Dose to population as a metric in the design of optimised exposure control in digital mammography

This paper describes a method for automatic optimisation of parameters (AOP) in digital mammography systems. Using a model of the image chain, contrast to noise ratio (CNR) and average glandular dose (AGD) are computed for possible X-ray parameters and breast types. The optimisation process consists of the determination of the operating points providing the lowest possible AGD for each CNR level and breast type. The proposed metric for the dose used in the design of an AOP mode is the resulting dose to the population, computed by averaging the AGD values over the distribution of breast types in the population. This method has been applied to the automatic exposure control of new digital mammography equipment. Breast thickness and composition are estimated from a low dose pre-exposure and used to index tables containing sets of optimised operating points. The resulting average dose to the population ranges from a level comparable to state-of-the-art screen/film mammography to a reduction by a factor of two. Using this method, both CNR and dose are kept under control for all breast types, taking into consideration both individual and collective risk.     

Method of simulating dose reduction for digital radiographic systems

The optimisation of image quality vs. radiation dose is an important task in medical imaging. To obtain maximum validity of the optimisation, it must be based on clinical images. Images at different dose levels can then either be obtained by collecting patient images at the different dose levels sought to investigate-including additional exposures and permission from an ethical committee-or by manipulating images to simulate different dose levels. The aim of the present work was to develop a method of simulating dose reduction for digital radiographic systems. The method uses information about the detective quantum efficiency and noise power spectrum at the original and simulated dose levels to create an image containing filtered noise. When added to the original image this results in an image with noise which, in terms of frequency content, agrees with the noise present in an image collected at the simulated dose level. To increase the validity, the method takes local dose variations in the original image into account. The method was tested on a computed radiography system and was shown to produce images with noise behaviour similar to that of images actually collected at the simulated dose levels. The method can, therefore, be used to modify an image collected at one dose level so that it simulates an image of the same object collected at any lower dose level.     

Using simple mathematical functions to simulate pathological structures--input for digital mammography clinical trial

In this study a set of structures has been simulated to represent a range of clinically relevant breast cancer mammographic lesions including solid tumours and microcalcifications. All structures have been created using simple random-based mathematical functions and have been inserted into a subset of digital mammography images at appropriate contrast levels into various regions of the breast, including dense fibroglandular and adipose tissue. These structures and their appearance in these clinical images were evaluated in terms of how realistic they looked. They will be used as the input to a large-scale clinical trial designed to examine the effect of significant dose reduction in digital mammography by comparing the detectability of such structures in images acquired at full and quarter automatic exposure control (AEC) dose level and in images with simulated noise levels in between.     

Nodule detection in digital chest radiography: effect of system noise

Apart from the image content that is the reproduction of anatomy and possible lesions, an X-ray image also contains system noise due to the limited number of photons and other internal noise sources in the system (image plate artefacts, electronic noise, etc.). The aim of this study was to determine the extent to which the system noise influences the detection of subtle lung nodules in five different regions of the chest. This was done by conducting a receiver operating characteristic (ROC) study with five observers on two different sets of images; clinical chest X-ray images and images of a LucAl phantom at similar dose levels found in the different regions of the chest. In both image types, mathematically simulated nodules (with a full-width-at-fifth-maximum of 10 mm) were added to the images at varying contrast levels. As a measure of the influence of system noise on the detection of subtle lung nodules, the ratio between the contrast needed to obtain an area under the ROC curve of 0.80 in the system noise images to that needed in the clinical images was used. The contrast ratio between system noise images and clinical images ranged from approximately 0.02 (in the hilar region) to 0.18 (in the lower mediastinal region). The maximum difference in contrast needed for the corresponding system noise images, collected at the lowest and the highest dose represented in the anatomical image, was a factor of 2. These results indicate that probably no region in a chest X-ray image is limited by the number of quanta to the detector for the detection of 10 mm lung nodules when a radiation dose corresponding to a system with speed class 200 (leading to a detector dose of approximately 9 muGy behind the parenchyma) is used.     

