A method to reduce the Gibbs ringing artifact in MRI scans while keeping tissue boundary integrity

Brain tissue segmentation in neurosurgery: a systematic analysis for quantitative tractography approaches

Diffusion magnetic resonance imaging (dMRI) is a cutting-edge imaging method that provides a macro-scale in vivo map of the white matter pathways in the brain. The measurement of brain microstructure and the enhancement of tractography rely heavily on dMRI tissue segmentation. Anatomical MRI technique (e.g., T1- and T2-weighted imaging) is the most widely used method for segmentation in dMRI. In comparison to anatomical MRI, dMRI suffers from higher image distortions, lower image quality, and making inter-modality registration more difficult. The dMRI tractography study of brain connectivity has become a major part of the neuroimaging landscape in recent years. In this research, we provide a high-level overview of the methods used to segment several brain tissues types, including grey and white matter and cerebrospinal fluid, to enable quantitative studies of structural connectivity in the brain in health and illness. In the first part of our review, we discuss the three main phases in the quantitative analysis of tractography, which are correction, segmentation, and quantification. Methodological possibilities are described for each phase, along with their popularity and potential benefits and drawbacks. After that, we will look at research that used quantitative tractography approaches to examine the white and grey matter of the brain, with an emphasis on neurodevelopment, ageing, neurological illnesses, mental disorders, and neurosurgery as possible applications. Even though there have been substantial advancements in methodological technology and the spectrum of applications, there is still no consensus regarding the "optimal" approach in the quantitative analysis of tractography. As a result, researchers should tread carefully when interpreting the findings of quantitative analysis of tractography.

ROI-based analysis of diffusion indices in healthy subjects and subjects with deficit or non-deficit syndrome schizophrenia

We analyzed DTI data involving 22 healthy subjects (HC), 15 patients with deficit syndrome schizophrenia (DSZ), and 25 patients with non-deficit syndrome schizophrenia (NDSZ). We used a 1.5-T MRI scanner to collect diffusion-weighted images and T1 images, which were employed to correct distortions and deformations within the diffusion-weighted images. For 156 regions of interest (ROI), we calculated the average fractional anisotropy (FA), mean diffusion (MD), and radial diffusion (RD). Each ROI underwent a group-wise comparison using permutation F-test, followed by post hoc pairwise comparisons with Bonferroni correction. In general, we observed lower FA in both schizophrenia groups compared to HC (i.e., HC>(DSZ=NDSZ)), while MD and RD showed the opposite pattern. Notably, specific ROIs with reduced FA in schizophrenia patients included bilateral nucleus accumbens, left fusiform area, brain stem, anterior corpus callosum, left rostral and caudal anterior cingulate, right posterior cingulate, left thalamus, left hippocampus, left inferior temporal cortex, right superior temporal cortex, left pars triangularis and right lingual gyrus. Significantly, the right cuneus exhibited lower FA in the DSZ group compared to other groups ((HC=NDSZ)>DSZ), without affecting MD and RD. These results indicate that compromised neural integrity in the cuneus may contribute to the pathophysiological distinctions between DSZ and NDSZ.

Automated filter selection for suppression of Gibbs ringing artefacts in MRI

Gibbs ringing creates artefacts in magnetic resonance images that can mislead clinicians. Reconstruction algorithms attempt to suppress Gibbs ringing, or an additional ringing suppression algorithm may be applied post reconstruction. Novel reconstruction algorithms are often compared with filtered Fourier reconstruction, but the choices of filters and filter parameters can be arbitrary and sub-optimal. Evaluation of different reconstruction and post-processing algorithms is difficult to automate or subjective: many metrics have been used in the literature. In this paper, we evaluate twelve of those metrics and demonstrate that none of them are fit for purpose. We propose a novel metric and demonstrate its efficacy in 1D and 2D simulations. We use our new metric to optimise and compare 17 smoothing filters for suppression of Gibbs artefacts. We examine the transfer functions of the optimised filters, with counter-intuitive results regarding the highest-performing filters. Our results will simplify and improve the comparison of novel MRI reconstruction and post-processing algorithms, and lead to the automation of ringing suppression in MRI. They also apply more generally to other applications in which data is captured in the Fourier domain.

