Cross noise level PET denoising with continuous adversarial domain generalization

Objective.Performing positron emission tomography (PET) denoising within the image space proves effective in reducing the variance in PET images. In recent years, deep learning has demonstrated superior denoising performance, but models trained on a specific noise level typically fail to generalize well on different noise levels, due to inherent distribution shifts between inputs. The distribution shift usually results in bias in the denoised images. Our goal is to tackle such a problem using a domain generalization technique.Approach.We propose to utilize the domain generalization technique with a novel feature space continuous discriminator (CD) for adversarial training, using the fraction of events as a continuous domain label. The core idea is to enforce the extraction of noise-level invariant features. Thus minimizing the distribution divergence of latent feature representation for different continuous noise levels, and making the model general for arbitrary noise levels. We created three sets of 10%, 13%-22% (uniformly randomly selected), or 25% fractions of events from 9718F-MK6240 tau PET studies of 60 subjects. For each set, we generated 20 noise realizations. Training, validation, and testing were implemented using 1400, 120, and 420 pairs of 3D image volumes from the same or different sets. We used 3D UNet as the baseline and implemented CD to the continuous noise level training data of 13%-22% set.Main results.The proposed CD improves the denoising performance of our model trained in a 13%-22% fraction set for testing in both 10% and 25% fraction sets, measured by bias and standard deviation using full-count images as references. In addition, our CD method can improve the SSIM and PSNR consistently for Alzheimer-related regions and the whole brain.Significance.To our knowledge, this is the first attempt to alleviate the performance degradation in cross-noise level denoising from the perspective of domain generalization. Our study is also a pioneer work of continuous domain generalization to utilize continuously changing source domains.

[Construction and preliminary validation of a risk prediction model for the recurrence of diabetic foot ulcer in diabetic patients]

Objective: To develop a risk prediction model for the recurrence of diabetic foot ulcer (DFU) in diabetic patients and primarily validate its predictive value. Methods: Meta-analysis combined with retrospective cohort study was conducted. The Chinese and English papers on risk factors related to DFU recurrence publicly published in China Biology Medicine disc, China National Knowledge Infrastructure, Wanfang Database, VIP Database, and PubMed, Embase, Cochrane Library, and Web of Science, and the search time was from the establishment date of each database until March 31st, 2022. The papers were screened and evaluated, the data were extracted, a meta-analysis was performed using RevMan 5.4.1 statistical software to screen risk factors for DFU recurrence, and Egger's linear regression was used to assess the publication bias of the study results. Risk factors for DFU recurrence mentioned in ≥3 studies and with statistically significant differences in the meta-analysis were selected as the independent variables to develop a logistic regression model for risk prediction of DFU recurrence. The medical records of 101 patients with DFU who met the inclusion criteria and were admitted to Affiliated Hospital of Guizhou Medical University from January 2019 to June 2022 were collected. There were 69 males and 32 females, aged (63±14) years. The receiver operating characteristic (ROC) curve of the predictive performance of the above constructed predictive model for DFU recurrence was drawn, and the area under the ROC curve, maximum Youden index, and sensitivity and specificity at the point were calculated. Dataset including data of 8 risk factors for DFU recurrence and the DFU recurrence rates of 10 000 cases was simulated using RStudio software and a scatter plot was drawn to determine two probabilities for risk division of DFU recurrence. Using the β coefficients corresponding to 8 DFU recurrence risk factors ×10 and taking the integer as the score of coefficient weight of each risk factor, the total score was obtained by summing up, and the cutoff scores for risk level division were calculated based on the total score × two probabilities for risk division of DFU recurrence. Results: Finally, 20 papers were included, including 3 case-control studies and 17 cohort studies, with a total of 4 238 cases and DFU recurrence rate of 22.7% to 71.2%. Meta-analysis showed that glycosylated hemoglobin >7.5% and with plantar ulcer, diabetic peripheral neuropathy, diabetic peripheral vascular disease, smoking, osteomyelitis, history of amputation/toe amputation, and multidrug-resistant bacterial infection were risk factors for the recurrence of DFU (with odds ratios of 3.27, 3.66, 4.05, 3.94, 1.98, 7.17, 11.96, 3.61, 95% confidence intervals of 2.79-3.84, 2.06-6.50, 2.50-6.58, 2.65-5.84, 1.65-2.38, 2.29-22.47, 4.60-31.14, 3.13-4.17, respectively, P<0.05). There were no statistically significant differences in publication biases of diabetic peripheral neuropathy, diabetic peripheral vascular disease, glycosylated hemoglobin >7.5%, plantar ulcer, smoking, multidrug-resistant bacterial infection, or osteomyelitis (P>0.05), but there was a statistically significant difference in the publication bias of amputation/toe amputation (t=-30.39, P<0.05). The area under the ROC curve of the predictive model was 0.81 (with 95% confidence interval of 0.71-0.91) and the maximum Youden index was 0.59, at which the sensitivity was 72% and the specificity was 86%. Ultimately, 29.0% and 44.8% were identified respectively as the cutoff for dividing the probability of low risk and medium risk, and medium risk and high risk for DFU recurrence, while the corresponding total scores of low, medium, and high risks of DFU recurrence were <37, 37-57, and 58-118, respectively. Conclusions: Eight risk factors for DFU recurrence are screened through meta-analysis and the risk prediction model for DFU recurrence is developed, which has moderate predictive accuracy and can provide guidance for healthcare workers to take interventions for patient with DFU recurrence risk.

Arterial spin labeled perfusion imaging with balanced steady-state free precession readout and radial sampling

PURPOSE

To develop an arterial spin labeling (ASL) perfusion imaging method with balanced steady-state free precession (bSSFP) readout and radial sampling for improved SNR and robustness to motion and off-resonance effects.

METHODS

An ASL perfusion imaging method was developed with pseudo-continuous arterial spin labeling (pCASL) and bSSFP readout. Three-dimensional (3D) k-space data were collected in segmented acquisitions following a stack-of-stars sampling trajectory. Multiple phase-cycling technique was utilized to improve the robustness to off-resonance effects. Parallel imaging with sparsity-constrained image reconstruction was used to accelerate imaging or increase the spatial coverage.

RESULTS

ASL with bSSFP readout showed higher spatial and temporal SNRs of the gray matter perfusion signal compared to those from spoiled gradient-recalled acquisition (SPGR). Cartesian and radial sampling schemes showed similar spatial and temporal SNRs, regardless of the imaging readout. In case of severe B0 inhomogeneity, single-RF phase incremented bSSFP acquisitions showed banding artifacts. These artifacts were significantly reduced when multiple phase-cycling technique (N = 4) was employed. The perfusion-weighted images obtained by the Cartesian sampling scheme showed respiratory motion-related artifacts when a high segmentation number was used. The perfusion-weighted images obtained by the radial sampling scheme did not show these artifacts. Whole brain perfusion imaging was feasible in 1.15 min or 4.6 min for cases without and with phase-cycling (N = 4), respectively, using the proposed method with parallel imaging.

CONCLUSIONS

The developed method allows non-invasive perfusion imaging of the whole-brain with relatively high SNR and robustness to motion and off-resonance effects in a practically feasible imaging time.

Generative Adversarial Network-Enhanced Ultra-Low-Dose [18F]-PI-2620 τ PET/MRI in Aging and Neurodegenerative Populations

BACKGROUND AND PURPOSE

With the utility of hybrid τ PET/MR imaging in the screening, diagnosis, and follow-up of individuals with neurodegenerative diseases, we investigated whether deep learning techniques can be used in enhancing ultra-low-dose [18F]-PI-2620 τ PET/MR images to produce diagnostic-quality images.

MATERIALS AND METHODS

Forty-four healthy aging participants and patients with neurodegenerative diseases were recruited for this study, and [18F]-PI-2620 τ PET/MR data were simultaneously acquired. A generative adversarial network was trained to enhance ultra-low-dose τ images, which were reconstructed from a random sampling of 1/20 (approximately 5% of original count level) of the original full-dose data. MR images were also used as additional input channels. Region-based analyses as well as a reader study were conducted to assess the image quality of the enhanced images compared with their full-dose counterparts.

RESULTS

The enhanced ultra-low-dose τ images showed apparent noise reduction compared with the ultra-low-dose images. The regional standard uptake value ratios showed that while, in general, there is an underestimation for both image types, especially in regions with higher uptake, when focusing on the healthy-but-amyloid-positive population (with relatively lower τ uptake), this bias was reduced in the enhanced ultra-low-dose images. The radiotracer uptake patterns in the enhanced images were read accurately compared with their full-dose counterparts.

CONCLUSIONS

The clinical readings of deep learning-enhanced ultra-low-dose τ PET images were consistent with those performed with full-dose imaging, suggesting the possibility of reducing the dose and enabling more frequent examinations for dementia monitoring.

