Noise simulation in cone beam CT imaging with parallel computing

We developed a computer noise simulation model for cone beam computed tomography imaging using a general purpose PC cluster. This model uses a mono-energetic x-ray approximation and allows us to investigate three primary performance components, specifically quantum noise, detector blurring and additive system noise. A parallel random number generator based on the Weyl sequence was implemented in the noise simulation and a visualization technique was accordingly developed to validate the quality of the parallel random number generator. In our computer simulation model, three-dimensional (3D) phantoms were mathematically modelled and used to create 450 analytical projections, which were then sampled into digital image data. Quantum noise was simulated and added to the analytical projection image data, which were then filtered to incorporate flat panel detector blurring. Additive system noise was generated and added to form the final projection images. The Feldkamp algorithm was implemented and used to reconstruct the 3D images of the phantoms. A 24 dual-Xeon PC cluster was used to compute the projections and reconstructed images in parallel with each CPU processing 10 projection views for a total of 450 views. Based on this computer simulation system, simulated cone beam CT images were generated for various phantoms and technique settings. Noise power spectra for the flat panel x-ray detector and reconstructed images were then computed to characterize the noise properties. As an example among the potential applications of our noise simulation model, we showed that images of low contrast objects can be produced and used for image quality evaluation.     

A Blurring Index for Medical Images

This study was undertaken to investigate a useful image blurring index. This work is based on our previously developed method, the Moran peak ratio. Medical images are often deteriorated by noise or blurring. Image processing techniques are used to eliminate these two factors. The denoising process may improve image visibility with a trade-off of edge blurring and may introduce undesirable effects in an image. These effects also exist in images reconstructed using the lossy image compression technique. Blurring and degradation in image quality increases with an increase in the lossy image compression ratio. Objective image quality metrics [e.g., normalized mean square error (NMSE)] currently do not provide spatial information about image blurring. In this article, the Moran peak ratio is proposed for quantitative measurement of blurring in medical images. We show that the quantity of image blurring is dependent upon the ratio between the processed peak of Moran's Z histogram and the original image. The peak ratio of Moran's Z histogram can be used to quantify the degree of image blurring. This method produces better results than the standard gray level distribution deviation. The proposed method can also be used to discern blurriness in an image using different image compression algorithms.     

Improved contrast deep optoacoustic imaging using displacement-compensated averaging: breast tumour phantom studies

For real-time optoacoustic (OA) imaging of the human body, a linear array transducer and reflection mode optical irradiation is usually preferred. Such a setup, however, results in significant image background, which prevents imaging structures at the ultimate depth determined by the light distribution and the signal noise level. Therefore, we previously proposed a method for image background reduction, based on displacement-compensated averaging (DCA) of image series obtained when the tissue sample under investigation is gradually deformed. OA signals and background signals are differently affected by the deformation and can thus be distinguished. The proposed method is now experimentally applied to image artificial tumours embedded inside breast phantoms. OA images are acquired alternately with pulse-echo images using a combined OA/echo-ultrasound device. Tissue deformation is accessed via speckle tracking in pulse echo images, and used to compensate in the OA images for the local tissue displacement. In that way, OA sources are highly correlated between subsequent images, while background is decorrelated and can therefore be reduced by averaging. We show that image contrast in breast phantoms is strongly improved and detectability of embedded tumours significantly increased, using the DCA method.     

X-ray imaging optimization using virtual phantoms and computerized observer modelling

This study develops and demonstrates a realistic x-ray imaging simulator with computerized observers to maximize lesion detectability and minimize patient exposure. A software package, ViPRIS, incorporating two computational patient phantoms, has been developed for simulating x-ray radiographic images. A tomographic phantom, VIP-Man, constructed from Visible Human anatomical colour images is used to simulate the scattered portion using the ESGnrc Monte Carlo code. The primary portion of an x-ray image is simulated using the projection ray-tracing method through the Visible Human CT data set. To produce a realistic image, the software simulates quantum noise, blurring effects, lesions, detector absorption efficiency and other imaging artefacts. The primary and scattered portions of an x-ray chest image are combined to form a final image for computerized observer studies and image quality analysis. Absorbed doses in organs and tissues of the segmented VIP-Man phantom were also obtained from the Monte Carlo simulations. Approximately 25,000 simulated images and 2,500,000 data files were analysed using computerized observers. Hotelling and Laguerre-Gauss Hotelling observers are used to perform various lesion detection tasks. Several model observer tasks were used including SKE/BKE, MAFC and SKEV. The energy levels and fluence at the minimum dose required to detect a small lesion were determined with respect to lesion size, location and system parameters.     