Objective assessment of image quality in conventional and digital mammography taking into account dynamic range

The goal of this work is to develop a method to objectively compare the performance of a digital and a screen-film mammography system in terms of image quality. The method takes into account the dynamic range of the image detector, the detection of high and low contrast structures, the visualisation of the images and the observer response. A test object, designed to represent a compressed breast, was constructed from various tissue equivalent materials ranging from purely adipose to purely glandular composition. Different areas within the test object permitted the evaluation of low and high contrast detection, spatial resolution and image noise. All the images (digital and conventional) were captured using a CCD camera to include the visualisation process in the image quality assessment. A mathematical model observer (non-prewhitening matched filter), that calculates the detectability of high and low contrast structures using spatial resolution, noise and contrast, was used to compare the two technologies. Our results show that for a given patient dose, the detection of high and low contrast structures is significantly better for the digital system than for the conventional screen-film system studied. The method of using a test object with a large tissue composition range combined with a camera to compare conventional and digital imaging modalities can be applied to other radiological imaging techniques. In particular it could be used to optimise the process of radiographic reading of soft copy images.     

Optimisation of X-ray examinations in Lithuania: start of implementation in mammography

Optimisation of medical X-ray examinations is very important for the enhancement of the reliability of the examination and for the reduction of the radiation dose to patients. Results of investigations of doses to patients during mammography using thermoluminescence dosemeters at different hospitals are presented together with a brief overview of the situation for mammography in Lithuania. It is shown that the entrance surface air kerma varies in a broad range and differed from hospital to hospital. Nevertheless the calculated values of average glandular dose (AGD) for a 'standard' breast being relatively high were comparable with those (3.2 mGy per exposure at net optical density 1.4) currently accepted by international authorities. Differences in AGD values evaluated at different hospitals demonstrate the existing potential for optimisation of the mammography screening procedures. The results of this investigation will be included in a database for patient doses in Lithuania and used for establishing a national reference dose level for mammography. Currently, reference levels recommended by international authorities are used in Lithuania.     

Application of fuzzy-rule-based postprocessing to correlation methods in pattern recognition

The use of fuzzy-logic techniques on the correlation output plane is analyzed as a method to improve the discrimination capabilities of pattern-recognition procedures. The study is divided into two parts: one recounts a computer-simulated example corresponding to pattern recognition by the use of input images that may be defocused, tilted, or corrupted by additive Gaussian noise, and the second part describes an experimental setup in which the deformation of foam material is studied.     

Can the average glandular dose in routine digital mammography screening be reduced? A pilot study using revised image quality criteria

There is a need for tools that in a simple way can be used for the evaluation of image quality related to clinical requirements in mammography. The aim of this work was to adjust the present European image quality criteria to be relevant also for digital mammography images, and to use as simple and as few criteria as possible. A pilot evaluation of the new set of criteria was made with mammograms of 28 women from a General Electric Senographe 2000D full-field digital mammography system. One breast was exposed using the standard automatic exposure mode, the other using about half of that absorbed dose. Three experienced radiologists evaluated the images using visual grading analysis technique. The results indicate that the new quality criteria can be used for the evaluation of image quality related to clinical requirements in digital mammography in a simple way. The results also suggest that absorbed doses for the mammography system used may be substantially reduced.     

Correction of systematic spatial noise in push-broom hyperspectral sensors: application to CHRIS/PROBA images

