3D DT-MRI using a reduced-FOV approach and saturation pulses.

Diffusion tensor imaging (DTI) can provide vital insights into brain connectivity, and may become an important tool for the diagnosis and treatment of neurological disease. However, DTI's intrinsic low signal-to-noise ratio (SNR) and vulnerability to ghosting artifacts can result in poor image quality with low spatial resolution, which limits its clinical applications. In this study, a new double-shot EPI sequence (half-FOV EPI) with high spatial resolution was developed. This method enables DT measurements to be obtained with high isotropic spatial resolution and whole-brain coverage. To avoid ghosting artifacts, the data are combined in image space rather than in k-space.     

Characteristic of evaluation of new image filter devised for improvement of workflow in CT examination

In this study, we processed reconstructed images with a new image filter (Siemens Medical Systems, Adaptive Image Filter: AIF). As one of its characteristics, the filter uses low-pass filtering. When an image that emphasizes a high-frequency element is changed to one with a reduced high-frequency element, an image suitable for clinical use can be obtained. For the resolving characteristic and the noise characteristic, we evaluated the degree of transition, using the modulation transfer factor (MTF) and Wiener spectrum (WS). Moreover, we used the signal-to-noise ratio (SNR) to examine the total loss of signal detection capability after use of the AIF. The results showed that, when we changed to images using the AIF and made it the same level as B30 and U40, we had to hold down the kernel level to at least B60 and U80. The use of an image filter did not recognize less of an SNR in comparison with the reconstruction image. In this study, changes in detailed characteristics of the image and SNR could be evaluated objectively using the AIF. As for the effective method by AIF that raw data isn't used for is available for the control of an image (times) using reconstruction and the change of an image on database. Therefore, we consider the AIF useful to improve workflow in CT examinations.     

Magnetic resonance imaging image intensity correction with extrapolation and adaptive smoothing.

A significant problem in magnetic resonance imaging (MRI) is the inhomogeneity of the image resulting from a number of factors that are hardware related. The obtained image can be treated as the true image multiplied by a signal modulator, which is usually smooth across the image. A class of MR image intensity correction methods extracts the slowly varying component from the image with low-pass filtering or smoothing to approximate the signal modulator. This usually causes the edge enhancement artifact in the corrected image. A novel method of extrapolating the image in advance is proposed to reduce this effect significantly. Closest point algorithm is implemented to minimize the calculation time for extrapolation. To remove bright spots caused by nonuniform sensitivity profiles, a gradient-weighted smoothing method is discussed in this work. The partial differential equations based model is applied for locally adaptive smoothing. The filtered gradient of the corrupted image is used as the weight for smoothing. Phantom and clinical data collected on various MRI systems are used for evaluation of our method. These experimental results show that the proposed method solves the edge enhancement and bright spots problem effectively and robustly.     

Smoothing that does not blur: Effects of the anisotropic approach for evaluating diffusion tensor imaging data in the clinic

Purpose:

To compare the effects of anisotropic and Gaussian smoothing on the outcomes of diffusion tensor imaging (DTI) voxel‐based (VB) analyses in the clinic, in terms of signal‐to‐noise ratio (SNR) enhancement and directional information and boundary structures preservation.

Materials and Methods:

DTI data of 30 Alzheimer's disease (AD) patients and 30 matched control subjects were obtained at 3T. Fractional anisotropy (FA) maps with variable degrees and quality (Gaussian and anisotropic) of smoothing were created and compared with an unsmoothed dataset. The two smoothing approaches were evaluated in terms of SNR improvements, capability to separate differential effects between patients and controls by a standard VB analysis, and level of artifacts introduced by the preprocessing.

Results:

Gaussian smoothing regionally biased the FA values and introduced a high variability of results in clinical analysis, greatly dependent on the kernel size. On the contrary, anisotropic smoothing proved itself capable of enhancing the SNR of images and maintaining boundary structures, with only moderate dependence of results on smoothing parameters.

Conclusion:

Our study suggests that anisotropic smoothing is more suitable in DTI studies; however, regardless of technique, a moderate level of smoothing seems to be preferable considering the artifacts introduced by this manipulation. J. Magn. Reson. Imaging 2010;31:690–697. © 2010 Wiley‐Liss, Inc.     

SENSE-DTI at 3 T.