Measurement of sub-zero temperatures in MRI using T1 temperature sensitive soft silicone materials: Applications for MRI-guided cryosurgery

PURPOSE

One standard method, proton resonance frequency shift, for measuring temperature using magnetic resonance imaging (MRI), in MRI-guided surgeries, fails completely below the freezing point of water. Because of this, we have developed a new methodology for monitoring temperature with MRI below freezing. The purpose of this paper is to show that a strong temperature dependence of the nuclear relaxation time T₁ in soft silicone polymers can lead to temperature-dependent changes of MRI intensity acquired with T₁ weighting. We propose the use of silicone filaments inserted in tissue for measuring temperature during MRI-guided cryoablations.

METHODS

The temperature dependence of T₁ in bio-compatible soft silicone polymers was measured using nuclear magnetic resonance spectroscopy and MRI. Phantoms, made of bulk silicone materials and put in an MRI-compatible thermal container with dry ice, allowed temperature measurements ranging from -60°C to + 20°C. T₁ -weighted gradient echo images of the phantoms were acquired at spatially uniform temperatures and with a gradient in temperature to determine the efficacy of using these materials as temperature indicators in MRI. Ex vivo experiments on silicone rods, 4 mm in diameter, inserted in animal tissue were conducted to assess the practical feasibility of the method.

RESULTS

Measurements of nuclear relaxation times of protons in soft silicone polymers show a monotonic, nearly linear, change with temperature (R²  > 0.98) and have a significant correlation with temperature (Pearson's r > 0.99, p < 0.01). Similarly, the intensity of the MR images in these materials, taken with a gradient echo sequence, are also temperature dependent. There is again a monotonic change in MRI intensity that correlates well with the measured temperature (Pearson's r < -0.98 and p < 0.01). The MRI experiments show that a temperature change of 3°C can be resolved in a distance of about 2.5 mm. Based on MRI images and external sensor calibrations for a sample with a gradient in temperature, temperature maps with 3°C isotherms are created for a bulk phantom. Experiments demonstrate that these changes in MRI intensity with temperature can also be seen in 4 mm silicone rods embedded in ex vivo animal tissue.

CONCLUSIONS

We have developed a new method for measuring temperature in MRI that potentially could be used during MRI-guided cryoablation operations, reducing both procedure time and cost, and making these surgeries safer.

Removal of partial Fourier-induced Gibbs (RPG) ringing artifacts in MRI

PURPOSE

To investigate and remove Gibbs-ringing artifacts caused by partial Fourier (PF) acquisition and zero filling interpolation in MRI data.

THEORY AND METHODS

Gibbs ringing of fully sampled data, leading to oscillations around tissue boundaries, is caused by the symmetric truncation of k-space. Such ringing can be removed by conventional methods, with the local subvoxel shifts method being the state-of-the-art. However, the asymmetric truncation of k-space in routinely used PF acquisitions leads to additional ringings of wider intervals in the PF sampling dimension that cannot be corrected solely based on magnitude images reconstructed via zero filling. Here, we develop a pipeline for the Removal of PF-induced Gibbs ringing (RPG) to remove ringing patterns of different periods by applying the conventional method twice. The proposed pipeline is validated on numerical phantoms, demonstrated on in vivo diffusion MRI measurements, and compared with the conventional method and neural network-based approach.

RESULTS

For PF = 7/8 and 6/8, Gibbs-ringings and subsequent bias in diffusion metrics induced by PF acquisition and zero filling are robustly removed by using the proposed RPG pipeline. For PF = 5/8, however, ringing removal via RPG leads to excessive image blurring due to the interplay of image phase and convolution kernel.