[Summary of the best evidence on exercise for the prevention and treatment of diabetic foot]

Objective: To summarize the best evidence on exercise for the prevention and treatment of diabetic foot. Methods: A bibliometric approach was used. Systematic searches were carried out to retrieve all the publicly published evidences till July 2022 on exercise for the prevention and treatment of diabetic foot, including guidelines, evidence summary, recommended practices, expert consensus, systematic review, and original research, from foreign language databases including BMJ Best Practice, UpToDate, Joanna Briggs Institute Evidence-Based Practice Database, Cochrane Library, Embase, PubMed, Guideline International Network, National Guideline Clearinghouse, Chinese databases including China National Knowledge Infrastructure, Wanfang Database, VIP Database, China Biology Medicine disc, China Clinical Guidelines Library, and the official websites of relevant academic organizations including National Institute for Health and Care Excellence of the United Kingdom, Registered Nurses' Association of Ontario of Canada, the International Working Group on the Diabetic Foot, International Diabetes Federation, American College of Sports Medicine, American Diabetes Association, and Chinese Diabetes Society. The literature was screened and evaluated for the quality, from which the evidences were extracted and evaluated to summarize the best evidences. Results: Nine guidelines, three expert consensuses, one evidence summary (with two systematic reviews being traced), two systematic reviews, 6 randomized controlled trials were retrieved and included, with good quality of literature. Totally 33 pieces of best evidences on exercise for the prevention and treatment of diabetic foot were summarized from the aspects of appropriate exercise prevention of diabetic foot, exercise therapy of diabetic foot, precautions for exercise, health education, and establishment of a multidisciplinary limb salvage team. Conclusions: Totally 33 pieces of best evidences on exercise for the prevention and treatment of diabetic foot were summarized from 5 aspects, providing decision-making basis for clinical guidance on exercise practice for patients with diabetic foot.

Impact of motion correction on [18F]-MK6240 tau PET imaging

Objective. Positron emission tomography (PET) imaging of tau deposition using [18F]-MK6240 often involves long acquisitions in older subjects, many of whom exhibit dementia symptoms. The resulting unavoidable head motion can greatly degrade image quality. Motion increases the variability of PET quantitation for longitudinal studies across subjects, resulting in larger sample sizes in clinical trials of Alzheimer's disease (AD) treatment.Approach. After using an ultra-short frame-by-frame motion detection method based on the list-mode data, we applied an event-by-event list-mode reconstruction to generate the motion-corrected images from 139 scans acquired in 65 subjects. This approach was initially validated in two phantoms experiments against optical tracking data. We developed a motion metric based on the average voxel displacement in the brain to quantify the level of motion in each scan and consequently evaluate the effect of motion correction on images from studies with substantial motion. We estimated the rate of tau accumulation in longitudinal studies (51 subjects) by calculating the difference in the ratio of standard uptake values in key brain regions for AD. We compared the regions' standard deviations across subjects from motion and non-motion-corrected images.Main results. Individually, 14% of the scans exhibited notable motion quantified by the proposed motion metric, affecting 48% of the longitudinal datasets with three time points and 25% of all subjects. Motion correction decreased the blurring in images from scans with notable motion and improved the accuracy in quantitative measures. Motion correction reduced the standard deviation of the rate of tau accumulation by -49%, -24%, -18%, and -16% in the entorhinal, inferior temporal, precuneus, and amygdala regions, respectively.Significance. The list-mode-based motion correction method is capable of correcting both fast and slow motion during brain PET scans. It leads to improved brain PET quantitation, which is crucial for imaging AD.

Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET

Background

In medical imaging, images are usually treated as deterministic, while their uncertainties are largely underexplored.

Purpose

This work aims at using deep learning to efficiently estimate posterior distributions of imaging parameters, which in turn can be used to derive the most probable parameters as well as their uncertainties.

Methods

Our deep learning-based approaches are based on a variational Bayesian inference framework, which is implemented using two different deep neural networks based on conditional variational auto-encoder (CVAE), CVAE-dual-encoder and CVAE-dual-decoder. The conventional CVAE framework, i.e., CVAE-vanilla, can be regarded as a simplified case of these two neural networks. We applied these approaches to a simulation study of dynamic brain PET imaging using a reference region-based kinetic model.

Results

In the simulation study, we estimated posterior distributions of PET kinetic parameters given a measurement of time-activity curve. Our proposed CVAE-dual-encoder and CVAE-dual-decoder yield results that are in good agreement with the asymptotically unbiased posterior distributions sampled by Markov Chain Monte Carlo (MCMC). The CVAE-vanilla can also be used for estimating posterior distributions, although it has an inferior performance to both CVAE-dual-encoder and CVAE-dual-decoder.

Conclusions

We have evaluated the performance of our deep learning approaches for estimating posterior distributions in dynamic brain PET. Our deep learning approaches yield posterior distributions, which are in good agreement with unbiased distributions estimated by MCMC. All these neural networks have different characteristics and can be chosen by the user for specific applications. The proposed methods are general and can be adapted to other problems.

Super-resolution in brain Positron Emission Tomography using a real-time motion capture system

Super-resolution (SR) is a methodology that seeks to improve image resolution by exploiting the increased spatial sampling information obtained from multiple acquisitions of the same target with accurately known sub-resolution shifts. This work aims to develop and evaluate an SR estimation framework for brain positron emission tomography (PET), taking advantage of a high-resolution infra-red tracking camera to measure shifts precisely and continuously. Moving phantoms and non-human primate (NHP) experiments were performed on a GE Discovery MI PET/CT scanner (GE Healthcare) using an NDI Polaris Vega (Northern Digital Inc), an external optical motion tracking device. To enable SR, a robust temporal and spatial calibration of the two devices was developed as well as a list-mode Ordered Subset Expectation Maximization (OSEM) PET reconstruction algorithm, incorporating the high-resolution tracking data from the Polaris Vega to correct motion for measured line of responses (LORs) on an event-by-event basis. For both phantoms and NHP studies, the SR reconstruction method yielded PET images with visibly increased spatial resolution compared to standard static acquisitions, allowing improved visualization of small structures. Quantitative analysis in terms of SSIM, CNR and line profiles were conducted and validated our observations. The results demonstrate that SR can be achieved in brain PET by measuring target motion in real-time using a high-resolution infrared tracking camera.

Posterior estimation using deep learning: a simulation study of compartmental modeling in dynamic positron emission tomography

BACKGROUND

In medical imaging, images are usually treated as deterministic, while their uncertainties are largely underexplored.

PURPOSE

This work aims at using deep learning to efficiently estimate posterior distributions of imaging parameters, which in turn can be used to derive the most probable parameters as well as their uncertainties.

METHODS

Our deep learning-based approaches are based on a variational Bayesian inference framework, which is implemented using two different deep neural networks based on conditional variational auto-encoder (CVAE), CVAE-dual-encoder, and CVAE-dual-decoder. The conventional CVAE framework, that is, CVAE-vanilla, can be regarded as a simplified case of these two neural networks. We applied these approaches to a simulation study of dynamic brain PET imaging using a reference region-based kinetic model.

RESULTS

In the simulation study, we estimated posterior distributions of PET kinetic parameters given a measurement of the time-activity curve. Our proposed CVAE-dual-encoder and CVAE-dual-decoder yield results that are in good agreement with the asymptotically unbiased posterior distributions sampled by Markov Chain Monte Carlo (MCMC). The CVAE-vanilla can also be used for estimating posterior distributions, although it has an inferior performance to both CVAE-dual-encoder and CVAE-dual-decoder.

CONCLUSIONS

We have evaluated the performance of our deep learning approaches for estimating posterior distributions in dynamic brain PET. Our deep learning approaches yield posterior distributions, which are in good agreement with unbiased distributions estimated by MCMC. All these neural networks have different characteristics and can be chosen by the user for specific applications. The proposed methods are general and can be adapted to other problems.

Mechanism of ALDH2 improves the neuronal damage caused by hypoxia/reoxygenation

OBJECTIVE

To investigate the protective effect and mechanism of ALDH2 on PC12 cells and brain nerve tissue injury under hypoxia.

MATERIALS AND METHODS

The hypoxia model of PC12 cells with low ALDH2 expression was established and screened. The eukaryotic expression vector of wild type pEGFP-N1-ALDH2 and blank plasmid pEGFP-N1 were constructed and transfected into PC12 hypoxia cells respectively. After reoxygenation culture, the morphology, quantity, ALDH2 expression level and apoptosis rate of the two groups were observed, and the role of ALDH2 in cell hypoxia injury was analyzed. Eighty SD rats were randomly divided into model group (ischemia-reperfusion injury group), Alda-1 group (intraperitoneal injection of alda-1 12 hours before and after modeling), DMSO group (intraperitoneal injection of dimethyl sulfoxide) and sham operation group, with 20 rats in each group. The neurobehavioral score, apoptosis rate of nerve cells, the content and activity of ALDH2 in active cerebral cortex and hippocampal CA1 area were compared.

