Artificial Intelligence for PET and SPECT Image Enhancement

Nuclear medicine imaging modalities such as PET and SPECT are confounded by high noise levels and low spatial resolution, necessitating postreconstruction image enhancement to improve their quality and quantitative accuracy. Artificial intelligence (AI) models such as convolutional neural networks, U-Nets, and generative adversarial networks have shown promising outcomes in enhancing PET and SPECT images. This review article presents a comprehensive survey of state-of-the-art AI methods for PET and SPECT image enhancement and seeks to identify emerging trends in this field. We focus on recent breakthroughs in AI-based PET and SPECT image denoising and deblurring. Supervised deep-learning models have shown great potential in reducing radiotracer dose and scan times without sacrificing image quality and diagnostic accuracy. However, the clinical utility of these methods is often limited by their need for paired clean and corrupt datasets for training. This has motivated research into unsupervised alternatives that can overcome this limitation by relying on only corrupt inputs or unpaired datasets to train models. This review highlights recently published supervised and unsupervised efforts toward AI-based PET and SPECT image enhancement. We discuss cross-scanner and cross-protocol training efforts, which can greatly enhance the clinical translatability of AI-based image enhancement tools. We also aim to address the looming question of whether the improvements in image quality generated by AI models lead to actual clinical benefit. To this end, we discuss works that have focused on task-specific objective clinical evaluation of AI models for image enhancement or incorporated clinical metrics into their loss functions to guide the image generation process. Finally, we discuss emerging research directions, which include the exploration of novel training paradigms, curation of larger task-specific datasets, and objective clinical evaluation that will enable the realization of the full translation potential of these models in the future.

AI-Driven sleep staging from actigraphy and heart rate

Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person's sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures-both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem

Trustworthiness is a core tenet of medicine. The patient-physician relationship is evolving from a dyad to a broader ecosystem of health care. With the emergence of artificial intelligence (AI) in medicine, the elements of trust must be revisited. We envision a road map for the establishment of trustworthy AI ecosystems in nuclear medicine. In this report, AI is contextualized in the history of technologic revolutions. Opportunities for AI applications in nuclear medicine related to diagnosis, therapy, and workflow efficiency, as well as emerging challenges and critical responsibilities, are discussed. Establishing and maintaining leadership in AI require a concerted effort to promote the rational and safe deployment of this innovative technology by engaging patients, nuclear medicine physicians, scientists, technologists, and referring providers, among other stakeholders, while protecting our patients and society. This strategic plan was prepared by the AI task force of the Society of Nuclear Medicine and Molecular Imaging.

Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development

The nuclear medicine field has seen a rapid expansion of academic and commercial interest in developing artificial intelligence (AI) algorithms. Users and developers can avoid some of the pitfalls of AI by recognizing and following best practices in AI algorithm development. In this article, recommendations on technical best practices for developing AI algorithms in nuclear medicine are provided, beginning with general recommendations and then continuing with descriptions of how one might practice these principles for specific topics within nuclear medicine. This report was produced by the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging.

Noise2Void: unsupervised denoising of PET images

Objective:Elevated noise levels in positron emission tomography (PET) images lower image quality and quantitative accuracy and are a confounding factor for clinical interpretation. The objective of this paper is to develop a PET image denoising technique based on unsupervised deep learning.Significance:Recent advances in deep learning have ushered in a wide array of novel denoising techniques, several of which have been successfully adapted for PET image reconstruction and post-processing. The bulk of the deep learning research so far has focused on supervised learning schemes, which, for the image denoising problem, require paired noisy and noiseless/low-noise images. This requirement tends to limit the utility of these methods for medical applications as paired training datasets are not always available. Furthermore, to achieve the best-case performance of these methods, it is essential that the datasets for training and subsequent real-world application have consistent image characteristics (e.g. noise, resolution, etc), which is rarely the case for clinical data. To circumvent these challenges, it is critical to develop unsupervised techniques that obviate the need for paired training data.Approach:In this paper, we have adapted Noise2Void, a technique that relies on corrupt images alone for model training, for PET image denoising and assessed its performance using PET neuroimaging data. Noise2Void is an unsupervised approach that uses a blind-spot network design. It requires only a single noisy image as its input, and, therefore, is well-suited for clinical settings. During the training phase, a single noisy PET image serves as both the input and the target. Here we present a modified version of Noise2Void based on a transfer learning paradigm that involves group-level pretraining followed by individual fine-tuning. Furthermore, we investigate the impact of incorporating an anatomical image as a second input to the network.Main Results:We validated our denoising technique using simulation data based on the BrainWeb digital phantom. We show that Noise2Void with pretraining and/or anatomical guidance leads to higher peak signal-to-noise ratios than traditional denoising schemes such as Gaussian filtering, anatomically guided non-local means filtering, and block-matching and 4D filtering. We used the Noise2Noise denoising technique as an additional benchmark. For clinical validation, we applied this method to human brain imaging datasets. The clinical findings were consistent with the simulation results confirming the translational value of Noise2Void as a denoising tool.

Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement

High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. Artificial intelligence models for image denoising and deblurring are becoming increasingly popular for the postreconstruction enhancement of PET images. We present a detailed review of recent efforts for artificial intelligence-based PET image enhancement with a focus on network architectures, data types, loss functions, and evaluation metrics. We also highlight emerging areas in this field that are quickly gaining popularity, identify barriers to large-scale adoption of artificial intelligence models for PET image enhancement, and discuss future directions.