CONCLUSIONS

RPG corrects Gibbs-ringing artifacts in magnitude images of PF acquired data and reduces the bias in quantitative MR metrics. Considering the benefit of PF acquisition and the feasibility of ringing removal, we suggest applying PF = 6/8 when PF acquisition is necessary.

Training a neural network for Gibbs and noise removal in diffusion MRI

PURPOSE

To develop and evaluate a neural network-based method for Gibbs artifact and noise removal.

METHODS

A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images.

RESULTS

Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions.

CONCLUSIONS

The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.

Intramedullary spinal tumor-like lesions

The development of magnetic resonance imaging (MRI) has led to an increasingly frequent detection of changes in the spinal cord. The most common intramedullary lesions are: demyelinating; vascular; inflammatory; infectious; and congenital, largely called tumor-like lesions. Spinal cord tumors are relatively rare, as compared with brain tumors. The hardest task is to conclude whether the spinal cord lesion is a tumor or a tumor-like lesion. This review is intended to help evaluate the spinal cord and gives an overview of the tumor-like lesions occurring in the spinal cord along with their characteristic.

[Elimination of Gibbs artifact based on local subpixel shift and interlaced local variation]

OBJECTIVE

To extend the application of Gibbs artifact reduction method that exploits local subvoxel- shifts (LSS) to zero- padded k-space magnetic resonance imaging (MRI) data.

METHODS

We investigated two approaches to extending the application of LSS-based method to under-sampled data. The first approach, namely LSS+ interpolation, utilized the original LSS-based method to minimize the local variation on nonzero-padding reconstructed images, followed by image interpolation to obtain the final images. The second approach, interlaced local variation, used zero-padded Fourier transformation followed by elimination of Gibbs artifacts by minimizing a novel interlaced local variations (iLV) term. We compared the two methods with the original LSS and Hamming window filter algorithms, and verified their feasibility and robustness in phantom and in vivo data.

RESULTS

The two methods proposed showed better performance than the original LSS and Hamming window filters and effectively eliminated Gibbs artifacts while preserving the image details. Compared to LSS + interpolation method, iLV method better preserved the details of the images.

CONCLUSIONS

The iLV and LSS+interpolation methods proposed herein both extend the application of the original LSS method and can eliminate Gibbs artifacts in zero-filled k-space data reconstruction images, and iLV method shows a more prominent advantage in retaining the image details.

Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline

This work evaluates the accuracy and precision of the Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline, developed to identify and minimize common sources of methodological variability including: thermal noise, Gibbs ringing artifacts, Rician bias, EPI and eddy current induced spatial distortions, and motion-related artifacts. Following this processing pipeline, iterative parameter estimation techniques were used to derive diffusion parameters of interest based on the diffusion tensor and kurtosis tensor. We evaluated accuracy using a software phantom based on 36 diffusion datasets from the Human Connectome project and tested the precision by analyzing data from 30 healthy volunteers scanned three times within one week. Preprocessing with both DESIGNER or a standard pipeline based on smoothing (instead of noise removal) improved parameter precision by up to a factor of 2 compared to preprocessing with motion correction alone. When evaluating accuracy, we report average decreases in bias (deviation from simulated parameters) over all included regions for fractional anisotropy, mean diffusivity, mean kurtosis, and axonal water fraction of 9.7%, 8.7%, 4.2%, and 7.6% using DESIGNER compared to the standard pipeline, demonstrating that preprocessing with DESIGNER improves accuracy compared to other processing methods.

Total variation-based neutron computed tomography

We perform the neutron computed tomography reconstruction problem via an inverse problem formulation with a total variation penalty. In the case of highly under-resolved angular measurements, the total variation penalty suppresses high-frequency artifacts which appear in filtered back projections. In order to efficiently compute solutions for this problem, we implement a variation of the split Bregman algorithm; due to the error-forgetting nature of the algorithm, the computational cost of updating can be significantly reduced via very inexact approximate linear solvers. We present the effectiveness of the algorithm in the significantly low-angular sampling case using synthetic test problems as well as data obtained from a high flux neutron source. The algorithm removes artifacts and can even roughly capture small features when an extremely low number of angles are used.

