Purpose:
To evaluate the image quality of virtual monochromatic images synthesized from dual-source dual-energy computed tomography (CT) in comparison with conventional polychromatic single-energy CT ...for the same radiation dose.
Methods:
In dual-energy CT, besides the material-specific information, one may also synthesize monochromatic images at different energies, which can be used for routine diagnosis similar to conventional polychromatic single-energy images. In this work, the authors assessed whether virtual monochromatic images generated from dual-source CT scanners had an image quality similar to that of polychromatic single-energy images for the same radiation dose. First, the authors provided a theoretical analysis of the optimal monochromatic energy for either the minimum noise level or the highest iodine contrast to noise ratio (CNR) for a given patient size and dose partitioning between the low- and high-energy scans. Second, the authors performed an experimental study on a dual-source CT scanner to evaluate the noise and iodine CNR in monochromatic images. A thoracic phantom with three sizes of attenuating rings was used to represent four adult sizes. For each phantom size, three dose partitionings between the low-energy (80 kV) and the high-energy (140 kV) scans were used in the dual-energy scan. Monochromatic images at eight energies (40 to 110 keV) were generated for each scan. Phantoms were also scanned at each of the four polychromatic single energy (80, 100, 120, and 140 kV) with the same radiation dose.
Results:
The optimal virtual monochromatic energy depends on several factors: phantom size, partitioning of the radiation dose between low- and high-energy scans, and the image quality metrics to be optimized. With the increase of phantom size, the optimal monochromatic energy increased. With the increased percentage of radiation dose on the low energy scan, the optimal monochromatic energy decreased. When maximizing the iodine CNR in monochromatic images, the optimal energy was lower than that when minimizing noise level. When the total radiation dose was equally distributed between low and high energy in dual-energy scans, for minimum noise, the optimal energies were 68, 71, 74, and 77 keV for small, medium, large, and extra-large (xlarge) phantoms, respectively; for maximum iodine CNR, the optimal energies were 66, 68, 70, 72 keV. With the optimal monochromatic energy, the noise level was similar to and the CNR was better than that in a single-energy scan at 120 kV for the same radiation dose. Compared to an 80 kV scan, however, the iodine CNR in monochromatic images was lower for the small, medium, and large phantoms.
Conclusions:
In dual-source dual-energy CT, optimal virtual monochromatic energy depends on patient size, dose partitioning, and the image quality metric optimized. With the optimal monochromatic energy, the noise level was similar to and the iodine CNR was better than that in 120 kV images for the same radiation dose. Compared to single-energy 80 kV images, the iodine CNR in virtual monochromatic images was lower for small to large phantom sizes.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The detection and recognition of objects in images is a key research topic in the computer vision community. Within this area, face recognition and interpretation has attracted increasing attention ...owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems. This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications. Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching;presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets;discusses recent pattern classification paradigms from a template matching perspective;illustrates the development of a real face recognition system;explores the use of advanced computer graphics techniques in the development of computer vision algorithms.Template Matching Techniques in Computer Visionis primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection. It is also of interest to graduate students undertaking studies in these areas.
Purpose:
Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. ...Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems.
Methods:
To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation.
Results:
The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods.
Conclusions:
The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT segmentation based on 15 patients.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Underwater images often have severe quality degradation and distortion due to light absorption and scattering in the water medium. A hazy image formation model is widely used to restore the image ...quality. It depends on two optical parameters: the background light (BL) and the transmission map (TM). Underwater images can also be enhanced by color and contrast correction from the perspective of image processing. In this paper, we propose an effective underwater image enhancement method for underwater images in composition of underwater image restoration and color correction. Firstly, a manually annotated background lights (MABLs) database is developed. With reference to the relationship between MABLs and the histogram distributions of various underwater images, robust statistical models of BLs estimation are provided. Next, the TM of R channel is roughly estimated based on the new underwater dark channel prior (NUDCP) via the statistic of clear and high resolution (HD) underwater images, then a scene depth map based on the underwater light attenuation prior (ULAP) and an adjusted reversed saturation map (ARSM) are applied to compensate and modify the coarse TM of R channel. Next, TMs of G-B channels are estimated based on the difference of attenuation ratios between R and G-B channels. Finally, to improve the color and contrast of the restored image with a dehazed and natural appearance, a variation of white balance is introduced as post-processing. In order to guide the priority of underwater image enhancement, sufficient evaluations are conducted to discuss the impacts of the key parameters including BL and TM, and the importance of the color correction. Comparisons with other state-of-the-art methods demonstrate that our proposed underwater image enhancement method can achieve higher accuracy of estimated BLs, lower computation time, overall superior performance, and better information retention.