Longitudinal predictive modeling of tau progression along the structural connectome

Tau neurofibrillary tangles, a pathophysiological hallmark of Alzheimer's disease (AD), exhibit a stereotypical spatiotemporal trajectory that is strongly correlated with disease progression and cognitive decline. Personalized prediction of tau progression is, therefore, vital for the early diagnosis and prognosis of AD. Evidence from both animal and human studies is suggestive of tau transmission along the brains preexisting neural connectivity conduits. We present here an analytic graph diffusion framework for individualized predictive modeling of tau progression along the structural connectome. To account for physiological processes that lead to active generation and clearance of tau alongside passive diffusion, our model uses an inhomogenous graph diffusion equation with a source term and provides closed-form solutions to this equation for linear and exponential source functionals. Longitudinal imaging data from two cohorts, the Harvard Aging Brain Study (HABS) and the Alzheimer's Disease Neuroimaging Initiative (ADNI), were used to validate the model. The clinical data used for developing and validating the model include regional tau measures extracted from longitudinal positron emission tomography (PET) scans based on the ¹⁸F-Flortaucipir radiotracer and individual structural connectivity maps computed from diffusion tensor imaging (DTI) by means of tractography and streamline counting. Two-timepoint tau PET scans were used to assess the goodness of model fit. Three-timepoint tau PET scans were used to assess predictive accuracy via comparison of predicted and observed tau measures at the third timepoint. Our results show high consistency between predicted and observed tau and differential tau from region-based analysis. While the prognostic value of this approach needs to be validated in a larger cohort, our preliminary results suggest that our longitudinal predictive model, which offers an in vivo macroscopic perspective on tau progression in the brain, is potentially promising as a personalizable predictive framework for AD.

250 AI-Supported Sleep Staging from Activity and Heart Rate

Introduction

Polysomnography (PSG) is considered the gold standard for sleep staging but is labor-intensive and expensive. Wrist wearables are an alternative to PSG because of their small form factor and continuous monitoring capability. In this work, we present a scheme to perform such automated sleep staging via deep learning in the MESA cohort validated against PSG. This scheme makes use of actigraphic activity counts and two coarse heart rate measures (only mean and standard deviation for 30-s sleep epochs) to perform multi-class sleep staging. Our method outperforms existing techniques in three-stage classification (i.e., wake, NREM, and REM) and is feasible for four-stage classification (i.e., wake, light, deep, and REM).

Methods

Our technique uses a combined convolutional neural network coupled and sequence-to-sequence network architecture to appropriate the temporal correlations in sleep toward classification. Supervised training with PSG stage labels for each sleep epoch as the target was performed. We used data from MESA participants randomly assigned to non-overlapping training (N=608) and validation (N=200) cohorts. The under-representation of deep sleep in the data leads to class imbalance which diminishes deep sleep prediction accuracy. To specifically address the class imbalance, we use a novel loss function that is minimized in the network training phase.

Results

Our network leads to accuracies of 78.66% and 72.46% for three-class and four-class sleep staging respectively. Our three-stage classifier is especially accurate at measuring NREM sleep time (predicted: 4.98 ± 1.26 hrs. vs. actual: 5.08 ± 0.98 hrs. from PSG). Similarly, our four-stage classifier leads to highly accurate estimates of light sleep time (predicted: 4.33 ± 1.20 hrs. vs. actual: 4.46 ± 1.04 hrs. from PSG) and deep sleep time (predicted: 0.62 ± 0.65 hrs. vs. actual: 0.63 ± 0.59 hrs. from PSG). Lastly, we demonstrate the feasibility of our method for sleep staging from Apple Watch-derived measurements.

Conclusion

This work demonstrates the viability of high-accuracy, automated multi-class sleep staging from actigraphy and coarse heart rate measures that are device-agnostic and therefore well suited for extraction from smartwatches and other consumer wrist wearables.

Support (if any)

This work was supported in part by the NIH grant 1R21AG068890-01 and the American Association for University Women.

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.

Investigation of groundwater and soil quality near to a municipal waste disposal site in Silchar, Assam, India

Unscientific management of municipal solid waste is one of the direct sources of contamination in developing countries, such as India. The present investigation carried out during Oct–Dec 2019 attempts to assess the parameters, such as quality of groundwater and soil along three depths (0–5, 5–15 and 15–30 cm), in proximity to a dumping site in Silchar, a rapidly evolving city of North-East India. Standard protocols of soil and water quality assessments were carried out. The pH values of the surface soils were found to be slightly acidic. Decrease in acidity with increasing depth was observed in the study site. The relative abundance of the analyzed elements at all soil depths was Zn > Fe > Ni > Cu > Cr. Weak correlation between the concentration of Cu, Fe and Zn, and the bulk density of the soil highlighted the micronutrient status of the soil. The impact of the nearby dumpsite on trace element contamination is indicated by the ‘extremely contaminated’ status of the soils with respect to geo-accumulation index. Majority of the groundwater samples exhibited pH levels below the desired limits, making it unfit for consumption by local communities. While Fe, Cu and Ni levels in groundwater samples exceeded the guideline values, Cr and Zn concentrations were found to be within limits except one sample. Principal Component Analysis of the observed data was carried out to ascertain the predominant sources of contamination. The observations indicate the negative impacts of nearby dumpsite on environmental parameters, such as groundwater and soil quality, as highlighted in this research.

A physics-informed geometric learning model for pathological tau spread in Alzheimer's disease

Tau tangles are a pathophysiological hallmark of Alzheimer's disease (AD) and exhibit a stereotypical pattern of spatiotemporal spread which has strong links to disease progression and cognitive decline. Preclinical evidence suggests that tau spread depends on neuronal connectivity rather than physical proximity between different brain regions. Here, we present a novel physics-informed geometric learning model for predicting tau buildup and spread that learns patterns directly from longitudinal tau imaging data while receiving guidance from governing physical principles. Implemented as a graph neural network with physics-based regularization in latent space, the model enables effective training with smaller data sizes. For training and validation of the model, we used longitudinal tau measures from positron emission tomography (PET) and structural connectivity graphs from diffusion tensor imaging (DTI) from the Harvard Aging Brain Study. The model led to higher peak signal-to-noise ratio and lower mean squared error levels than both an unregularized graph neural network and a differential equation solver. The method was validated using both two-timepoint and three-timepoint tau PET measures. The effectiveness of the approach was further confirmed by a cross-validation study.

