Quantitative perfusion and water transport time model from multi b-value diffusion magnetic resonance imaging validated against neutron capture microspheres

Intravoxel Incoherent Motion (IVIM) is a non-contrast magnetic resonance imaging diffusion-based scan that uses a multitude of b-values to measure various speeds of molecular perfusion and diffusion, sidestepping inaccuracy of arterial input functions or bolus kinetics in quantitative imaging. We test a new method of IVIM quantification and compare our values to reference standard neutron capture microspheres across normocapnia, CO2 induced hypercapnia, and middle cerebral artery occlusion in a controlled animal model. Perfusion quantification in ml/100g/min compared to microsphere perfusion uses the 3D gaussian probability distribution and defined water transport time as when 50% of the molecules remain in the tissue of interest. Perfusion, water transport time, and infarct volume was compared to reference standards. Simulations were studied to suppress non-specific cerebrospinal fluid (CSF). Linear regression analysis of quantitative perfusion returned correlation (slope = .55, intercept = 52.5, $R^2$= .64). Linear regression for water transport time asymmetry in infarcted tissue was excellent (slope = .59, intercept = .3, $R^2$ = .93). Strong linear agreement also was found for infarct volume (slope = 1.01, $R^2$= .79). Simulation of CSF suppression via inversion recovery returned blood signal reduced by 82% from combined T1 and T2 effects. Intra-physiologic state comparison of perfusion shows potential partial volume effects which require further study especially in disease states. The accuracy and sensitivity of IVIM provides evidence that observed signal changes reflect cytotoxic edema and tissue perfusion. Partial volume contamination of CSF may be better removed during post-processing rather than with inversion recovery to avoid artificial loss of blood signal.

On using the Microsoft Kinect$^{\rm TM}$ sensors in the analysis of human motion

The present paper aims at providing the theoretical background required for investigating the use of the Microsoft Kinect$^{\rm TM}$ (`Kinect', for short) sensors (original and upgraded) in the analysis of human motion. Our methodology is developed in such a way that its application be easily adaptable to comparative studies of other systems used in capturing human-motion data. Our future plans include the application of this methodology to two situations: first, in a comparative study of the performance of the two Kinect sensors; second, in pursuing their validation on the basis of comparisons with a marker-based system (MBS). One important feature in our approach is the transformation of the MBS output into Kinect-output format, thus enabling the analysis of the measurements, obtained from different systems, with the same software application, i.e., the one we use in the analysis of Kinect-captured data; one example of such a transformation, for one popular marker-placement scheme (`Plug-in Gait'), is detailed. We propose that the similarity of the output, obtained from the different systems, be assessed on the basis of the comparison of a number of waveforms, representing the variation within the gait cycle of quantities which are commonly used in the modelling of the human motion. The data acquisition may involve commercially-available treadmills and a number of velocity settings: for instance, walking-motion data may be acquired at $5$ km/h, running-motion data at $8$ and $11$ km/h. We recommend that particular attention be called to systematic effects associated with the subject's knee and lower leg, as well as to the ability of the Kinect sensors in reliably capturing the details in the asymmetry of the motion for the left and right parts of the human body. The previous versions of the study have been withdrawn due to the use of a non-representative database.