The influence of antiscatter x-ray grids on image quality in cone-beam computed tomography (CT) is evaluated through broad experimental investigation for various anatomical sites (head and body), ...scatter conditions (scatter-to-primary ratio (SPR) ranging from
∼
10
%
to 150%), patient dose, and spatial resolution in three-dimensional reconstructions. Studies involved linear grids in combination with a flat-panel imager on a system for kilovoltage cone-beam CT imaging and guidance of radiation therapy. Grids were found to be effective in reducing x-ray scatter “cupping” artifacts, with heavier grids providing increased image uniformity. The system was highly robust against ring artifacts that might arise in CT reconstructions as a result of gridline shadows in the projection data. The influence of grids on soft-tissue detectability was evaluated quantitatively in terms of absolute contrast, voxel noise, and contrast-to-noise ratio (CNR) in cone-beam CT reconstructions of
16
cm
“head” and
32
cm
“body” cylindrical phantoms. Imaging performance was investigated qualitatively in observer preference tests based on patient images (pelvis, abdomen, and head-and-neck sites) acquired with and without antiscatter grids. The results suggest that although grids reduce scatter artifacts and improve subject contrast, there is little strong motivation for the use of grids in cone-beam CT in terms of CNR and overall image quality under most circumstances. The results highlight the tradeoffs in contrast and noise imparted by grids, showing improved image quality with grids only under specific conditions of high x-ray scatter
(
SPR
>
100
%
)
, high imaging dose
(
D
center
>
2
cGy
)
, and low spatial resolution (voxel size
⩾
1
mm
).
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Quality evaluation of underwater images is a key goal of underwater video image retrieval and intelligent processing. To date, no metric has been proposed for underwater color image quality ...evaluation (UCIQE). The special absorption and scattering characteristics of the water medium do not allow direct application of natural color image quality metrics especially to different underwater environments. In this paper, subjective testing for underwater image quality has been organized. The statistical distribution of the underwater image pixels in the CIELab color space related to subjective evaluation indicates the sharpness and colorful factors correlate well with subjective image quality perception. Based on these, a new UCIQE metric, which is a linear combination of chroma, saturation, and contrast, is proposed to quantify the non-uniform color cast, blurring, and low-contrast that characterize underwater engineering and monitoring images. Experiments are conducted to illustrate the performance of the proposed UCIQE metric and its capability to measure the underwater image enhancement results. They show that the proposed metric has comparable performance to the leading natural color image quality metrics and the underwater grayscale image quality metrics available in the literature, and can predict with higher accuracy the relative amount of degradation with similar image content in underwater environments. Importantly, UCIQE is a simple and fast solution for real-time underwater video processing. The effectiveness of the presented measure is also demonstrated by subjective evaluation. The results show better correlation between the UCIQE and the subjective mean opinion score.
Purpose:
This work involved the development of a phantom-based method to quantify the performance of tube current modulation and iterative reconstruction in modern computed tomography (CT) systems. ...The quantification included resolution, HU accuracy, noise, and noise texture accounting for the impact of contrast, prescribed dose, reconstruction algorithm, and body size.
Methods:
A 42-cm-long, 22.5-kg polyethylene phantom was designed to model four body sizes. Each size was represented by a uniform section, for the measurement of the noise-power spectrum (NPS), and a feature section containing various rods, for the measurement of HU and the task-based modulation transfer function (TTF). The phantom was scanned on a clinical CT system (GE, 750HD) using a range of tube current modulation settings (NI levels) and reconstruction methods (FBP and ASIR30). An image quality analysis program was developed to process the phantom data to calculate the targeted image quality metrics as a function of contrast, prescribed dose, and body size.
Results:
The phantom fabrication closely followed the design specifications. In terms of tube current modulation, the tube current and resulting image noise varied as a function of phantom size as expected based on the manufacturer specification: From the 16- to 37-cm section, the HU contrast for each rod was inversely related to phantom size, and noise was relatively constant (<5% change). With iterative reconstruction, the TTF exhibited a contrast dependency with better performance for higher contrast objects. At low noise levels, TTFs of iterative reconstruction were better than those of FBP, but at higher noise, that superiority was not maintained at all contrast levels. Relative to FBP, the NPS of iterative reconstruction exhibited an ∼30% decrease in magnitude and a 0.1 mm−1 shift in the peak frequency.
Conclusions:
Phantom and image quality analysis software were created for assessing CT image quality over a range of contrasts, doses, and body sizes. The testing platform enabled robust NPS, TTF, HU, and pixel noise measurements as a function of body size capable of characterizing the performance of reconstruction algorithms and tube current modulation techniques.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Purpose:
The ionizing radiation imparted to patients during computed tomography exams is raising concerns. This paper studies the performance of a scheme called dose reduction using prior image ...constrained compressed sensing (DR-PICCS). The purpose of this study is to characterize the effects of a statistical model of x-ray detection in the DR-PICCS framework and its impact on spatial resolution.