Hyperspectral remote sensing images are affected by different types of noise. In addition to typical random noise, nonperiodic partially deterministic disturbance patterns generally appear in the data. These patterns, which are intrinsic to the image formation process, are characterized by a high degree of spatial and spectral coherence. We present a new technique that faces the problem of removing the spatially coherent noise known as vertical striping, usually found in images acquired by push-broom sensors. The developed methodology is tested on data acquired by the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-board Autonomy (PROBA) orbital platform, which is a typical example of a push-broom instrument exhibiting a relatively high noise component. The proposed correction method is based on the hypothesis that the vertical disturbance presents higher spatial frequencies than the surface radiance. A technique to exclude the contribution of the spatial high frequencies of the surface from the destriping process is introduced. First, the performance of the proposed algorithm is tested on a set of realistic synthetic images with added modeled noise in order to quantify the noise reduction and the noise estimation accuracy. Then, algorithm robustness is tested on more than 350 real CHRIS images from different sites, several acquisition modes (different spatial and spectral resolutions), and covering the full range of possible sensor temperatures. The proposed algorithm is benchmarked against the CHRIS reference algorithm. Results show excellent rejection of the noise pattern with respect to the original CHRIS images, especially improving the removal in those scenes with a natural high contrast. However, some low-frequency components still remain. In addition, the developed correction model captures and corrects the dependency of the noise patterns on sensor temperature, which confirms the robustness of the presented approach.     

Despeckling of medical ultrasound images

Speckle noise is an inherent property of medical ultrasound imaging, and it generally tends to reduce the image resolution and contrast, thereby reducing the diagnostic value of this imaging modality. As a result, speckle noise reduction is an important prerequisite, whenever ultrasound imaging is used for tissue characterization. Among the many methods that have been proposed to perform this task, there exists a class of approaches that use a multiplicative model of speckled image formation and take advantage of the logarithmical transformation in order to convert multiplicative speckle noise into additive noise. The common assumption made in a dominant number of such studies is that the samples of the additive noise are mutually uncorrelated and obey a Gaussian distribution. The present study shows conceptually and experimentally that this assumption is oversimplified and unnatural. Moreover, it may lead to inadequate performance of the speckle reduction methods. The study introduces a simple preprocessing procedure, which modifies the acquired radio-frequency images (without affecting the anatomical information they contain), so that the noise in the log-transformation domain becomes very close in its behavior to a white Gaussian noise. As a result, the preprocessing allows filtering methods based on assuming the noise to be white and Gaussian, to perform in nearly optimal conditions. The study evaluates performances of three different, nonlinear filters--wavelet denoising, total variation filtering, and anisotropic diffusion--and demonstrates that, in all these cases, the proposed preprocessing significantly improves the quality of resultant images. Our numerical tests include a series of computer-simulated and in vivo experiments.     

Nodule detection in digital chest radiography: effect of anatomical noise

The image background resulting from imaged anatomy can be divided into those components that are meaningful to the observers, in the sense that they are recognised as separate structures, and those that are not. These latter components (reffered to as anatomical noise) can be removed using a method developed within the RADIUS group. The aim of the present study was to investigate whether the removal of the anatomical noise results in images where lung nodules with lower contrast can be detected. A receiver operating characteristic (ROC) study was therefore conducted using two types of images: clinical chest images and chest images in which the anatomical noise had been removed. Simulated designer nodules with a full-width-at-fifth-maximum of 10 mm but with varying contrast were added to the images. The contrast needed to obtain an area under the ROC curve of 0.80, C0.8, was used as a measure of detectability (a low value of C0.8 represents a high detectability). Five regions of the chest X ray were investigated and it was found that in all regions the removal of anatomical noise led to images with lower C0.8 than the original images. On average, C0.8 was 20% higher in the original images, ranging from 7% (the lateral pulmonary regions) to 41% (the upper mediastinal regions).     

An approach for classification of malignant and benign microcalcification clusters

The only reliable and successful treatment of breast cancer is its detection through mammography at initial stage. Clusters of microcalcifications are important signs of breast cancer. Manual interpretation of mammographic images, in which the suspicious regions are indicated as areas of varying intensities, is not error free due to a number of reasons. These errors can be reduced by using computer-aided diagnosis systems that result in reduction of either false positives or true negatives. The purpose of the study in this paper is to develop a methodology for distinguishing malignant microcalcification clusters from benign microcalcification clusters. The proposed approach first enhances the region of interest by using morphological operations. Then, two types of features, cluster shape features and cluster texture features, are extracted. A Support Vector Machine is used for classification. A new set of shape features based on the recursive subsampling method is added to the feature set, which improves the classification accuracy of the system. It has been found that these features are capable of differentiating malignant and benign tissue regions. To investigate the performance of the proposed approach, mammogram images are taken from Digital Database for Screening Mammography database and an accuracy of 94.25% has been achieved. The experiments have shown that the proposed classification system minimizes the classification errors and is more efficient in correct diagnosis.     

