Abstract:A novel acquisition and processing method that enables realtime, single snapshot of optical properties (SSOP) and 3-dimensional (3D) profile measurements in the spatial frequency domain is described. This method makes use of a dual sinusoidal wave projection pattern permitting to extract the DC and AC components in the frequency domain to recover optical properties as well as the phase for measuring the 3D profile. In this method, the 3D profile is used to correct for the effect of sample's height and angle and directly obtain profile-corrected absorption and reduced scattering maps from a single acquired image. In this manuscript, the 3D-SSOP method is described and validated on tissue-mimicking phantoms as well as in vivo, in comparison with the standard profile-corrected SFDI (3D-SFDI) method. On average, in comparison with 3D-SFDI method, the 3D-SSOP method allows to recover the profile within 1.2mm and profilecorrected optical properties within 12% for absorption and 6% for reduced scattering over a large field-of-view and in real-time. 6498-6507 (2000).

The Allen Brain Atlases enable the study of spatially resolved, genome-wide gene expression patterns across the mammalian brain. Several explorative studies have applied linear dimensionality reduction methods such as Principal Component Analysis (PCA) and classical Multi-Dimensional Scaling (cMDS) to gain insight into the spatial organization of these expression patterns. In this paper, we describe a non-linear embedding technique called Barnes-Hut Stochastic Neighbor Embedding (BH-SNE) that emphasizes the local similarity structure of high-dimensional data points. By applying BH-SNE to the gene expression data from the Allen Brain Atlases, we demonstrate the consistency of the 2D, non-linear embedding of the sagittal and coronal mouse brain atlases, and across 6 human brains. In addition, we quantitatively show that BH-SNE maps are superior in their separation of neuroanatomical regions in comparison to PCA and cMDS. Finally, we assess the effect of higher-order principal components on the global structure of the BH-SNE similarity maps. Based on our observations, we conclude that BH-SNE maps with or without prior dimensionality reduction (based on PCA) provide comprehensive and intuitive insights in both the local and global spatial transcriptome structure of the human and mouse Allen Brain Atlases.

PurposeNear-infrared (NIR) fluorescence imaging can provide the surgeon with real-time visualization of, e.g., tumor margins and lymph nodes. We describe and evaluate the Artemis, a novel, handheld NIR fluorescence camera.ProceduresWe evaluated minimal detectable cell numbers (FaDu-luc2, 7D12-IRDye 800CW), preclinical intraoperative detection of sentinel lymph nodes (SLN) using indocyanine green (ICG), and of orthotopic tongue tumors using 7D12-800CW. Results were compared with the Pearl imager. Clinically, three patients with liver metastases were imaged using ICG.ResultsMinimum detectable cell counts for Artemis and Pearl were 2 × 105 and 4 × 104 cells, respectively. In vivo, seven SLNs were detected in four mice with both cameras. Orthotopic OSC-19-luc2-cGFP tongue tumors were clearly identifiable, and a minimum FaDu-luc2 tumor size of 1 mm3 could be identified. Six human malignant lesions were identified during three liver surgery procedures.ConclusionsBased on this study, the Artemis system has demonstrated its utility in fluorescence-guided cancer surgery.Electronic supplementary materialThe online version of this article (doi:10.1007/s11307-014-0799-z) contains supplementary material, which is available to authorized users.

Shape differences exist between control and OCD groups. This indicates that a geometry modulated biomechanical behavior of the talocrural joint may be a risk factor for OCD.

Abstract:With almost 50% of all surgeries in the U.S. being performed as minimally invasive procedures, there is a need to develop quantitative endoscopic imaging techniques to aid surgical guidance. Recent developments in widefield optical imaging make endoscopic implementations of real-time measurement possible. In this work, we introduce a proof-ofconcept endoscopic implementation of a functional widefield imaging technique called 3D single snapshot of optical properties (3D-SSOP) that provides quantitative maps of absorption and reduced scattering optical properties as well as surface topography with simple instrumentation added to a commercial endoscope. The system's precision and accuracy is validated using tissue-mimicking phantoms, showing a max error of 0.004 mm −1 , 0.05 mm −1 , and 1.1 mm for absorption, reduced scattering, and sample topography, respectively. This study further demonstrates video acquisition of a moving phantom and an in vivo sample with a framerate of approximately 11 frames per second.

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Purpose: Diffuse optical spectroscopy (DOS) has the potential to enable monitoring of tumor response during chemotherapy, particularly in the early stages of treatment. This study aims to assess feasibility of DOS for monitoring treatment response in HER2-negative breast cancer patients receiving neoadjuvant chemotherapy (NAC) and compare DOS with tumor response assessment by MRI.Experimental Design: Patients received NAC in six cycles of 3 weeks. In addition to standard treatment monitoring by dynamic contrast enhanced MRI (DCE-MRI), DOS scans were acquired after the first, third, and last cycle of chemotherapy. The primary goal was to assess feasibility of DOS for early assessment of tumor response. The predictive value of DOS and DCE-MRI compared with pathologic response was assessed.Results: Of the 22 patients, 18 patients had a partial or complete tumor response at pathologic examination, whereas 4 patients were nonresponders. As early as after the first chemotherapy cycle, a significant difference between responders and nonresponders was found using DOS (HbO 2 86% AE 25 vs. 136% AE 25, P ¼ 0.023). The differences between responders and nonresponders continued during treatment (halfway treatment, HbO 2 68% AE 22 vs. 110% AE 10, P ¼ 0.010). Using DCE-MRI, a difference between responders and nonresponders was found halfway treatment (P ¼ 0.005) using tumor volume measurement calculations.Conclusions: DOS allows for tumor response assessment and is able to differentiate between responders and nonresponders after the first chemotherapy cycle and halfway treatment. In this study, DOS was equally effective in predicting tumor response halfway treatment compared with DCE-MRI. Therefore, DOS may be used as a novel imaging modality for (early) treatment monitoring of NAC.

Mild Cognitive Impairment (MCI) is an intermediate stage between healthy and Alzheimer's disease (AD). To enable early intervention it is important to identify the MCI subjects that will convert to AD in an early stage. In this paper, we provide a new method to distinguish between MCI patients that either convert to Alzheimer's Disease (MCIc) or remain stable (MCIs), using only longitudinal T1-weighted MRI. Currently, most longitudinal studies focus on volumetric comparison of a few anatomical structures, thereby ignoring more detailed development inside and outside those structures. In this study we propose to exploit the anatomical development within the entire brain, as found by a non-rigid registration approach. Specifically, this anatomical development is represented by the Stationary Velocity Field (SVF) from registration between the baseline and follow-up images. To make the SVFs comparable among subjects, we use the parallel transport method to align them in a common space. The normalized SVF together with derived features are then used to distinguish between MCIc and MCIs subjects. This novel feature space is reduced using a Kernel Principal Component Analysis method, and a linear support vector machine is used as a classifier. Extensive comparative experiments are performed to inspect the influence of several aspects of our method on classification performance, specifically the feature choice, the smoothing parameter in the registration and the use of dimensionality reduction. The optimal result from a 10-fold cross-validation using 36 month follow-up data shows competitive results: accuracy 92%, sensitivity 95%, specificity 90%, and AUC 94%. Based on the same dataset, the proposed approach outperforms two alternative ones that either depends on the baseline image only, or uses longitudinal information from larger brain areas. Good results were also obtained when scans at 6, 12, or 24 months were used for training the classifier. Besides the classification power, the proposed method can quantitatively compare brain regions that have a significant difference in development between the MCIc and MCIs groups.