A High Resolution Dynamic Heart Model Based on Averaged MRI Data

Abstract

Abstract. We are in the process of constructing a high resolution, high signal to noise ratio (SNR) dynamic MRI dataset for the human heart using methodology similar to that employed to construct a low-noise standard brain at the Montreal Neurological Institute. Several high resolution, low SNR magnetic resonance images of 20 phases over the cardiac cycle were acquired from a single subject. Images from identical phases and temporally adjacent phases were registered, and the image intensities were averaged together to generate a high resolution, high SNR dynamic magnetic resonance image volume of the human heart. Although this work is still preliminary, and the results still demonstrate residual artifacts due to motion an sub-optimal alignment of interleaved image slices, our model has a SNR that is improved by a factor of 2.7 over a single volume, spatial resolution of 1.5 mm 3 , and a temporal resolution of 60 ms.

Volume Reconstruction by Inverse Interpolation: Application to Interleaved MR Motion Correction

“…The qualitative (visual appearance) and quantitative (approximation error; preservation of intensity distribution) results in this paper suggest that a simple cubic kernel is effective, and it is also computationally efficient (Table 1). For comparison, we have demonstrated the superiority of our inverse interpolation reconstruction approach over interpolation-and-averaging schemes described in the literature [2]. It is also worth mentioning that our method can reconstruct image volumes from lower-dimensional acquired data (e.g., freehand ultrasound), which interpolation and averaging can not.…”

“…A common and straightforward volume reconstruction method, pursued for example by Moore et al [2] in the construction of a cardiac MR atlas, is to interpolate the coregistered acquired images onto the target grid and average the interpolated images. This approach is limited, however, by the resolution of the input images (Fig.…”

“…One application for the algorithm introduced in this paper is to The average of co-registered pass images (analogous to Ref. [2]) is blurry. (d) The image reconstructed using the proposed algorithm is crisp and free of between-pass motion artifacts.…”

Inter-breath-hold Registration for the Production of High Resolution Cardiac MR Volumes

“…However, these three techniques all seek to correct for breath-hold discontinuities in thick sliced images whose features are somewhat different to those of high resolution near-isotropic images. To date, only Moore et al [7] have attempted to correct for breath-hold inconsistencies in images with high through-plane resolution. They performed successive rigid registrations between each slice and sagital and axial scout slices.…”

Towards Subject-Specific Models of the Dynamic Heart for Image-Guided Mitral Valve Surgery

“…left ventricular myocardium, right ventricle and atrium, etc.) were manually segmented from each image and the resulting data was then co-registered into a common high-resolution reference image (1.5 mm slice thickness) [11]. The image component of the prior model represents a measure of the MR appearance of the heart, and was obtained by performing a principal component analysis on the co-registered data.…”