Optimising the Use of Computed Radiography in Pediatric Chest Imaging

The objective of this study was to analyze image quality of chest examinations in pediatric patients using computed radiography (CR) obtained with a wide range of doses to suggest the appropriate parameters for optimal image quality. A sample of 240 chest images in four age ranges was randomly selected from the examinations performed during 2004. Images were obtained using a CR system and were evaluated independently by three radiologists. Each image was scored using criteria proposed by the European Guidelines on Quality Criteria in Pediatrics. Mean global scoring and scoring of individual criteria more sensitive to noise were used to evaluate image quality. Agfa dose level (DL) was in the range 1.20 to 2.85. It was found that there was not significant correlation (R < 0.5) between image quality and DL for any of the age ranges for either global score or for individual criteria more related to noise. The mean value of DL was in the ranges 1.9–2.1 for the four age bands. From this study, a DL value of 1.6 is proposed for pediatric CR chest imaging. This could yield a reduction of approximately a factor of 2.5 in mean patient entrance surface doses.     

显微荧光去卷积成像评价方法的研究

目的 为了降低荧光显微图像的噪声和离焦平面的信息,提高图像清晰度,并评价去卷积成像方法的性能,本文进行显微荧光去卷积成像评价方法的研究.方法 首先,将测得的系统点扩散函数作用于共聚焦显微镜采集的endoG-GFP标记的人乳腺癌细胞MCF-7图像,得到模糊图像,然后采用Richardson-Lucy（R-L）算法对模糊的荧光图像进行去卷积运算,并对去卷积成像方法的性能进行了分析和评估.结果 显微荧光去卷积成像方法可有效去除焦平面外荧光信息,显著提高荧光图像的清晰度.结论 显微荧光去卷积算法结合有效的评价方法在未来有望用于微流控细胞培养的成像,对活体细胞和组织的三维结构的研究有一定的参考价值.     

Resolution at oblique incidence angles of a flat panel imager for breast tomosynthesis

Oblique incidence of x rays on an imaging detector causes blurring that reduces spatial resolution. For simple projection imaging this effect is small and often ignored. However, for breast tomosynthesis, the incidence angle can be larger (>20 degrees), leading to increased blur for some of the projections. The modulation transfer function (MTF) is measured for a typical phosphor-coupled flat-panel detector versus angular incidence of the x-ray beam for two x-ray spectra: 26 kV Mo/Mo and 40 kV Rh/Al. At an incidence angle of 40 degrees the MTF at 5 mm(-1) falls by 35% and 40% for each spectrum, respectively (and 65%/80% at 8 mm(-1)). Increasing the detector absorber thickness to achieve improved quantum efficiency will cause the blurring effect due to beam obliquity to become greater. The impact of this blur is likely to cause misregistration and increased relative noise in tomosynthesis reconstructed images.     

