Cerebral palsy (CP) develops as a consequence of white matter damage (WMD) in approximately one out of every 10 very preterm infants. Ultrasound (US) is widely used to screen for a variety of brain injuries in this patient population, but early US often fails to detect WMD. We hypothesized that quantitative texture measures on US images obtained within one week of birth are associated with the subsequent development of CP. In this retrospective study, using images from a variety of US machines, we extracted unique texture measures by means of adaptive processing and high resolution feature enhancement. We did not standardize the images, but used patients as their own controls. We did not remove speckle, as it may contain information. To test our hypothesis, we used the "random forest" algorithm to create a model. The random forest classifier achieved a 72% match to the health outcome of the patients (CP versus no CP), whereas designating all patients as having CP would have resulted in 53% error. This suggests that quantitative early texture measures contain diagnostic information relevant to the development of CP.

Motion artifacts have been identified as a problem in medical tomography systems. While computed tomography (CT) imaging has been getting faster, there remains a need to detect and compensate for motions in clinical follow-up of neurological patients (multiple sclerosis, tumors, stroke, etc.), in cardiac imaging, and in any area in which failing to detect a motion artifact may lead to misdiagnosis. We have developed a novel algorithm to detect motion in brain images. The algorithm deals with detecting and isolating motion in the object domain using only the information available in the sinogram domain. The new "opposite ray algorithm" (ORA) addresses the issue of motion in the interior elements of the object. The ORA combines information from projections that are opposite in space and separated in time to isolate and identify the motion. A sinogram of motion is created, integrated and reconstructed to isolate the moving component. The algorithm can be used with conventional clinical scanners employing quarter-detector offset. The significant effect of quarter-detector offset on the ORA is investigated. The effects that a finite beamwidth and noise have on the ORA are also investigated. Both the similarity index and a correlation coefficient are used to evaluate the algorithm. The algorithm is successful when applied to cases exhibiting translational and translational-rotational motion. A similarity index of 0.88 is obtained in a typical case with both translational and rotational motion. Further development is recommended in the deformation case.

Hydrocephalus, characterized by increased fluid in the cerebral ventricles, is traditionally evaluated by a visual assessment of serial CT scans. The complex shape of the ventricular system makes accurate visual comparison of CT scans difficult. The current research developed a quantitative method to measure the change in cerebral ventricular volume over time. Key elements of the developed framework are: adaptive image registration based on mutual information and wavelet multiresolution analysis; adaptive segmentation with novel feature extraction based on the Dual-Tree Complex Wavelet Transform; volume calculation. The framework, when tested on physical phantoms, had an error of 2.3%. When validated on clinical cases, results showed that cases deemed to be normal/stable had a calculated volume change less than 5%. Those with progressive/treated hydrocephalus had a calculated change greater than 20%. These findings indicate that the framework is reasonable and has potential for development as a tool in the evaluation of hydrocephalus.

Premature infants are prone to white matter damage (WMD), which is associated with cerebral palsy (CP) and cognitive impairment. Ultrasound (US) is the preferred imaging modality to detect WMD. To improve on existing diagnostic rates, quantitative measures incorporating new information are needed. We are investigating US texture measures as new indicators of white matter health.We have developed algorithms to enhance texture features and then obtain a measure of the tissue texture. Using our texture measures, data from 18 patients (12 with normal outcome, 6 who developed CP) form separate populations based on patient outcome. Our algorithms are applied to B-mode cranial US images without compensating for operator-dependent machine settings and without suppressing speckle. The results of the preliminary study will form the basis for the design of a computer aided diagnosis system for the early detection of white matter damage.