This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.
Diffuse correlation spectroscopy (DCS) is a novel optical technique that appears to be an excellent tool for assessing cerebral blood flow in a continuous and non-invasive manner at the bedside. We present new clinical validation of the DCS methodology by demonstrating strong agreement between DCS indices of relative cerebral blood flow and indices based on phase-encoded velocity mapping magnetic resonance imaging (VENC MRI) of relative blood flow in the jugular veins and superior vena cava. Data were acquired from 46 children with single ventricle cardiac lesions during a hypercapnia intervention. Significant increases in cerebral blood flow, measured both by DCS and by VENC MRI, as well as significant increases in oxyhemoglobin concentration, and total hemoglobin concentration, were observed during hypercapnia. Comparison of blood flow changes measured by VENC MRI in the jugular veins and by DCS revealed a strong linear relationship, R = 0.88, p<0.001, slope = 0.91±0.07. Similar correlations were observed between DCS and VENC MRI in the superior vena cava, R = 0.77, slope = 0.99±0.12, p<0.001. The relationship between VENC MRI in the aorta and DCS, a negative control, was weakly correlated, R = 0.46, slope = 1.77±0.45, p<0.001.
KEYWORDS: Breast, Shape analysis, Tissues, Statistical analysis, 3D image processing, Breast imaging, Digital breast tomosynthesis, Computed tomography, Analytical research, Cancer
Recent advances in high-resolution 3D breast imaging, namely, digital breast tomosynthesis and dedicated breast CT,
have enabled detailed analysis of the shape and distribution of anatomical structures in the breast. Such analysis is
critically important, since the projections of breast anatomical structures make up the parenchymal pattern in clinical
images which can mask the existing abnormalities or introduce false alarms; the parenchymal pattern is also correlated
with the risk of cancer. As a first step towards the shape analysis of anatomical structures in the breast, we have
analyzed an anthropomorphic software breast phantom. The phantom generation is based upon the recursive splitting of
the phantom volume using octrees, which produces irregularly shaped tissue compartments, qualitatively mimicking the
breast anatomy. The shape analysis was performed by fitting ellipsoids to the simulated tissue compartments. The
ellipsoidal semi-axes were calculated by matching the moments of inertia of each individual compartment and of an
ellipsoid. The distribution of Dice coefficients, measuring volumetric overlap between the compartment and the
corresponding ellipsoid, as well as the distribution of aspect ratios, measuring relative orientations of the ellipsoids, were
used to characterize various classes of phantoms with qualitatively distinctive appearance. A comparison between input
parameters for phantom generation and the properties of fitted ellipsoids indicated the high level of user control in the
design of software breast phantoms. The proposed shape analysis could be extended to clinical breast images, and used
to inform the selection of simulation parameters for improved realism.
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