Respiratory motion blur identification and reduction in ungated thoracic PET imaging

Respiratory motion results in significant motion blur in thoracic positron emission tomography (PET) imaging. Existing approaches to correct the blurring artifact involve acquiring the images in gated mode and using complicated reconstruction algorithms. In this paper, we propose a post-reconstruction framework to estimate respiratory motion and reduce the motion blur of PET images acquired in ungated mode. Our method includes two steps: one is to use minmax directional derivative analysis and local auto-correlation analysis to identify the two parameters blur direction and blur extent, respectively, and another is to employ WRL, à trous wavelet-denoising modified Richardson-Lucy (RL) deconvolution, to reduce the motion blur based on identified parameters. The mobile phantom data were first used to test the method before it was applied to 32 cases of clinical lung tumor PET data. Results showed that the blur extent of phantom images in different directions was accurately identified, and WRL can remove the majority of motion blur within ten iterations. The blur extent of clinical images was estimated to be 12.1 ± 3.7 mm in the direction of 74 ± 3° relative to the image horizontal axis. The quality of clinical images was significantly improved, both from visual inspection and quantitative evaluation after deconvolution. It was demonstrated that WRL outperforms RL and a Wiener filter in reducing the motion blur with one to two more iterations. The proposed method is easy to implement and thus could be a useful tool to reduce the effect of respiration in ungated thoracic PET imaging.     

Accurate Determination of Imaging Modality using an Ensemble of Text- and Image-Based Classifiers

Imaging modality can aid retrieval of medical images for clinical practice, research, and education. We evaluated whether an ensemble classifier could outperform its constituent individual classifiers in determining the modality of figures from radiology journals. Seventeen automated classifiers analyzed 77,495 images from two radiology journals. Each classifier assigned one of eight imaging modalities—computed tomography, graphic, magnetic resonance imaging, nuclear medicine, positron emission tomography, photograph, ultrasound, or radiograph—to each image based on visual and/or textual information. Three physicians determined the modality of 5,000 randomly selected images as a reference standard. A “Simple Vote” ensemble classifier assigned each image to the modality that received the greatest number of individual classifiers’ votes. A “Weighted Vote” classifier weighted each individual classifier’s vote based on performance over a training set. For each image, this classifier’s output was the imaging modality that received the greatest weighted vote score. We measured precision, recall, and F score (the harmonic mean of precision and recall) for each classifier. Individual classifiers’ F scores ranged from 0.184 to 0.892. The simple vote and weighted vote classifiers correctly assigned 4,565 images (F score, 0.913; 95% confidence interval, 0.905–0.921) and 4,672 images (F score, 0.934; 95% confidence interval, 0.927–0.941), respectively. The weighted vote classifier performed significantly better than all individual classifiers. An ensemble classifier correctly determined the imaging modality of 93% of figures in our sample. The imaging modality of figures published in radiology journals can be determined with high accuracy, which will improve systems for image retrieval.     

Removal of Out-of-Plane Fluorescence for Single Cell Visualization and Quantification in Cryo-Imaging

We developed a cryo-imaging system, which alternates between sectioning (10–40 μm) and imaging bright field and fluorescence block-face image volumes with micron-scale-resolution. For applications requiring single-cell detection of fluorescently labeled cells anywhere in a mouse, we are developing software for reduction of out-of-plane fluorescence. In mouse experiments, we imaged GFP-labeled cancer and stem cells, and cell-sized fluorescent microspheres. To remove out-of-plane fluorescence, we used a simplified model of light-tissue interaction whereby the next-image was scaled, blurred, and subtracted from the current image. We estimated scaling and blurring parameters by minimizing an objective function on subtracted images. Tissue-specific attenuation parameters [μ _T: heart (267 ± 47.6 cm⁻¹), liver (218 ± 27.1 cm⁻¹), brain (161 ± 27.4 cm⁻¹)] were found to be within the range of estimates in the literature. “Next-image” processing removed out-of-plane fluorescence equally well across multiple tissues (brain, kidney, liver, etc.), and analysis of 200 microsphere images gave 97 ± 2% reduction of out-of-plane fluorescence. Next-image processing greatly improved axial-resolution, enabled high quality 3D volume renderings, and improved automated enumeration of single cells by up to 24%. The method has been used to identify metastatic cancer sites, determine homing of stem cells to injury sites, and show microsphere distribution correlated with blood flow patterns.     

