A novel magnetic resonance imaging technique provides direct imaging of motion by spatially modulating the degree of magnetization prior to imaging. The preimaging pulse sequence consists of a radio-frequency (RF) pulse to produce transverse magnetization, a magnetic field gradient to "wrap" the phase along the direction of the gradient, and a second RF pulse to mix the modulated transverse magnetization with the longitudinal magnetization. The resulting images show periodic stripes due to the modulation. Motion between the time of striping and image formation is directly demonstrated as a corresponding displacement of the stripes. This technique can be used to study heart wall motion, to distinguish slowly moving blood from thrombus, and to study the flow of blood and cerebrospinal fluid.

A previously reported method of using magnetic resonance (MR) imaging to study heart wall motion involves a pair of nonselective radio-frequency (RF) pulses, separated by a magnetic field gradient pulse, prior to imaging; this produces images with a regular pattern of stripes that move with the heart wall and that have a sinusoidal intensity profile. It is demonstrated in this study that the substitution of more RF pulses, with their relative amplitudes distributed according to the binomial sequence, results in sharper stripes. This permits the use of a two-dimensional grid of stripes for more detailed studies of heart wall motion and provides a unique method of analyzing regional ventricular myocardial strain.

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OBJECTIVE The objective of our study was to predict response to chemoradiation therapy in patients with head and neck squamous cell carcinoma (HNSCC) by combined use of diffusion-weighted imaging (DWI) and high-spatial-resolution, high-temporal-resolution dynamic contrast-enhanced MRI (DCE-MRI) parameters from primary tumors and metastatic nodes. SUBJECTS AND METHODS Thirty-two patients underwent pretreatment DWI and DCE-MRI using a modified radial imaging sequence. Postprocessing of data included motion-correction algorithms to reduce motion artifacts. The median apparent diffusion coefficient (ADC), volume transfer constant (Ktrans), extracellular extravascular volume fraction (ve), and plasma volume fraction (vp) were computed from primary tumors and nodal masses. The quality of the DCE-MRI maps was estimated using a threshold median chi-square value of 0.10 or less. Multivariate logistic regression and receiver operating characteristic curve analyses were used to determine the best model to discriminate responders from nonresponders. RESULTS Acceptable χ2 values were observed from 84% of primary tumors and 100% of nodal masses. Five patients with unsatisfactory DCE-MRI data were excluded and DCEMRI data for three patients who died of unrelated causes were censored from analysis. The median follow-up for the remaining patients (n = 24) was 23.72 months. When ADC and DCE-MRI parameters (Ktrans, ve, vp) from both primary tumors and nodal masses were incorporated into multivariate logistic regression analyses, a considerably higher discriminative accuracy (area under the curve [AUC] = 0.85) with a sensitivity of 81.3% and specificity of 75% was observed in differentiating responders (n = 16) from nonresponders (n = 8). CONCLUSION The combined use of DWI and DCE-MRI parameters from both primary tumors and nodal masses may aid in prediction of response to chemoradiation therapy in patients with HNSCC.

Magnetic resonance tissue tagging allows noninvasive in vivo measurement of soft tissue deformation. Planes of magnetic saturation are created, orthogonal to the imaging plane, which form dark lines (stripes) in the image. The authors describe a method for tracking stripe motion in the image plane, and show how this information can be incorporated into a finite element model of the underlying deformation. Human heart data were acquired from several imaging planes in different orientations and were combined using a deformable model of the left ventricle wall. Each tracked stripe point provided information on displacement orthogonal to the original tagging plane, i.e., a one-dimensional (1-D) constraint on the motion. Three-dimensional (3-D) motion and deformation was then reconstructed by fitting the model to the data constraints by linear least squares. The average root mean squared (rms) error between tracked stripe points and predicted model locations was 0.47 mm (n=3,100 points). In order to validate this method and quantify the errors involved, the authors applied it to images of a silicone gel phantom subjected to a known, well-controlled, 3-D deformation. The finite element strains obtained were compared to an analytic model of the deformation known to be accurate in the central axial plane of the phantom. The average rms errors were 6% in both the reconstructed shear strains and 16% in the reconstructed radial normal strain.