Performance evaluation of two-dimensional phase unwrapping algorithms

We present a performance evaluation of eight two-dimensional phase unwrapping methods with respect to correct phase unwrapping and execution times. The evaluated methods are block least squares (BLS), adaptive integration (AI), quality guided path following (QUAL), mask cut (MCUT), multigrid (MGRID), preconditioned conjugate gradient (PCG), Flynn's (FLYNN), and Liang's (LIANG). This set included integration- (path following), least-squares-, L(1)-, and model-based methods. The methods were tested on several synthetic images, on two magnetic resonance images, and on two interferometry images. The synthetic images were designed to demonstrate different aspects of the phase unwrapping problem. To test the noise robustness of the methods, independent noise was added to the synthetic images to yield different signal-to-noise ratios. Each experiment was performed 50 times with different noise realizations to test the stability of the methods. The results of the experiments showed that the congruent minimum L(1) norm FLYNN method was best overall and the most noise robust of the methods, but it was also one of the slowest methods. The integration-based QUAL method was the only method that correctly unwrapped the two interferometry images. The least-squares-based methods (MGRID, PCG) gave worse results on average than did the integration- (or path following) based methods (BLS, AI, QUAL, MCUT) and were also slower. The model-based LIANG method was sensitive to noise and resulted in large errors for the magnetic resonance images and the interferometry images. In conclusion, for a particular application there is a trade-off between the quality of the unwrapping and the execution time when we attempt to select the most appropriate method.     

Evaluation of image quality of lumbar spine images: a comparison between FFE and VGA

PURPOSE: The aim of the present study is to compare two different methods for evaluation of the quality of clinical X-ray images. METHODS: Based on fifteen lumbar spine radiographs, two new sets of images were created. A hybrid image set was created by adding two distributions of artificial lesions to each original image. The image quality parameters spatial resolution and noise were manipulated and a total of 210 hybrid images were created. A set of 105 disease-free images was created by applying the same combinations of spatial resolution and noise to the original images. The hybrid images were evaluated with the free-response forced error experiment (FFE) and the normal images with visual grading analysis (VGA) by nine experienced radiologists. RESULTS: In the VGA study, images with low noise were preferred over images with higher noise levels. The alteration of the MTF had a limited influence on the VGA score. For the FFE study, the visibility of the lesions was independent of the sharpness and the noise level. No correlation was found between the two image quality measures. CONCLUSIONS: FFE is a precise method for evaluation of image quality, but the results are only valid for the type of lesion used in the study, whereas VGA is a more general method for clinical image quality assessment. The results of the FFE study indicate that there might be a potential to lower the dose levels in lumbar spine radiography without losing important diagnostic information.     

