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T2 lesion quantification plays a crucial role in monitoring disease progression and evaluating treatment response in multiple sclerosis (MS). We developed a 3D, multi-arm U-Net for T2 lesion segmentation, which was trained on a large, multicenter clinical trial dataset of relapsing MS. We investigated its generalization to other relapsing and primary progressive MS clinical trial datasets, and to an external dataset from the MICCAI 2016 MS lesion segmentation challenge. Additionally, we assessed the model’s ability to reproduce the separation of T2 lesion volumes between treatment and control arms; and the association of baseline T2 lesion volumes with clinical disability scores compared with manual lesion annotations. The trained model achieved a mean dice coefficient of ≥ 0.66 and a lesion detection sensitivity of ≥ 0.72 across the internal test datasets. On the external test dataset, the model achieved a mean dice coefficient of 0.62, which is comparable to 0.59 from the best model in the challenge, and a lesion detection sensitivity of 0.68. Lesion detection performance was reduced for smaller lesions (≤ 30 μL, 3–10 voxels). The model successfully maintained the separation of the longitudinal changes in T2 lesion volumes between the treatment and control arms. Such tools could facilitate semi-automated MS lesion quantification; and reduce rater burden in clinical trials.

We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging. We report a variant of U-Net architecture, multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view. Further, we develop data normalization methods for accurate prediction of myelin distribution over large brain regions. We show that experimental defects in labeling the human tissue can be rescued with quantitative label-free imaging and neural network model. We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue. Microscopy is central to biological research and has enabled scientist to study the structure and dynamics of cells and their components within. Often, fluorescent dyes or trackers are used that can be detected under the microscope. However, this procedure can sometimes interfere with the biological processes being studied. Now, Guo, Yeh, Folkesson et al. have developed a new approach to examine structures within tissues and cells without the need for a fluorescent label. The technique, called QLIPP, uses the phase and polarization of the light passing through the sample to get information about its makeup. A computational model was used to decode the characteristics of the light and to provide information about the density and orientation of molecules in live cells and brain tissue samples of mice and human. This way, Guo et al. were able to reveal details that conventional microscopy would have missed. Then, a type of machine learning, known as ‘deep learning’, was used to translate the density and orientation images into fluorescence images, which enabled the researchers to predict specific structures in human brain tissue sections. QLIPP can be added as a module to a microscope and its software is available open source. Guo et al. hope that this approach can be used across many fields of biology, for example, to map the connectivity of nerve cells in the human brain or to identify how cells respond to infection. However, further work in automating other aspects, such as sample preparation and analysis, will be needed to realize the full benefits.

The 2016 World Health Organization Classification of Tumors of the Central Nervous System incorporates the use of molecular information into the classification of brain tumors, including grade II and III gliomas, providing new prognostic information that cannot be delineated based on histopathology alone. We hypothesized that these genomic subgroups may also have distinct imaging features. A retrospective single institution study was performed on 40 patients with pathologically proven infiltrating WHO grade II/III gliomas with a pre-treatment MRI and molecular data on IDH, chromosomes 1p/19q and ATRX status. Two blinded Neuroradiologists qualitatively assessed MR features. The relationship between each parameter and molecular subgroup (IDH-wildtype; IDH-mutant-1p/19q codeleted- ATRX intact; IDH-mutant-1p/19q intact- ATRX loss) was evaluated with Fisher’s exact test. Progression free survival (PFS) was also analyzed. A border that could not be defined on FLAIR was most characteristic of IDH-wildtype tumors, whereas IDH-mutant tumors demonstrated either well-defined or slightly ill-defined borders (p = 0.019). Degree of contrast enhancement and presence of restricted diffusion did not distinguish molecular subgroups. Frontal lobe predominance was associated with IDH-mutant tumors (p = 0.006). The IDH-wildtype subgroup had significantly shorter PFS than the IDH-mutant groups (p < 0.001). No differences in PFS were present when separating by tumor grade. FLAIR border patterns and tumor location were associated with distinct molecular subgroups of grade II/III gliomas. These imaging features may provide fundamental prognostic and predictive information at time of initial diagnostic imaging.

Radiation therapy (RT) is a critical treatment modality for patients with brain tumors, although it can cause adverse effects. Recent data suggest that brain RT is associated with dose-dependent cortical atrophy, which could disrupt neocortical networks. This study examines whether brain RT affects structural network properties in brain tumor patients. We applied graph theory to MRI-derived cortical thickness estimates of 54 brain tumor patients before and after RT. Cortical surfaces were parcellated into 68 regions and correlation matrices were created for patients pre- and post-RT. Significant changes in graph network properties were tested using nonparametric permutation tests. Linear regressions were conducted to measure the association between dose and changes in nodal network connectivity. Increases in transitivity, modularity, and global efficiency (n = 54, p < 0.0001) were all observed in patients post-RT. Decreases in local efficiency (n = 54, p = 0.007) and clustering coefficient (n = 54, p = 0.005) were seen in regions receiving higher RT doses, including the inferior parietal lobule and rostral anterior cingulate. These findings demonstrate alterations in global and local network topology following RT, characterized by increased segregation of brain regions critical to cognition. These pathological network changes may contribute to the late delayed cognitive impairments observed in many patients following brain RT.

In the initial online publication, the values in the last two rows in Table 1 were in the wrong rows. The original article has been corrected.In the initial online publication, the values in the last two rows in Table 1 were in the wrong rows. The original article has been corrected.