The Relative Impact of Ghosting and Noise on the Perceived Quality of MR Images

Magnetic resonance (MR) imaging is vulnerable to a variety of artifacts, which potentially degrade the perceived quality of MR images and, consequently, may cause inefficient and/or inaccurate diagnosis. In general, these artifacts can be classified as structured or unstructured depending on the correlation of the artifact with the original content. In addition, the artifact can be white or colored depending on the flatness of the frequency spectrum of the artifact. In current MR imaging applications, design choices allow one type of artifact to be traded off with another type of artifact. Hence, to support these design choices, the relative impact of structured versus unstructured or colored versus white artifacts on perceived image quality needs to be known. To this end, we conducted two subjective experiments. Clinical application specialists rated the quality of MR images, distorted with different types of artifacts at various levels of degradation. The results demonstrate that unstructured artifacts deteriorate quality less than structured artifacts, while colored artifacts preserve quality better than white artifacts.

Fast simultaneous noncontrast angiography and intraplaque hemorrhage (fSNAP) sequence for carotid artery imaging

PURPOSE

To propose a fast simultaneous noncontrast angiography and intraplaque hemorrhage (fSNAP) sequence for carotid artery imaging.

METHODS

The proposed fSNAP sequence uses a low-resolution reference acquisition for phase-sensitive reconstruction to speed up the scan, and an inversion recovery acquisition with arbitrary k-space filling order to generate similar contrast to conventional SNAP. Four healthy volunteers and eight patients were recruited to test the performance of fSNAP in vivo. The lumen area quantification, muscle-blood CNR, IPH-blood CNR, lumen SNR, and standard deviation and intraplaque hemorrhage (IPH) detection accuracy were compared between fSNAP and SNAP.

RESULTS

By using a low-resolution reference acquisition with 1/4 matrix size of the full-resolution reference scan, the scan time of fSNAP was 37.5% less than that of SNAP. A high agreement of lumen area measurement (ICC = 0.97, 95% CI: 0.96-0.99) and IPH detection (Kappa = 1) were found between fSNAP and SNAP. Also, no significant difference was found for muscle-blood CNR (P = 0.25), IPH-blood CNR (P = 0.35), lumen SNR (P = 0.60), and standard deviation (P = 0.46) between the two techniques.

CONCLUSION

The feasibility of fSNAP was validated. fSNAP can improve the imaging efficiency with similar performance to SNAP on carotid artery imaging. Magn Reson Med 77:753-758, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

The effect of Gibbs ringing artifacts on measures derived from diffusion MRI

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a unique method to investigate microstructural tissue properties noninvasively and is one of the most popular methods for studying the brain white matter in vivo. To obtain reliable statistical inferences with diffusion MRI, however, there are still many challenges, such as acquiring high-quality DW-MRI data (e.g., high SNR and high resolution), careful data preprocessing (e.g., correcting for subject motion and eddy current induced geometric distortions), choosing the appropriate diffusion approach (e.g., diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), or diffusion spectrum MRI), and applying a robust analysis strategy (e.g., tractography based or voxel based analysis). Notwithstanding the numerous efforts to optimize many steps in this complex and lengthy diffusion analysis pipeline, to date, a well-known artifact in MRI--i.e., Gibbs ringing (GR)--has largely gone unnoticed or deemed insignificant as a potential confound in quantitative DW-MRI analysis. Considering the recent explosion of diffusion MRI applications in biomedical and clinical applications, a systematic and comprehensive investigation is necessary to understand the influence of GR on the estimation of diffusion measures. In this work, we demonstrate with simulations and experimental DW-MRI data that diffusion estimates are significantly affected by GR artifacts and we show that an off-the-shelf GR correction procedure based on total variation already can alleviate this issue substantially.