First report of porcine respirovirus 1 in South America

Porcine respirovirus 1 (PRV1) is an emerging virus in pigs that has been previously described in the USA and China. There are no reports of its presence in the rest of the world. The objective of this study was to determine the occurrence of PRV1 in Chile and to determine its phylogeny. Thus, we collected samples (oral fluids, nasal swabs, and lungs) from a swine influenza A virus (IAV) surveillance program, most of which belonged to pigs with respiratory disease. The samples were analyzed by RT-PCR, and the viral sequencing was obtained using RNA whole-genome sequencing approach. Maximum likelihood phylogeny was constructed with the available references. Thirty-one of 164 samples (18.9 %) were RT-PCR positive for PRV1: 62.5 % oral fluids, 19.0 % nasal swabs, and 8.6 % lungs. All 6 farms in this study had at least one positive sample, with 6-40 % of positive results per farm, which suggests that PRV1 is disseminated in Chilean swine farms. Twenty-one of 31 (677%) PRV1-positive samples were also positive for IAV, so the role of PRV1 as secondary pathogen in respiratory disease needs to be further evaluated. Near to complete genome of two PRV1s were obtained from two farms. The phylogenies, in general, showed low bootstrap support, except the concatenated genome and the L gene trees which showed clustering of the Chilean PRV1 with Asian sequences, suggesting a close genetic relationship. This is the first report of PRV1 in the Southern Hemisphere. Further studies are necessary to determine the genetic diversity of this virus in Chile.

PET image super-resolution using generative adversarial networks

The intrinsically low spatial resolution of positron emission tomography (PET) leads to image quality degradation and inaccurate image-based quantitation. Recently developed supervised super-resolution (SR) approaches are of great relevance to PET but require paired low- and high-resolution images for training, which are usually unavailable for clinical datasets. In this paper, we present a self-supervised SR (SSSR) technique for PET based on dual generative adversarial networks (GANs), which precludes the need for paired training data, ensuring wider applicability and adoptability. The SSSR network receives as inputs a low-resolution PET image, a high-resolution anatomical magnetic resonance (MR) image, spatial information (axial and radial coordinates), and a high-dimensional feature set extracted from an auxiliary CNN which is separately-trained in a supervised manner using paired simulation datasets. The network is trained using a loss function which includes two adversarial loss terms, a cycle consistency term, and a total variation penalty on the SR image. We validate the SSSR technique using a clinical neuroimaging dataset. We demonstrate that SSSR is promising in terms of image quality, peak signal-to-noise ratio, structural similarity index, contrast-to-noise ratio, and an additional no-reference metric developed specifically for SR image quality assessment. Comparisons with other SSSR variants suggest that its high performance is largely attributable to simulation guidance.

Super-Resolution PET Imaging Using Convolutional Neural Networks

Positron emission tomography (PET) suffers from severe resolution limitations which reduce its quantitative accuracy. In this paper, we present a super-resolution (SR) imaging technique for PET based on convolutional neural networks (CNNs). To facilitate the resolution recovery process, we incorporate high-resolution (HR) anatomical information based on magnetic resonance (MR) imaging. We introduce the spatial location information of the input image patches as additional CNN inputs to accommodate the spatially-variant nature of the blur kernels in PET. We compared the performance of shallow (3-layer) and very deep (20-layer) CNNs with various combinations of the following inputs: low-resolution (LR) PET, radial locations, axial locations, and HR MR. To validate the CNN architectures, we performed both realistic simulation studies using the BrainWeb digital phantom and clinical studies using neuroimaging datasets. For both simulation and clinical studies, the LR PET images were based on the Siemens HR+ scanner. Two different scenarios were examined in simulation: one where the target HR image is the ground-truth phantom image and another where the target HR image is based on the Siemens HRRT scanner - a high-resolution dedicated brain PET scanner. The latter scenario was also examined using clinical neuroimaging datasets. A number of factors affected relative performance of the different CNN designs examined, including network depth, target image quality, and the resemblance between the target and anatomical images. In general, however, all deep CNNs outperformed classical penalized deconvolution and partial volume correction techniques by large margins both qualitatively (e.g., edge and contrast recovery) and quantitatively (as indicated by three metrics: peak signal-to-noise-ratio, structural similarity index, and contrast-to-noise ratio).

PET Image Deblurring and Super-Resolution with an MR-Based Joint Entropy Prior

The intrinsically limited spatial resolution of PET confounds image quantitation. This paper presents an image deblurring and super-resolution framework for PET using anatomical guidance provided by high-resolution MR images. The framework relies on image-domain post-processing of already-reconstructed PET images by means of spatially-variant deconvolution stabilized by an MR-based joint entropy penalty function. The method is validated through simulation studies based on the BrainWeb digital phantom, experimental studies based on the Hoffman phantom, and clinical neuroimaging studies pertaining to aging and Alzheimer's disease. The developed technique was compared with direct deconvolution and deconvolution stabilized by a quadratic difference penalty, a total variation penalty, and a Bowsher penalty. The BrainWeb simulation study showed improved image quality and quantitative accuracy measured by contrast-to-noise ratio, structural similarity index, root-mean-square error, and peak signal-to-noise ratio generated by this technique. The Hoffman phantom study indicated noticeable improvement in the structural similarity index (relative to the MR image) and gray-to-white contrast-to-noise ratio. Finally, clinical amyloid and tau imaging studies for Alzheimer's disease showed lowering of the coefficient of variation in several key brain regions associated with two target pathologies.

GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR ALZHEIMER'S DISEASE CLASSIFICATION

Graph convolutional neural networks (GCNNs) aim to extend the data representation and classification capabilities of convolutional neural networks, which are highly effective for signals defined on regular Euclidean domains, e.g. image and audio signals, to irregular, graph-structured data defined on non-Euclidean domains. Graph-theoretic tools that enable us to study the brain as a complex system are of great significance in brain connectivity studies. Particularly, in the context of Alzheimer's disease (AD), a neurodegenerative disorder associated with network dysfunction, graph-based tools are vital for disease classification and staging. Here, we implement and test a multi-class GCNN classifier for network-based classification of subjects on the AD spectrum into four categories: cognitively normal, early mild cognitive impairment, late mild cognitive impairment, and AD. We train and validate the network using structural connectivity graphs obtained from diffusion tensor imaging data. Using receiver operating characteristic curves, we show that the GCNN classifier outperforms a support vector machine classifier by margins that are reliant on disease category. Our findings indicate that the performance gap between the two methods increases with disease progression from CN to AD. We thus demonstrate that GCNN is a competitive tool for staging and classification of subjects on the AD spectrum.

