Robust dynamic susceptibility contrast MR perfusion using 4D nonlinear noise filters

A dynamic susceptibility contrast MRI digital reference object for testing software with leakage correction: Effect of background simulation

PURPOSE

Dynamic susceptibility contrast (DSC)-MRI is a perfusion imaging technique from which useful quantitative imaging biomarkers can be derived. Relative cerebral blood volume (rCBV) is such a biomarker commonly used for evaluating brain tumors. To account for the extravasation of contrast agents in tumors, post-processing leakage correction is often applied to improve rCBV accuracy. Digital reference objects (DRO) are ideal for testing the post-processing software, because the biophysical model used to generate the DRO can be matched to the one that the software uses. This study aims to develop DROs to validate the leakage correction of software using Weisskoff model and to examine the effect of background signal time curves that are required by the model.

METHODS

Three DROs were generated using the Weisskoff model, each composed of nine foreground lesion objects with combinations of different levels of rCBV and contrast leakage parameter (K2). Three types of background were implemented for these DROs: (1) a multi-compartment brain-like background, (2) a sphere background with a constant signal time curve, and (3) a sphere background with signal time curve identical to that of the brain-like DRO's white matter (WM). The DROs were then analyzed with an FDA-cleared software with and without leakage correction. Leakage correction was tested with and without brain segmentation.

RESULTS

Accuracy of leakage correction was able to be verified using the brain-like phantom and the sphere phantom with WM background. The sphere with constant background did not perform well with leakage correction with or without brain segmentation. The DROs were able to verify that for the particular software tested, leakage correction with brain segmentation achieved the lowest error.

CONCLUSIONS

DSC-MRI DROs with biophysical model matched to that of the post-processing software can be well used for the software's validation, provided that the background signals are also properly simulated for generating the reference time curve required by the model. Care needs to be taken to consider the interaction of the design of the DRO with the software's implementation of brain segmentation to extract the reference time curve.

CUDA-based acceleration and BPN-assisted automation of bilateral filtering for brain MR image restoration

PURPOSE

Bilateral filters have been substantially exploited in numerous magnetic resonance (MR) image restoration applications for decades. Due to the deficiency of theoretical basis on the filter parameter setting, empirical manipulation with fixed values and noise variance-related adjustments has generally been employed. The outcome of these strategies is usually sensitive to the variation of the brain structures and not all the three parameter values are optimal. This article is in an attempt to investigate the optimal setting of the bilateral filter, from which an accelerated and automated restoration framework is developed.

METHODS

To reduce the computational burden of the bilateral filter, parallel computing with the graphics processing unit (GPU) architecture is first introduced. The NVIDIA Tesla K40c GPU with the compute unified device architecture (CUDA) functionality is specifically utilized to emphasize thread usages and memory resources. To correlate the filter parameters with image characteristics for automation, optimal image texture features are subsequently acquired based on the sequential forward floating selection (SFFS) scheme. Subsequently, the selected features are introduced into the back propagation network (BPN) model for filter parameter estimation. Finally, the k-fold cross validation method is adopted to evaluate the accuracy of the proposed filter parameter prediction framework.

RESULTS

A wide variety of T1-weighted brain MR images with various scenarios of noise levels and anatomic structures were utilized to train and validate this new parameter decision system with CUDA-based bilateral filtering. For a common brain MR image volume of 256 × 256 × 256 pixels, the speed-up gain reached 284. Six optimal texture features were acquired and associated with the BPN to establish a "high accuracy" parameter prediction system, which achieved a mean absolute percentage error (MAPE) of 5.6%. Automatic restoration results on 2460 brain MR images received an average relative error in terms of peak signal-to-noise ratio (PSNR) less than 0.1%. In comparison with many state-of-the-art filters, the proposed automation framework with CUDA-based bilateral filtering provided more favorable results both quantitatively and qualitatively.

CONCLUSIONS

Possessing unique characteristics and demonstrating exceptional performances, the proposed CUDA-based bilateral filter adequately removed random noise in multifarious brain MR images for further study in neurosciences and radiological sciences. It requires no prior knowledge of the noise variance and automatically restores MR images while preserving fine details. The strategy of exploiting the CUDA to accelerate the computation and incorporating texture features into the BPN to completely automate the bilateral filtering process is achievable and validated, from which the best performance is reached.

