Showing papers on "Image quality published in 2002"

A universal image quality index

Abstract: We propose a new universal objective image quality index, which is easy to calculate and applicable to various image processing applications. Instead of using traditional error summation methods, the proposed index is designed by modeling any image distortion as a combination of three factors: loss of correlation, luminance distortion, and contrast distortion. Although the new index is mathematically defined and no human visual system model is explicitly employed, our experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error. Demonstrative images and an efficient MATLAB implementation of the algorithm are available online at http://anchovy.ece.utexas.edu//spl sim/zwang/research/quality_index/demo.html.

Generalized autocalibrating partially parallel acquisitions (GRAPPA).

Abstract: In this study, a novel partially parallel acquisition (PPA) method is presented which can be used to accelerate image acquisition using an RF coil array for spatial encoding. This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD-AUTO-SMASH reconstruction techniques. As in those previous methods, a detailed, highly accurate RF field map is not needed prior to reconstruction in GRAPPA. This information is obtained from several k-space lines which are acquired in addition to the normal image acquisition. As in PILS, the GRAPPA reconstruction algorithm provides unaliased images from each component coil prior to image combination. This results in even higher SNR and better image quality since the steps of image reconstruction and image combination are performed in separate steps. After introducing the GRAPPA technique, primary focus is given to issues related to the practical implementation of GRAPPA, including the reconstruction algorithm as well as analysis of SNR in the resulting images. Finally, in vivo GRAPPA images are shown which demonstrate the utility of the technique.

HAMMER: hierarchical attribute matching mechanism for elastic registration

Abstract: A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate very high accuracy in superposition of images from different subjects. There are two major novelties in the proposed algorithm. First, it uses an attribute vector, i.e., a set of geometric moment invariants (GMIs) that are defined on each voxel in an image and are calculated from the tissue maps, to reflect the underlying anatomy at different scales. The attribute vector, if rich enough, can distinguish between different parts of an image, which helps establish anatomical correspondences in the deformation procedure; it also helps reduce local minima, by reducing ambiguity in potential matches. This is a fundamental deviation of our method, referred to as the hierarchical attribute matching mechanism for elastic registration (HAMMER), from other volumetric deformation methods, which are typically based on maximizing image similarity. Second, in order to avoid being trapped by local minima, i.e., suboptimal poor matches, HAMMER uses a successive approximation of the energy function being optimized by lower dimensional smooth energy functions, which are constructed to have significantly fewer local minima. This is achieved by hierarchically selecting the driving features that have distinct attribute vectors, thus, drastically reducing ambiguity in finding correspondence. A number of experiments demonstrate that the proposed algorithm results in accurate superposition of image data from individuals with significant anatomical differences.

No-reference perceptual quality assessment of JPEG compressed images

Abstract: Human observers can easily assess the quality of a distorted image without examining the original image as a reference. By contrast, designing objective No-Reference (NR) quality measurement algorithms is a very difficult task. Currently, NR quality assessment is feasible only when prior knowledge about the types of image distortion is available. This research aims to develop NR quality measurement algorithms for JPEG compressed images. First, we established a JPEG image database and subjective experiments were conducted on the database. We show that Peak Signal-to-Noise Ratio (PSNR), which requires the reference images, is a poor indicator of subjective quality. Therefore, tuning an NR measurement model towards PSNR is not an appropriate approach in designing NR quality metrics. Furthermore, we propose a computational and memory efficient NR quality assessment model for JPEG images. Subjective test results are used to train the model, which achieves good quality prediction performance.

Why is image quality assessment so difficult

Abstract: Image quality assessment plays an important role in various image processing applications. A great deal of effort has been made in recent years to develop objective image quality metrics that correlate with perceived quality measurement. Unfortunately, only limited success has been achieved. In this paper, we provide some insights on why image quality assessment is so difficult by pointing out the weaknesses of the error sensitivity based framework, which has been used by most image quality assessment approaches in the literature. Furthermore, we propose a new philosophy in designing image quality metrics: The main function of the human eyes is to extract structural information from the viewing field, and the human visual system is highly adapted for this purpose. Therefore, a measurement of structural distortion should be a good approximation of perceived image distortion. Based on the new philosophy, we implemented a simple but effective image quality indexing algorithm, which is very promising as shown by our current results.