Higher-order singular value decomposition-based lung parcellation for breathing motion management

Positron emission tomography (PET) imaging of the lungs is confounded by respiratory motion-induced blurring artifacts that degrade quantitative accuracy. Gating and motion-compensated image reconstruction are frequently used to correct these motion artifacts in PET. In the absence of voxel-by-voxel deformation measures, surrogate signals from external markers are used to track internal motion and generate gated PET images. The objective of our work is to develop a group-level parcellation framework for the lungs to guide the placement of markers depending on the location of the internal target region. We present a data-driven framework based on higher-order singular value decomposition (HOSVD) of deformation tensors that enables identification of synchronous areas inside the torso and on the skin surface. Four-dimensional (4-D) magnetic resonance (MR) imaging based on a specialized radial pulse sequence with a one-dimensional slice-projection navigator was used for motion capture under free-breathing conditions. The deformation tensors were computed by nonrigidly registering the gated MR images. Group-level motion signatures obtained via HOSVD were used to cluster the voxels both inside the volume and on the surface. To characterize the parcellation result, we computed correlation measures across the different regions of interest (ROIs). To assess the robustness of the parcellation technique, leave-one-out cross-validation was performed over the subject cohort, and the dependence of the result on varying numbers of gates and singular value thresholds was examined. Overall, the parcellation results were largely consistent across these test cases with Jaccard indices reflecting high degrees of overlap. Finally, a PET simulation study was performed which showed that, depending on the location of the lesion, the selection of a synchronous ROI may lead to noticeable gains in the recovery coefficient. Accurate quantitative interpretation of PET images is important for lung cancer management. Therefore, a guided motion monitoring approach is of utmost importance in the context of pulmonary PET imaging.

Nanocomposite functionalized membranes based on silica nanoparticles cross-linked to electrospun nanofibrous support for arsenic(v) adsorption from contaminated underground water

Nanocomposite functionalized membranes were synthesized using surface functionalized mesoporous silica nanoparticles (MCM-NH₂ or MCM-PEI) cross-linked to a modified polyacrylonitrile (mPAN) nanofibrous substrate for the removal of 1 mg L⁻¹ of As(v); a concentration much higher than what has been reported for underground water in Argentina. Adsorption studies were carried out in batch mode at pH 8 with nanoparticles in colloidal form, as well as the nanoparticles supported on the modified PAN membranes (mPAN/MCM-NH₂ and mPAN/MCM-PEI). Results indicate a twenty-fold improvement in As(v) adsorption with supported nanoparticles (nanocomposite membranes) as opposed to their colloidal form. The adsorption efficiency could be further enhanced by modifying the nanocomposite membrane surface with Fe³⁺ (mPAN/MCM-NH₂-Fe³⁺ and mPAN/MCM-PEI-Fe³⁺) which resulted in more than 95% arsenic being removed within the first 15 minutes and a specific arsenic adsorption capacity of 4.61 mg g⁻¹ and 5.89 mg g⁻¹ for mPAN/MCM-NH₂-Fe³⁺ and mPAN/MCM-PEI-Fe³⁺ nanocomposite membranes, respectively. The adsorption characteristics were observed to follow a pseudo-first order behavior. The results suggest that the synthesized materials are excellent for quick and efficient reduction of As(v) concentrations below the WHO guidelines and show promise for future applications.

Development of nanocomposite functionalized membranes for the removal of As(v) from contaminated water.

A novel depth-of-interaction rebinning strategy for ultrahigh resolution PET

Small animal positron emission tomography (PET) imaging often requires high resolution (∼few hundred microns) to enable accurate quantitation in small structures such as animal brains. Recently, we have developed a prototype ultrahigh resolution depth-of-interaction (DOI) PET system that uses CdZnTe detectors with a detector pixel size of 350 μm and eight DOI layers with a 250 μm depth resolution. Due to the large number of line-of-response (LOR) combinations of DOIs, the system matrix for reconstruction is 64 times larger than that without DOI. While a high resolution virtual ring geometry can be employed to simplify the system matrix and create a sinogram, the LORs in such a sinogram tend to be sparse and irregular, leading to potential degradation of the reconstructed image quality. In this paper, we propose a novel high resolution sinogram rebinning method in which a uniform sub-sampling DOI strategy is employed. However, even with the high resolution rebinning strategy, the reconstructed image tends to be very noisy due to insufficient photon counts in many high resolution sinogram pixels. To reduce noise effects, we developed a penalized maximum likelihood reconstruction framework with the Poisson log-likelihood and a non-convex total variation penalty. Here, an ordered subsets separable quadratic surrogate and alternating direction method of multipliers are utilized to solve the optimization. To evaluate the performance of the proposed sub-sampling method and the penalized maximum likelihood reconstruction technique, we perform simulations and preliminary point source experiments. By comparing the reconstructed images and profiles based on sinograms without DOI, with rebinned DOI and with sub-sampled DOI, we demonstrate that the proposed method with sub-sampled DOIs can significantly improve the image quality with lower dose and yield a high resolution of  <300 μm.