Noise Estimation and Reduction in Magnetic Resonance Imaging Using a New Multispectral Nonlocal Maximum-likelihood Filter

Denoising of magnetic resonance (MR) images enhances diagnostic accuracy, the quality of image manipulations such as registration and segmentation, and parameter estimation. The first objective of this paper is to introduce a new, high-performance, nonlocal filter for noise reduction in MR image sets consisting of progressively-weighted, that is, multispectral, images. This filter is a multispectral extension of the nonlocal maximum likelihood filter (NLML). Performance was evaluated on synthetic and in-vivo T₂ - and T₁ -weighted brain imaging data, and compared to the nonlocal-means (NLM) and its multispectral version, that is, MS-NLM, and the nonlocal maximum likelihood (NLML) filters. Visual inspection of filtered images and quantitative analyses showed that all filters provided substantial reduction of noise. Further, as expected, the use of multispectral information improves filtering quality. In addition, numerical and experimental analyses indicated that the new multispectral NLML filter, MS-NLML, demonstrated markedly less blurring and loss of image detail than seen with the other filters evaluated. In addition, since noise standard deviation (SD) is an important parameter for all of these nonlocal filters, a multispectral extension of the method of maximum likelihood estimation (MLE) of noise amplitude is presented and compared to both local and nonlocal MLE methods. Numerical and experimental analyses indicated the superior performance of this multispectral method for estimation of noise SD.

A robust noise reduction technique for time resolved CT

PURPOSE

To develop a noise reduction method for time resolved CT data, especially those with significant patient motion.

METHODS

PArtial TEmporal Nonlocal (PATEN) means is a technique that uses the redundant information in time-resolved CT data to achieve noise reduction. In this method, partial temporal profiles are used to determine the similarity (or weight) between pixels, and the similarity search makes use of both spatial and temporal information, providing robustness to patient motion. The performance of the PATEN filter was qualitatively and quantitatively evaluated with nine cardiac CT patient data sets and five CT brain perfusion patient data sets. In cardiac CT, PATEN was applied to reduce noise primarily in the reduced-dose phases created with electrocardiographic (ECG) pulsing. CT number accuracy and noise reduction were evaluated in both full-dose phases and reduced-dose phases between filtered backprojection images and PATEN filtered images. In CT brain perfusion, simulated quarter dose data were obtained by adding noise to the raw data of a routine dose scan. PATEN was applied to the simulated low-dose images. Image noise, time-intensity profile accuracy, and perfusion parameter maps were compared among low-dose, low-dose+PATEN filter, and full-dose images. The noise reduction performance of PATEN was compared to a previously proposed noise reduction method, time-intensity profile similarity (TIPS) bilateral filtering.

RESULTS

In 4D cardiac CT, after PATEN filtering, the image noise in the reduced-dose phases was greatly reduced, making anatomical structures easier to identify. The mean decreases in noise values between the original and PATEN images were 11.0% and 53.8% for the full and reduced-dose phases of the cardiac cycle, respectively. TIPS could not achieve effective noise reduction. In CT brain perfusion, PATEN achieved a 55.8%-66.3% decrease in image noise in the low-dose images. The contrast to noise ratio in the axial images was increased and was comparable to the full-dose images. Differentiation of anatomical structure in the PATEN images and corresponding quantitative perfusion parameter maps were preferred by two neuroradiologists compared to the simulated low-dose and TIPS results. The mean perfusion parameters calculated from the PATEN images agreed with those determined from full-dose data to within 12% and 20% for normal and diseased regions.

CONCLUSIONS

In ECG-gated cardiac CT, where the dose had already been reduced by a factor of 5 in the reduced-dose phases, PATEN achieved a 53.8% noise reduction, which decreased the noise level in the reduced-dose phases close to that of the full-dose phases. In CT brain perfusion, a fourfold dose reduction was demonstrated to be achievable by PATEN filtering, which improved quantitative perfusion analysis. PATEN can be used to effectively reduce image noise to improve image quality, even when significant motion occurred between temporal samples.

Analysis of perfusion MRI in stroke: To deconvolve, or not to deconvolve

PURPOSE

There is currently controversy regarding the benefits of deconvolution-based parameters in stroke imaging, with studies suggesting a similar infarct prediction using summary parameters. We investigate here the performance of deconvolution-based parameters and summary parameters for dynamic-susceptibility contrast (DSC) MRI analysis, with particular emphasis on precision.

METHODS

Numerical simulations were used to assess the contribution of noise and arterial input function (AIF) variability to measurement precision. A realistic AIF range was defined based on in vivo data from an acute stroke clinical study. The simulated tissue curves were analyzed using two popular singular value decomposition (SVD) based algorithms, as well as using summary parameters.

