An estimator for functional data with application to MRI.

We propose a method for restoring the underlying true signal in noisy functional images. The Nadaraya-Watson (NW) estimator described in, e.g., [1] is a classical nonparametric estimator for this problem. Since the true scene in many applications contains abrupt changes between pixels of different types, a modification of the NW estimator is needed. In the data we study, the characteristics of each pixel are given as a function of time. This means that a curve of data points is observed at each pixel. Utilizing this time information, the NW weights can be modified to obtain a weighted average over pixels with the same true value. Theoretical results showing the estimator's properties are developed. Several parameters play an important role for the restoration result. Practical guidelines are given for how these parameters can be selected. Finally, we demonstrate how the method can be successfully applied both to artificial data and Magnetic Resonance Images.

Feature extraction and classification of dynamic contrast-enhanced T2*-weighted breast image data.

UNLABELLED: The relatively low specificity of dynamic contrast-enhanced T1-weighted magnetic resonance imaging (MR) imaging of breast cancer has lead several groups to investigate different approaches to data acquisition, one of them being the use of rapid T2*-weighted imaging. Analyses of such data are difficult due to susceptibility artifacts and breathing motion. MATERIALS AND METHODS: One-hundred-twenty-seven patients with breast tumors underwent MR examination with rapid, single-slice T2*-weighted imaging of the tumor. Different methods for classifying the image data set using leave-one-out cross validation were tested. Furthermore, a semi-automatic region of interest (ROI) definition tool was presented and compared with manual ROI definitions from a previous study. Finally, pixel-by-pixel analysis was done and compared with ROI analysis. The analyses were done with and without noise reduction. RESULTS: The minimum enhancement parameter was the most robust and accurate of the parameters tested. The semi-automatic ROI definition method was fast and produced similar results as the manually defined ROIs. Noise reduction improved both sensitivity and specificity, but the improvement was not statistically significant. The pixel-based analysis methods used in the present study did not improve classification results. CONCLUSIONS: Analysis of T2*-weighted breast images can be done in a rapid and robust manner by using semi-automatic ROI definition tools in combination with noise reduction. Minimum enhancement gives an indication of malignancy in T2*-weighted imaging.

Analysis of dynamic magnetic resonance images.

Dynamic magnetic resonance (MR) imaging with contrast agents is a very promising technique for studying tissue perfusion in vivo. A temporal series of magnetic resonance images of the same slice are acquired following the injection of a contrast agent into the blood stream. The image intensity depends on the local concentration of the contrast agent, so that tissue perfusion can be studied by the image series. A new method of analyzing such series is described here. Nonparametric linear regression is used for modeling the image intensity along the series on a pixel by pixel basis. After modeling, some relevant quantities describing the time series are obtained and displayed as images. Due to its flexibility, this approach is preferred to parametric modeling when pathology is present since this can induce a wide spread of patterns for the pixel image intensity along time. Results of the application of the method to series of dynamic magnetic resonance images from ischaemic rat brains after the injection of the susceptibility agent Sprodiamide Inj. (Dy-DTPA-BMA) are shown and compared to results from a related known method.

Combined perfusion and diffusion-weighted magnetic resonance imaging in a rat model of reversible middle cerebral artery occlusion.

BACKGROUND AND PURPOSE: Diffusion-weighted imaging and dynamic first-pass bolus tracking of susceptibility contrast agents (perfusion imaging) are two new magnetic resonance imaging techniques that offer the possibility of early diagnosis of stroke. The present study was performed to evaluate the diagnostic information derived from these two methods in a rat model of temporary focal ischemia. METHODS: Fifteen male Wistar rats were assigned to 45 (n = 7) or 120 minutes (n = 8) of middle cerebral artery occlusion followed by reperfusion using the intraluminal filament technique. The diffusion-weighted images were collected, and areas of hyperintensity were compared with histologically assessed areas of ischemic injury. The magnetic resonance perfusion image series were postprocessed to produce topographic maps reflecting the maximum reduction in the signal obtained during the first passage of the contrast agent and the time delay between the arrival of the bolus and the point of maximum contrast-agent effect. RESULTS: Hyperintensity in diffusion-weighted images was demonstrated after 30 minutes of middle cerebral artery occlusion and was mainly expressed in the lateral caudoputamen and parts of the lower frontoparietal cortex. Reperfusion after 45 minutes of occlusion reduced the area of hyperintensity from 24.2% to 9.9% of hemispheric area. In the group with 120 minutes of occlusion, the hyperintense area increased from 24.4% to 29.1%. Relative to the nonischemic hemisphere, the changes in the topographic maps of maximum signal reduction occurred in the lateral caudoputamen and adjacent lower neocortical areas. Increased time delay to maximum effect, however, was seen also in the upper frontoparietal cortex. CONCLUSIONS: Hyperintensity in diffusion-weighted images was reversible after 45 minutes but not after 120 minutes of middle cerebral artery occlusion. Analysis of the signal-reduction and time-delay parametric maps demonstrated regions of different perfusion changes in the ischemic hemisphere.