Noise-adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels.

Anisotropic diffusion filtering is widely used for MR image enhancement. However, the anisotropic filter is nonoptimal for MR images with spatially varying noise levels, such as images reconstructed from sensitivity-encoded data and intensity inhomogeneity-corrected images. In this work, a new method for filtering MR images with spatially varying noise levels is presented. In the new method, a priori information regarding the image noise level spatial distribution is utilized for the local adjustment of the anisotropic diffusion filter. Our new method was validated and compared with the standard filter on simulated and real MRI data. The noise-adaptive method was demonstrated to outperform the standard anisotropic diffusion filter in both image error reduction and image signal-to-noise ratio (SNR) improvement. The method was also applied to inhomogeneity-corrected and sensitivity encoding (SENSE) images. The new filter was shown to improve segmentation of MR brain images with spatially varying noise levels.     

Reconstruction of 3D dynamic contrast-enhanced magnetic resonance imaging using nonlocal means

Purpose

To develop and test a nonlocal means‐based reconstruction algorithm for undersampled 3D dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) of tumors.

Materials and Methods

We propose a reconstruction technique that is based on the recently proposed nonlocal means (NLM) filter which can relax trade‐offs in spatial and temporal resolutions in dynamic imaging. Unlike the original application of NLM for image denoising, the MR reconstruction framework here can offer high‐quality images from undersampled k‐space data. The method is based on enforcing similarity constraints in terms of neighborhoods of pixels rather than individual pixels. The method was applied on undersampled 3D DCE imaging of breast and brain tumor datasets and the results were compared to sliding window reconstructions and to a compressed sensing method using total variation constraints on the images.

Results

Undersampling factors of up to five were obtained with the proposed approach while preserving the spatial and temporal characteristics. The NLM reconstruction method offered improved performance over the sliding window and the total variation constrained reconstruction techniques.

Conclusion

The reconstruction framework here can give high‐quality images from undersampled DCE MRI data and has the potential to improve the quality of DCE tumor imaging. J. Magn. Reson. Imaging 2010;32:1217–1227. © 2010 Wiley‐Liss, Inc.     

Adaptive retrospective correction of motion artifacts in cranial MRI with multicoil three‐dimensional radial acquisitions

Despite reduction in imaging times through improved hardware and rapid acquisition schemes, motion artifacts can compromise image quality in magnetic resonance imaging, especially in three‐dimensional imaging with its prolonged scan durations. Direct extension of most state‐of‐the‐art two‐dimensional rigid body motion compensation techniques to the three‐dimensional case is often challenging or impractical due to a significant increase in sampling requirements. This article introduces a novel motion correction technique that is capable of restoring image quality in motion corrupted two‐dimensional and three‐dimensional radial acquisitions without a priori assumptions about when motion occurs. The navigating properties of radial acquisitions—corroborated by multiple receiver coils—are exploited to detect actual instances of motion. Pseudorandom projection ordering provides flexibility of reconstructing navigator images from the obtained motion‐free variable‐width subsets for subsequent estimation of rigid body motion parameters by coregistration. The proposed approach does not require any additional navigators or external motion estimation schemes. The capabilities and limitations of the method are described and demonstrated through simulations and representative volunteer cranial acquisitions. Magn Reson Med 69:1094–1103, 2013. © 2012 Wiley Periodicals, Inc.     

Registration of prostate histology images to ex vivo MR images via strand‐shaped fiducials

Purpose:

To present and evaluate a method for registration of whole‐mount prostate digital histology images to ex vivo magnetic resonance (MR) images.

Materials and Methods:

Nine radical prostatectomy specimens were marked with 10 strand‐shaped fiducial markers per specimen, imaged with T1‐ and T2‐weighted 3T MRI protocols, sliced at 4.4‐mm intervals, processed for whole‐mount histology, and the resulting histological sections (3–5 per specimen, 34 in total) were digitized. The correspondence between fiducial markers on histology and MR images yielded an initial registration, which was refined by a local optimization technique, yielding the least‐squares best‐fit affine transformation between corresponding fiducial points on histology and MR images. Accuracy was quantified as the postregistration 3D distance between landmarks (3–7 per section, 184 in total) on histology and MR images, and compared to a previous state‐of‐the‐art registration method.

Results:

The proposed method and previous method had mean (SD) target registration errors of 0.71 (0.38) mm and 1.21 (0.74) mm, respectively, requiring 3 and 11 hours of processing time, respectively.

Conclusion:

The proposed method registers digital histology to prostate MR images, yielding 70% reduced processing time and mean accuracy sufficient to achieve 85% overlap on histology and ex vivo MR images for a 0.2 cc spherical tumor. J. Magn. Reson. Imaging 2012; 36:1402–1412. © 2012 Wiley Periodicals, Inc.     

Highly accelerated contrast‐enhanced MR angiography: Improved reconstruction accuracy and reduced noise amplification with complex subtraction

Contrast‐enhanced magnetic resonance angiography is routinely performed using parallel imaging to best capture the first pass of contrast material through the target vasculature, followed by digital subtraction to suppress the appearance of unwanted signal from background tissue. Both processes, however, amplify noise and can produce uninterpretable images when large acceleration factors are used. Using a phantom study of contrast‐enhanced magnetic resonance angiography, we show that complex subtraction processing prior to partially parallel reconstruction improves reconstruction accuracy relative to magnitude subtraction processing for reduction factors as large as 12. Time‐resolved contrast‐enhanced magnetic resonance angiographic data obtainedwith complex subtraction in volunteers supported the results of the phantom study and when compared with magnitude subtraction processing demonstrated reduced geometry factors as well as improved image quality at large reduction factors. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.