Aliased noise in X-ray CT images and band-limiting processing as a preventive measure

X-ray CT projection data often include components with frequencies that are markedly higher than the pixel Nyquist frequency f PN, which is determined by the pixel size. Noise components higher than f PN are folded back into a region lower than f PN through the backprojection process, thereby creating aliased noise. With clinical CT scanners, we evaluated the aliased noise using an aliasing prevention measure, band-limiting processing (BLP), which suppresses frequency components higher than f PN in the projection data. Indices we used to evaluate improvement by BLP were the noise power spectrum (NPS), modulation transfer function (MTF), signal-to-noise-ratio (SNR) spectrum, matched filter SNR (MF SNR), and two-alternative forced-choice (2-AFC) test. With BLP, the NPS was decreased not only beyond f PN, but also within f PN. The same level of MTF was maintained as that without BLP within f PN. No remarkable reduction in spatial resolution was observed. The SNR spectrum and the MF SNR of the BLP image nearly agreed with those of an ideal state without aliased noise. A notable improvement in the visuoperceptual image quality by BLP was recognized with a reconstruction field of view (FOV) of more than 45 cm. We then applied BLP to clinical data and confirmed that significant aliased noise of a large FOV image was removed without notable side effects. The results showed that at least some CTs suffering from aliased noise can be improved by proper band-limiting.

Single acquisition quantitative single-point electron paramagnetic resonance imaging

PURPOSE

Electron paramagnetic resonance imaging has emerged as a promising noninvasive technology to dynamically image tissue oxygenation. Owing to its extremely short spin-spin relaxation times, electron paramagnetic resonance imaging benefits from a single-point imaging scheme where the entire free induction decay signal is captured using pure phase encoding. However, direct T2 (*)/pO2 quantification is inhibited owing to constant magnitude gradients which result in time-decreasing field of view. Therefore, conventional acquisition techniques require repeated imaging experiments with differing gradient amplitudes (typically 3), which results in long acquisition time.

METHODS

In this study, gridding was evaluated as a method to reconstruct images with equal field of view to enable direct T2 (*)/pO2 quantification within a single imaging experiment. Additionally, an enhanced reconstruction technique that shares high spatial k-space regions throughout different phase-encoding time delays was investigated (k-space extrapolation).

RESULTS

The combined application of gridding and k-space extrapolation enables pixelwise quantification of T2 (*) from a single acquisition with improved image quality across a wide range of phase-encoding time delays. The calculated T2 (*)/pO2 does not vary across this time range.

CONCLUSIONS

By utilizing gridding and k-space extrapolation, accurate T2 (*)/pO2 quantification can be achieved within a single data set to allow enhanced temporal resolution (by a factor of 3).

Removal of truncation artefacts in magnetic resonance images by recovering missing spectral data

Truncation artefacts are often present in many archived clinical magnetic resonance (MR) images due to the need of shortening the acquisition time by sampling a part of their k-space. This artificial information degrades the quality of the image and may hamper clinical diagnosis. In this paper, we propose a novel method to remove the artefacts by recovering the missing k-space or spectral data. The method consists of four steps: (a) estimating the truncated k-space from the images containing truncations artefacts, (b) computing the parameters of the sparse representation of the difference image of an image from the estimated truncated k-space, (c) recovering the missing spectral data using the parameters computed in (b), and (d) obtaining the artefact-removed image through inverse Fourier transform of the estimated and the recovered spectral data. Experiments on both simulated and real MR images have shown that the proposed method effectively removes truncation artefacts while preserving image quality and outperforms both the conventional Hamming window method and the popular TV method.