Statistical evaluation of image quality measures

Abstract: In this work we comprehensively categorize image qual- ity measures, extend measures defined for gray scale images to their multispectral case, and propose novel image quality measures. They are categorized into pixel difference-based, correlation-based, edge-based, spectral-based, context-based and human visual sys- tem (HVS)-based measures. Furthermore we compare these mea- sures statistically for still image compression applications. The sta- tistical behavior of the measures and their sensitivity to coding artifacts are investigated via analysis of variance techniques. Their similarities or differences are illustrated by plotting their Kohonen maps. Measures that give consistent scores across an image class and that are sensitive to coding artifacts are pointed out. It was found that measures based on the phase spectrum, the multireso- lution distance or the HVS filtered mean square error are computa- tionally simple and are more responsive to coding artifacts. We also demonstrate the utility of combining selected quality metrics in build- ing a steganalysis tool. © 2002 SPIE and IS&T.

New paradigm for imaging systems

Abstract: We describe a new paradigm for designing hybrid imaging systems. These imaging systems use optics with a special aspheric surface to code the image so that the point-spread function or the modulation transfer function has specified characteristics. Signal processing then decodes the detected image. The coding can be done so that the depth of focus can be extended. This allows the manufacturing tolerance to be reduced, focus-related aberrations to be controlled, and imaging systems to be constructed with only one optical element plus some signal processing. OCIS codes: 080.3620, 110.0110, 110.2990, 110.0180, 110.4850, 180.0180. 1. Introduction and Background The new paradigm that we describe for the design of imaging systems has been termed wave-front coding. These coded optical systems are arrived at by means of designing the coding optics and the signal processing as an integrated imaging system. The results are imaging systems with previously unobtainable imaging modalities and require a modification of the optics for coding the wave in the aperture stop or an image of the aperture stop. This coding produces an intermediate image formed by the optical portion of the system that gathers the image. Signal processing is then required for decoding the intermediate image to produce a final image. The coding can be designed to make the imaging system invariant to certain parameters or to optimize the imaging system’s sensitivity to those parameters. One example is the use of image coding to preserve misfocus and hence, range or distance information. Another example is the use of different types of codes to make the image invariant to misfocus. These new focusinvariant imaging systems can have more than an order of magnitude increase in the depth of field. Our emphasis in this paper is on the use of the increased depth of focus to design new types of imaging systems. An example of the new imaging systems that can be constructed is a single-element lens that has a small F#, wide field of view, and diffractionlimited imaging. It also can have greatly relaxed assembly tolerances, because of its invariance to focus-related aberrations. Coding of signals for optimally conveying particular information is not new. In radar the transmitted pulses are coded for optimally providing information concerning a target’s range, for example. The appropriate signal processing to extract the range information is designed in conjunction with the transmitted signal. The integrated design of the optical image-gathering portion along with the signal processing normally is not done in the design of imaging systems. There are exceptions such as tomography, coded aperture imaging, and sometimes, interferometric imaging. In 1984 a group that was investigating the limits of resolution pointed out the potential of increasing the performance of imaging systems by jointly designing the optics and the signal processing. 1

Improved viewing resolution of three-dimensional integral imaging by use of nonstationary micro-optics

Abstract: We propose the use of synchronously moving micro-optics (lenslet arrays) for image pickup and display in three-dimensional integral imaging to overcome the upper resolution limit imposed by the Nyquist sampling theorem. With the proposed technique, we show experimentally that the viewing resolution can be improved without reducing the three-dimensional viewing aspect of the reconstructed image.

