Comparative Study Between a Customized Bimodal Endoscope and a Benchtop Microscope for Quantitative Tissue Diagnosis

BACKGROUND Electrical properties (EPs) of brain tissue, specifically brain tumors, crucially determine the field distribution of Tumor Treating Fields (TTFields), an anti-mitotic treatment approved for glioblastoma multiforme (GBM). Due to the correlation of TTFields efficacy and field intensity at the tumor region, the knowledge of EPs in each patient is of great importance for patient-specific planning of treatment. Water content electrical properties tomography (wEPT) is a non-invasive imaging technique using water content (WC) maps obtained from rapidly acquired and processed conventional sequences to estimate the EPs of brain tissue at 128 MHz. The WC maps of this approach are constructed from two spin echo sequences similar to a T1 and a PD image. Following previous studies in rat tumor models demonstrating promising wEPT mapping of EPs in the brain at 200, this study examines the feasibility of this approach in human GBM patients. MATERIAL AND METHODS For three patients of the EF-14 trial population, we divided T1 and PD images pixel-by-pixel to obtain the image ratio. Using a transfer function, WC maps were generated and maps of the electrical conductivity σ and the relative permittivity ε r at 200 kHz were calculated with two different equations. RESULTS The median value of estimated WC remains similar in healthy brain tissues among all patients, ~73.5% in the white matter, ~82% in the gray matter. The median values of wEPT-estimated σ at 200 kHz in the white matter is ~0.09 S/m and in the gray matter ~0.18 S/m, corresponding median values of ε r at 200 kHz are ~2100 and ~3000 in white and gray matter respectively. Contrary, in the tumor the spread between the median values of WC and EPs is much higher. Stating the most important findings, in the necrosis median WC are 90.3%, 92.3%, 85.2% in patients 1–3 respectively with corresponding median σ values of 0.494, 0.657, 0.25 S/m. In the enhancing tumor the spread of median WC is even higher (67.2%, 83.6%, 85.5%), yet lower spread but also very heterogeneous median σ values of 0.075 S/m, 0.208, 0.259 S/m are estimated with wEPT. CONCLUSION Our results demonstrate the adaption of wEPT for mapping of WC and EPs at 200 kHz in three human GBM patients. In contrast to the vastly irregular tumor tissue, our estimations in healthy brain tissue are similar between patients and in accordance with EPs experimentally measured during our animal experiments and consistent with reported values in the literature. Hence, wEPT is a promising, fast technique based on regular MRI that might help patient-specific treatment planning of TTFields therapy, although the mapping of tumor tissue needs further confirmation in a greater population and investigations of EPs of excised tumor tissue samples should be conducted.

Unlocking the Blood–Brain Barrier: A Role for RMP-7 in Brain Tumor Therapy

Brain tumors, like many other disorders, can cause brain injury through the formation of clots. The MRI picture clearly shows the brain tumor. Healthy brain tissue and brain tumor tissue seem quite similar under the microscope, making it easy to confuse the two. The brain tumor must be properly diagnosed. When assessing brain tumors, segmentation is the gold standard. Brain tumor segmentation is conducted to get around this difficulty by isolating tumor tissue from normal brain tissue, edematous brain tissue, and cerebrospinal fluid. However, this cannot be accomplished until the MRI picture has been median filtered to preserve its edges. An iterative thresholding approach is required to extract the greatest area from the tumor segmentation. After using the watershed method to separate the brain from the rest of the head, the cropping procedure is used to remove any remaining skull tissue. After ALO has improved the settings of ELM, a brain tumor detection system based on the ALO-ELM combination will have been created by identifying the input nodes, hidden layer nodes, and output nodes. The technique outperforms both the ALO and ELM models, with an accuracy of around 98.8%.

Robust classification of pharmaceuticals in an industrial process is an important step for validation of the final product. Especially for pharmaceuticals with similar visual appearance a quality control is only possible if a reliable algorithm based on easily obtainable spectroscopic data is available. We used Principal Component Analysis (PCA) and Support Vector Machines (SVM) on Raman spectroscopy data from a compact Raman system to classify several look-alike pharmaceuticals. This paper describes the data gathering and analysis process to robustly discriminate 19 different pharmaceuticals with similar visual appearance. With the described process we successfully identified all given pharmaceuticals which had a significant amount of active ingredients. Thus automatic validation of these pharmaceuticals in a process can be used to prevent wrong administration of look-alike drugs in an industrial setting, e.g. patient individual blistering.