ROC Study of Four LCD Displays Under Typical Medical Center Lighting Conditions

Nine observers reviewed a previously assembled library of 320 chest computed radiography (CR) images. Observers participated in four sessions, reading a different 1/4 of the sample on each of four liquid crystal displays: a 2-megapixel (MP) consumer color display, a 2-MP business color display, a 2-MP medical-grade gray display, and a 3-MP gray display. Each display was calibrated according to the DICOM Part 14 standard. The viewing application required observer login, then randomized the order of the subsample seen on the display, and timed the responses of the observer to render a 1–5 judgment on the absence or presence of ILD on chest CRs. Selections of 1–2 were considered negative, 3 was indeterminate, and 4–5 were positive. The order of viewing sessions was also randomized for each observer. The experiment was conducted under controlled lighting, temperature, and sound conditions to mimic conditions typically found in a patient examination room. Lighting was indirect, and illuminance at the display face was 195 ± 8% lux and was monitored over the course of the experiment. The average observer sensitivity for the 2 MP color consumer, 2 MP business color, 2 MP gray, and 3 MP gray displays were 83.7%, 84.1%, 85.5%, and 86.7%, respectively. The only pairwise significant difference was between the 2-MP consumer color and the 2-MP gray (P = 0.05). Effect of order within a session was not signitfficant (P = 0.21): period 1 (84.3%), period 2 (86.2%), period 3 (85.4%), period 4 (84.1%). Observer specificity for the various displays was not statistically significant (P = 0.21). Finally, a timing analysis showed no significant difference between the displays for the user group (P = 0.13), ranging from 5.3 s (2 MP color business) to 5.9 s (3 MP Gray). There was, however, a reduction in time over the study that was significant (P <<< 0.001) for all users; the group average decreased from 6.5 to 4.7 s per image. Physical measurements of the resolution, contrast, and noise properties of the displays were acquired. Most notably, the noise of the displays varied by 3.5× between the lowest and highest noise displays. Differences in display noise were indicative of observer performance. However, the large difference in the magnitude of the noise was not predictive of the small difference (3%) in the observer sensitivity for various displays. This is likely because detection of interstitial lung disease is limited by “““““anatomical noise””” rather than display or x-ray image noise.     

Influence of x-ray pulse parameters on the image quality for moving objects in digital cardiac imaging

The image quality of a single frame in a modern cardiac imaging x-ray facility can be improved by adjusting the automatic pulse exposure parameters. The effects of acquisition rate on patient dose and the detectability of moving objects have been fully described in scientific literature. However, the influence of automatic pulse exposure parameters is still to be determined. Images of a moving wheel (with lead wires) were acquired using an H5000 Philips Integris cardiac x-ray system. Poly(methylmethacrylate) plastic samples 20 and 30 cm thick were employed as the build-up phantom to simulate a patient. The images were obtained using preset clinical parameters for cardiac imaging procedures. The signal detectability and motion blur of a contrast bar at a transversal speed in the range of 100-150 mm/s were evaluated with a cine pulse width of 3, 5, 7, and 10 ms under automatic mA kV regulation. Two levels of exposure at the image intensifier entrance were included in this study. Signal detectability was analyzed in terms of the signal-to-noise ratio (SNR) and the value of SNR2/entrance surface dose. The blurring was modeled as a Gaussian-shaped blurring function, and the motion blur was expressed in terms of the peak full width at half maximum and amplitude (apparent contrast) of the resolution functions. A contrast bar simulating a vessel in motion at the maximum velocities of typical cardiac structures was exposed. Severe loss of image quality occurred at pulse widths > or =7 ms. It is also shown that below 5 ms static nonlinearities, likely caused by the need to use a large focus for cine acquisition, dominate the blurring process.     