RESULTS

The number of PC12 cells in hypoxia group was lower than that in control group. The expression level of ALDH2 protein in PC12 cells after 4 hours of hypoxia was lower than that in normal culture group. The number of PC12 cells transfected with wild-type recombinant plasmid was significantly more than that of blank plasmid group. Compared with the hypoxia group, the pre apoptotic and post apoptotic cells in wild type transfection group decreased after hypoxia treatment. Compared with sham operation group, nerve injury and apoptosis were increased in group M and DMSO, while ALDH2 activity and expression did not change significantly. Compared with M group and DMSO group, the nerve injury and apoptosis in Alda-1 group were improved, ALDH2 activity was increased, and ALDH2 expression was not significantly changed in Alda-1 group.

CONCLUSIONS

Increasing the expression of ALDH2 or enhancing the activity of ALDH2 can improve the injury of neurons induced by hypoxia/reoxygenation.

Investigation of a Model-based Time-over-threshold Technique for Phoswich Crystal Discrimination

The best crystal identification (CI) algorithms proposed so far for phoswich detectors are based on adaptive filtering and pulse shape discrimination (PSD). However, these techniques require free running analog to digital converters, which is no longer possible with the ever increasing pixelization of new detectors. We propose to explore the dual-threshold time-over-threshold (ToT) technique, used to measure events energy and time of occurence, as a more robust solution for crystal identification with broad energy windows in phoswich detectors. In this study, phoswich assemblies made of various combinations of LGSO and LYSO scintillators with decay times in the range 30 to 65 ns were investigated for the LabPET II detection front-end. The electronic readout is based on a 4 × 8 APD array where pixels are individually coupled to charge sensitive preamplifiers followed by first order CR-RC shapers with 75 ns peaking time. Crystal identification data were sorted out based on the measurements of likeliness between acquired signals and a time domain model of the analog front-end. Results demonstrate that crystal identification can be successfully performed using a dual-threshold ToT scheme with a discrimination accuracy of 99.1% for LGSO (30 ns)/LGSO (45 ns), 98.1% for LGSO (65 ns)/LYSO (40 ns) and 92.1% for LYSO (32 ns)/LYSO (47 ns), for an energy window of [350-650] keV. Moreover, the method shows a discrimination accuracy >97% for the two first pairs and ~90% for the last one when using a wide energy window of [250-650] keV.

Surgical Treatment and Clinical Outcomes of Petroclival Meningiomas: A Single-Center Experience of 107 Patients

Objective

This study aimed to establish optimal surgical strategies via reviewing the clinical outcomes of various surgical approaches for the pertroclival meningiomas (PCMs).

Methods

This retrospective study enrolled 107 patients with PCMs at the authors’ institution from year 2010 to 2020. Patient demographics, the clinical characteristics, various operative approaches, major morbidity, post-operative cranial nerve deficits and tumor progression or recurrence were analyzed.

Results

The subtemporal transtentorial approach (STA), the Kawase approach (KA), the retrosigmoid approach (RSA) and the anterior sigmoid approach (ASA), namely the posterior petrosal approach (PPA) were adopted for 17 cases, 22 cases, 31 cases and 34 cases respectively. Total or subtotal resection was achieved in 96 cases (89.7%). The incidence of new-onset and aggravated cranial nerve dysfunction were 13.1% (14/107) and 10.4% (15/144), respectively. Furthermore, 14 cases suffered from intracranial infection, 9 cases had cerebrospinal fluid leakage, and 3 cases sustained intracranial hematoma (1 case underwent second operation). The mean preoperative and postoperative Karnofsky Performance Status (KPS) score was 80 (range 60-100) and 78.6 (range 0-100), but this was not statistically significant (P>0.05). After a mean follow-up of 5.1 years (range 0.3- 10.6 years), tumor progression or recurrence was confirmed in 23 cases. Two cases died from postoperative complications.

Conclusions

For the treatment of PCMs, it is still a challenge to achieve total resection. With elaborate surgical plans and advanced microsurgical skills, most patients with PCMs can be rendered tumor resection with satisfactory extent and functional preservation, despite transient neurological deterioration during early postoperative periods.

Impact of reconstruction parameters on lesion detection and localization in joint ictal/inter-ictal SPECT reconstruction

PURPOSE

Previously, a joint ictal/inter-ictal SPECT reconstruction was proposed to reconstruct a differential image representing the change of brain SPECT image from an inter-ictal to an ictal study. The so-called joint method yielded better performance for epileptic foci localization than the conventional subtraction method. In this study, we evaluated the performance of different reconstruction settings of the joint reconstruction of ictal/inter-ictal SPECT data, which creates a differential image showing the difference between ictal and inter-ictal images, in lesion detection and localization in epilepsy imaging.

METHODS

Differential images reconstructed from phantom data using the joint and the subtraction methods were compared based on lesion detection performance (channelized Hotelling observer signal-to-noise ratio (SNR_CHO) averaged across four lesion-to-background contrast levels) at the optimal iteration. The joint-initial method which was the joint method that was initialized by the subtraction method at optimal iteration was also used to reconstruct differential images. These three methods with respective optimal iteration and the subtraction method with four iterations were applied to epileptic patient datasets. A human observer lesion localization study was performed based on localization receiver operating characteristic (LROC) analysis.

RESULTS

From the phantom study, at their respective optimal iteration, the joint method yielded an improvement in lesion detection performance over the subtraction method of 26%, which increased to 145% when using the joint-initial method. From the patient study, the joint-initial method yielded the highest area under the LROC curve as compared with those of the joint and the subtraction methods with optimal iteration and with 4 iterations (0.44 vs 0.41, 0.39 and 0.36, respectively).

CONCLUSIONS

In lesion detection and localization, the joint method at optimal iteration outperformed the subtraction method at optimal iteration and at iteration typically used in clinical practice. Furthermore, initialization by the subtraction method improved the performance of the joint method.

Detecting lumbar lesions in 99m Tc-MDP SPECT by deep learning: Comparison with physicians

PURPOSE

^99m Tc-MDP single-photon emission computed tomography (SPECT) is an established tool for diagnosing lumbar stress, a common cause of low back pain (LBP) in pediatric patients. However, detection of small stress lesions is complicated by the low quality of SPECT, leading to significant interreader variability. The study objectives were to develop an approach based on a deep convolutional neural network (CNN) for detecting lumbar lesions in ^99m Tc-MDP scans and to compare its performance to that of physicians in a localization receiver operating characteristic (LROC) study.

METHODS

Sixty-five lesion-absent (LA) ^99m Tc-MDP studies performed in pediatric patients for evaluating LBP were retrospectively identified. Projections for an artificial focal lesion were acquired separately by imaging a ^99m Tc capillary tube at multiple distances from the collimator. An approach was developed to automatically insert lesions into LA scans to obtain realistic lesion-present (LP) ^99m Tc-MDP images while ensuring knowledge of the ground truth. A deep CNN was trained using 2.5D views extracted in LP and LA ^99m Tc-MDP image sets. During testing, the CNN was applied in a sliding-window fashion to compute a 3D "heatmap" reporting the probability of a lesion being present at each lumbar location. The algorithm was evaluated using cross-validation on a ^99m Tc-MDP test dataset which was also studied by five physicians in a LROC study. LP images in the test set were obtained by incorporating lesions at sites selected by a physician based on clinical likelihood of injury in this population.

RESULTS

The deep learning (DL) system slightly outperformed human observers, achieving an area under the LROC curve (AUC_LROC ) of 0.830 (95% confidence interval [CI]: [0.758, 0.924]) compared with 0.785 (95% CI: [0.738, 0.830]) for physicians. The AUC_LROC for the DL system was higher than that of two readers (difference in AUC_LROC [ΔAUC_LROC ] = 0.049 and 0.053) who participated to the study and slightly lower than that of two other readers (ΔAUC_LROC  = -0.006 and -0.012). Another reader outperformed DL by a more substantial margin (ΔAUC_LROC  = -0.053).

CONCLUSION

The DL system provides comparable or superior performance than physicians in localizing small ^99m Tc-MDP positive lumbar lesions.

Tissue at Risk and Ischemic Core Estimation Using Deep Learning in Acute Stroke

BACKGROUND AND PURPOSE

In acute stroke patients with large vessel occlusions, it would be helpful to be able to predict the difference in the size and location of the final infarct based on the outcome of reperfusion therapy. Our aim was to demonstrate the value of deep learning-based tissue at risk and ischemic core estimation. We trained deep learning models using a baseline MR image in 3 multicenter trials.

MATERIALS AND METHODS

Patients with acute ischemic stroke from 3 multicenter trials were identified and grouped into minimal (≤20%), partial (20%-80%), and major (≥80%) reperfusion status based on 4- to 24-hour follow-up MR imaging if available or into unknown status if not. Attention-gated convolutional neural networks were trained with admission imaging as input and the final infarct as ground truth. We explored 3 approaches: 1) separate: train 2 independent models with patients with minimal and major reperfusion; 2) pretraining: develop a single model using patients with partial and unknown reperfusion, then fine-tune it to create 2 separate models for minimal and major reperfusion; and 3) thresholding: use the current clinical method relying on apparent diffusion coefficient and time-to-maximum of the residue function maps. Models were evaluated using area under the curve, the Dice score coefficient, and lesion volume difference.