While holding vast potential, diffusion tensor imaging (DTI) with single-excitation protocols still faces serious challenges. Limited spatial resolution, susceptibility to magnetic field inhomogeneity, and low signal-to-noise ratio (SNR) may be considered the most prominent limitations. It is demonstrated that all of these shortcomings can be effectively mitigated by the transition to parallel imaging technology and high magnetic field strength. Using the sensitivity encoding (SENSE) technique at 3 T, brain DTI was performed in nine healthy volunteers. Despite enhanced field inhomogeneity, parallel acquisition permitted both controlling geometric distortions and enhancing spatial resolution up to 0.8 mm in-plane. Heightened SNR requirements were met in part by high base sensitivity at 3 T. A further significant increase in SNR efficiency was accomplished by SENSE acquisition, exploiting enhanced encoding speed for echo time reduction. Based on the resulting image data, high-resolution tensor mapping is demonstrated.     

Digital scanned projection radiography: optimization of image characteristics

Fast interactive programs for the investigation of the effects of different processing methods were developed in an attempt to optimize the diagnostic value of digital scanned projection radiography (SPR) data. Filters from two classes of functions with varied frequency characteristics were evaluated. Arbitrary combinations of filtered and unfiltered images were generated. Speed was achieved by implementing the filtering as a convolution operation with standard computed tomography hardware (20 sec/image). The criteria for the choice of the reconstruction parameters are discussed. The clinical results show that suppressing the low-frequency content of the image, as in smoothing, low-pass filtering, and subtracting the background from the original image, optimizes the diagnostic value of SPR images.     

Single‐shot multiecho parallel echo‐planar imaging (EPI) for diffusion tensor imaging (DTI) with improved signal‐to‐noise ratio (SNR) and reduced distortion

In this work, a multiecho parallel echo‐planar imaging (EPI) acquisition strategy is introduced as a way to improve the acquisition efficiency in parallel diffusion tensor imaging (DTI). With the use of an appropriate echo combination strategy, the sequence can provide signal‐to‐noise ratio (SNR) enhancement while maintaining the advantages of parallel EPI. Simulations and in vivo experiments demonstrate that a weighted summation of multiecho images provides a significant gain in SNR over the first echo image. It is experimentally demonstrated that this SNR gain can be utilized to reduce the number of measurements often required to ensure adequate SNR for accurate DTI measures. Furthermore, the multiple echoes can be used to derive a T₂ map, providing additional information that might be useful in some applications. Magn Reson Med 60:1512–1517, 2008. © 2008 Wiley‐Liss, Inc.     

Utilizing the diffusion-to-noise ratio to optimize magnetic resonance diffusion tensor acquisition strategies for improving measurements of diffusion anisotropy.

It is well known that quantitative anisotropy measurements derived from the diffusion tensor are extremely sensitive to noise contamination. The level of noise in the diffusion tensor imaging (DTI) experiment is usually measured from some estimate of the signal-to-noise ratio (SNR) in the component diffusion-weighted (DW) images. This measure is, however, highly dependent on experimental parameters, such as the diffusion attenuation b-value and the diffusion coefficient of the subject. Conversely, the diffusion-to-noise ratio (DNR), defined as the SNR of the calculated diffusion tensor trace map, provides a reliable estimate of noise contamination, which is largely independent of such parameters. In this work it is demonstrated how reliable anisotropy measurements can be obtained using an image acquisition strategy that optimizes the DNR of the DTI experiment. This acquisition scheme is shown to provide noise-independent measurements of typical diffusion anisotropy values found in the human brain.     

Accelerating MR diffusion tensor imaging via filtered reduced-encoding projection-reconstruction.

MR diffusion tensor imaging (DTI) is a promising tool for characterizing the microstructure of ordered tissues. However, its practical applications are hampered by relatively low signal-to-noise-ratio and spatial and temporal resolution. Reduced-encoding imaging (REI) via k-space sharing with constrained reconstruction has previously been shown to be effective for accelerating DTI, although the implementation was based on rectilinear k-space sampling. Due to the intrinsic oversampling of central k-space and allowance for isotropic downsampling, projection-reconstruction (PR) imaging may be better suited for REI. In this study, regularization procedures, including radial filtering and baseline signal correction to adequately reconstruct reduced encoded PR imaging data, are investigated. The proposed filtered reduced-encoding projection-reconstruction (FREPR) technique is applied to DTI tissue fiber orientation and fractional anisotropy (FA) measurements. Results show that FREPR offers improved reconstructions of the reduced encoded images and on an equal total scan-time basis provides more accurate fiber orientation and FA measurements compared to rectilinear k-space sampling-based REI methods or a control experiment consisting of only fully encoded images. These findings suggest a potentially significant role of FREPR in accelerating repeated imaging and improving the data acquisition-time efficiency of DTI experiments.     