Vehicle make and model classification is crucial to the operation of an intelligent transportation system (ITS). Fine-grained vehicle information such as make and model can help officers uncover cases of traffic violations when license plate information cannot be obtained. Various techniques have been developed to perform vehicle make and model classification. However, it is very hard to identify the make and model of vehicles with highly similar visual appearances. The classifier contains a lot of potential for mistakes because the vehicles look very similar but have different models and manufacturers. To solve this problem, a fine-grained classifier based on convolutional neural networks with a multi-task learning approach is proposed in this paper. The proposed method takes a vehicle image as input and extracts features using the VGG-16 architecture. The extracted features will then be sent to two different branches, with one branch being used to classify the vehicle model and the other to classify the vehicle make. The performance of the proposed method was evaluated using the InaV-Dash dataset, which contains an Indonesian vehicle model with a highly similar visual appearance. The experimental results show that the proposed method achieves 98.73% accuracy for vehicle make and 97.69% accuracy for vehicle model. Our study also demonstrates that the proposed method is able to improve the performance of the baseline method on highly similar vehicle classification problems.

Optical spectroscopy for brain tumor demarcation was investigated in this study. Fluorescence and diffuse reflectance spectra were measured from normal and tumorous human brain tissues in vitro. A fluorescence peak was consistently observed around 460 nm (+/- 10 nm) emission from both normal and tumorous brain tissues using 337 nm excitation. Intensity of this fluorescence peak (F460) from normal brain tissues was greater than that from primary brain tumorous tissues. In addition, diffuse reflectance (Rd) between 650 and 800 nm from white matter was significantly stronger than that from primary and secondary brain tumors. A good separation between gray matter and brain tumors was found using the ratio of F460 and Rd at 460 nm (Rd460). Two empirical discrimination algorithms based on F460, Rd625, and F460/Rd460 were developed. These algorithms yielded an average sensitivity and specificity of 96% and 93%, respectively.

With the completion of the fission converter epithermal neutron beam (FCB) at MIT, BNCT irradiations conducted by the Harvard-MIT BNCT program could be reduced from approximately 2 1/2 hours per beam to a few minutes.1 Therefore, the timing of the irradiation becomes important. An optimal therapeutic gain is expected to be gained when patients are irradiated during a time window which has the best tumor/normal tissue 10B concentration ratio, and the boron concentration is high. Since in the current clinical trial, blood 10B concentration is used as a surrogate for normal brain tissue 10B concentration, while the metabolism of BPA-f in tumor and normal brain tissues is not well understood, it is important to determine the biodistribution and phar-macokinetics of boron in these tissues. In addition, to be able to prescribe accurate radiation dose to radiation sensitive areas such as the optic chiasm, knowledge of the spatial biodistribution of BPA-f in these tissues is essential. A GL261 murine glioma tumor model is being used to study in detail the time-dependent biodistribution of BPA-f in tumor and several anatomic sites in the normal brain. Preliminary results indicate that there are slight differences in the temporal uptake of BPA-f in different regions of the normal brain, and that the pharmacokinetics of BPA-f in GL261 tumor and normal brain are vastly different.

Different features describe different views of visual appearance, multi-view based methods can integrate the information contained in each view and improve the image clustering performance. Most of the existing methods assume that the importance of one type of feature is the same to all the data. However, the visual appearance of images are different, so the description abilities of different features vary with different images. To solve this problem, we propose a group-aware multi-view fusion approach. Images are partitioned into groups which consist of several images sharing similar visual appearance. We assign different weights to evaluate the pairwise similarity between different groups. Then the clustering results and the fusion weights are learned by an iterative optimization procedure. Experimental results indicate that our approach achieves promising clustering performance compared with the existing methods.

Proton therapy is increasingly used to treat pediatric brain tumors. However, the response of both tumors and healthy tissues to proton therapy is currently under investigation. One way of assessing this response is magnetic resonance (MR) diffusion tensor imaging (DTI), which can measure molecular mobility at the cellular level, quantified by the apparent diffusion coefficient (ADC). In addition, DTI may reveal axonal fiber directional information in white matter, quantified by fractional anisotropy (FA). Here we report use of DTI to assess tumor and unexposed healthy brain tissue responses in a child who received proton therapy for juvenile pilocytic astrocytoma. A 10-year-old boy with recurrent juvenile pilocytic astrocytoma of the left thalamus received proton therapy to a dose of 50.4Gy (RBE) in 28 fractions. Functional magnetic resonance imaging was used to select beam angles for treatment planning. Over the course of the 7-year follow-up period, magnetic resonance imaging including DTI was done to assess response. The MR images were registered to the treatment-planning computed tomography scan, and the gross tumor volume (GTV) was mapped onto the MR images at each follow-up. The GTV contour was then mirrored to the right side of brain through the midline to represent unexposed healthy brain tissue. Proton therapy delivered the full prescribed dose to the target while completely sparing the contralateral brain. The MR ADC images obtained before and after proton therapy showed that enhancement corresponding to the GTV had nearly disappeared by 25 months. The ADC and FA measurements confirmed that contralateral healthy brain tissue was not affected, and the GTV reverted to clinically normal ADC and FA values. Use of DTI allowed quantitative evaluation of tumor and healthy brain tissue responses to proton therapy.