RESULTS

SVD-based deconvolution methods were found to considerably reduce the AIF-dependency, but a residual AIF bias remained on the calculated parameters. Summary parameters, in turn, show a lower sensitivity to noise. The residual AIF-dependency for deconvolution methods and the large AIF-sensitivity of summary parameters was greatly reduced when normalizing them relative to normal tissue.

CONCLUSION

Consistent with recent studies suggesting high performance of summary parameters in infarct prediction, our results suggest that DSC-MRI analysis using properly normalized summary parameters may have advantages in terms of lower noise and AIF-sensitivity as compared to commonly used deconvolution methods. Magn Reson Med 76:1282-1290, 2016. © 2015 Wiley Periodicals, Inc.

Evaluation of the effectiveness of simple nuclei-segmentation methods on Caenorhabditis elegans embryogenesis images

BACKGROUND

For the analysis of spatio-temporal dynamics, various automated processing methods have been developed for nuclei segmentation. These methods tend to be complex for segmentation of images with crowded nuclei, preventing the simple reapplication of the methods to other problems. Thus, it is useful to evaluate the ability of simple methods to segment images with various degrees of crowded nuclei.

RESULTS

Here, we selected six simple methods from various watershed based and local maxima detection based methods that are frequently used for nuclei segmentation, and evaluated their segmentation accuracy for each developmental stage of the Caenorhabditis elegans. We included a 4D noise filter, in addition to 2D and 3D noise filters, as a pre-processing step to evaluate the potential of simple methods as widely as possible. By applying the methods to image data between the 50- to 500-cell developmental stages at 50-cell intervals, the error rate for nuclei detection could be reduced to ≤ 2.1% at every stage until the 350-cell stage. The fractions of total errors throughout the stages could be reduced to ≤ 2.4%. The error rates improved at most of the stages and the total errors improved when a 4D noise filter was used. The methods with the least errors were two watershed-based methods with 4D noise filters. For all the other methods, the error rate and the fraction of errors could be reduced to ≤ 4.2% and ≤ 4.1%, respectively. The minimum error rate for each stage between the 400- to 500-cell stages ranged from 6.0% to 8.4%. However, similarities between the computational and manual segmentations measured by volume overlap and Hausdorff distance were not good. The methods were also applied to Drosophila and zebrafish embryos and found to be effective.

CONCLUSIONS

The simple segmentation methods were found to be useful for detecting nuclei until the 350-cell stage, but not very useful after the 400-cell stage. The incorporation of a 4D noise filter to the simple methods could improve their performances. Error types and the temporal biases of errors were dependent on the methods used. Combining multiple simple methods could also give good segmentations.

The 39 steps: evading error and deciphering the secrets for accurate dynamic susceptibility contrast MRI

Dynamic susceptibility contrast (DSC) MRI is the most commonly used MRI method to assess cerebral perfusion and other related haemodynamic parameters. Although the technique is well established and used routinely in clinical centres, there are still many problems that impede accurate perfusion quantification. In this review article, we present 39 steps which guide the reader through the theoretical principles, practical decisions, potential problems, current limitations and latest advances in DSC-MRI. The 39 steps span the collection, analysis and interpretation of DSC-MRI data, expounding issues and possibilities relating to the contrast agent, the acquisition of DSC-MRI data, data pre-processing, the contrast concentration-time course, the arterial input function, deconvolution, common perfusion parameters, post-processing possibilities, patient studies, absolute versus relative quantification and automated analysis methods.

Parametric imaging via kinetics-induced filter for dynamic positron emission tomography

Due to the noisy measurement of the voxel-wise time activity curve (TAC), parametric imaging for dynamic positron emission tomography (PET) is a challenging task. To address this problem, some spatial filters, such as Gaussian filter, bilateral filter, wavelet-based filter, and so on, are often performed to reduce the noise of each frame. However, these filters usually just consider local properties of each frame without exploring the kinetic information. In this paper, aiming to improve the quantitative accuracy of parametric imaging, we present a kinetics-induced filter to lower the noise of dynamic PET images by incorporating the kinetic information. The present kinetics-induced filter is designed via the similarity between voxel-wise TACs under the framework of bilateral filter. Experimental results with a simulation study demonstrate that the present kinetics-induced filter can achieve noticeable gains than other existing methods for parametric images in terms of quantitative accuracy measures.

Suitability of bilateral filtering for edge-preserving noise reduction in PET

BACKGROUND

To achieve an acceptable signal-to-noise ratio (SNR) in PET images, smoothing filters (SF) are usually employed during or after image reconstruction preventing utilisation of the full intrinsic resolution of the respective scanner. Quite generally Gaussian-shaped moving average filters (MAF) are used for this purpose. A potential alternative to MAF is the group of so-called bilateral filters (BF) which provide a combination of noise reduction and edge preservation thus minimising resolution deterioration of the images. We have investigated the performance of this filter type with respect to improvement of SNR, influence on spatial resolution and for derivation of SUVmax values in target structures of varying size.