Penalized PET Reconstruction Using Deep Learning Prior and Local Linear Fitting

Motivated by the great potential of deep learning in medical imaging, we propose an iterative positron emission tomography reconstruction framework using a deep learning-based prior. We utilized the denoising convolutional neural network (DnCNN) method and trained the network using full-dose images as the ground truth and low dose images reconstructed from downsampled data by Poisson thinning as input. Since most published deep networks are trained at a predetermined noise level, the noise level disparity of training and testing data is a major problem for their applicability as a generalized prior. In particular, the noise level significantly changes in each iteration, which can potentially degrade the overall performance of iterative reconstruction. Due to insufficient existing studies, we conducted simulations and evaluated the degradation of performance at various noise conditions. Our findings indicated that DnCNN produces additional bias induced by the disparity of noise levels. To address this issue, we propose a local linear fitting function incorporated with the DnCNN prior to improve the image quality by preventing unwanted bias. We demonstrate that the resultant method is robust against noise level disparities despite the network being trained at a predetermined noise level. By means of bias and standard deviation studies via both simulations and clinical experiments, we show that the proposed method outperforms conventional methods based on total variation and non-local means penalties. We thereby confirm that the proposed method improves the reconstruction result both quantitatively and qualitatively.

Partial volume correction for PET quantification and its impact on brain network in Alzheimer's disease

Amyloid positron emission tomography (PET) imaging is a valuable tool for research and diagnosis in Alzheimer’s disease (AD). Partial volume effects caused by the limited spatial resolution of PET scanners degrades the quantitative accuracy of PET image. In this study, we have applied a method to evaluate the impact of a joint-entropy based partial volume correction (PVC) technique on brain networks learned from a clinical dataset of AV-45 PET image and compare network properties of both uncorrected and corrected image-based brain networks. We also analyzed the region-wise SUVRs of both uncorrected and corrected images. We further performed classification tests on different groups using the same set of algorithms with same parameter settings. PVC has sometimes been avoided due to increased noise sensitivity in image registration and segmentation, however, our results indicate that appropriate PVC may enhance the brain network structure analysis for AD progression and improve classification performance.

Hybrid Pixel-Waveform (HPWF) Enabled CdTe Detectors for Small Animal Gamma-Ray Imaging Applications

This paper presents the design and preliminary evaluation of small-pixel CdTe gamma ray detectors equipped with a hybrid pixel-waveform (HPWF) readout system for gamma ray imaging applications with additional discussion on CZT due to its similarity. The HPWF readout system utilizes a pixelated anode readout circuitry which is designed to only provide the pixel address. This readout circuitry works in coincidence with a high-speed digitizer to sample the cathode waveform which provides the energy, timing, and depth-of-interaction (DOI) information. This work focuses on the developed and experimentally evaluated prototype HPWF-CdTe detectors with a custom CMOS pixel-ASIC to readout small anode pixels of 350 μm in size, and a discrete waveform sampling circuitry to digitize the signal waveform induced on the large cathode. The intrinsic timing, energy, and spatial resolution were experimentally evaluated in this paper in conjunction with methods for depth of interaction (DOI) partitioning of the CdTe crystal. While the experimental studies discussed in this paper are primarily for evaluating HPWF detectors for small animal PET imaging, these detectors could find their applications for ultrahigh-resolution SPECT and other imaging modalities.

Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning With Deep Belief Networks

This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A three-layer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models' robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. A total of 10 300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 91.99%, using a ten-fold cross validation experiment. The analysis of DBN weights showed that there was a good visual spatial relationship between the underlying critical features of different layers. Our findings show that the most sensitive features obtained from the DBN weights are consistent with the consensus showed by clinical rules and standards for COPD diagnostics. We, thus, demonstrate that DBN is a competitive tool for exacerbation risk assessment for patients suffering from COPD.

Association of vitamin D receptor gene polymorphisms with polycystic ovary syndrome among Indian women

Background & objectives:

The Vitamin-D receptor (VDR) regulates vitamin D levels and calcium metabolism in the body and these are known to be associated with endocrine dysfunctions, insulin resistance and type-2 diabetes in polycystic ovarian syndrome (PCOS). Studies on VDR polymorphisms among PCOS women are sparse. We undertook this study to investigate the association pattern of VDR polymorphisms (Cdx2, Fok1, Apa1 and Taq1) with PCOS among Indian women.

Methods:

For the present study, 250 women with PCOS and 250 normal healthy control women were selected from Hyderabad city, Telangana, India. The four VDR polymorphisms were genotyped and analysed using ASM-PCR (allele specific multiple PCR) and PCR-RFLP (restriction fragment length polymorphism).

Results:

The genotype and allele frequency distributions of only Cdx2 showed significant difference between the PCOS cases and control women, indicating protective role of this SNP against PCOS phenotype. However, significant association was observed between VDR genotypes and some of the PCOS specific clinical/biochemical traits. For example, Fok1 showed a significant genotypic difference for the presence of infertility and Cdx2 genotpes showed association with testosterone levels. Further, the two haplotypes, ACCA and ACTA, were found to be significantly associated with PCOS indicating haplotype specific risk.

Interpretation & conclusions:

Although VDR polymorphisms have not shown significant association with PCOS, in view of functional significance of the SNPs considered, one cannot yet rule out the possibility of their association with PCOS. Further, specifically designed studies on large cohorts are required to conclusively establish the role of VDR polymorphisms in PCOS, particularly including data on vitamin D levels.

Pulmonary imaging using respiratory motion compensated simultaneous PET/MR

PURPOSE

Pulmonary positron emission tomography (PET) imaging is confounded by blurring artifacts caused by respiratory motion. These artifacts degrade both image quality and quantitative accuracy. In this paper, the authors present a complete data acquisition and processing framework for respiratory motion compensated image reconstruction (MCIR) using simultaneous whole body PET/magnetic resonance (MR) and validate it through simulation and clinical patient studies.

METHODS

The authors have developed an MCIR framework based on maximum a posteriori or MAP estimation. For fast acquisition of high quality 4D MR images, the authors developed a novel Golden-angle RAdial Navigated Gradient Echo (GRANGE) pulse sequence and used it in conjunction with sparsity-enforcing k-t FOCUSS reconstruction. The authors use a 1D slice-projection navigator signal encapsulated within this pulse sequence along with a histogram-based gate assignment technique to retrospectively sort the MR and PET data into individual gates. The authors compute deformation fields for each gate via nonrigid registration. The deformation fields are incorporated into the PET data model as well as utilized for generating dynamic attenuation maps. The framework was validated using simulation studies on the 4D XCAT phantom and three clinical patient studies that were performed on the Biograph mMR, a simultaneous whole body PET/MR scanner.