Lossless data embedding for all image formats

Abstract: Lossless data embedding has the property that the distortion due to embedding can be completely removed from the watermarked image without accessing any side channel. This can be a very important property whenever serious concerns over the image quality and artifacts visibility arise, such as for medical images, due to legal reasons, for military images or images used as evidence in court that may be viewed after enhancement and zooming. We formulate two general methodologies for lossless embedding that can be applied to images as well as any other digital objects, including video, audio, and other structures with redundancy. We use the general principles as guidelines for designing efficient, simple, and high-capacity lossless embedding methods for three most common image format paradigms - raw, uncompressed formats (BMP), lossy or transform formats (JPEG), and palette formats (GIF, PNG). We close the paper with examples of how the concept of lossless data embedding can be used as a powerful tool to achieve a variety of non-trivial tasks, including elegant lossless authentication using fragile watermarks. Note on terminology: some authors coined the terms erasable, removable, reversible, invertible, and distortion-free for the same concept.

Model observers for assessment of image quality

Abstract: Image quality should be defined and assessed in terms of specific tasks and observers. For classification tasks, the observers can be humans, computer algorithms, or a theoretical construct called the Ideal Observer. In this talk, we review the current state of knowledge regarding computational methods for predicting the performance of human observers and estimating the performance of the Ideal Observer.

Image Distortion Correction in EPI: Comparison of Field Mapping With Point Spread Function Mapping

Abstract: Echo-planar imaging (EPI) can provide rapid imaging by acquiring a complete k-space data set in a single acquisition. However, this approach suffers from distortion effects in geometry and intensity, resulting in poor image quality. The distortions, caused primarily by field inhomogeneities, lead to intensity loss and voxel shifts, the latter of which are particularly severe in the phase-encode direction. Two promising approaches to correct the distortion in EPI are field mapping and point spread function (PSF) mapping. The field mapping method measures the field distortions and translates these into voxel shifts, which can be used to assign image intensities to the correct voxel locations. The PSF approach uses acquisitions with additional phase-encoding gradients applied in the x, y, and/or z directions to map the 1D, 2D, or 3D PSF of each voxel. These PSFs encode the spatial information about the distortion and the overall distribution of intensities from a single voxel. The measured image is the convolution of the undistorted density and the PSF. Measuring the PSF allows the distortion in geometry and intensity to be corrected. This work compares the efficacy of these methods with equal time allowed for field mapping and PSF mapping.

A study of real-time packet video quality using random neural networks

Abstract: An important and unsolved problem today is that of automatic quantification of the quality of video flows transmitted over packet networks. In particular, the ability to perform this task in real time (typically for streams sent themselves in real time) is especially interesting. The problem is still unsolved because there are many parameters affecting video quality, and their combined effect is not well identified and understood. Among these parameters, we have the source bit rate, the encoded frame type, the frame rate at the source, the packet loss rate in the network, etc. Only subjective evaluations give good results but, by definition, they are not automatic. We have previously explored the possibility of using artificial neural networks (NNs) to automatically quantify the quality of video flows and we showed that they can give results well correlated with human perception. In this paper, our goal is twofold. First, we report on a significant enhancement of our method by means of a new neural approach, the random NN model, and its learning algorithm, both of which offer better performances for our application. Second, we follow our approach to study and analyze the behavior of video quality for wide range variations of a set of selected parameters. This may help in developing control mechanisms in order to deliver the best possible video quality given the current network situation, and in better understanding of QoS aspects in multimedia engineering.