METHODS

Data of ten patients with head and neck cancer were evaluated. The patients had been investigated by routine whole body scans (ECAT EXACT HR+, Siemens, Erlangen). Tomographic images were reconstructed (OSEM 6i/16s) using a Gaussian filter (full width half maximum (FWHM): Γ0 = 4 mm). Image data were then post-processed with a Gaussian MAF (FWHM: ΓM = 7 mm) and a Gaussian BF (spatial domain: ΓS = 9 mm, intensity domain: ΓI = 2.5 SUV), respectively. Images were assessed regarding SNR as well as spatial resolution. Thirty-four lesions (volumes of about 1-100 mL) were analysed with respect to their SUVmax values in the original as well as in the MAF and BF filtered images.

RESULTS

With the chosen filter parameters both filters improved SNR approximately by a factor of two in comparison to the original data. Spatial resolution was significantly better in the BF-filtered images in comparison to MAF (MAF: 9.5 mm, BF: 6.8 mm). In MAF-filtered data, the SUVmax was lower by 24.1 ± 9.9% compared to the original data and showed a strong size dependency. In the BF-filtered data, the SUVmax was lower by 4.6 ± 3.7% and no size effects were observed.

CONCLUSION

Bilateral filtering allows to increase the SNR of PET image data while preserving spatial resolution and preventing smoothing-induced underestimation of SUVmax values in small lesions. Bilateral filtering seems a promising and superior alternative to standard smoothing filters.

TIPS bilateral noise reduction in 4D CT perfusion scans produces high-quality cerebral blood flow maps

Cerebral computed tomography perfusion (CTP) scans are acquired to detect areas of abnormal perfusion in patients with cerebrovascular diseases. These 4D CTP scans consist of multiple sequential 3D CT scans over time. Therefore, to reduce radiation exposure to the patient, the amount of x-ray radiation that can be used per sequential scan is limited, which results in a high level of noise. To detect areas of abnormal perfusion, perfusion parameters are derived from the CTP data, such as the cerebral blood flow (CBF). Algorithms to determine perfusion parameters, especially singular value decomposition, are very sensitive to noise. Therefore, noise reduction is an important preprocessing step for CTP analysis. In this paper, we propose a time-intensity profile similarity (TIPS) bilateral filter to reduce noise in 4D CTP scans, while preserving the time-intensity profiles (fourth dimension) that are essential for determining the perfusion parameters. The proposed TIPS bilateral filter is compared to standard Gaussian filtering, and 4D and 3D (applied separately to each sequential scan) bilateral filtering on both phantom and patient data. Results on the phantom data show that the TIPS bilateral filter is best able to approach the ground truth (noise-free phantom), compared to the other filtering methods (lowest root mean square error). An observer study is performed using CBF maps derived from fifteen CTP scans of acute stroke patients filtered with standard Gaussian, 3D, 4D and TIPS bilateral filtering. These CBF maps were blindly presented to two observers that indicated which map they preferred for (1) gray/white matter differentiation, (2) detectability of infarcted area and (3) overall image quality. Based on these results, the TIPS bilateral filter ranked best and its CBF maps were scored to have the best overall image quality in 100% of the cases by both observers. Furthermore, quantitative CBF and cerebral blood volume values in both the phantom and the patient data showed that the TIPS bilateral filter resulted in realistic mean values with a smaller standard deviation than the other evaluated filters and higher contrast-to-noise ratios. Therefore, applying the proposed TIPS bilateral filtering method to 4D CTP data produces higher quality CBF maps than applying the standard Gaussian, 3D bilateral or 4D bilateral filter. Furthermore, the TIPS bilateral filter is computationally faster than both the 3D and 4D bilateral filters.

Multicomponent MR Image Denoising

Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality. Many of these methods use the information of a single image without taking into consideration the intrinsic multicomponent nature of MR images. In this paper we propose a new filter to reduce random noise in multicomponent MR images by spatially averaging similar pixels using information from all available image components to perform the denoising process. The proposed algorithm also uses a local Principal Component Analysis decomposition as a postprocessing step to remove more noise by using information not only in the spatial domain but also in the intercomponent domain dealing in a higher noise reduction without significantly affecting the original image resolution. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical multicomponent MR images showing an improved performance in all cases analyzed.