This document may be broadly used as a standard reference regarding the current state of the IVOCT imaging modality, intended for researchers and clinicians who use IVOCT and analyze IVOCT data.

Near‐infrared (NIR) fluorescence imaging is gaining clinical acceptance over the last years and has been used for detection of lymph nodes, several tumor types, vital structures and tissue perfusion. This review focuses on NIR fluorescence imaging with indocyanine green and methylene blue for different clinical applications in abdominal surgery with an emphasis on oncology, based on a systematic literature search. Furthermore, practical information on doses, injection times, and intraoperative use are provided.

The identification of tumor subpopulations that adversely affect patient outcomes is essential for a more targeted investigation into how tumors develop detrimental phenotypes, as well as for personalized therapy. Mass spectrometry imaging has demonstrated the ability to uncover molecular intratumor heterogeneity. The challenge has been to conduct an objective analysis of the resulting data to identify those tumor subpopulations that affect patient outcome. Here we introduce spatially mapped t-distributed stochastic neighbor embedding (t-SNE), a nonlinear visualization of the data that is able to better resolve the biomolecular intratumor heterogeneity. In an unbiased manner, t-SNE can uncover tumor subpopulations that are statistically linked to patient survival in gastric cancer and metastasis status in primary tumors of breast cancer.intratumor heterogeneity | mass spectrometry imaging | t-SNE | biomarker | cancer M ass spectrometry imaging (MSI) is a technology that simultaneously provides the spatial distribution of hundreds of biomolecules directly from tissue (1, 2). The two most common techniques, matrix-assisted laser desorption and desorption electrospray ionization, lead to minimal loss of histological information. Accordingly, the same tissue section can be histologically assessed and registered to the MSI dataset. In this manner, the mass spectral signatures of specific cell types or histopathological entities (e.g., tumor cells) can be extracted from the often highly heterogeneous tissues encountered in patient tumors (3). This high cellular specificity is behind the increasing popularity of MSI in cancer research and its proven ability to identify diagnostic and prognostic biomarkers (4).There is growing awareness that MSI also can be used to annotate tissues based on the local mass spectrometry profiles and thereby differentiate tissues/regions that are not histologically distinct. Deininger et al. (5) were the first to report that MSI may reveal the biomolecular intratumor heterogeneity associated with a tumor's clonal development. A hierarchical cluster analysis of the MSI data revealed a patchwork of molecularly distinct regions, which were postulated to reflect the tumor's clonal evolution. It was recently demonstrated that such an approach, using multivariate analysis of the MSI data to identify regions with distinct mass spectral signatures and then linking these molecularly distinct regions to patient outcome, enables the identification of tumor subpopulations that are statistically associated with poor survival and tumor metastasis (6).All methods used to date for revealing intratumor heterogeneity have been linear dimensionality-reduction techniques, but this linearity constraint focuses the results on the global characteristics of the data space at the expense of finer details (7). Accordingly, linear methods might not be sensitive to the subtle changes expected to demarcate the clonal progression of tumors, in which the molecular differences between nearly sequential subpopulations may...