RESULTS

The authors compared MCIR (MC) results with ungated (UG) and one-gate (OG) reconstruction results. The XCAT study revealed contrast-to-noise ratio (CNR) improvements for MC relative to UG in the range of 21%-107% for 14 mm diameter lung lesions and 39%-120% for 10 mm diameter lung lesions. A strategy for regularization parameter selection was proposed, validated using XCAT simulations, and applied to the clinical studies. The authors' results show that the MC image yields 19%-190% increase in the CNR of high-intensity features of interest affected by respiratory motion relative to UG and a 6%-51% increase relative to OG.

CONCLUSIONS

Standalone MR is not the traditional choice for lung scans due to the low proton density, high magnetic susceptibility, and low T2 (∗) relaxation time in the lungs. By developing and validating this PET/MR pulmonary imaging framework, the authors show that simultaneous PET/MR, unique in its capability of combining structural information from MR with functional information from PET, shows promise in pulmonary imaging.

First Report of Nigrospora Leaf Blight on Tea Caused by Nigrospora sphaerica in India

Tea [Camellia sinensis (L.) O. Kuntze] is an economically important non-alcoholic caffeine-containing beverage crop widely cultivated for leaves in India, especially in the Darjeeling district of West Bengal. In May 2012, distinct blight symptoms were observed on leaves of popular tea cultivars AV-2, Tukdah 78, Rungli Rungliot 17/144, and Bannockburn 157 in commercial tea estates of the Darjeeling district. This disease reduces yield and quality of the leaves. The initial symptoms were frequently observed on the young leaf margins and apices. Foliar symptoms are characterized by grayish to brown, semicircular or irregular shaped lesions, often surrounded by pale yellow zones up to 9 mm in diameter. The lesions later expand and the affected leaves turn grayish to dark brown and eventually the dried tissue falls, leading to complete defoliation of the plant. The disease causes damage to leaves of all ages and is severe in young leaves. A portion of the symptomatic leaf tissues were surface sterilized in 70% ethanol for 30 s, then in 2% NaClO for 3 min, rinsed three times in sterile distilled water, and plated onto potato dextrose agar (PDA). The fungal colonies were initially white and then became grayish to brown with sporulation. Conidia were spherical to sub spherical, single-celled, black, 19 to 21 μm in diameter, and were borne on a hyaline vesicle at the tip of each conidiophore. Morphological characteristics of the isolates were concurring to those of Nigrospora sphaerica (1). Moreover, the internal transcribed spacer (ITS) region of the ribosomal RNA was amplified by using primers ITS1 and ITS4 and sequenced (GenBank Accession No. KJ767520). The sequence was compared to the GenBank database through nucleotide BLAST search and the isolate showed 100% similarity to N. sphaerica (KC519729.1). On the basis of morphological characteristics and nucleotide homology, the isolate was identified as N. sphaerica. Koch's postulates were fulfilled in the laboratory on tea leaves inoculated with N. sphaerica conidial suspension (10⁶ conidia ml^-1) collected from a 7-day-old culture on PDA. Six inoculated 8-month-old seedlings of tea cultivars AV-2 and S.3/3 were incubated in a controlled environment chamber at 25°C and 80 to 85% humidity with a 12-h photoperiod. In addition, three plants of each cultivar were sprayed with sterile distilled water to serve as controls. Twelve to 14 days after inoculation, inoculated leaves developed blight symptoms similar to those observed on naturally infected tea leaves in the field. No symptoms were observed on the control leaves. The pathogen was re-isolated from lesions and its identity was confirmed by morphological characteristics. It was reported that N. sphaerica is frequently encountered as a secondary invader or as a saprophyte on many plant species and also as a causative organism of foliar disease on several hosts worldwide (2,3). To our knowledge, this is first report of N. sphaerica as a foliar pathogen of Camellia sinensis in Darjeeling, West Bengal, India, or worldwide. References: (1) M. B. Ellis. Dematiaceous Hyphomycetes. CMI, Kew, Surrey, UK, 1971. (2) D. F. Farr and A. Y. Rossman. Fungal Databases, Syst. Mycol. Microbiol. Lab., ARS, USDA. Retrieved from http://nt.ars-grin.gov/fungaldatabases/ July 01, 2013. (3) E. R. Wright et al. Plant Dis. 92:171, 2008.

Role of surface defects on visible light enabled plasmonic photocatalysis in Au–ZnO nanocatalysts

Visible light photocatalytic activity of the plasmonic gold–zinc oxide (Au–ZnO) nanorods (NRs) is investigated with respect to the surface defects of the ZnO NRs, controlled by annealing the NRs in ambient at different temperatures.

Transmission of methicillin-resistant Staphylococcus aureus via deceased donor liver transplantation confirmed by whole genome sequencing

Donor-derived bacterial infection is a recognized complication of solid organ transplantation (SOT). The present report describes the clinical details and successful outcome in a liver transplant recipient despite transmission of methicillin-resistant Staphylococcus aureus (MRSA) from a deceased donor with MRSA endocarditis and bacteremia. We further describe whole genome sequencing (WGS) and complete de novo assembly of the donor and recipient MRSA isolate genomes, which confirms that both isolates are genetically 100% identical. We propose that similar application of WGS techniques to future investigations of donor bacterial transmission would strengthen the definition of proven bacterial transmission in SOT, particularly in the presence of highly clonal bacteria such as MRSA. WGS will further improve our understanding of the epidemiology of bacterial transmission in SOT and the risk of adverse patient outcomes when it occurs.