Realistic analytical phantoms for parallel magnetic resonance imaging

The quantitative validation of reconstruction algorithms requires reliable data. Rasterized simulations are popular but they are tainted by an aliasing component that impacts the assessment of the performance of reconstruction. We introduce analytical simulation tools that are suited to parallel magnetic resonance imaging and allow one to build realistic phantoms. The proposed phantoms are composed of ellipses and regions with piecewise-polynomial boundaries, including spline contours, Bézier contours, and polygons. In addition, they take the channel sensitivity into account, for which we investigate two possible models. Our analytical formulations provide well-defined data in both the spatial and k-space domains. Our main contribution is the closed-form determination of the Fourier transforms that are involved. Experiments validate the proposed implementation. In a typical parallel magnetic resonance imaging reconstruction experiment, we quantify the bias in the overly optimistic results obtained with rasterized simulations-the inverse-crime situation. We provide a package that implements the different simulations and provide tools to guide the design of realistic phantoms.

Suppression of MRI truncation artifacts using total variation constrained data extrapolation

The finite sampling of k-space in MRI causes spurious image artifacts, known as Gibbs ringing, which result from signal truncation at the border of k-space. The effect is especially visible for acquisitions at low resolution and commonly reduced by filtering at the expense of image blurring. The present work demonstrates that the simple assumption of a piecewise-constant object can be exploited to extrapolate the data in k-space beyond the measured part. The method allows for a significant reduction of truncation artifacts without compromising resolution. The assumption translates into a total variation minimization problem, which can be solved with a nonlinear optimization algorithm. In the presence of substantial noise, a modified approach offers edge-preserving denoising by allowing for slight deviations from the measured data in addition to supplementing data. The effectiveness of these methods is demonstrated with simulations as well as experimental data for a phantom and human brain in vivo.

Differential diagnosis of T2 hyperintense spinal cord lesions: Part A

Hyperintense spinal cord signal on T2-weighted images is seen in a wide-ranging variety of spinal cord processes including; simple MR artefacts, congenital anomalies and most disease categories. Characterization of the abnormal areas of T2 signal as well as their appearance on other MR imaging sequences, when combined with clinical context and laboratory investigations, will often allow a unique diagnosis, or at least aid in narrowing the differential diagnosis. A wide range of instructive cases is discussed here with review of the published reports focusing on pertinent MR features to aid in diagnosis.

Late gadolinium enhancement of the right ventricular myocardium: is it really different from the left ?

It has been suggested that, in late gadolinium enhancement, the signal of right ventricular myocardium is nulled at a shorter inversion time than the left. While we initially made the same observation, we believe that the difference is not real, but results from artifacts. We present 7 cases as well as computer simulations to describe the nature of these artifacts and explain how they can create the impression of different inversion times for the right and left ventricle. At inversion times that are shorter than ideal for the myocardium a black rim can be seen at the border of the myocardium with blood on the inside and with fat on the outside. This is most likely a partial volume effect. The thin myocardium of the right ventricle is sandwiched between these black rims and, at a low spatial resolution, is no longer visible. In this case, the adjacent black rims may then be misinterpreted as myocardium. While black rims also occur on the left side, the myocardium is thicker and remains discernable as a separate layer. As a consequence, the optimal inversion time for the right ventricle only appears different from that for the left. In fact, in the presence of hypertrophy of the right ventricle or during systolic wall thickening we did not find a difference in inversion times between the left and right ventricle. We conclude that sufficient spatial resolution is important for adequate late gadolinium enhancement of the right ventricle.

Phase correction-based singularity function analysis for partial k-space reconstruction

Partial k-space acquisition is a conventional method in magnetic resonance imaging (MRI) for reducing imaging time while maintaining image quality. In this field, image reconstruction from partial k-space is a key issue. This paper proposes an approach fundamentally different from traditional techniques for reconstructing magnetic resonance (MR) images from partial k-space. It uses a so-called singularity function analysis (SFA) model based on phase correction. With such a reconstruction approach, some nonacquired negative spatial frequencies are first recovered by means of phase correction and Hermitian symmetry property, and then the other nonacquired negative and/or positive spatial frequencies are estimated using the mathematical SFA model. The method is particularly suitable for asymmetrical partial k-space acquisition owing to its ability of overcoming reconstruction limitations due to k-space truncations. The performance of this approach is evaluated using both simulated and real MR brain images, and compared with existing techniques. The results demonstrate that the proposed SFA based on phase correction achieves higher image quality than the initial SFA or the projection-onto-convex sets (POCS) method.