Deblurring of breathing motion artifacts in thoracic PET images by deconvolution methods

In FDG-PET imaging of thoracic tumors, blurring due to breathing motion often significantly degrades the quality of the observed image, which then obscures the tumor boundary. We demonstrate a deblurring technique that combines patient-specific motion estimates of tissue trajectories with image deconvolution techniques, thereby partially eliminating breathing-motion induced artifacts. Two data sets were used to evaluate the methodology including mobile phantoms and clinical images. The clinical images consist of PET/CT co-registered images of patients diagnosed with lung cancer. A breathing motion model was used to locally estimate the location-dependent tissue location probability function (TLP) due to breathing. The deconvolution process is carried by an expectation-maximization (EM) iterative algorithm using the motion-based TLP. Several methods were used to improve the robustness of the deblurring process by mitigating noise amplification and compensating for motion estimate uncertainties. The mobile phantom study with controlled settings demonstrated significant reduction in underestimation error of concentration in high activity case without significant superiority between the different applied methods. In case of medium activity concentration (moderate noise levels), less improvement was reported (10%-15% reduction in underestimation error relative to 15%-20% reduction in high concentration). Residual denoising using wavelets offered the best performance for this case. In the clinical data case, the image spatial resolution was significantly improved, especially in the direction of greatest motion (cranio-caudal). The EM algorithm converged within 15 and 5 iterations in the large and small tumor cases, respectively. A compromise between a figure-of-merit and entropy minimization was suggested as a stopping criterion. Regularization techniques such as wavelets and Bayesian methods provided further refinement by suppressing noise amplification. Our initial results show that the proposed method provides a feasible framework for improving PET thoracic images, without the need for gated/4-D PET imaging, when 4-D CT is available to estimate tumor motion.     

A Motion Compounding Technique for Speckle Reduction in Ultrasound Images

The quality of ultrasound images is usually influenced by speckle noise and the temporal decorrelation of the speckle patterns. To reduce the speckle noise, compounding techniques have been widely applied. Partially correlated images scanned on the same subject cross-section are combined to generate a compound image with improved image quality. However, the compounding technique might introduce image blurring if the transducer or the target moves too fast. This blurring effect becomes especially critical when assessing tissue deformation in clinical motion examinations. In this paper, an ultrasound motion compounding system is proposed to improve the quality of ultrasound motion sequences. The proposed motion compounding technique uses a hierarchical adaptive feature weighted motion estimation method to realign the frames before compounding. Each frame is first registered and warped to the reference frame before being compounded to reduce the speckle noise. Experimental results showed that the motion could be assessed accurately and better visualization could be achieved for the compound images, with improved signal-to-noise and contrast-to-noise ratios.     

Attenuation correction for three-dimensional PET using uncollimated flood-source transmission measurements

An attenuation-correction method for three-dimensional PET imaging, which obtains attenuation-correction factors from transmission measurements using an uncollimated flood source, is described. This correction is demonstrated for two different phantoms using transmission data acquired with QPET, a rotating imaging system with two planar detectors developed for imaging small volumes. The scatter amplitude in the transmission projections was a maximum of 30%; to obtain accurate attenuation-correction factors the scatter distribution was first calculated and subtracted. The attenuation-corrected emission images for both phantoms indicate that their original uniform amplitudes have been restored. The attenuation correction adds only a small amount of noise to the emission images, as evaluated from the standard deviation over a central region. For the first phantom, with maximum attenuation of 48%, the noise added was 2.6%. The second phantom was attenuated by a maximum of 37%, and 1.9% noise was added. Because the transmission data are smoothed, some artifacts are visible at the edges of the phantom where the correction factors change abruptly within the emission image.     

Retinal image restoration by means of blind deconvolution

Retinal imaging plays a key role in the diagnosis and management of ophthalmologic disorders, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. Because of the acquisition process, retinal images often suffer from blurring and uneven illumination. This problem may seriously affect disease diagnosis and progression assessment. Here we present a method for color retinal image restoration by means of multichannel blind deconvolution. The method is applied to a pair of retinal images acquired within a lapse of time, ranging from several minutes to months. It consists of a series of preprocessing steps to adjust the images so they comply with the considered degradation model, followed by the estimation of the point-spread function and, ultimately, image deconvolution. The preprocessing is mainly composed of image registration, uneven illumination compensation, and segmentation of areas with structural changes. In addition, we have developed a procedure for the detection and visualization of structural changes. This enables the identification of subtle developments in the retina not caused by variation in illumination or blur. The method was tested on synthetic and real images. Encouraging experimental results show that the method is capable of significant restoration of degraded retinal images.     