Current methods of determining the treatment margin for Stereotactic Radiotherapy (SRT) of glioma patients are inadequate as recurrences/secondary tumors often occur at or near the boundary of the treatment margin. To account for the microscopic spread of disease, radiation oncologists usually include a 2-3 cm isotropic margin for SRT of gliomas. This margin is unnecessarily large in certain directions where no microscopic spread is present, causing significant damage to normal brain tissue and loss of cognitive function. The margin is often too small in some directions where microscopic cancer spread is present leading to recurrences. My hypothesis is that tumor cells migrate preferentially along white matter tracts, determined non-invasively using Magnetic Resonance Diffusion Tensor Imaging (MR-DTI). By performing tractography through the primary tumor using MR-DTI data sets obtained retrospectively in glioma patients, we have shown that there exists a high qualitative correlation between the location of the secondary tumor and the fibers passing through the primary tumor. We developed a random walk model of tumor cell migration, seeded from the surface of the primary tumor and constrained by the local fiber architecture determined using DTI data sets obtained as a part of both prospective and retrospective clinical studies. The areas of higher flux of tumor cells determined by the model were coincident with the direction/location of recurrences and/or secondary tumors. If my hypothesis is true then future SRT treatment margins would be modified to increase the margin along areas of higher flux of tumor cells determined by the model and decrease the margin in low-risk directions to improve patient outcomes.

In modern microscopy imaging systems, optical components are carefully designed to obtain diffraction-limited resolution. However, live imaging of large biological samples rarely attains this limit because of sample induced refractive index inhomogeneities that create unknown temporally variant optical aberrations. Importantly, these aberrations are also spatially variant, thus making it challenging to correct over wide fields of view. Here, we present a framework for deep-learning based wide-field optical aberration sensing and correction. Our model consists of two modules which take in a set of three phase-diverse images and (i) estimate the wavefront phase in terms of its constituent Zernike polynomial coefficients and (ii) perform blind-deconvolution to yield an aberration-free image. First, we demonstrate our framework on simulations that incorporate optical aberrations, spatial variance, and realistic modelling of sensor noise. We find that our blind deconvolution achieves a 2-fold improvement in frequency support compared to input images, and our phase-estimation achieves a coefficient of determination (r^2) of at least 80% when estimating astigmatism, spherical aberration and coma. Second, we show that our results mostly hold for strongly varying spatially-variant aberrations with a 30% resolution improvement. Third, we demonstrate practical usability for light-sheet microscopy: we show a 46% increase in frequency support even in imaging regions affected by detection and illumination scattering.

Quantitative imaging of biological architecture with fluorescent labels is not as scalable as genomic or proteomic measurements. Here, we combine quantitative label-free imaging and deep neural networks for scalable analysis of complex structures. We reconstruct quantitative three-dimensional density, anisotropy, and orientation in live cells and tissue slices from polarization- and depth-resolved images. We report a computationally efficient variant of U-Net architecture that predicts a 3D fluorescent structure from its morphology and physical properties. We evaluate the performance of our models by predicting F-actin and nuclei in mouse kidney tissue. Further, we report label-free imaging of axon tracts and predict level of myelination in human brain tissue sections. We demonstrate the model's ability to rescue inconsistent labeling. We anticipate that the proposed approach will enable quantitative analysis of architectural order across scales of organelles to tissues. Footnotes * We have added supplementary figure (Fig. 2 - supplement 4) to illustrate background correction method and revised the text. * https://github.com/mehta-lab/reconstruct-order * https://github.com/czbiohub/microdl

Each year in the United States, approximately 17,000 new cases of primary brain cancer are diagnosed (Ries et al. 2006). e common primary brain tumors are anaplastic astrocytomas, glioblastoma multiforme (GBM or simply glioblastoma), oligodendrogliomas, meningiomas, and medulloblastomas.

This study was conducted to test the suitability of using nine types of millets namely finger millet, pearl millet, white and yellow sorghum, little millet, barnyard millet, proso millet, kodo millet, and browntop millet in seven popular Indian meal preparations based on sensory characteristics and nutrient value. The popular Indian meal preparations tested were boiled grain, dosa, idli, bisi belle bath, pulao, puttu, and pongal. In total, 53 variations in meal preparations were developed using the millets and seven polished white rice-based meal preparations were developed as control. The main findings indicated that meal preparation crafted from various millets garnered overall sensory scores closely resembling to those derived from polished white rice. Notably, little millet exhibited high scores in pongal and dosa, and achieved elevated overall sensory scores compared to meal preparation from polished white rice. Bisi belle bath made of barnyard millet scored higher in overall sensory score than polished white rice. Moreover, there was significant association between some types of millets’ overall sensory characteristics (p < 0.005) with polished white rice-based meal preparations. In terms of nutrient value, all the millet-based meal preparations had significantly high nutritional value compared to those made with polished white rice (p < 0.05). Especially calcium content of the meal prepared with finger millet was significantly higher compared to polished white rice-based meals (p < 0.05). Puttu, idli, and dosa prepared with finger millet had calcium content of 59.4, 10.8, and 70.9 mg/100 g compared to those prepared with the polished white rice which had only 1.3, 6.3, and 9.2 mg/100 g. The magnesium content of all millet-based meal preparations was generally several-folds higher compared to the polished white rice-based meal preparations (p < 0.05). There is a significant difference in the fiber content of the meals prepared with millets compared to the meals prepared with polished white rice (p < 0.05). This study was conducted using millets that are locally available and does not represent all the millet varieties available globally, as each type of millet has a wide range of varieties. Therefore, it is important to understand and choose the type and variety of millet while enhancing the nutritional value of diets.