RESULTS

Two hundred thirty-seven patients were included (minimal, major, partial, and unknown reperfusion: n = 52, 80, 57, and 48, respectively). The pretraining approach achieved the highest median Dice score coefficient (tissue at risk = 0.60, interquartile range, 0.43-0.70; core = 0.57, interquartile range, 0.30-0.69). This was higher than the separate approach (tissue at risk = 0.55; interquartile range, 0.41-0.69; P = .01; core = 0.49; interquartile range, 0.35-0.66; P = .04) or thresholding (tissue at risk = 0.56; interquartile range, 0.42-0.65; P = .008; core = 0.46; interquartile range, 0.16-0.54; P < .001).

CONCLUSIONS

Deep learning models with fine-tuning lead to better performance for predicting tissue at risk and ischemic core, outperforming conventional thresholding methods.

Quantitative PET in the 2020s: a roadmap

Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years, a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semi-quantitative 'hot spot' imaging for oncologic applications has meant that the capability of PET to quantify biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total-body PET, have opened up the ability to address a vast range of new research questions, from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next ten years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.

[Association of hyperuricemia-induced renal damage with sirtuin 1 and endothelial nitric oxide synthase in rats]

Objective: To investigate the association of hyperuricemia-induced renal damage with sirtuin 1 (SIRT1) and endothelial nitric oxide synthase (eNOS) in rats. Methods: Using the random number table method, 32 Sprague-Dawley rats were randomly divided into 4 groups: control group, model A group (the model was generated using oxonic acid potassium salt alone), model B group (hyperuricemia model was generated using oxonic acid potassium salt combined with uric acid) and resveratrol group, with 8 rats in each group. The experiment lasted 12 weeks. Serum uric acid and cystatin C levels were monitored regularly. In week 12, serum creatinine and urea nitrogen levels were measured, and the kidneys were extracted. The expression of SIRT1 and eNOS in renal tissues was measured and determined by immunohistochemistry, quantitative reverse-transcription polymerase chain reaction (RT-qPCR) and western blotting. Immunohistochemistry of alpha-smooth muscle actin combined with Masson staining was employed to evaluate the degree of renal fibrosis, and pathological changes were observed based on hematoxylin and eosin staining. Results: In week 12, the uric acid levels in both the model A and model B groups were higher than those in the control group [(316±43) μmol/L, (297±40) μmol/L vs (118±44) μmol/L, both P<0.05]. The levels of cystatin C in the model A, model B, and resveratrol groups were all higher than those in the control group [(156±20) ng/ml, (143±29) ng/ml, (128±26) ng/ml vs (62±18) ng/ml, all P<0.05]. Creatinine levels were higher in the model A and model B groups than those in the control group [(68.5±10.3) μmol/L, (64.5±13.9) μmol/L vs (43.2±10.6) μmol/L, both P<0.05]. The levels of uric acid, cystatin C and creatinine in the resveratrol group were lower than those in the model A group (all P<0.05). Immunohistochemistry, RT-qPCR, and Western blotting for renal SIRT1 and eNOS showed that the expression in the model A and model B groups was inhibited, while the expression in the resveratrol group was not significantly inhibited, compared with that in the control group. Microscopically, obvious abnormalities were not found in the renal tissue of the control group. Renal inflammatory cell aggregation and edema occurred, and interstitial fibrosis was obvious in both the model A and model B groups, while these lesions in the resveratrol group were significantly improved. Conclusions: Hyperuricemia may cause renal injury by inhibiting the expression of SIRT1 and eNOS.

Motion correction for PET data using subspace-based real-time MR imaging in simultaneous PET/MR

Image quality of positron emission tomography (PET) reconstructions is degraded by subject motion occurring during the acquisition. Magnetic resonance (MR)-based motion correction approaches have been studied for PET/MR scanners and have been successful at capturing regular motion patterns, when used in conjunction with surrogate signals (e.g. navigators) to detect motion. However, handling irregular respiratory motion and bulk motion remains challenging. In this work, we propose an MR-based motion correction method relying on subspace-based real-time MR imaging to estimate motion fields used to correct PET reconstructions. We take advantage of the low-rank characteristics of dynamic MR images to reconstruct high-resolution MR images at high frame rates from highly undersampled k-space data. Reconstructed dynamic MR images are used to determine motion phases for PET reconstruction and estimate phase-to-phase nonrigid motion fields able to capture complex motion patterns such as irregular respiratory and bulk motion. MR-derived binning and motion fields are used for PET reconstruction to generate motion-corrected PET images. The proposed method was evaluated on in vivo data with irregular motion patterns. MR reconstructions accurately captured motion, outperforming state-of-the-art dynamic MR reconstruction techniques. Evaluation of PET reconstructions demonstrated the benefits of the proposed method in terms of motion artifacts reduction, improving the contrast-to-noise ratio by up to a factor 3 and achieveing a target-to-background ratio up to 90% superior compared to standard/uncorrected methods. The proposed method can improve the image quality of motion-corrected PET reconstructions in clinical applications.

PET imaging of neurotransmission using direct parametric reconstruction

Receptor ligand-based dynamic Positron Emission Tomography (PET) permits the measurement of neurotransmitter release in the human brain. For single-scan paradigms, the conventional method of estimating changes in neurotransmitter levels relies on fitting a pharmacokinetic model to activity concentration histories extracted after PET image reconstruction. However, due to the statistical fluctuations of activity concentration data at the voxel scale, parametric images computed using this approach often exhibit low signal-to-noise ratio, impeding characterization of neurotransmitter release. Numerous studies have shown that direct parametric reconstruction (DPR) approaches, which combine image reconstruction and kinetic analysis in a unified framework, can improve the signal-to-noise ratio of parametric mapping. However, there is little experience with DPR in imaging of neurotransmission and the performance of the approach in this application has not been evaluated before in humans. In this report, we present and evaluate a DPR methodology that computes 3-D distributions of ligand transport, binding potential (BP_ND) and neurotransmitter release magnitude (γ) from a dynamic sequence of PET sinograms. The technique employs the linear simplified reference region model (LSRRM) of Alpert et al. (2003), which represents an extension of the simplified reference region model that incorporates time-varying binding parameters due to radioligand displacement by release of neurotransmitter. Estimation of parametric images is performed by gradient-based optimization of a Poisson log-likelihood function incorporating LSRRM kinetics and accounting for the effects of head movement, attenuation, detector sensitivity, random and scattered coincidences. A ¹¹C-raclopride simulation study showed that the proposed approach substantially reduces the bias and variance of voxel-wise γ estimates as compared to standard methods. Moreover, simulations showed that detection of release could be made more reliable and/or conducted using a smaller sample size using the proposed DPR estimator. Likewise, images of BP_ND computed using DPR had substantially improved bias and variance properties. Application of the method in human subjects was demonstrated using ¹¹C-raclopride dynamic scans and a reward task, confirming the improved quality of the estimated parametric images using the proposed approach.

Orexin A alleviates neuroinflammation via OXR2/CaMKKβ/AMPK signaling pathway after ICH in mice

Background

Orexins are two neuropeptides (orexin A, OXA; orexin B, OXB) secreted mainly from the lateral hypothalamus, which exert a wide range of physiological effects by activating two types of receptors (orexin receptor 1, OXR1; orexin receptor 2, OXR2). OXA has equal affinity for OXR1 and OXR2, whereas OXB binds preferentially to OXR2. OXA rapidly crosses the blood-brain barrier by simple diffusion. Many studies have reported OXA’s protective effect on neurological diseases via regulating inflammatory response which is also a fundamental pathological process in intracerebral hemorrhage (ICH). However, neuroprotective mechanisms of OXA have not been explored in ICH.

Methods

ICH models were established using stereotactic injection of autologous arterial blood into the right basal ganglia of male CD-1 mice. Exogenous OXA was administered intranasally; CaMKKβ inhibitor (STO-609), OXR1 antagonist (SB-334867), and OXR2 antagonist (JNJ-10397049) were administered intraperitoneally. Neurobehavioral tests, hematoma volume, and brain water content were evaluated after ICH. Western blot and ELISA were utilized to evaluate downstream mechanisms.

Results

OXA, OXR1, and OXR2 were expressed moderately in microglia and astrocytes and abundantly in neurons. Expression of OXA decreased whereas OXR1 and OXR2 increased after ICH. OXA treatment significantly improved not only short-term but also long-term neurofunctional outcomes and reduced brain edema in ipsilateral hemisphere. OXA administration upregulated p-CaMKKβ, p-AMPK, and anti-inflammatory cytokines while downregulated p-NFκB and pro-inflammatory cytokines after ICH; this effect was reversed by STO-609 or JNJ-10397049 but not SB-334867.

Conclusions

OXA improved neurofunctional outcomes and mitigated brain edema after ICH, possibly through alleviating neuroinflammation via OXR2/CaMKKβ/AMPK pathway.

