A fast model independent method for automatic correction of intensity nonuniformity in MRI data.

A novel nonparametric approach for correcting intensity nonuniformity in magnetic resonance (MR) images is described. This approach is based solely on the assumption that the various sources of nonuniformity in MR imaging give rise to smooth variations in image intensity, and that these variations can be extracted and corrected for. The advantage of this computationally fast method is that it can be applied early in quantitative analysis while being independent of pulse sequence and is insensitive to pathological processes. This algorithm has been tested on both simulated and real data. Application to tissue segmentation and functional MR imaging has shown a marked improvement in quantitative analysis. J. Magn. Reson. Imaging 1999;10:550-562.

Improved high resolution MR imaging for surface coils using automated intensity non-uniformity correction: feasibility study in the orbit.

This study examined the effects of a recently developed automated intensity non-uniformity correction on surface coil images using the orbit as an exemplar. Images were obtained using a standard head coil and a range of surface coils. Slices through the optic nerve head and cavernous sinus were subjected to the correction algorithm. Blind forced-choice rankings of the subjective image quality were performed. Quantitative measurements were taken of the similarity between vitreous humor at two depths from the coil, and of the conspicuity between orbital fat and temporalis muscle intensities. The combined qualitative ranks for corrected surface coil images were higher than for the equivalent uncorrected images in all cases. Intensity non-uniformity correction produced statistically significant improvements in orbital surface coil images, bringing their intensity uniformity in homogeneous tissue to the level of head coil images. The subjective quality of the corrected surface coil images was superior to head coil images, due to increased spatial resolution combined with improved signal to noise ratio across the image.

Is clinical growth index a reliable predictor of tumour growth in vestibular schwannomas?

We have assessed the clinical growth index as an indicator of tumour growth rate in 50 patients with a vestibular schwannoma. Clinical growth index was calculated by measuring the length of history and dividing it by the maximum tumour diameter. Total tumour volumes were also measured from all MRI examinations and an effective tumour volume doubling time was calculated. Radiological growth measurements demonstrated involution in 10/50 patients. The median volume doubling time was 1.65 years (range 20.9-46.3 months, skewness 1.72 years). The median clinical growth index was 0.030 cm per month (range 0-0.270 cm per month, skewness 2.398). There was no significant correlation between volume doubling time and clinical growth index. Identification of rapidly growing tumours with clinical growth index >0.025 cm/month had a positive predictive value of 61%, negative predictive value of 48%, false-positive rate of 30% and false-negative rate of 52%. In conclusion, we have shown that the growth rate of vestibular schwannoma is not related to the clinical growth index and we recommend that this measure should be abandoned in the clinical management of patients where conservative management regimes are being considered.