Motivation: To get insight into altered brain glucose metabolism in epilepsy and glioblastoma. Goal(s): To assess glucose metabolism in glioblastoma, epileptogenic, and healthy brain tissue by analyzing the tricarboxylic acid cycle rate (VTCA) using deuterium metabolic imaging (DMI) and metabolic modeling. Approach: Dynamic DMI data were acquired in five healthy controls, five epilepsy patients and five glioblastoma patients. A metabolic model was used to estimate VTCA across different ROIs. Results: VTCA is lower in glioblastoma tissue than in healthy controls. No difference was found between epileptogenic and healthy tissue or epileptogenic and glioblastoma tissue, although the results depend heavily on assumed Glx pool sizes. Impact: This study demonstrates the potential of deuterium metabolic imaging with metabolic modeling to estimate the tricarboxylic acid cycle rate in brain tissue, providing insights into epilepsy and glioblastoma. Findings could enhance non-invasive diagnostics and individualized treatment strategies.

We are developing a dietary assessment system that records daily food intake through the use of food images. Recognizing food in an image is difficult due to large visual variance with respect to eating or preparation conditions. This task becomes even more challenging when different foods have similar visual appearance. In this paper we propose to incorporate two types of contextual dietary information, food co-occurrence patterns and personalized learning models, in food image analysis to reduce ambiguity in food visual appearance and improve food recognition accuracy. We evaluate our model on 1453 food images acquired by 45 participants in natural eating conditions. The result shows that incorporating contextual dietary information improves the food categorization accuracy by about 10%.

: Treating breast cancer that has metastasized to the central nervous system remains a formidable challenge due to, 1) its high malignancy, 2) the difficulty in differentiating between tumor and healthy brain tissue, 3) the sensitivity of normal brain tissue to the toxicities of current therapies, 4) intrinsic cellular resistance of BMBC cells to chemotherapeutic drugs, and 5) the blood-brain barrier s (BBB) ability to prevent the passage of extrinsic substances such as drugs and contrast agents. Our aim is to develop magnetic contrast agents and targeted siRNA therapies capable of overcoming the blood brain barrier and to aid in both the diagnosis and treatment brain metastatic breast cancer. Magnetic nanoparticles with high susceptibility have been developed. These nanoparticles have been characterized to be monodispersed are working to engineer a nanovector that will aid in addressing the major challenges associated with the diagnosis and treatment of BMBC. The major components of the nanovector include a Fe3O4 superparamagnetic nanoparticle (NP) core and a shell comprised of a copolymer of polyethylene glycol (PEG)-grafted chitosan (C)-polyethyleneimine (P), or CP. The tumor targeting peptide, chlorotoxin (CTX), and siRNA (designed to knockdown the pro metastasis gene MENA) also are conjugated to the NP polymer overcoat.

Development of a plastic waste treatment process by combining deep eutectic solvent (DES) pretreatment and bioaugmentation with a plastic-degrading bacterial consortium

In this work, image feature vectors are formed for blocks containing sufficient information, which are selected using a singular-value criterion. When the ratio between the first two SVs are below a given threshold, the block is considered informative. A total of 12 features including statistics of brightness, color components and texture measures are used to form intermediate vectors. Principal component analysis is then performed to reduce the dimension to 6 to give the final feature vectors. Relevance of the constructed feature vectors is demonstrated by experiments in which k-means clustering is used to group the vectors hence the blocks. Blocks falling into the same group show similar visual appearances.

ObjectiveTo evaluate the hypothesis that anthropometric dimensions derived from a person’s manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient metabolic disease risk stratification in clinical settings.MethodsManifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software.ResultsFollowing exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R2s, 0.75–0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%D ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80–0.99 and small but significant bias (p < 0.05 – 0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements.Conclusions3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.