Efficient DCT-domain blind measurement and reduction of blocking artifacts

Abstract: Blocking artifacts continue to be among the most serious defects that occur in images and video streams compressed to low bit rates using block discrete cosine transform (DCT)-based compression standards (e.g., JPEG, MPEG, and H.263). It is of interest to be able to numerically assess the degree of blocking artifact in a visual signal, for example, in order to objectively determine the efficacy of a compression method, or to discover the quality of video content being delivered by a web server. We propose new methods for efficiently assessing, and subsequently reducing, the severity of blocking artifacts in compressed image bitstreams. The method is blind, and operates only in the DCT domain. Hence, it can be applied to unknown visual signals, and it is efficient since the signal need not be compressed or decompressed. In the algorithm, blocking artifacts are modeled as 2-D step functions. A fast DCT-domain algorithm extracts all parameters needed to detect the presence of, and estimate the amplitude of blocking artifacts, by exploiting several properties of the human vision system. Using the estimate of blockiness, a novel DCT-domain method is then developed which adaptively reduces detected blocking artifacts. Our experimental results show that the proposed method of measuring blocking artifacts is effective and stable across a wide variety of images. Moreover, the proposed blocking-artifact reduction method exhibits satisfactory performance as compared to other post-processing techniques. The proposed technique has a low computational cost hence can be used for real-time image/video quality monitoring and control, especially in applications where it is desired that the image/video data be processed directly in the DCT-domain.

Segmentation of ultrasound B-mode images with intensity inhomogeneity correction

Abstract: Displayed ultrasound (US) B-mode images often exhibit tissue intensity inhomogeneities dominated by nonuniform beam attenuation within the body. This is a major problem for intensity-based, automatic segmentation of video-intensity images because conventional threshold-based or intensity-statistic-based approaches do not work well in the presence of such image distortions. Time gain compensation (TGC) is typically used in standard US machines in an attempt to overcome this. However this compensation method is position-dependent which means that different tissues in the same TGC time-range (or corresponding depth range) will be, incorrectly, compensated by the same amount. Compensation should really be tissue-type dependent but automating this step is difficult. The main contribution of this paper is to develop a method for simultaneous estimation of video-intensity inhomogeneities and segmentation of US image tissue regions. The method uses a combination of the maximum a posteriori (MAP) and Markov random field (MRF) methods to estimate the US image distortion field assuming it follows a multiplicative model while at the same time labeling image regions based on the corrected intensity statistics. The MAP step is used to estimate the intensity model parameters while the MRF step provides a novel way of incorporating the distributions of image tissue classes as a spatial smoothness constraint. We explain how this multiplicative model can be related to the ultrasonic physics of image formation to justify our approach. Experiments are presented on synthetic images and a gelatin phantom to evaluate quantitatively the accuracy of the method. We also discuss qualitatively the application of the method to clinical breast and cardiac US images. Limitations of the method and potential clinical applications are outlined in the conclusion.

Processing and analysis of in vivo high-resolution MR images of trabecular bone for longitudinal studies: reproducibility of structural measures and micro-finite element analysis derived mechanical properties.

Abstract: The authors have developed a system for the characterization of trabecular bone structure from high-resolution MR images. It features largely automated coil inhomogeneity correction, trabecular bone region segmentation, serial image registration, bone/marrow binarization, and structural calculation steps. The system addresses problems of efficiency and inter- and intra-operator variability inherent in previous analyses. The system is evaluated on repetitive scans of 8 volunteers for both two-dimensional (2D) apparent structure calculations and three-dimensional (3D) mechanical calculations using micro-finite element analysis. Coil correction methods based on a priori knowledge of the coil sensitivity and on low-pass filtering of the high-resolution mages are compared and found to perform similarly. Image alignment is found to cause small but significant changes in some structural parameters. Overall the automated system provides on the order of a 3-fold decrease in trained operator time over previous manual methods. Reproducibility is found to be dependent on image quality for most parameters. For 7 subjects with good image quality, reproducibility of 2–4% is found for 2D structural parameters, while 3D mechanical parameters vary by 4–9%, with percent standardized coefficients of variation in the ranges of 15–34% and 20–38% respectively.

Handbook of Image Quality: Characterization and Prediction

Abstract: With 300 figures, tables, and equations, this book presents a unified approach to image quality research and modeling The author discusses the results of different, calibrated psychometric experiments can be rigorously integrated to construct predictive software using Monte Carlo simulations and provides numerous examples of viable field applications for product design and verification of modeling predictions He covers perceptual measurements for the assessment of individual quality attributes and overall quality, explores variation in scene susceptibility, observer sensitivity, and preference, and includes methods of analysis for testing and refining metrics based on psychometric data

Information hiding technique with double phase encoding.