Quantitative statistical methods for image quality assessment

Quantitative measures of image quality and reliability are critical for both qualitative interpretation and quantitative analysis of medical images. While, in theory, it is possible to analyze reconstructed images by means of Monte Carlo simulations using a large number of noise realizations, the associated computational burden makes this approach impractical. Additionally, this approach is less meaningful in clinical scenarios, where multiple noise realizations are generally unavailable. The practical alternative is to compute closed-form analytical expressions for image quality measures. The objective of this paper is to review statistical analysis techniques that enable us to compute two key metrics: resolution (determined from the local impulse response) and covariance. The underlying methods include fixed-point approaches, which compute these metrics at a fixed point (the unique and stable solution) independent of the iterative algorithm employed, and iteration-based approaches, which yield results that are dependent on the algorithm, initialization, and number of iterations. We also explore extensions of some of these methods to a range of special contexts, including dynamic and motion-compensated image reconstruction. While most of the discussed techniques were developed for emission tomography, the general methods are extensible to other imaging modalities as well. In addition to enabling image characterization, these analysis techniques allow us to control and enhance imaging system performance. We review practical applications where performance improvement is achieved by applying these ideas to the contexts of both hardware (optimizing scanner design) and image reconstruction (designing regularization functions that produce uniform resolution or maximize task-specific figures of merit).

Demonstration of side coupling to cladding modes through zinc oxide nanorods grown on multimode optical fiber

A novel concept is introduced that utilizes the scattering properties of zinc oxide nanorods to control light guidance and leakage inside optical fibers coated with nanorods. The effect of the hydrothermal growth conditions of the nanorods on light scattering and coupling to optical fiber are experimentally investigated. At optimum conditions, 5% of the incident light is side coupled to the cladding modes. This coupling scheme could be used in different applications such as distributed sensors and light combing. Implementation of the nanorods on fiber provides low cost and controllable nonlithography-based solutions for free space to fiber coupling. Higher coupling efficiencies can be achieved with further optimization.

Expression of oxidative stress in metastatic retinoblastoma- a comparative study

OBJECTIVE

To compare oxidative stress between primary retinoblastoma and retinoblastoma with distant metastasis.

PATIENTS AND METHODS

Forty consecutive patients presented with primary retinoblastoma and the same number of patients presented with distant metastasis, attending the outpatient department of our hospital between August 2002 and April 2005. All the patients with retinoblastoma underwent a standard metastasis workup and were subsequently categorized into two groups (without metastasis and with metastasis).Venous blood samples were drawn from each patient. After proper centrifugation, serum was collected and antioxidant enzymes and reactive oxygen species (ROS) were assayed.

MAIN OUTCOME MEASURES

Serum collected from the patients was subjected to biochemical assay of the antioxidant enzymes (superoxide dismutase, catalase and peroxidise) and ROS to determine any difference in enzyme activity between the two groups.

RESULTS

Antioxidant levels were found to be less in the metastasis group as compared to the primary intraocular retinoblastoma group(p less than 0.05).Mean ROS activity was found to be increased in metastatic group (p less than 0.05).

CONCLUSION

The decreased antioxidant enzymes level along with increased ROS activity in patients with metastatic retinoblastoma reflect increased oxidative stress as compared to primary intraocular retinoblastoma patients.

Effect of mitomycin C-aided trabeculectomy on conjunctival goblet cell density

INTRODUCTION

Mitomycin C is gaining widespread popularity as an adjunctive with trabeculectomy, as it significantly increases the success rate of the procedure. But it is associated with serious sight-threatening complications.

MATERIALS AND METHODS

Twenty eyes planned for trabeculectomy from the glaucoma clinic were enrolled for the study after obtaining an informed consent. The baseline impression cytology was taken. Ten eyes underwent trabeculectomy with mitomycin C (Group A) and the rest underwent trabeculectomy without any antimetabolites (Group B). Impression cytology samples were taken on months 1, 3, 6, 9 and 12.

RESULTS

In Group A, the difference between goblet cell density preoperatively and 12 month postoperatively was statistically significant (p less than 0.0001). In Group B, the difference was not statistically significant. (p = 0.27).

CONCLUSION

Mitomycin C, though used to augment the success rate of trabeculectomy, has deleterious effect on the conjunctival goblet cell population as is evident from the conjunctival impression cytology.

Joint L1 and total variation regularization for fluorescence molecular tomography

Fluorescence molecular tomography (FMT) is an imaging modality that exploits the specificity of fluorescent biomarkers to enable 3D visualization of molecular targets and pathways in vivo in small animals. Owing to the high degree of absorption and scattering of light through tissue, the FMT inverse problem is inherently ill-conditioned making image reconstruction highly susceptible to the effects of noise and numerical errors. Appropriate priors or penalties are needed to facilitate reconstruction and to restrict the search space to a specific solution set. Typically, fluorescent probes are locally concentrated within specific areas of interest (e.g., inside tumors). The commonly used L(2) norm penalty generates the minimum energy solution, which tends to be spread out in space. Instead, we present here an approach involving a combination of the L(1) and total variation norm penalties, the former to suppress spurious background signals and enforce sparsity and the latter to preserve local smoothness and piecewise constancy in the reconstructed images. We have developed a surrogate-based optimization method for minimizing the joint penalties. The method was validated using both simulated and experimental data obtained from a mouse-shaped phantom mimicking tissue optical properties and containing two embedded fluorescent sources. Fluorescence data were collected using a 3D FMT setup that uses an EMCCD camera for image acquisition and a conical mirror for full-surface viewing. A range of performance metrics was utilized to evaluate our simulation results and to compare our method with the L(1), L(2) and total variation norm penalty-based approaches. The experimental results were assessed using the Dice similarity coefficients computed after co-registration with a CT image of the phantom.