Plaque constitution on computed tomography coronary angiography (CTA) is associated with prognosis. At present only visual assessment of plaque constitution is possible. An accurate automatic, quantitative approach for CTA plaque constitution assessment would improve reproducibility and allows higher accuracy. The present study assessed the feasibility of a fully automatic and quantitative analysis of atherosclerosis on CTA. Clinically derived CTA and intravascular ultrasound virtual histology (IVUS VH) datasets were used to investigate the correlation between quantitatively automatically derived CTA parameters and IVUS VH. A total of 57 patients underwent CTA prior to IVUS VH. First, quantitative CTA quantitative computed tomography (QCT) was performed. Per lesion stenosis parameters and plaque volumes were assessed. Using predefined HU thresholds, CTA plaque volume was differentiated in 4 different plaque types necrotic core (NC), dense calcium (DC), fibrotic (FI) and fibro-fatty tissue (FF). At the identical level of the coronary, the same parameters were derived from IVUS VH. Bland-Altman analyses were performed to assess the agreement between QCT and IVUS VH. Assessment of plaque volume using QCT in 108 lesions showed excellent correlation with IVUS VH (r = 0.928, p < 0.001) (Fig. 1). The correlation of both FF and FI volume on IVUS VH and QCT was good (r = 0.714, p < 0.001 and r = 0.695, p < 0.001 respectively) with corresponding bias and 95 % limits of agreement of 24 mm(3) (-42; 90) and 7.7 mm(3) (-54; 70). Furthermore, NC and DC were well-correlated in both modalities (r = 0.523, p < 0.001) and (r = 0.736, p < 0.001). Automatic, quantitative CTA tissue characterization is feasible using a dedicated software tool. Fig. 1 Schematic illustration of the characterization of coronary plaque on CTA: cross-correlation with IVUS VH. First, the 3-dimensional centerline was generated from the CTA data set using an automatic tree extraction algorithm (Panel I). Using a unique registration a complete pullback series of IVUS images was mapped on the CTA volume using true anatomical markers (Panel II). Fully automatic lumen and vessel wall contour detection was performed for both imaging modalities (Panel III). Finally, fusion-based quantification of atherosclerotic lesions was based on the lumen and vessel wall contours as well as the corresponding reference lines (estimate of normal tapering of the coronary artery), as shown in panel IV. At the level of the minimal lumen area (MLA) (yellow lines), stenosis parameters, could be calculated for both imaging techniques. Additionally, plaque volumes and plaque types were derived for the whole coronary artery lesion, ranging from the proximal to distal lesion marker (blue markers). Fibrotic tissue was labeled in dark green, Fibro-fatty tissue in light green, dense calcium in white and necrotic core was labeled in red.

Abstract

Coronary computed tomographic angiography (CCTA) is a non-invasive imaging modality for the visualization of the heart and coronary arteries. To fully exploit the potential of the CCTA datasets and apply it in clinical practice, an automated coronary artery extraction approach is needed. The purpose of this paper is to present and validate a fully automatic centerline extraction algorithm for coronary arteries in CCTA images. The algorithm is based on an improved version of Frangi’s vesselness filter which removes unwanted step-edge responses at the boundaries of the cardiac chambers. Building upon this new vesselness filter, the coronary artery extraction pipeline extracts the centerlines of main branches as well as side-branches automatically. This algorithm was first evaluated with a standardized evaluation framework named Rotterdam Coronary Artery Algorithm Evaluation Framework used in the MICCAI Coronary Artery Tracking challenge 2008 (CAT08). It includes 128 reference centerlines which were manually delineated. The average overlap and accuracy measures of our method were 93.7% and 0.30 mm, respectively, which ranked at the 1st and 3rd place compared to five other automatic methods presented in the CAT08. Secondly, in 50 clinical datasets, a total of 100 reference centerlines were generated from lumen contours in the transversal planes which were manually corrected by an expert from the cardiology department. In this evaluation, the average overlap and accuracy were 96.1% and 0.33 mm, respectively. The entire processing time for one dataset is less than 2 min on a standard desktop computer. In conclusion, our newly developed automatic approach can extract coronary arteries in CCTA images with excellent performances in extraction ability and accuracy.

Automated quantification of coronary plaque on CT is feasible using dedicated quantitative software with a novel 3D registration algorithm.

Previous MRI studies reported cortical iron accumulation in early-onset (EOAD) compared to late-onset (LOAD) Alzheimer disease patients. However, the pattern and origin of iron accumulation is poorly understood. This study investigated the histopathological correlates of MRI contrast in both EOAD and LOAD. T2*-weighted MRI was performed on postmortem frontal cortex of controls, EOAD, and LOAD. Images were ordinally scored using predefined criteria followed by histology. Nonlinear histology-MRI registration was used to calculate pixel-wise spatial correlations based on the signal intensity. EOAD and LOAD were distinguishable based on 7T MRI from controls and from each other. Histology-MRI correlation analysis of the pixel intensities showed that the MRI contrast is best explained by increased iron accumulation and changes in cortical myelin, whereas amyloid and tau showed less spatial correspondence with T2*-weighted MRI. Neuropathologically, subtypes of Alzheimer's disease showed different patterns of iron accumulation and cortical myelin changes independent of amyloid and tau that may be detected by high-field susceptibility-based MRI.