Evaluation of four disease management programs: evidence from blue cross blue shield of Louisiana

Aims: Chronic diseases impose a substantial healthcare burden. This study sought to evaluate the clinical and economic impact of new disease management (DM) programs, targeting four major chronic disease groups: diabetes, coronary heart disease (CHD)/hypertension (HTN), asthma/chronic obstructive pulmonary disease (COPD), and congestive heart failure (CHF)/chronic kidney disease (CKD).Materials and methods: Between March 1, 2015, and February 28, 2018, members with Blue Cross Blue Shield of Louisiana insurance were contacted and enrolled in a DM program if they were aged 18 years through 64 years, eligible for a DM program, and had not been previously enrolled in a DM program. Active enrollees of a DM program ("IN" group) were compared to members who were not yet enrolled ("OUT" group). Average per member per month (PMPM) costs were aggregated annually to document any descriptive trends. Multivariable model estimates were used to compare PMPM costs for all IN subjects and all OUT subjects. Total medical savings were evaluated for the following time intervals: 1-12 months, 13-24 months, and 25-36 months.Results: For all four DM programs, average costs PMPM trended upward over time for the OUT cohort, while they remained relatively stable for the IN cohort. Some evidence also showed that DM programs improved clinical outcomes, such as hemoglobin A1c values. A difference in difference analysis showed PMPM savings for all four programs combined of $31.61, $50.45, and $53.72 after 1, 2, and 3 years, respectively. Multivariable modeling results showed total savings after 3 years of $14,460,174 for all DM programs combined.Limitations: Although multivariable models adjusted for several clinical, demographic, and economic characteristics; it is possible that some important confounders were missing due to lack of data.Conclusions: DM programs implemented to control diabetes, CHD/HTN, CHF/CKD, and asthma/COPD are cost-effective and show some evidence of improved clinical outcomes.

MR-based PET attenuation correction using a combined ultrashort echo time/multi-echo Dixon acquisition

PURPOSE

To develop a magnetic resonance (MR)-based method for estimation of continuous linear attenuation coefficients (LACs) in positron emission tomography (PET) using a physical compartmental model and ultrashort echo time (UTE)/multi-echo Dixon (mUTE) acquisitions.

METHODS

We propose a three-dimensional (3D) mUTE sequence to acquire signals from water, fat, and short T₂ components (e.g., bones) simultaneously in a single acquisition. The proposed mUTE sequence integrates 3D UTE with multi-echo Dixon acquisitions and uses sparse radial trajectories to accelerate imaging speed. Errors in the radial k-space trajectories are measured using a special k-space trajectory mapping sequence and corrected for image reconstruction. A physical compartmental model is used to fit the measured multi-echo MR signals to obtain fractions of water, fat, and bone components for each voxel, which are then used to estimate the continuous LAC map for PET attenuation correction.

RESULTS

The performance of the proposed method was evaluated via phantom and in vivo human studies, using LACs from computed tomography (CT) as reference. Compared to Dixon- and atlas-based MRAC methods, the proposed method yielded PET images with higher correlation and similarity in relation to the reference. The relative absolute errors of PET activity values reconstructed by the proposed method were below 5% in all of the four lobes (frontal, temporal, parietal, and occipital), cerebellum, whole white matter, and gray matter regions across all subjects (n = 6).

CONCLUSIONS

The proposed mUTE method can generate subject-specific, continuous LAC map for PET attenuation correction in PET/MR.

Dependence of fluorodeoxyglucose (FDG) uptake on cell cycle and dry mass: a single-cell study using a multi-modal radiography platform

High glucose uptake by cancer compared to normal tissues has long been utilized in fluorodeoxyglucose-based positron emission tomography (FDG-PET) as a contrast mechanism. The FDG uptake rate has been further related to the proliferative potential of cancer, specifically the proliferation index (PI) - the proportion of cells in S, G2 or M phases. The underlying hypothesis was that the cells preparing for cell division would consume more energy and metabolites as building blocks for biosynthesis. Despite the wide clinical use, mixed reports exist in the literature on the relationship between FDG uptake and PI. This may be due to the large variation in cancer types or methods adopted for the measurements. Of note, the existing methods can only measure the average properties of a tumor mass or cell population with highly-heterogeneous constituents. In this study, we have built a multi-modal live-cell radiography system and measured the [18F]FDG uptake by single HeLa cells together with their dry mass and cell cycle phase. The results show that HeLa cells take up twice more [18F]FDG in S, G2 or M phases than in G1 phase, which confirms the association between FDG uptake and PI at a single-cell level. Importantly, we show that [18F]FDG uptake and cell dry mass have a positive correlation in HeLa cells, which suggests that high [18F]FDG uptake in S, G2 or M phases can be largely attributed to increased dry mass, rather than the activities preparing for cell division. This interpretation is consistent with recent observations that the energy required for the preparation of cell division is much smaller than that for maintaining house-keeping proteins.

Free-Breathing Three-Dimensional T1 Mapping of the Heart Using Subspace-Based Data Acquisition and Image Reconstruction

Mapping the longitudinal relaxation time constant (T1) of the myocardium using Magnetic Resonance Imaging (MRI) is an emerging technique for quantitative assessment of the morphology and viability of the myocardium. However, three-dimensional (3D) T1 mapping of the heart is challenging due to the high dimensionality of the signal and the presence of cardiac and respiratory motions. We propose a subspace-based method for free-breathing 3D T1 mapping of the heart without respiratory gating. The image function is represented as a high-order partially separable (PS) function to explore the inherent spatiotemporal correlations of the underlying signal. A special data acquisition scheme enabled by the high-order PS model is used for sparse sampling of the (k,t)-space, where complementary sparse datasets are acquired, each covering only a small portion of the (k,t)-space to characterize a single subspace (spatial or temporal). High-resolution dynamic MR images are reconstructed from the highly undersampled (k,t)-space using low-rank tensor and sparsity constraints. We demonstrate the feasibility of our proposed method using in vivo data obtained from healthy subjects on a 3T MR scanner. The proposed method can enable new clinical applications of T1 mapping in cardiac MR.

Homocysteine induced oxidative stress in human umbilical vein endothelial cells via regulating methylation of SORBS1

OBJECTIVE

The aim of the present study was to investigate the mechanism of homocysteine (Hcy) induced oxidative stress in the human umbilical vein endothelial cells (HUVECs).

PATIENTS AND METHODS

The HUVECs were isolated from umbilical vein vascular wall of 12 patients and treated with Hcy. The malondialdehyde (MDA) level was measured using the thiobarbituric acid (TBA) method. The expressions of superoxide dismutase 2 (SOD2), endothelial nitric oxide synthase (eNOS), and intercellular adhesion molecule 1 (ICAM-1) were detected by Western blot and RT-PCR. The genome-wide DNA methylation assay was performed using the Infinium Human Methylation 450 BeadChip. The specific DNA methylation was determined using bisulfite sequencing analysis. To evaluate the role of sorbin and SH3 domain-containing protein 1 (SORBS1), the HUVECs were transfected with small interfere RNA (siRNA) targeting SORBS1 (SORBS1-siRNA).

RESULTS

Hcy induced MDA level in HUVECs, and increased ICAM-1 expression both in protein and mRNA levels. The protein and mRNA levels of SOD2 and eNOS were inhibited by Hcy induction. However, the effects of Hcy on MDA level and expressions of SOD2, eNOS, and ICAM-1 were attenuated by folic acid (Fc) and vitamin B12 (B12) treatment. DNA total methylation level in Hcy treated cells was significantly decreased compared to the control group, while the DNA total methylation levels were increased after treatment with Fc and B12. The methylation level of SORBS1 in Hcy treatment group was higher than that of control group. And the methylation level of SORBS1 induced by Hcy was attenuated by Fc and B12 treatment. SORBS1-siRNA transfection induced the MDA levels and reduced the expressions of SOD2 in HUVECs.

CONCLUSIONS

We indicated that Hcy induced oxidative stress in HUVECs via regulating methylation of SORBS1. We also found that Fc and B12 treatment attenuated the oxidative stress and inflammation induced by Hcy in HUVECs. The findings indicated that Fc and B12 might be effective agents for the treatment of Hcy induced AS.

Body motion detection and correction in cardiac PET: Phantom and human studies

PURPOSE

Patient body motion during a cardiac positron emission tomography (PET) scan can severely degrade image quality. We propose and evaluate a novel method to detect, estimate, and correct body motion in cardiac PET.

METHODS

Our method consists of three key components: motion detection, motion estimation, and motion-compensated image reconstruction. For motion detection, we first divide PET list-mode data into 1-s bins and compute the center of mass (COM) of the coincidences' distribution in each bin. We then compute the covariance matrix within a 25-s sliding window over the COM signals inside the window. The sum of the eigenvalues of the covariance matrix is used to separate the list-mode data into "static" (i.e., body motion free) and "moving" (i.e. contaminated by body motion) frames. Each moving frame is further divided into a number of evenly spaced sub-frames (referred to as "sub-moving" frames), in which motion is assumed to be negligible. For motion estimation, we first reconstruct the data in each static and sub-moving frame using a rapid back-projection technique. We then select the longest static frame as the reference frame and estimate elastic motion transformations to the reference frame from all other static and sub-moving frames using nonrigid registration. For motion-compensated image reconstruction, we reconstruct all the list-mode data into a single image volume in the reference frame by incorporating the estimated motion transformations in the PET system matrix. We evaluated the performance of our approach in both phantom and human studies.