On the dark rim artifact in dynamic contrast-enhanced MRI myocardial perfusion studies

A dark band or rim along parts of the subendocardial border of the left ventricle (LV) and the myocardium has been noticed in some dynamic contrast-enhanced MR perfusion studies. The artifact is thought to be due to susceptibility effects from the gadolinium bolus, motion, or resolution, or a combination of these. Here motionless ex vivo hearts in which the cavity was filled with gadolinium are used to show that dark rim artifacts can be consistent with resolution effects alone.

Improved accuracy of reconstructed diffuse optical tomographic images by means of spatial deconvolution: two-dimensional quantitative characterization

Systematic characterization studies are presented, relating to a previously reported spatial deconvolution operation that seeks to compensate for the information-blurring property of first-order perturbation algorithms for diffuse optical tomography (DOT) image reconstruction. In simulation results that are presented, this deconvolution operation has been applied to two-dimensional DOT images reconstructed by solving a first-order perturbation equation. Under study was the effect on algorithm performance of control parameters in the measurement (number and spatial distribution of sources and detectors, presence of noise, and presence of systematic error), target (medium shape; and number, location, size, and contrast of inclusions), and computational (number of finite-element-method mesh nodes, length of filter-generating linear system, among others) parameter spaces associated with computation and the use of the deconvolution operators. Substantial improvements in reconstructed image quality, in terms of recovered inclusion location, size, and contrast, are found in all cases. A finding of practical importance is that the method is robust to appreciable differences between the optical coefficients of the media used for filter generation and those of the target media to which the filters are subsequently applied.

Enhancing ultrasound images by morphology filter and eliminating ringing effect

Various medical image compression techniques have been proposed for accelerating image propagation in many applications. JPEG2000 is a new generation technique that can encode near lossless ultrasound images at medium bit-rate with diagnostically acceptable quality. Because the coder of JPEG2000 is based on wavelet transform, the reconstructed image will contain some ringing artifacts. Some de-ringing algorithm must be applied to enhance image quality. This study presents quad-tree decomposition and a set of morphological filters for reducing the ringing artifacts of ultrasound images. Specifically, the presented morphological filters use eight predefined morphological operations, including four structuring elements (SE) that include both dilation and erosion. The proposed voting strategy can be used to select the morphological filter for each block to optimize decoded image quality. Image quality can be enhanced by applying the appropriate morphological filter to each block. Experimental results demonstrate that the proposed technique enhances reconstructed ultrasound image quality compared to JPEG2000 at the same bit-rate in terms of both PSNR and the perceptual results.

Improving the accuracy of volumetric segmentation using pre-processing boundary detection and image reconstruction

The concentration edge -detection and Gegenbauer image-reconstruction methods were previously shown to improve the quality of segmentation in magnetic resonance imaging. In this study, these methods are utilized as a pre-processing step to the Weibull E-SD field segmentation. It is demonstrated that the combination of the concentration edge detection and Gegenbauer reconstruction method improves the accuracy of segmentation for the simulated test data and real magnetic resonance images used in this study.

Improving tissue segmentation of human brain MRI through preprocessing by the Gegenbauer reconstruction method

The Gegenbauer image reconstruction method, previously shown to improve the quality of magnetic resonance images, is utilized in this study as a segmentation preprocessing step. It is demonstrated that, for all simulated and real magnetic resonance images used in this study, the Gegenbauer reconstruction method improves the accuracy of segmentation. Although it is more desirable to use the k-space data for the Gegenbauer reconstruction method, only information acquired from MR images is necessary for the reconstruction, making the procedure completely self-contained and viable for all human brain segmentation algorithms.