Methods:
Both numerical simulations with known ground truth andin vivo animal dataset were used in this study. In numerical simulations, a phantom was simulated with Poisson noise and with varying levels of eccentricity. Both the conventional filtered backprojection (FBP) and the PICCS algorithms were used to reconstruct images. In PICCS reconstructions, the prior image was generated using two different denoising methods: a simple Gaussian blur and a more advanced diffusion filter. Due to the lack of shift-invariance in nonlinear image reconstruction such as the one studied in this paper, the concept of local spatial resolution was used to study the sharpness of a reconstructed image. Specifically, a directional metric of image sharpness, the so-called pseudopoint spread function (pseudo-PSF), was employed to investigate local spatial resolution.
Results:
In the numerical studies, the pseudo-PSF was reduced from twice the voxel width in the prior image down to less than 1.1 times the voxel width in DR-PICCS reconstructions when the statistical model was not included. At the same noise level, when statistical weighting was used, the pseudo-PSF width in DR-PICCS reconstructed images varied between 1.5 and 0.75 times the voxel width depending on the direction along which it was measured. However, this anisotropy was largely eliminated when the prior image was generated using diffusion filtering; the pseudo-PSF width was reduced to below one voxel width in that case. In thein vivo study, a fourfold improvement in CNR was achieved while qualitatively maintaining sharpness; images also had a qualitatively more uniform noise spatial distribution when including a statistical model.
Conclusions:
DR-PICCS enables to reconstruct CT images with lower noise than FBP and the loss of spatial resolution can be mitigated to a large extent. The introduction of statistical modeling in DR-PICCS may improve some noise characteristics, but it also leads to anisotropic spatial resolution properties. A denoising method, such as the directional diffusion filtering, has been demonstrated to reduce anisotropy in spatial resolution effectively when it was combined with DR-PICCS with statistical modeling.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Purpose:
To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy.
Methods:
The authors ...assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer‐aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave‐one‐case‐out validation method.
Results:
In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN‐based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01).
Conclusions:
This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Purpose:
Prior image constrained compressed sensing (PICCS) is an image reconstruction framework which incorporates an often available prior image into the compressed sensing objective function. The ...images are reconstructed using an optimization procedure. In this paper, several alternative unconstrained minimization methods are used to implement PICCS. The purpose is to study and compare the performance of each implementation, as well as to evaluate the performance of the PICCS objective function with respect to image quality.
Methods:
Six different minimization methods are investigated with respect to convergence speed and reconstruction accuracy. These minimization methods include the steepest descent (SD) method and the conjugate gradient (CG) method. These algorithms require a line search to be performed. Thus, for each minimization algorithm, two line searching algorithms are evaluated: a backtracking (BT) line search and a fast Newton-Raphson (NR) line search. The relative root mean square error is used to evaluate the reconstruction accuracy. The algorithm that offers the best convergence speed is used to study the performance of PICCS with respect to the prior image parameter α and the data consistency parameter λ. PICCS is studied in terms of reconstruction accuracy, low-contrast spatial resolution, and noise characteristics. A numerical phantom was simulated and an animal model was scanned using a multirow detector computed tomography (CT) scanner to yield the projection datasets used in this study.
Results:
For λ within a broad range, the CG method with Fletcher-Reeves formula and NR line search offers the fastest convergence for an equal level of reconstruction accuracy. Using this minimization method, the reconstruction accuracy of PICCS was studied with respect to variations in α and λ. When the number of view angles is varied between 107, 80, 64, 40, 20, and 16, the relative root mean square error reaches a minimum value for α ≈ 0.5. For values of α near the optimal value, the spatial resolution of the reconstructed image remains relatively constant and the noise texture is very similar to that of the prior image, which was reconstructed using the filtered backprojection (FBP) algorithm.
Conclusions:
Regarding the performance of the minimization methods, the nonlinear CG method with NR line search yields the best convergence speed. Regarding the performance of the PICCS image reconstruction, three main conclusions can be reached. (1) The performance of PICCS is optimal when the weighting parameter of the prior image parameter is selected to be near α = 0.5. (2) The spatial resolution measured for static objects in images reconstructed using PICCS from undersampled datasets is not degraded with respect to the fully-sampled reconstruction for α near its optimal value. (3) The noise texture of PICCS reconstructions is similar to that of the prior image, which was reconstructed using the conventional FBP method.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
PDF