Assessing DTI data quality using bootstrap analysis.

Diffusion tensor imaging (DTI) is an established method for characterizing and quantifying ultrastructural brain tissue properties. However, DTI-derived variables are affected by various sources of signal uncertainty. The goal of this study was to establish an objective quality measure for DTI based on the nonparametric bootstrap methodology. The confidence intervals (CIs) of white matter (WM) fractional anisotropy (FA) and Clinear were determined by bootstrap analysis and submitted to histogram analysis. The effects of artificial noising and edge-preserving smoothing, as well as enhanced and reduced motion were studied in healthy volunteers. Gender and age effects on data quality as potential confounds in group comparison studies were analyzed. Additional noising showed a detrimental effect on the mean, peak position, and height of the respective CIs at 10% of the original background noise. Inverse changes reflected data improvement induced by edge-preserving smoothing. Motion-dependent impairment was also well depicted by bootstrap-derived parameters. Moreover, there was a significant gender effect, with females displaying less dispersion (attributable to elevated SNR). In conclusion, the bootstrap procedure is a useful tool for assessing DTI data quality. It is sensitive to both noise and motion effects, and may help to exclude confounding effects in group comparisons.     

MRI diffusion-based filtering: a note on performance characterisation.

Frequently MRI data is characterised by a relatively low signal to noise ratio (SNR) or contrast to noise ratio (CNR). When developing automated Computer Assisted Diagnostic (CAD) techniques the errors introduced by the image noise are not acceptable. Thus, to limit these errors, a solution is to filter the data in order to increase the SNR. More importantly, the image filtering technique should be able to reduce the level of noise, but not at the expense of feature preservation. In this paper we detail the implementation of a number of 3D diffusion-based filtering techniques and we analyse their performance when they are applied to a large collection of MR datasets of varying type and quality.     

Optimal filtering of constant velocity torque data

The purpose of this investigation was to implement an optimal filtering strategy for processing in vivo dynamometric data. The validity of employing commonly accepted analog smoothing methods was also appraised. An inert gravitational model was used to assess the filtering requirements of two Cybex II constant velocity dynamometers at 10 pre-set speeds with three selected loads. Speed settings were recorded as percentages of the servomechanism's maximum tachometer feedback voltage (10 to 100% Vfb max). Spectral analyses of unsmoothed torque and associated angular displacement curves, followed by optimized low-pass digital filtering, revealed the presence of two superimposed contaminating influences: a damped oscillation, representing successive sudden braking and releasing of the servomechanism control system; a relatively stationary oscillatory series, which was attributed to the Cybex motor. The optimal cutoff frequency for any data set was principally a positive function of % Vfb max. This association was represented for each machine by a different, but reliable, third order least-squares polynomial, which could be used to accurately predict the correct smoothing required for any speed setting. Unacceptable errors may be induced, especially when measuring peak torques, if data are inappropriately filtered. Over-smoothing disguises inertial artefacts. The use of Cybex recorder damping settings should be discouraged. Optimal filtering is a minimal requirement of valid data processing.     

Parallel MRI reconstruction using variance partitioning regularization.

Multiple receivers can be utilized to enhance the spatiotemporal resolution of MRI by employing the parallel imaging technique. Previously, we have reported the L-curve Tikhonov regularization technique to mitigate noise amplification resulting from the geometrical correlations between channels in a coil array. Nevertheless, one major disadvantage of regularized image reconstruction is lengthy computational time in regularization parameter estimation. At a fixed noise level, L-curve regularization parameter estimation was also found not to be robust across repetitive measurements, particularly for low signal-to-noise ratio (SNR) acquisitions. Here we report a computationally efficient and robust method to estimate the regularization parameter by partitioning the variance of the noise-whitened encoding matrix based on the estimated SNR of the aliased pixel set in parallel MRI data. The proposed Variance Partitioning Regularization (VPR) method can improve computational efficiency by 2-5-fold, depending on image matrix sizes and acceleration rates. Our anatomical and functional MRI results show that the VPR method can be applied to both static and dynamic MRI experiments to suppress noise amplification in parallel MRI reconstructions for improved image quality.     