Abstract: We propose a technique for information hiding using double phase encoding. The proposed method uses a weighted double phase-encoded hidden image added to a host image referred to as the transmitted image. We develop an analytical presentation for the system performance using the statistical properties of double phase encoding. The peak signal-to-noise-ratio metric is used as a measure for the degradation in the quality of the host image and the recovered hidden image. We test, analytically, the distortion of the hidden image that is due to the host image and the effect of occlusion of the pixels of the transmitted image (that is, the host image containing the hidden image). Moreover, we discuss the effect of using only the real part of the transmitted image to recover the hidden image. Computer simulations are presented to test the system performance against these types of distortion. The simulations illustrate the system ability to recover the hidden image under distortions and the robustness of the hidden image against removal trials.

High resolution image formation from low resolution frames using Delaunay triangulation

Abstract: An algorithm based on spatial tessellation and approximation of each triangle patch in the Delaunay (1934) triangulation (with smoothness constraints) by a bivariate polynomial is advanced to construct a high resolution (HR) high quality image from a set of low resolution (LR) frames. The high resolution algorithm is accompanied by a site-insertion algorithm for update of the initial HR image with the availability of more LR frames till the desired image quality is attained. This algorithm, followed by post filtering, is suitable for real-time image sequence processing because of the fast expected (average) time construction of Delaunay triangulation and the local update feature.

Extended visual cryptography for natural images

Abstract: Extended Visual Cryptography[Ateni01] is a type of cryptography which encodes a number of images in the way that when the images on transparencies are stacked together, the hidden message appears without a trace of original images The decryption is done directly by the human visual system with no special cryptographic calculations This paper presents a system which takes three pictures as an input and generates two images which correspond to two of the three input pictures The third picture is reconstructed by printing the two output images onto transparencies and stacking them together While the previous researches basically handle only binary images, this paper establishes the extended visual cryptography scheme suitable for natural images Generally, visual cryptography suffers from the deterioration of the image quality This paper also describes the method to improve the quality of the output images The trade-off between the image quality and the security are discussed and assessed by observing the actual results of this method Furthermore, the optimization of the image quality is discussed

Blind image quality assessment

Abstract: Blind image quality assessment refers to the problem of evaluating the visual quality of an image without any reference. It addresses a fundamental distinction between fidelity and quality, i.e. human vision system usually does not need any reference to determine the subjective quality of a target image. In this paper, we propose to appraise the image quality by three objective measures: edge sharpness level, random noise level and structural noise level. They jointly provide a heuristic approach of characterizing the most important aspects of visual quality. We investigate various mathematical tools (analytical, statistical and PDE-based) for accurately and robustly estimating those three levels. Extensive experiment results are used to justify the validity of our approach.

A vision-based masking model for spread-spectrum image watermarking

Abstract: We present a perceptual model for hiding a spread-spectrum watermark of variable amplitude and density in an image. The model takes into account the sensitivity and masking behavior of the human visual system by means of a local isotropic contrast measure and a masking model. We compare the insertion of this watermark in luminance images and in the blue channel of color images. We also evaluate the robustness of such a watermark with respect to its embedding density. Our results show that this approach facilitates the insertion of a more robust watermark while preserving the visual quality of the original. Furthermore, we demonstrate that the maximum watermark density generally does not provide the best detection performance.

Foveated video quality assessment

Abstract: Most image and video compression algorithms that have been proposed to improve picture quality relative to compression efficiency have either been designed based on objective criteria such as signal-to-noise-ratio (SNR) or have been evaluated, post-design, against competing methods using an objective sample measure. However, existing quantitative design criteria and numerical measurements of image and video quality both fail to adequately capture those attributes deemed important by the human visual system, except, perhaps, at very low error rates. We present a framework for assessing the quality of and determining the efficiency of foveated and compressed images and video streams. Image foveation is a process of nonuniform sampling that accords with the acquisition of visual information at the human retina. Foveated image/video compression algorithms seek to exploit this reduction of sensed information by nonuniformly reducing the resolution of the visual data. We develop unique algorithms for assessing the quality of foveated image/video data using a model of human visual response. We demonstrate these concepts on foveated, compressed video streams using modified (foveated) versions of H.263 that are standard-compliant. We rind that quality vs. compression is enhanced considerably by the foveation approach.