RESULTS

Visually, the motion-corrected (MC) PET images obtained using the proposed method have better quality and fewer motion artifacts than the images reconstructed without motion correction (NMC). Quantitative analysis indicates that MC yields higher myocardium to blood pool concentration ratios. MC also yields sharper myocardium than NMC.

CONCLUSIONS

The proposed body motion correction method improves image quality of cardiac PET.

MR-based cardiac and respiratory motion correction of PET: application to static and dynamic cardiac 18F-FDG imaging

Motion of the myocardium deteriorates the quality and quantitative accuracy of cardiac PET images. We present a method for MR-based cardiac and respiratory motion correction of cardiac PET data and evaluate its impact on estimation of activity and kinetic parameters in human subjects. Three healthy subjects underwent simultaneous dynamic ¹⁸F-FDG PET and MRI on a hybrid PET/MR scanner. A cardiorespiratory motion field was determined for each subject using navigator, tagging and golden-angle radial MR acquisitions. Acquired coincidence events were binned into cardiac and respiratory phases using electrocardiogram and list mode-driven signals, respectively. Dynamic PET images were reconstructed with MR-based motion correction (MC) and without motion correction (NMC). Parametric images of ¹⁸F-FDG consumption rates (K_i) were estimated using Patlak's method for both MC and NMC images. MC alleviated motion artifacts in PET images, resulting in improved spatial resolution, improved recovery of activity in the myocardium wall and reduced spillover from the myocardium to the left ventricle cavity. Significantly higher myocardium contrast-to-noise ratio and lower apparent wall thickness were obtained in MC versus NMC images. Likewise, parametric images of K_i calculated with MC data had improved spatial resolution as compared to those obtained with NMC. Consistent with an increase in reconstructed activity concentration in the frames used during kinetic analyses, MC led to the estimation of higher K_i values almost everywhere in the myocardium, with up to 18% increase (mean across subjects) in the septum as compared to NMC. This study shows that MR-based motion correction of cardiac PET results in improved image quality that can benefit both static and dynamic studies.

[Clinical characteristics and prognosis of 34 cases of acute myeloid leukemia with FLT3 internal tandem duplication and MLL gene rearrangement]

目的

探讨同时伴FLT3-ITD突变及MLL基因异常的急性髓系白血病（AML）患者的临床特征及转归。

方法

回顾性分析34例同时伴FLT3-ITD突变及MLL基因异常的AML患者的临床资料，比较化疗、化疗加靶向药物治疗及allo-HSCT的疗效及影响因素。

结果

34例同时伴FLT3-ITD突变及MLL基因异常的AML患者占同期住院AML患者的2.02%。入院时WBC>30×10⁹/L的患者占63.6%，其中WBC>50×10⁹/L者占39.4%。FAB亚型中以M₅比例最高，占35.3%，染色体核型异常者达63.6%，其中复杂异常占12.1%。34例患者中仅有FLT3-ITD及MLL基因异常（双基因异常）者11例（32.4%），具FLT3及MLL以外的1种及1种以上的基因异常（多基因异常）者23例（67.6%）。34例患者2个疗程完全缓解（CR）率为29.4%，7例（20.6%）化疗≥3个疗程后CR，CR患者的早期复发率为52.9%。WBC>50×10⁹/L以及多基因异常的患者2个疗程CR率较低（7.7%、5.4%），其中具有3种以上基因异常的患者无一例CR。34例患者2年总生存（OS）率为28.8%（95%CI 13.5%~46.0%），2年无病生存（DFS）率为27.1%（95% CI 12.5%~44.0%）。18例仅使用化疗或化疗加靶向药物治疗的患者，17例在2年内死亡，1例放弃治疗后失访。接受allo-HSCT治疗的患者3年OS率为43.4%（95%CI 13.7%~70.4%），3年DFS率为42.7%（95% CI 13.4%~69.7%）。

结论

同时伴FLT3-ITD突变及MLL基因异常的AML患者FAB分型以M₅多见，常伴高白细胞血症、细胞遗传学异常及多基因异常。患者化疗缓解率低，早期复发率高，长期生存率低。高白细胞血症、多基因异常可能是此类患者疗效差的重要原因，allo-HSCT可改善患者的转归。

[Efficacy and safety of IA regimen containing different doses of idarubicin in de-novo acute myeloid leukemia for adult patients]

目的

探讨含不同剂量去甲氧柔红霉素（IDA 8、10、12 mg/m²）的IA方案诱导治疗成人初发急性髓系白血病（AML）（非急性早幼粒细胞白血病）的临床疗效和安全性。

方法

采用多中心、单盲、非随机、临床对照研究，纳入2011年5月至2015年3月苏州大学附属第一医院及其他36家单位收治的1 215例成人初发AML患者，根据诱导化疗方案中IDA的剂量对患者进行分组，分析不同剂量IDA联合阿糖胞苷（100 mg/m²）组成的IA方案在成人初发AML诱导治疗中的完全缓解（CR）率、血液学及非血液学不良事件。

结果

可纳入缓解率分析的AML患者共1 207例，IDA 8 mg/m²、10 mg/m²和12 mg/m²组的CR率分别为73.6%（215/292）、84.1%（662/787）和86.7%（111/128），差异有统计学意义（P<0.001）；以IDA 8 mg/m²组为参照组，在调整了年龄、骨髓原始细胞比例、FAB分型、危险度分层后，IDA 10 mg/m²和IDA 12 mg/m²为影响患者CR的有利因素[OR=0.49（95% CI 0.34~0.70），P<0.001；OR=0.36（95%CI 0.18~0.71），P=0.003]。在中、低危组中三组CR率分别为76.5%（163/213）、86.9%（506/582）和86.1%（68/79），差异有统计学意义（P=0.007）；在调整了年龄、骨髓原始细胞比例、FAB分型因素后，IDA 10 mg/m²为影响患者CR的有利因素[OR=0.47（95% CI 0.31~0.71），P<0.001]。在高危组中，三组CR率分别为50.0%（18/36）、60.6%（43/71）和81.8%（18/22），差异无统计学意义（P=0.089），但在调整了年龄、骨髓原始细胞比例、FAB分型因素后，IDA 12 mg/m²为影响患者CR的有利因素[OR=0.22（95% CI 0.06~0.80），P=0.022]。8 mg/m²、10 mg/m²和12 mg/m²组中性粒细胞≤0.5×10⁹/L的中位持续时间分别为14（11~18）、15（11~20）和18（14~22）d，差异有统计学意义（P=0.012）；三组PLT≤20×10⁹/L的中位持续时间分别为14（7~17）、15（11~20）和17（15~21）d，差异有统计学意义（P=0.001）；三组肺部感染发生率分别为9.8%、13.5%和25.2%，差异有统计学意义（P<0.001）。

结论

在中国成人（18~60岁）初发AML中，建议中、低危组患者采用含IDA 10 mg/m²的IA方案进行诱导治疗；而高危组AML建议选择含IDA 12 mg/m²的IA方案进行诱导治疗。

Giant Magneto-Impedance (GMI) Effect in Single-Layer Soft Magnetic Film Under Stress

The stress-induced magnetic anisotropy can significantly affect giant magneto-impedance (GMI) effect of the soft magnetic film. This paper is devoted to the GMI effect of the single layer soft magnetic film implied without and with a stress. By simulating a physical model with MATLAB and COMSOL software, the impedance expression of the single layer soft magnetic film and the relation between external magnetic field and magnetic permeability are deduced. We observed that, without a stress, the sensitive region increased firstly and then decreased with the increasing of the excitation current frequency from 1 MHz to 200 MHz. While the film was subjected to the stress in the direction of the current with one end stressed, the stress on the film was gradually reduced from stressed end to free end. Also, the impedance change rate of the film changed when the stress was added, which is similar to the effect of adding a bias magnetic field on the film. More importantly, the addition of stress σ can induce the bias of the GMI measurement range and improve its sensitivity near zero magnetic fields. This may provide a new way for designing a GMI sensor with higher sensitivity and adjustable measurement range.