Acoustic radiation force impulse (ARFI) imaging of zebrafish embryo by high-frequency coded excitation sequence

Acoustic radiation force impulse (ARFI) imaging has been developed as a non-invasive method for quantitative illustration of tissue stiffness or displacement. Conventional ARFI imaging (2–10 MHz) has been implemented in commercial scanners for illustrating elastic properties of several organs. The image resolution, however, is too coarse to study mechanical properties of micro-sized objects such as cells. This article thus presents a high-frequency coded excitation ARFI technique, with the ultimate goal of displaying elastic characteristics of cellular structures. Tissue mimicking phantoms and zebrafish embryos are imaged with a 100-MHz lithium niobate (LiNbO₃) transducer, by cross-correlating tracked RF echoes with the reference. The phantom results show that the contrast of ARFI image (14 dB) with coded excitation is better than that of the conventional ARFI image (9 dB). The depths of penetration are 2.6 and 2.2 mm, respectively. The stiffness data of the zebrafish demonstrate that the envelope is harder than the embryo region. The temporal displacement change at the embryo and the chorion is as large as 36 and 3.6 μm. Consequently, this high-frequency ARFI approach may serve as a remote palpation imaging tool that reveals viscoelastic properties of small biological samples.     

Digital Radiographic Image Denoising Via Wavelet-Based Hidden Markov Model Estimation

This paper presents a technique for denoising digital radiographic images based upon the wavelet-domain Hidden Markov tree (HMT) model. The method uses the Anscombes transformation to adjust the original image, corrupted by Poisson noise, to a Gaussian noise model. The image is then decomposed in different subbands of frequency and orientation responses using the dual-tree complex wavelet transform, and the HMT is used to model the marginal distribution of the wavelet coefficients. Two different correction functions were used to shrink the wavelet coefficients. Finally, the modified wavelet coefficients are transformed back into the original domain to get the denoised image. Fifteen radiographic images of extremities along with images of a hand, a line-pair, and contrast–detail phantoms were analyzed. Quantitative and qualitative assessment showed that the proposed algorithm outperforms the traditional Gaussian filter in terms of noise reduction, quality of details, and bone sharpness. In some images, the proposed algorithm introduced some undesirable artifacts near the edges.     

Near-field thermoacoustic tomography of small animals

Near-field radiofrequency thermoacoustic (NRT) tomography is a new imaging method that was developed to mitigate limitations of conventional thermoacoustic imaging approaches, related to hard compromises between signal strength and spatial resolution. By utilizing ultrahigh-energy electromagnetic impulses at ～20 ns duration along with improved energy absorption coupling in the near-field, this method can deliver high-resolution images without compromising signal to noise ratio. NRT is a promising modality, offering cost-effectiveness and ease of implementation and it can be conveniently scaled to image small animals and humans. However, several of the performance metrics of the method are not yet documented. In this paper, we characterize the expected imaging performance via numerical simulations based on a finite-integration time-domain (FITD) technique and experiments using tissue mimicking phantoms and different biological samples. Furthermore, we show for the first time whole-body tomographic imaging results from mice, revealing clear anatomical details along with highly dissipative inclusions introduced for control. The best spatial resolution achieved for those experiments was 150 μm.     

基于非局部均值平滑的体素内纤维结构估计

为了克服球面反卷积法对扩散加权图像噪声敏感的问题,提出一种基于非局部均值平滑的体素内纤维结构估计方法,该方法建立了一种面向磁共振扩散加权图像的非局部均值方法,对磁共振扩散加权图像进行平滑后再采用球面反卷积法对体素内纤维结构进行估计,从而提高球面反卷积法的抗噪性能。数值仿真数据和仿真实体数据实验结果表明,与直接采用球面反卷积方法和对数据进行非局部均值平滑后使用球面反卷积相比,采用本研究提出的方法得到的体素内纤维结构的平均角度误差更小,且较少存在边缘模糊现象。