Supporting image and video applications in a multihop radio environment using path diversity and multiple description coding

Abstract: This paper examines the effectiveness of combining multiple description coding (MDC) and multiple path transport (MPT) for video and image transmission in a multihop mobile radio network. The video and image information is encoded nonhierarchically into multiple descriptions with the following objectives. The received picture quality should be acceptable, even if only one description is received and every additional received description contributes to enhanced picture quality. Typical applications will need a higher bandwidth/higher reliability connection than that provided by a single link in current mobile networks. To support these applications, a mobile node may need to set up and use multiple paths to the desired destination, either simply because of the lack of raw bandwidth on a single channel or because of its poor error characteristics, which reduce its effective throughput. The principal reason for considering such an architecture is to provide high bandwidth and more robust end-to-end connections. We describe a protocol architecture that addresses this need and, with the help of simulations, we demonstrate the feasibility of this system and compare the performance of the MDC-MPT scheme to a system using layered coding and asymmetrical paths for the base and enhancement layers.

Application of sensitivity-encoded echo-planar imaging for blood oxygen level-dependent functional brain imaging.

Abstract: Functional magnetic resonance imaging (fMRI) studies based on blood oxygen level-dependent (BOLD) contrast require rapid scan techniques that are robust under conditions such as subject motion and tissue pulsations. This is because the signal changes due to neuronal activation are only a small percentage of the full MRI signal, and thus are difficult to distinguish from other signal fluctuations. In fMRI, single-shot techniques such as echo-planar imaging (EPI) and spiral imaging are often preferred over multishot techniques because of their reduced sensitivity for shot-to-shot signal variations (1,2). However, these techniques require rapid gradient switching, leading to high levels of acoustic noise and potentially to peripheral nerve stimulation. In addition, image quality is affected by blurring and warping, caused by T* 2 and off-resonance effects. These effects increase with B0-field strength and limit the achievable spatial resolution. With single-shot EPI techniques, blurring can be reduced by using reduced data acquisition techniques, such as half k-space acquisition, combined with homodyne image reconstruction (3,4). These techniques appear to have some benefits for fMRI (5), although they can potentially lead to image artifacts caused by errors in background phase.

Digital watermarking by a holographic technique

Abstract: A holographic technique is applied for digital watermarking by a computer. A digital-watermark image to be hidden is phase modulated in a random fashion, and its Fourier-transformed hologram is superposed on a content image. The watermark is reconstructed by means of a holographic-reconstruction technique from the bit-map image that hides it. In the study the processes of constructing and reconstructing a digital hologram are described on the basis of the theory of Fourier optics. The conditions for superposing the hologram onto the content images are investigated in detail. The validity of the present method is verified by changing the weighting of the hologram relative to that of the content image. The effect of image size is also discussed with respect to reconstruction of the watermark, and it is shown that watermark information in a form of a diffuse-type Fourier-transform hologram cannot be removed by cutting it out of the host image.

Vision-model-based impairment metric to evaluate blocking artifacts in digital video

Abstract: In this paper investigations are conducted to simplify and refine a vision-model-based video quality metric without compromising its prediction accuracy. Unlike other vision-model-based quality metrics, the proposed metric is parameterized using subjective quality assessment data recently provided by the Video Quality Experts Group. The quality metric is able to generate a perceptual distortion map for each and every video frame. A perceptual blocking distortion metric (PBDM) is introduced which utilizes this simplified quality metric. The PBDM is formulated based on the observation that blocking artifacts are noticeable only in certain regions of a picture. A method to segment blocking dominant regions is devised, and perceptual distortions in these regions are summed up to form an objective measure of blocking artifacts. Subjective and objective tests are conducted and the performance of the PBDM is assessed by a number of measures such as the Spearman rank-order correlation, the Pearson correlation, and the average absolute error The results show a strong correlation between the objective blocking ratings and the mean opinion scores on blocking artifacts.

