman correlation coefficients to examine the relationship between sleep quality (discrete variable or dichotomous variable) and sperm parameters, reproductive hormones. RESULTS: A negative correlation was found between the general PSQI scores and several semen parameters: total motility (r= -0.187979, p< 0.001), progressive motility (r= -0.192902, p< 0.001), concentration (r= -0.167063, p< 0.001), total sperm number (r= -0.160008, p< 0.001), and normal sperm morphology (r= -0.124511, p< 0.001). However, there was no significant correlation between the semen volume, all reproductive hormones and the general PSQI scores. After adjusting for confounders, men with poor sleep had lower total motility (ß= -9.287; 95% CI, -12.050, -6.523), progressive motility (ß= -8.853; 95% CI, -11.526, -6.180), concentration (log scale, ß= -0.131; 95% CI, -0.181, -0.082), total sperm number (log scale, ß= -0.137; 95% CI, -0.189, -0.084), and normal sperm morphology (ß= -1.195; 95% CI, -1.844, -0.547), but semen volume and all reproductive hormones were not markedly altered. CONCLUSION: Poor sleep quality might be related to impaired semen quality, but we found no evidence that poor sleep quality affects reproductive hormones.

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).

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.