Progress in antimicrobial activities of chitin, chitosan and its oligosaccharides: a systematic study needs for food applications

In recent years, active biomolecules such as chitosan and its derivatives are undergoing a significant and very fast development in food application area. Due to recent outbreaks of contaminations associated with food products, there have been growing concerns regarding the negative environmental impact of packaging materials of antimicrobial biofilms, which have been studied. Chitosan has a great potential for a wide range of applications due to its biodegradability, biocompatibility, antimicrobial activity, nontoxicity and versatile chemical and physical properties. It can be formed into fibers, films, gels, sponges, beads or nanoparticles. Chitosan films have been used as a packaging material for the quality preservation of a variety of foods. Chitosan has high antimicrobial activities against a wide variety of pathogenic and spoilage microorganisms, including fungi, and Gram-positive and Gram-negative bacteria. A tremendous effort has been made over the past decade to develop and test films with antimicrobial properties to improve food safety and shelf-life. This review highlights the preparation, mechanism, antimicrobial activity, optimization of biocide properties of chitosan films and applications including biocatalysts for the improvement of quality and shelf-life of foods.

Nanostructured zinc sulphide phosphors

Zinc sulphide (ZnS) particles are efficient phosphors for application in flat-panel displays. Spherical ZnS particles were prepared by precipitation from a homogeneous solution. Nanoparticles of 20 to 40 nm having a very narrow size distribution could be synthesized by using complexing chelates such as acetate and acetylacetonate. Complexing of the precipitating cation with the anions present in the system lead to a limited concentration of free cations in the solution. This strongly influences the kinetics of the primary particle agglomeration/growth, resulting in nanometer-sized ZnS particles. Nanostructured ZnS synthesized in this way are polycrystalline particles composed of crystallites of 5–10 nm. The synthesis of very small, non-agglomerated, nanocrystalline particles in the 5–10 nm size range was also possible, making use of a strong complexing ligand (thioglycerol) during the synthesis. The synthesis of controlled monosized ZnS particles will be presented and discussed. The photoluminescence characteristics of ZnS make this material a suitable candidate as phosphor for application in low voltage display technology. The effect of Mn2+ doping on the luminescence characteristics of ZnS will also be discussed.

Tin Dioxide Nano-Powders for Gas Sensor Applications

SnO2 nanoparticles are of interest for gas sensor applications because the surface area is much larger compared to conventional powders. Thus, interactions between the material and the gases, which occur on the surface sites of the particles, are increased considerably. The preparation of SnO2 powders has been investigated following two forced precipitation systems: the hydrolysis reaction of SnC14 in an emulsion media and the hydrolysis reaction of Sn2+ in the presence of a complexing ligand (CH3COO−). Spherical nanoparticles in the 10 to 100 nm range and with a narrow size distribution were synthesized by both precipitating routes. In both cases, it has been demonstrated that the most important parameter which controlled the particle size was the nature of the associated anion. When this associated anion or ligand is able to form a strong complex with the colloidal subunits, a barrier against Van der Waals attraction is created which results in little growth. This greatly influences the agglomeration/growth kinetics during the precipitation. The effect of acetate chelating ligands which resulted in the SnO2 nano-powders formed of 5–10 nm crystallites will be presented and discussed. Preliminary results on the gas (N2, NO) adsorption studies on pellets formed from these powders are also presented.

DigiWarp: a method for deformable mouse atlas warping to surface topographic data

For pre-clinical bioluminescence or fluorescence optical tomography, the animal's surface topography and internal anatomy need to be estimated for improving the quantitative accuracy of reconstructed images. The animal's surface profile can be measured by all-optical systems, but estimation of the internal anatomy using optical techniques is non-trivial. A 3D anatomical mouse atlas may be warped to the estimated surface. However, fitting an atlas to surface topography data is challenging because of variations in the posture and morphology of imaged mice. In addition, acquisition of partial data (for example, from limited views or with limited sampling) can make the warping problem ill-conditioned. Here, we present a method for fitting a deformable mouse atlas to surface topographic range data acquired by an optical system. As an initialization procedure, we match the posture of the atlas to the posture of the mouse being imaged using landmark constraints. The asymmetric L(2) pseudo-distance between the atlas surface and the mouse surface is then minimized in order to register two data sets. A Laplacian prior is used to ensure smoothness of the surface warping field. Once the atlas surface is normalized to match the range data, the internal anatomy is transformed using elastic energy minimization. We present results from performance evaluation studies of our method where we have measured the volumetric overlap between the internal organs delineated directly from MRI or CT and those estimated by our proposed warping scheme. Computed Dice coefficients indicate excellent overlap in the brain and the heart, with fair agreement in the kidneys and the bladder.

Illumination pattern optimization for fluorescence tomography: theory and simulation studies

Fluorescence molecular tomography is a powerful tool for 3D visualization of molecular targets and pathways in vivo in small animals. Owing to the high degrees of absorption and scattering of light through tissue, the fluorescence tomographic inverse problem is inherently ill-posed. In order to improve source localization and the conditioning of the light propagation model, multiple sets of data are acquired by illuminating the animal surface with different spatial patterns of near-infrared light. However, the choice of these patterns in most experimental setups is ad hoc and suboptimal. This paper presents a systematic approach for designing efficient illumination patterns for fluorescence tomography. Our objective here is to determine how to optimally illuminate the animal surface so as to maximize the information content in the acquired data. We achieve this by improving the conditioning of the Fisher information matrix. We parameterize the spatial illumination patterns and formulate our problem as a constrained optimization problem that, for a fixed number of illumination patterns, yields the optimal set of patterns. For geometric insight, we used our method to generate a set of three optimal patterns for an optically homogeneous, regular geometrical shape and observed expected symmetries in the result. We also generated a set of six optimal patterns for an optically homogeneous cuboidal phantom set up in the transillumination mode. Finally, we computed optimal illumination patterns for an optically inhomogeneous realistically shaped mouse atlas for different given numbers of patterns. The regularized pseudoinverse matrix, generated using the singular value decomposition, was employed to reconstruct the point spread function for each set of patterns in the presence of a sample fluorescent point source deep inside the mouse atlas. We have evaluated the performance of our method by examining the singular value spectra as well as plots of average spatial resolution versus estimator variance corresponding to different illumination schemes.