Video quality assessment using structural distortion measurement

Abstract: Objective image/video quality measures play important roles in various image/video processing applications, such as compression, communication, printing, analysis, registration, restoration and enhancement. Most proposed quality assessment approaches in the literature are error sensitivity-based methods. We follow a new philosophy in designing image/video quality metrics, which uses structural distortion as an estimation of perceived visual distortion. We develop a new approach for video quality assessment. Experiments on the video quality experts group (VQEG) test data set shows that the new quality measure has higher correlation with subjective quality measurement than the proposed methods in VQEG's Phase I tests for full-reference video quality assessment.

A constrained adaptive beamformer for medical ultrasound: initial results

Abstract: Adaptive beamforming has been widely used as a way to improve image quality in medical ultrasound applications by correcting phase and amplitude aberration errors resulting from tissue inhomogeneity. A less-studied concern in ultrasound beamforming is the deleterious contribution of bright off-axis targets. This paper describes a new approach, the constrained adaptive beamformer (CAB), which builds on classic array processing methods from radar and sonar. Given a desired frequency response for the mainlobe beam, the CAB reduces off-axis signals by imposing an optimal set of weights on the receive aperture. A linearly constrained adaptive filter dynamically adjusts the aperture weights in response to the incoming data. Initial results show a factor of two improvement in point target resolution and a 60% contrast improvement for low echogenicity cysts. The CAB could considerably improve cardiac and abdominal image quality. We address implementation issues and discuss future work.

Heart-rate-adapted image reconstruction in multidetector-row cardiac CT: influence of physiological and technical prerequisite on image quality.

Abstract: The purpose of this study was to develop strategies for optimal image reconstruction in multidetector-row cardiac CT and to discuss the results in the context of individual heart rate, cardiac physiology, and technical prerequisite. Sixty-four patients underwent multidetector-row cardiac CT. Depending on the heart rate either a single-segmental reconstruction (SSR) or an adaptive two-segmental reconstruction (ASR) was applied. Image reconstruction was done either antegrade (a) or retrograde (r) in relation to the R-peak. Reconstruction of all data sets was performed at multiple time points within the t-wave/p-wave interval, differing from each other by 50 ms. In addition, each reconstruction was assigned to one of six reconstruction intervals (A–F), each corresponding to a specific event in the cardiac cycle. While no significant time points were found for absolute values, the following interval/reconstruction technique combinations provided significant better image quality: F/r at HR 65 bpm for all segments (p≤0.002). The results show that in order to achieve optimal image quality, image reconstruction has to be adjusted to each patient's ECG curve and heart rate individually. The moment of reconstruction should be determined as absolute rather than as relative distance from the previous R-peak.

Improvement of image quality in diffuse optical tomography by use of full time-resolved data.

Abstract: In the field of diffuse optical tomography (DOT), it is widely accepted that time-resolved (TR) measurement can provide the richest information on photon migration in a turbid medium, such as biological tissue. However, the currently available image reconstruction algorithms for TR DOT are based mostly on the cw component or some featured data types of original temporal profiles, which are related to the solution of a time-independent diffusion equation. Although this methodology can greatly simplify the reconstruction process, it suffers from low spatial resolution and poor quantitativeness owing to the limitation of effectively applicable data types. To improve image quality, it has been argued that exploiting the full TR data is essential. We propose implementation of a DOT algorithm by using full TR data and furthermore a variant algorithm with time slices of TR data to alleviate the computational complexity and enhance noise robustness. Compared with those algorithms where the featured data types are used, our evaluations on the spatial resolution and quantitativeness show that a significant improvement in imaging quality can be achieved when full TR data are used, which convinces the DOT community of the potential advantage of the TR domain over cw and frequency domains.