Multimodality offers anthropologists an inflection on the way we do research, produce scholarship, teach students, and relate to diverse publics. Advancing an expanding array of tools, practices,... Show moreMultimodality offers anthropologists an inflection on the way we do research, produce scholarship, teach students, and relate to diverse publics. Advancing an expanding array of tools, practices, and concepts, multimodality signals a change in the way we pay attention and attend to the diverse possibilities for understanding the human experience. Multimodality recognizes the way smartphones, social media, and digital software transform research dynamics in unprecedented ways, while also drawing upon long-standing practices of recording and presenting research through images, sounds, objects, and text. Rather than flatten out ethnographic participant observation into logocentric practices of people-writing, multimodal ethnographies diversify their modes of inquiry to produce more-than-textual mediations of sensorial research experiences. By emphasizing kaleidoscopic qualities that give shape to an emergent, multidimensional, and diversifying anthropology, multimodality proposes alternatives to enduring and delimiting dichotomies, particularly text/image. These new configurations invite unrealized disciplinary constellations and research collaborations to emerge, but also require overhauling the infrastructures that support training, dissemination, and assessment. Show less
Fluorescence-guided surgery (FGS) is an intraoperative imaging technique already introduced and validated in the clinic for sentinel lymph node mapping and biliary imaging. Conjugating a NIR... Show moreFluorescence-guided surgery (FGS) is an intraoperative imaging technique already introduced and validated in the clinic for sentinel lymph node mapping and biliary imaging. Conjugating a NIR-dye to a specific tumor-targeting vehicle dramatically enhances the specificity of this technique. Hence, a powerful synergy can be achieved when fluorescent imaging is combined with nuclear imaging. The (NIR) fluorescent signal aids the surgeon to accurately recognize and resect malignant tissues and detect (nearby) vital structures in real-time during surgery, while its nuclear counterpart can be used to preoperative assess tumor spread and aid in the surgical planning and guidance. Patients may benefit directly from better tumor detection as the surgical status (R0 or R1) is one of the most import parameters for morbidity and patient survival. This thesis focus on the evaluation of potential targets for image-guided surgery applications and describes the preclinical evaluation of novel tracers for (hybrid) image-guided surgery. Show less
KleinJan, G.H.; Bunschoten, A.; Berg, N.S. van den; Olmos, R.A.V.; Klop, W.M.C.; Horenblas, S.; ... ; Leeuwen, F.W.B. van 2016
Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease.... Show moreMagnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification. (C) 2016 The Authors. Published by Elsevier Inc. Show less
Schouten, T.M.; Koini, M.; Vos, F. de; Seiler, S.; Van der Grond, J.; Lechner, A.; ... ; Rombouts, S.A.R.B. 2016
