Adaptive optics fluorescence lifetime imaging ophthalmoscopy of in vivo human retinal pigment epithelium

The intrinsic fluorescence properties of lipofuscin – naturally occurring granules that accumulate in the retinal pigment epithelium – are a potential biomarker for the health of the eye. A new modality is described here which combines adaptive optics technology with fluorescence lifetime detection, allowing for the investigation of functional and compositional differences within the eye and between subjects. This new adaptive optics fluorescence lifetime imaging ophthalmoscope was demonstrated in 6 subjects. Repeated measurements between visits had a minimum intraclass correlation coefficient of 0.59 Although the light levels were well below maximum permissible exposures, the safety of the imaging paradigm was tested using clinical measures; no concerns were raised. This new technology allows for in vivo adaptive optics fluorescence lifetime imaging of the human RPE mosaic.     

3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set

Purpose

Brain tumor segmentation is a required step before any radiation treatment or surgery. When performed manually, segmentation is time consuming and prone to human errors. Therefore, there have been significant efforts to automate the process. But, automatic tumor segmentation from MRI data is a particularly challenging task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. In our work, we propose an automatic brain tumor segmentation method that addresses these last two difficult problems.

Methods

We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multidimensional feature set. Then, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this work is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned region statistics in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters from the normal brain region to be in the tumor region. This leads to a better disambiguation of the tumor from brain tissue.

Results

We evaluated the performance of our automatic segmentation method on 15 real MRI scans of brain tumor patients, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Validation with the expert segmentation labels yielded encouraging results: Jaccard (58%), Precision (81%), Recall (67%), Hausdorff distance (24 mm).

Conclusions

Using priors on the brain/tumor appearance, our proposed automatic 3D variational segmentation method was able to better disambiguate the tumor from the surrounding tissue.

     

Quantitative assessment of field strength,total intracranial volume,sex, and age effects on the goodness of harmonization for volumetric analysis on the ADNI database

When analyzing large multicenter databases, the effects of multiple confounding covariates increase the variability in the data and may reduce the ability to detect changes due to the actual effect of interest, for example, changes due to disease. Efficient ways to evaluate the effect of covariates toward the data harmonization are therefore important. In this article, we showcase techniques to assess the “goodness of harmonization” of covariates. We analyze 7,656 MR images in the multisite, multiscanner Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We present a comparison of three methods for estimating total intracranial volume to assess their robustness and correct the brain structure volumes using the residual method and the proportional (normalization by division) method. We then evaluated the distribution of brain structure volumes over the entire ADNI database before and after accounting for multiple covariates such as total intracranial volume, scanner field strength, sex, and age using two techniques: (a) Zscapes, a panoramic visualization technique to analyze the entire database and (b) empirical cumulative distributions functions. The results from this study highlight the importance of assessing the goodness of data harmonization as a necessary preprocessing step when pooling large data set with multiple covariates, prior to further statistical data analysis.     

Beyond sarcopenia: Characterization and integration of skeletal muscle quantity and radiodensity in a curable breast cancer population

Skeletal muscle loss, commonly known as sarcopenia, is highly prevalent and prognostic of adverse outcomes in oncology. However, there is limited information on adults with early breast cancer and examination of other skeletal muscle indices, despite the potential prognostic importance. This study characterizes and examines age‐related changes in body composition of adults with early breast cancer and describes the creation of a novel integrated muscle measure. Female patients diagnosed with stage I‐III breast cancer with abdominal computerized tomography (CT) scans within 12 weeks from diagnosis were identified from local tumor registry (N = 241). Skeletal muscle index (muscle area per height [cm²/m²]), skeletal muscle density, and subcutaneous and visceral adipose tissue areas, were determined from CT L3 lumbar segments. We calculated a novel integrated skeletal measure, skeletal muscle gauge, which combines skeletal muscle index and density (SMI × SMD). 241 patients were identified with available CT imaging. Median age 52 years and range of 23‐87. Skeletal muscle index and density significantly decreased with age. Using literature based cut‐points, older adults (≥65 years) had significantly higher proportions of sarcopenia (63 vs 28%) and myosteatosis (90 vs 11%) compared to younger adults (<50 years). Body mass index was positively correlated with skeletal muscle index and negatively correlated with muscle density. Skeletal muscle gauge correlated better with increasing age (ρ = 0.52) than with either skeletal muscle index (ρ = 0.20) or density (ρ = 0.46). Wide variations and age‐related changes in body composition metrics were found using routinely obtained abdominal CT imaging. Skeletal muscle index and density provide independent, complementary information, and the product of the two metrics, skeletal muscle gauge, requires further research to explore its impact on outcomes in women with curable breast cancer.     

Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI,AIBL, OASIS,and MIRIAD databases

Biomarkers for dementia of Alzheimer's type (DAT) are sought to facilitate accurate prediction of the disease onset, ideally predating the onset of cognitive deterioration. T1‐weighted magnetic resonance imaging (MRI) is a commonly used neuroimaging modality for measuring brain structure in vivo, potentially providing information enabling the design of biomarkers for DAT. We propose a novel biomarker using structural MRI volume‐based features to compute a similarity score for the individual's structural patterns relative to those observed in the DAT group. We employed ensemble‐learning framework that combines structural features in most discriminative ROIs to create an aggregate measure of neurodegeneration in the brain. This classifier is trained on 423 stable normal control (NC) and 330 DAT subjects, where clinical diagnosis is likely to have the highest certainty. Independent validation on 8,834 unseen images from ADNI, AIBL, OASIS, and MIRIAD Alzheimer's disease (AD) databases showed promising potential to predict the development of DAT depending on the time‐to‐conversion (TTC). Classification performance on stable versus progressive mild cognitive impairment (MCI) groups achieved an AUC of 0.81 for TTC of 6 months and 0.73 for TTC of up to 7 years, achieving state‐of‐the‐art results. The output score, indicating similarity to patterns seen in DAT, provides an intuitive measure of how closely the individual's brain features resemble the DAT group. This score can be used for assessing the presence of AD structural atrophy patterns in normal aging and MCI stages, as well as monitoring the progression of the individual's brain along with the disease course.     

Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score

¹⁸F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) enables in‐vivo capture of the topographic metabolism patterns in the brain. These images have shown great promise in revealing the altered metabolism patterns in Alzheimer's disease (AD). The AD pathology is progressive, and leads to structural and functional alterations that lie on a continuum. There is a need to quantify the altered metabolism patterns that exist on a continuum into a simple measure. This work proposes a 3D convolutional neural network with residual connections that generates a probability score useful for interpreting the FDG‐PET images along the continuum of AD. This network is trained and tested on images of stable normal control and stable Dementia of the Alzheimer's type (sDAT) subjects, achieving an AUC of 0.976 via repeated fivefold cross‐validation. An independent test set consisting of images in between the two extreme ends of the DAT spectrum is used to further test the generalization performance of the network. Classification performance of 0.811 AUC is achieved in the task of predicting conversion of mild cognitive impairment to DAT for conversion time of 0–3 years. The saliency and class activation maps, which highlight the regions of the brain that are most important to the classification task, implicate many known regions affected by DAT including the posterior cingulate cortex, precuneus, and hippocampus.     

Acute and subacute toxicity of chloroform and hexaneextracts of root of Xanthium strumarium

Xanthium strumarium Linn. is used in Indian and Chinese complementary medicines for its various therapeutic properties. There are reports stating that the plant is toxic and lethal to cattle and swine. We have studied the oral toxicity of chloroform and hexane soluble fractions of root extract of X. strumarium in Wistar rats. The toxicity determination was done according to the Organization for Economic Cooperation and Development guidelines. In the acute study with a single oral dose of extracts at 5?g/kg body weight, there was no mortality or signs of toxicity. In the subacute study, none of the blood elements were significantly affected in male and female rats after 28?days. Acute and subacute administration of chloroform and hexane soluble fractions of root did not produce any adverse reactions or any significant changes in organ weight, mortality, and the body weights as compared to untreated controls. Administration of these extracts did not alter the hepatic and renal function, and did not produce any change in the hematological parameters. The study indicates that acute and subacute administration of X. strumarium did not produce any toxicity to rats as evident from weight change, mortality, and limited biochemical and histopathological analysis. Hence, the extracts are considered as unclassified and are found to be safe.