Near-infrared spectroscopy is a non-invasive neuroimaging method which uses light to measure changes in cerebral blood oxygenation associated with brain activity. In this work, we demonstrate the ability to record and analyze images of brain activity in real-time using a 16-channel continuous wave optical NIRS system. We propose a novel real-time analysis framework using an adaptive Kalman filter and a state–space model based on a canonical general linear model of brain activity. We show that our adaptive model has the ability to estimate single-trial brain activity events as we apply this method to track and classify experimental data acquired during an alternating bilateral self-paced finger tapping task.

We compare the consistency and accuracy of two image binning approaches used in 4D-CT imaging. One approach, phase binning (PB), assigns each breathing cycle 2pi rad, within which the images are grouped. In amplitude binning (AB), the images are assigned bins according to the breathing signal's full amplitude. To quantitate both approaches we used a NEMA NU2-2001 IEC phantom oscillating in the axial direction and at random frequencies and amplitudes, approximately simulating a patient's breathing. 4D-CT images were obtained using a four-slice GE Lightspeed CT scanner operating in cine mode. We define consistency error as a measure of ability to correctly bin over repeated cycles in the same field of view. Average consistency error mue+/-sigmae in PB ranged from 18%+/-20% to 30%+/-35%, while in AB the error ranged from 11%+/-14% to 20%+/-24%. In PB nearly all bins contained sphere slices. AB was more accurate, revealing empty bins where no sphere slices existed. As a proof of principle, we present examples of two non-small cell lung carcinoma patients' 4D-CT lung images binned by both approaches. While AB can lead to gaps in the coronal images, depending on the patient's breathing pattern, PB exhibits no gaps but suffers visible artifacts due to misbinning, yielding images that cover a relatively large amplitude range. AB was more consistent, though often resulted in gaps when no data existed due to patients' breathing pattern. We conclude AB is more accurate than PB. This has important consequences to treatment planning and diagnosis.

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In this paper we discuss a new set of symmetric tight frame wavelets with the associated filterbank outputs downsampled by four at each stage. The frames consist of seven generators obtained from the lowpass filter using spectral factorization, with the lowpass filter obtained via a simple method using Taylor polynomials. The filters are simple to construct, and offer smooth scaling functions and wavelets. Additionally, the filterbanks presented in this paper have limited redundancy while maintaining the smoothness of underlying limit functions. The filters are linear phase (symmetric), FIR, and the resulting wavelets possess vanishing moments.

In this paper we explore the design of tight frame wavelets with a dilation factor M = 4. The resulting limit functions are significantly smoother than their orthogonal counterparts. An advantage of the proposed filters over the dyadic filterbanks is that the proposed filterbanks result in a reduced redundancy when compared with dyadic tight frames, while maintaining smoothness. The proposed filterbanks generate five wavelets and a scaling function with the underlying filters related as follows: H5−i(z) = Hi(−z), i = 0 . . . 5.