Sodium TQF NMR and intracellular sodium in isolated crystalloid perfused rat heart.

The feasibility of monitoring intracellular sodium changes using Na triple quantum filtered NMR without a chemical shift reagent (SR) was investigated in an isolated rat heart during a variety of interventions for Na(i) loading. Perfusion with 1 mM ouabain or without K+ present in the perfusate for 30 min produced a rise of the Na TQF signal with a plateau of approximately 190% and approximately 228% relative to the preintervention level, respectively. Stop-flow ischemia for 30 min resulted in a TQF signal growth of approximately 147%. The maximal Na TQF signal increase of 460% was achieved by perfusion without K+/Ca2+, corresponding to an elimination of the Na transmembrane gradient. The observed values of Na NMR TQF growth in the physiological and pathological ranges are in agreement with reported data by other methods and have a linear correlation with intracellular sodium content as determined in this study by Co-EDTA method and by sucrose-histidine washout of the extracellular space. Our data indicate that the increase in Na TQF NMR signal is determined by the growth of Na(i), and the extracellular Na contribution to the total TQF signal is unchanged at approximately 64%. In conclusion, Na TQF NMR without using SR offers a unique and noninvasive opportunity to monitor alterations of intracellular sodium. It may provide valuable insights for developing cardioprotective strategies and for observing the effects of pharmaceutical treatments on sodium homeostasis.

A new approach to study cardiac motion: the optical flow of cine MR images.

We present a novel approach to study the intricate motion of the heart using the optical flow technique applied to cine MR images. The method uses image brightness variations between consecutive frames to compute in-plane displacements or velocities of moving image features. The dense velocity field thereby obtained throughout the myocardial wall allows to not only characterize segmental left ventricular wall motion but also to resolve "fine" motion such as intramural differences in displacements and therefore in thickening. Other subtle features of systole like the descent of the base towards the apex or the counterclockwise rotation of the apex with respect to the base can also be detected by the algorithm. Contrary to other techniques proposed earlier, this noninvasive method presents the additional advantages of not requiring any special pulse sequence nor well defined endocardial and epicardial outlines.

Optimization neural networks for the segmentation of magnetic resonance images.

The application of the Hopfield neural network for the multispectral unsupervised classification of MR images is reported. Winner-take-all neurons were used to obtain a crisp classification map using proton density-weighted and T(2)-weighted images in the head. The preliminary studies indicate that the number of iterations needed to reach ;good' solutions was nearly constant with the number of clusters chosen for the problem.

Application of an artificial neural network in radiographic diagnosis.

The description of 44 cases of bone tumors was used by an artificial neural network to rank the likelihood of 55 possible pathologic diagnoses. The performance of the artificial neural network was compared with the performance of experienced (3 or more years of radiology training) residents and inexperienced (less than 1 year of radiology training) residents. The artificial neural network was trained using descriptions of 110 radiographs of bone tumors with known diagnoses. The descriptions of a separate set of 44 cases were used to test the neural network. The neural network ranked 55 possible pathologic diagnoses on a scale from 1 to 55. Experienced and inexperienced residents also ranked the possible diagnoses in the same 44 cases. Inexperienced residents had a significantly lower mean proportion of diagnoses ranked first or second than did the neural network. Experienced residents had a significantly higher proportion of correct diagnoses ranked first than did the network. Otherwise, a significant difference between the performance of the network and experienced or inexperienced residents was not identified. These results demonstrate that artificial neural networks can be trained to classify bone tumors. Whether neural network performance in classification of bone tumors can be made accurate enough to assist radiologists in clinical practice remains an open question. These preliminary results indicate that further investigation of this technology for interpretation assistance is warranted.

Tissue segmentation for three-dimensional display of human spines.

This paper presents a morphological tissue segmentation technique for the three-dimensional visualization of the human spine. Magnetic resonance images of the human spine were obtained using motion-compensated fast imaging with steady-state free precession (FISP) pulse sequences. The images acquired with these sequences exhibit very high contrast between the bright cerebrospinal fluid (CSF) and the dark spinal cord and nerve roots. This phenomenon has enabled us to apply mathematical morphological processing techniques to extract the spinal nerve roots within the CSF space. The nerve roots were extracted from a set of slices and displayed in a three-dimensional format.

Segmentation of magnetic resonance images using an artificial neural network.

Signal intensities from intermediate and T2 weighted spin echo images of the brain were used as inputs into an artificial neural network (ANN). The signal intensities were used to train the network to recognize anatomically-important segments. The ANN was a self-organizing map (SOM) neural network which develops a continuous topographical map of the signal intensities within the two images. The neural network segmented images demonstrated good correlation with white matter, gray matter, and cerebral spinal fluid (CSF) spaces. This technique was rated better than manual thresholding of the intermediate images, but not as good as manual thresholding of the T2 weighted images.