Joint reconstruction of rest/stress myocardial perfusion SPECT

Myocardial perfusion imaging (MPI) using rest/stress single photon emission computed tomography (SPECT) allows non-invasive assessment of reversible cardiac perfusion defects. Conventionally, reversible defects are identified using a difference image, called reversible map, obtained by subtracting the stress image from the rest image after registration and normalization of the two images. The identification of reversible defects using the conventional subtraction method is however limited by noise. We propose to jointly reconstruct rest and stress projection data to directly obtain the reversible map in a single reconstruction framework to improve the detectability of reversible defects. To evaluate the performance of the proposed method, we performed phantom studies to mimic reversible defects with different levels of severity and doses. As compared to the conventional subtraction method, the joint method yielded reversible maps with much lower noise and improved defect detectability. At a normal clinical dose level, the joint method improved the signal to noise ratio (SNR) of defect contrast in reversible maps from 13.2 to 66.4, 9.7 to 35.0, 6.1 to 13.2, and 3.1 to 6.5, for defect to normal myocardium concentration ratios of 0%, 25%, 50%, and 75%, respectively. The SNRs obtained using the joint method were improved from 6.1 to 13.2, 3.9 to 9.4, 3.0 to 8.0, and 2.1 to 7.1, for 100%, 75%, 50%, and 25% of the normal clinical dose as compared to the conventional subtraction method. To access clinical feasibility, we applied the joint method to a rest/stress SPECT MPI patient study. The joint method yielded a reversible map with much lower noise, translating into a much higher defect detectability as compared to the conventional subtraction method. Our results indicate that the joint method has the potential to improve radiologists' performance for assessing defects in rest/stress SPECT MPI. In addition, the joint method can be used to reduce dose or imaging time.

A comparison of the biomechanical stability of pedicle-lengthening screws and traditional pedicle screws: an in vitro instant and fatigue-resistant pull-out test

Aims

The aim of this study was to compare the peak pull-out force (PPF) of pedicle-lengthening screws (PLS) and traditional pedicle screws (TPS) using instant and cyclic fatigue testing.

Materials and Methods

A total of 60 lumbar vertebrae were divided into six groups: PLS submitted to instant pull-out and fatigue-resistance testing (groups A1 and A2, respectively), TPS submitted to instant pull-out and fatigue-resistance testing (groups B1 and B2, respectively) and PLS augmented with 2 ml polymethylmethacrylate, submitted to instant pull-out and fatigue-resistance testing (groups C1 and C2, respectively). The PPF and normalized PPF (PPFn) for bone mineral density (BMD) were compared within and between all groups.

Results

In all groups, BMD was significantly correlated with PPF (r = 0.83, p < 0.001). The PPFn in A1 was significantly less than in B1 (p = 0.006) and C1 (p = 0.002). The PPFn of A2 was significantly less than in B2 (p < 0.001) and C2 (p < 0.001). The PPFn in A1, B1, and C1 was significantly greater than in A2 (p = 0.002), B2 (p = 0.027), and C2 (p = 0.003). There were no significant differences in PPFn between B1 and C1, or between B2 and C2.

Conclusion

Pedicle lengthening screws with cement augmentation can provide the same fixation stability as traditional pedicle screws and may be a viable clinical option. Cite this article: Bone Joint J 2018;100-B:516-21.

MR-based motion correction for cardiac PET parametric imaging: a simulation study

BACKGROUND

Both cardiac and respiratory motions bias the kinetic parameters measured by dynamic PET. The aim of this study was to perform a realistic positron emission tomography-magnetic resonance (PET-MR) simulation study using 4D XCAT to evaluate the impact of MR-based motion correction on the estimation of PET myocardial kinetic parameters using PET-MR. Dynamic activity distributions were obtained based on a one-tissue compartment model with realistic kinetic parameters and an arterial input function. Realistic proton density/T1/T2 values were also defined for the MRI simulation. Two types of motion patterns, cardiac motion only (CM) and both cardiac and respiratory motions (CRM), were generated. PET sinograms were obtained by the projection of the activity distributions. PET image for each time frame was obtained using static (ST), gated (GA), non-motion-corrected (NMC), and motion-corrected (MC) methods. Voxel-wise unweighted least squares fitting of the dynamic PET data was then performed to obtain K₁ values for each study. For each study, the mean and standard deviation of K₁ values were computed for four regions of interest in the myocardium across 25 noise realizations.

RESULTS

Both cardiac and respiratory motions introduce blurring in the PET parametric images if the motion is not corrected. Conventional cardiac gating is limited by high noise level on parametric images. Dual cardiac and respiratory gating further increases the noise level. In contrast to GA, the MR-based MC method reduces motion blurring in parametric images without increasing noise level. It also improves the myocardial defect delineation as compared to NMC method. Finally, the MR-based MC method yields lower bias and variance in K₁ values than NMC and GA, respectively. The reductions of K₁ bias by MR-based MC are 7.7, 5.1, 15.7, and 29.9% in four selected 0.18-mL myocardial regions of interest, respectively, as compared to NMC for CRM. MR-based MC yields 85.9, 75.3, 71.8, and 95.2% less K₁ standard deviation in the four regions, respectively, as compared to GA for CRM.

CONCLUSIONS

This simulation study suggests that the MR-based motion-correction method using PET-MR greatly reduces motion blurring on parametric images and yields less K₁ bias without increasing noise level.

[Distribution and drug resistance of pathogens at hematology department of Jiangsu Province from 2014 to 2015: results from a multicenter, retrospective study]

Objective: To describe the distribution and drug resistance of pathogens at hematology department of Jiangsu Province from 2014 to 2015 to provide reference for empirical anti-infection treatment. Methods: Pathogens were from hematology department of 26 tertiary hospitals in Jiangsu Province from 2014 to 2015. Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or agar dilution method. Collection of drug susceptibility results and corresponding patient data were analyzed. Results: The separated pathogens amounted to 4 306. Gram-negative bacteria accounted for 64.26%, while the proportions of gram-positive bacteria and funguses were 26.99% and 8.75% respectively. Common gram-negative bacteria were Escherichia coli (20.48%) , Klebsiella pneumonia (15.40%) , Pseudomonas aeruginosa (8.50%) , Acinetobacter baumannii (5.04%) and Stenotropho-monas maltophilia (3.41%) respectively. CRE amounted to 123 (6.68%) . Common gram-positive bacteria were Staphylococcus aureus (4.92%) , Staphylococcus hominis (4.88%) and Staphylococcus epidermidis (4.71%) respectively. Candida albicans were the main fungus which accounted for 5.43%. The rates of Escherichia coli and Klebsiella pneumonia resistant to carbapenems were 3.5%-6.1% and 5.0%-6.3% respectively. The rates of Pseudomonas aeruginosa resistant to tobramycin and amikacin were 3.2% and 3.3% respectively. The resistant rates of Acinetobacter baumannii towards tobramycin and cefoperazone/sulbactam were both 19.2%. The rates of Stenotrophomonas maltophilia resistant to minocycline and sulfamethoxazole were 3.5% and 9.3% respectively. The rates of Staphylococcus aureus, Enterococcus faecium and Enterococcus faecalis resistant wards vancomycin were 0, 6.4% and 1.4% respectively; also, the rates of them resistant to linezolid were 1.2%, 0 and 1.6% respectively; in addition, the rates of them resistant to teicoplanin were 2.8%, 14.3% and 8.0% respectively. Furthermore, MRSA accounted for 39.15% (83/212) . Conclusions: Pathogens were mainly gram-negative bacteria. CRE accounted for 6.68%. The rates of Escherichia coli and Klebsiella pneumonia resistant to carbapenems were lower compared with other antibacterial agents. The rates of gram-positive bacteria resistant to vancomycin, linezolid and teicoplanin were still low. MRSA accounted for 39.15%.

mTORC1 activation downregulates FGFR3 and PTH/PTHrP receptor in articular chondrocytes to initiate osteoarthritis

OBJECTIVE

Articular chondrocyte activation, involving aberrant proliferation and prehypertrophic differentiation, is essential for osteoarthritis (OA) initiation and progression. Disruption of mechanistic target of rapamycin complex 1 (mTORC1) promotes chondrocyte autophagy and survival, and decreases the severity of experimental OA. However, the role of cartilage mTORC1 activation in OA initiation is unknown. In this study, we elucidated the specific role of mTORC1 activation in OA initiation, and identify the underlying mechanisms.

METHOD

Expression of mTORC1 in articular cartilage of OA patients and OA mice was assessed by immunostaining. Cartilage-specific tuberous sclerosis complex 1 (Tsc1, mTORC1 upstream inhibitor) knockout (TSC1CKO) and inducible Tsc1 KO (TSC1CKO^ER) mice were generated. The functional effects of mTORC1 in OA initiation and development on its downstream targets were examined by immunostaining, western blotting and qPCR.

RESULTS

Articular chondrocyte mTORC1 was activated in early-stage OA and in aged mice. TSC1CKO mice exhibited spontaneous OA, and TSC1CKO^ER mice (from 2 months) exhibited accelerated age-related and DMM-induced OA phenotypes, with aberrant chondrocyte proliferation and hypertrophic differentiation. This was associated with hyperactivation of mTORC1 and dramatic downregulation of FGFR3 and PPR, two receptors critical for preventing chondrocyte proliferation and differentiation. Rapamycin treatment reversed these phenotypes in KO mice. Furthermore, in vitro rescue experiments demonstrated that p73 and ERK1/2 may mediate the negative regulation of FGFR3 and PPR by mTORC1.