Work in progress: the application of temporal filtering techniques to hybrid subtraction in digital subtraction angiography

Temporal filtering methods were applied to iodine signal-to-noise ratio (SNR) restoration in intravenous hybrid subtraction digital subtraction angiography (DSA). For equal detected exposure rates hybrid subtraction had approximately 35% of the SNR of temporal subtraction. When matched filtering was applied to a DSA run, the filtered result had approximately two times higher SNR than the peak contrast image in the run. Thus, when matched filtering techniques were applied to the hybrid image sequence, the resultant SNR increased to about 70% of that of temporal subtraction. With an additional factor-of-two increase in exposure rate for the hybrid run, SNR parity with temporal subtraction could be achieved. This compared with a factor-of-nine increase in exposure that would be required if no filtering were performed. Experimental hybrid matched filter results, generated with intravenous canine DSA studies, supported the predictions in SNR performance.     

k-space correction of eddy-current-induced distortions in diffusion-weighted echo-planar imaging.

This paper describes a method for correcting eddy-current (EC)-induced distortions in diffusion-weighted echo-planar imaging (DW-EPI). First, reference measurements of EC fields within the EPI acquisition window are performed for DW gradient pulses applied separately along each physical axis of the gradient set and for a range of gradient amplitudes. EC fields caused by the DW gradients of the DW-MRI protocol are then calculated using the reference EC measurements. Finally, these calculated fields are used to correct the respective DW-EPI raw (k-space) data during image reconstruction. The technique was implemented in a small-bore MRI scanner with no digital preemphasis. It corrected EC-induced image distortions in both phantom and in vivo brain diffusion tensor imaging (DTI) data more effectively than commonly used image-based techniques. The method did not increase imaging time, since the same reference EC measurements were used to correct data acquired from different phantoms, subjects, and DTI protocols. Because of the simplicity of the reference EC measurements, the method can easily be implemented in clinical scanners.     

Spatially filtering functional magnetic resonance imaging data

When constructing MR images from acquired spatial frequency data, it can be beneficial to apply a low-pass filter to remove high frequency noise from the resulting images. This amounts to attenuating high spatial frequency fluctuations that can affect detected MR signal. A study is presented of spatially filtering MR data and possible ramifications on detecting regionally specific activation signal. It is shown that absolute activation levels are strongly dependent on the parameters of the filter used in image construction and that significance of an activation signal can be enhanced through appropriate filter selection. A comparison is made between spatially filtering MR image data and applying a Gaussian convolution kernel to statistical parametric maps.     

Effect of correction function on image characteristics of positologica: A positron CT device for the head

The effects of correction function on image characteristics were studied experimentally for a positron CT device Positologica. Correction functions were obtained by smoothing the Shepp and Logan function by convolution of Gaussian functions. Signal-to-noise ratio (SNR) and spatial resolution were measured for phantoms and related to the magnitude of smoothing. The relation between SNR and spatial resolution is also discussed. Some suggestions are made to indicate how to select correction functions for clinical images. A flow of data processing for Positologica is described together with an outline of its hardware.     

MR subtraction angiography with a matched filter

The technique of matched filtering (MF) has been used in the past with X-ray digital subtraction angiography as a method of improving signal-to-noise ratio (SNR) in subtraction angiographic images. In this work we describe how MF can be applied to a series of images produced by cinematographic magnetic resonance (cine MR) to produce angiographic images. Likewise, a simple subtraction image can be formed by subtracting an image in which flow is not well visualized from an image at the same location but with flow visualization. Theory predicts that a subtraction image resulting from the MF technique will yield typical SNR improvements of 60% over results from simple subtraction. Twenty-one studies of the human popliteal, canine aorta, and canine carotid artery were undertaken in which MF was compared with simple subtraction. It was determined that cine MR can be used to produce subtraction angiographic images and that MF can produce a modest improvement in SNR over simple subtraction.     

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.     

Adaptive filtering for high resolution magnetic resonance images

We present a study of least mean square (LMS) based adaptive filters for high resolution magnetic resonance (MR) images to improve signal-to-noise ratio (SNR) while maintaining sharp edges. Five variations of a new technique that senses the type of noise or the presence of an edge in the filtering window, called adaptive filtering with noise estimation (AFEN) are presented and compared with the basic two-dimensional LMS (TDLMS) algorithm, adaptive filtering with a mean estimator (AFLME), a two-dimensional averaged LMS (TDALMS) algorithm, and a two-dimensional median weighted LMS (TDMLMS) algorithm. Although TDLMS, TDALMS, and TDMLMS filters give better SNR improvement when applied uniformly to an image, they significantly blur edges. The AFLME and AFEN filters both show approximately a factor of 2 SNR improvement with much better retention of edges, with AFEN showing slightly better performance for both SNR and edge sharpness in phantom and in vivo inner ear images.