CONCLUSION

mTORC1 activation stimulates articular chondrocyte proliferation and differentiation to initiate OA, in part by downregulating FGFR3 and PPR.

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in vivo studies

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves-TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans-each containing 1/8th of the total number of events-were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard ordered subset expectation maximization (OSEM) reconstruction algorithm on one side, and the one-step late maximum a posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of [Formula: see text], the tracer transport rate (ml · min-1 · ml-1), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced [Formula: see text] maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced [Formula: see text] estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance.

Joint reconstruction of Ictal/inter-ictal SPECT data for improved epileptic foci localization

PURPOSE

To improve the performance for localizing epileptic foci, we have developed a joint ictal/inter-ictal SPECT reconstruction method in which ictal and inter-ictal SPECT projections are simultaneously reconstructed to obtain the differential image.

METHODS

We have developed a SPECT reconstruction method that jointly reconstructs ictal and inter-ictal SPECT projection data. We performed both phantom and patient studies to evaluate the performance of our joint method for epileptic foci localization as compared with the conventional subtraction method in which the differential image is obtained by subtracting the inter-ictal image from the co-registered ictal image. Two low-noise SPECT projection datasets were acquired using ^99m Tc and a Hoffman head phantom at two different positions and orientations. At one of the two phantom locations, a low-noise dataset was also acquired using a ^99m Tc-filled 3.3-cm sphere with a cold attenuation background identical to the Hoffman phantom. These three datasets were combined and scaled to mimic low-noise clinical ictal (three different lesion-to-background contrast levels: 1.25, 1.55, and 1.70) and inter-ictal scans. For each low-noise dataset, 25 noise realizations were generated by adding Poisson noise to the projections. The mean and standard deviation (SD) of lesion contrast in the differential images were computed using both the conventional subtraction and our joint methods. We also applied both methods to the 35 epileptic patient datasets. Each differential image was presented to two nuclear medicine physicians to localize a lesion and specify a confidence level. The readers' data were analyzed to obtain the localized-response receiver operating characteristic (LROC) curves for both the subtraction and joint methods.

RESULTS

For the phantom study, the difference between the mean lesion contrast in the differential images obtained using the conventional subtraction versus our joint method decreases as the iteration number increases. Compared with the conventional subtraction approach, the SD reduction of lesion contrast at the 10^th iteration using our joint method ranges from 54.7% to 68.2% (P < 0.0005), and 33.8% to 47.9% (P < 0.05) for 2 and 4 million total inter-ictal counts, respectively. In the patient study, our joint method increases the area under LROC from 0.24 to 0.34 and from 0.15 to 0.20 for the first and second reader, respectively. We have demonstrated improved performance of our method as compared to the standard subtraction method currently used in clinical practice.

CONCLUSION

The proposed joint ictal/inter-ictal reconstruction method yields better performance for epileptic foci localization than the conventional subtraction method.

The segment-dependent changes in lumbar intervertebral space height during flexion-extension motion

Objectives

Many studies have investigated the kinematics of the lumbar spine and the morphological features of the lumbar discs. However, the segment-dependent immediate changes of the lumbar intervertebral space height during flexion-extension motion are still unclear. This study examined the changes of intervertebral space height during flexion-extension motion of lumbar specimens.

Methods

First, we validated the accuracy and repeatability of a custom-made mechanical loading equipment set-up. Eight lumbar specimens underwent CT scanning in flexion, neural, and extension positions by using the equipment set-up. The changes in the disc height and distance between adjacent two pedicle screw entry points (DASEP) of the posterior approach at different lumbar levels (L3/4, L4/5 and L5/S1) were examined on three-dimensional lumbar models, which were reconstructed from the CT images.

Results

All the vertebral motion segments (L3/4, L4/5 and L5/S1) had greater changes in disc height and DASEP from neutral to flexion than from neutral to extension. The change in anterior disc height gradually increased from upper to lower levels, from neutral to flexion. The changes in anterior and posterior disc heights were similar at the L4/5 level from neutral to extension, but the changes in anterior disc height were significantly greater than those in posterior disc height at the L3/4 and L5/S1 levels, from neutral to extension.

Conclusions

The lumbar motion segment showed level-specific changes in disc height and DASEP. The data may be helpful in understanding the physiologic dynamic characteristics of the lumbar spine and in optimising the parameters of lumbar surgical instruments.

Cite this article: M. Fu, Q. Ye, C. Jiang, L. Qian, D. Xu, Y. Wang, P. Sun, J. Ouyang. The segment-dependent changes in lumbar intervertebral space height during flexion-extension motion. Bone Joint Res 2017;6:245–252. DOI: 10.1302/2046-3758.64.BJR-2016-0245.R1.

Impact of motion and partial volume effects correction on PET myocardial perfusion imaging using simultaneous PET-MR

PET is an established modality for myocardial perfusion imaging (MPI) which enables quantification of absolute myocardial blood flow (MBF) using dynamic imaging and kinetic modeling. However, heart motion and partial volume effects (PVE) significantly limit the spatial resolution and quantitative accuracy of PET MPI. Simultaneous PET-MR offers a solution to the motion problem in PET by enabling MR-based motion correction of PET data. The aim of this study was to develop a motion and PVE correction methodology for PET MPI using simultaneous PET-MR, and to assess its impact on both static and dynamic PET MPI using 18F-Flurpiridaz, a novel 18F-labeled perfusion tracer. Two dynamic 18F-Flurpiridaz MPI scans were performed on healthy pigs using a PET-MR scanner. Cardiac motion was tracked using a dedicated tagged-MRI (tMR) sequence. Motion fields were estimated using non-rigid registration of tMR images and used to calculate motion-dependent attenuation maps. Motion correction of PET data was achieved by incorporating tMR-based motion fields and motion-dependent attenuation coefficients into image reconstruction. Dynamic and static PET datasets were created for each scan. Each dataset was reconstructed as (i) Ungated, (ii) Gated (end-diastolic phase), and (iii) Motion-Corrected (MoCo), each without and with point spread function (PSF) modeling for PVE correction. Myocardium-to-blood concentration ratios (MBR) and apparent wall thickness were calculated to assess image quality for static MPI. For dynamic MPI, segment- and voxel-wise MBF values were estimated by non-linear fitting of a 2-tissue compartment model to tissue time-activity-curves. MoCo and Gating respectively decreased mean apparent wall thickness by 15.1% and 14.4% and increased MBR by 20.3% and 13.6% compared to Ungated images (P  <  0.01). Combined motion and PSF correction (MoCo-PSF) yielded 30.9% (15.7%) lower wall thickness and 82.2% (20.5%) higher MBR compared to Ungated data reconstructed without (with) PSF modeling (P  <  0.01). For dynamic PET, mean MBF across all segments were comparable for MoCo (0.72  ±  0.21 ml/min/ml) and Gating (0.69  ±  0.18 ml/min/ml). Ungated data yielded significantly lower mean MBF (0.59  ±  0.16 ml/min/ml). Mean MBF for MoCo-PSF was 0.80  ±  0.22 ml/min/ml, which was 37.9% (25.0%) higher than that obtained from Ungated data without (with) PSF correction (P  <  0.01). The developed methodology holds promise to improve the image quality and sensitivity of PET MPI studies performed using PET-MR.

A Bayesian spatial temporal mixtures approach to kinetic parametric images in dynamic positron emission tomography

PURPOSE

Estimation of parametric maps is challenging for kinetic models in dynamic positron emission tomography. Since voxel kinetics tend to be spatially contiguous, the authors consider groups of homogeneous voxels together. The authors propose a novel algorithm to identify the groups and estimate kinetic parameters simultaneously. Uncertainty estimates for kinetic parameters are also obtained.

METHODS

Mixture models were used to fit the time activity curves. In order to borrow information from spatially nearby voxels, the Potts model was adopted. A spatial temporal model was built incorporating both spatial and temporal information in the data. Markov chain Monte Carlo was used to carry out parameter estimation. Evaluation and comparisons with existing methods were carried out on cardiac studies using both simulated data sets and a pig study data. One-compartment kinetic modeling was used, in which K1 is the parameter of interest, providing a measure of local perfusion.

RESULTS

Based on simulation experiments, the median standard deviation across all image voxels, of K1 estimates were 0, 0.13, and 0.16 for the proposed spatial mixture models (SMMs), standard curve fitting, and spatial K-means methods, respectively. The corresponding median mean squared biases for K1 were 0.04, 0.06, and 0.06 for abnormal region of interest (ROI); 0.03, 0.03, and 0.04 for normal ROI; and 0.007, 0.02, and 0.05 for the noise region.

CONCLUSIONS

SMM is a fully Bayesian algorithm which determines the optimal number of homogeneous voxel groups, voxel group membership, parameter estimation, and parameter uncertainty estimation simultaneously. The voxel membership can also be used for classification purposes. By borrowing information from spatially nearby voxels, SMM substantially reduces the variability of parameter estimates. In some ROIs, SMM also reduces mean squared bias.