A framework for optimising the radiographic technique in digital X-ray imaging

The transition to digital radiology has provided new opportunities for improved image quality, made possible by the superior detective quantum efficiency and post-processing capabilities of new imaging systems, and advanced imaging applications, made possible by rapid digital image acquisition. However, this transition has taken place largely without optimising the radiographic technique used to acquire the images. This paper proposes a framework for optimising the acquisition of digital X-ray images. The proposed approach is based on the signal and noise characteristics of the digital images and the applied exposure. Signal is defined, based on the clinical task involved in an imaging application, as the difference between the detector signal with and without a target present against a representative background. Noise is determined from the noise properties of uniformly acquired images of the background, taking into consideration the absorption properties of the detector. Incident exposure is estimated or otherwise measured free in air, and converted to dose. The main figure of merit (FOM) for optimisation is defined as the signal-difference-to-noise ratio (SdNR) squared per unit exposure or (more preferably) dose. This paper highlights three specific technique optimisation studies that used this approach to optimise the radiographic technique for digital chest and breast applications. In the first study, which was focused on chest radiography with a CsI flat-panel detector, a range of kV(p) (50-150) and filtration (Z = 13-82) were examined in terms of their associated FOM as well as soft tissue to bone contrast, a factor of importance in digital chest radiography. The results indicated that additive Cu filtration can improve image quality. A second study in digital mammography using a selenium direct flat-panel detector indicated improved SdNR per unit exposure with the use of a tungsten target and a rhodium filter than conventional molybdenum target/molybdenum filter techniques. Finally, a third study focusing on cone-beam computed tomography of the breast using a CsI flat-panel detector indicated that high Z filtration of a tungsten target X-ray beam can notably improve the signal and noise characteristics of the image. The general findings highlight the fact that the techniques that are conventionally assumed to be optimum may need to be revisited for digital radiography.     

Evaluation of patient dose in some mammography centres in Iran

High diagnostic sensitivity and specificity while maintaining the least dose to the patient is the ideal mammography. The objective of this work was to evaluate patient dose and image quality of mammograms to propose corrective actions. The image quality for 1242 patient in 7 mammography facilities in Tehran city was evaluated based on selected image quality criteria using a three-point scale. Clinical image quality, the entrance surface air kerma, the average glandular dose and optical density of films for standard PMMA phantom of 4.5 cm thickness were evaluated. The results showed that up to 72 % of mammograms were in good condition to be diagnosed, and only about 3.4 % of the images were unacceptable or with suboptimal quality. The entrance surface air kerma values were in the range of 3.8-10.5 mGy, average glandular dose 0.5-1.8 mGy and optical density of films 0.74-2.03. The image quality evaluation after correction actions, periodic image quality evaluation and using the correct equipment certainly will improve patient dose.     

Enhanced reconstruction of three-dimensional shape and texture from integral photography images

A method for the reconstruction of 3D shape and texture from integral photography (IP) images is presented. Sharing the same principles with stereoscopic-based object reconstruction, it offers increased robustness to noise and occlusions due to the unique characteristics of IP images. A coarse-to-fine approach is used, employing what we believe to be a novel grid refinement step in order to increase the quality of the reconstructed objects. The proposed method's properties include configurable depth accuracy and direct and seamless triangulation. We evaluate our method using synthetic data from a computer-simulated IP setup as well as real data from a simple yet effective digital IP setup. Experiments show reconstructed objects of high-quality indicating that IP can be a competitive modality for 3D object reconstruction.     

Attenuating noise from computed tomography medical images using a coefficients‐driven total variation denoising algorithm

The aim of image denoising is to recover a visually accepted image from its noisy observation with as much detail as possible. The noise exists in computed tomography images due to hardware errors, software faults and/or low radiation dose. Because of noise, the analysis and extraction of accurate medical information is a challenging task for specialists. Therefore, a novel modification on the total variational denoising algorithm is proposed in this article to attenuate the noise from CT images and provide a better visual quality. The newly developed algorithm can properly detect noise from the other image components using four new noise distinguishing coefficients and reduce it using a novel minimization function. Moreover, the proposed algorithm has a fast computation speed, a simple structure, a relatively low computational cost and preserves the small image details while reducing the noise efficiently. Evaluating the performance of the proposed algorithm is achieved through the use of synthetic and real noisy images. Likewise, the synthetic images are appraised by three advanced accuracy methods –Gradient Magnitude Similarity Deviation (GMSD), Structural Similarity (SSIM) and Weighted Signal‐to‐Noise Ratio (WSNR). The empirical results exhibited significant improvement not only in noise reduction but also in preserving the minor image details. Finally, the proposed algorithm provided satisfying results that outperformed all the comparative methods.     

Noise reduction in digital speckle pattern interferometry using bidimensional empirical mode decomposition

We propose a bidimensional empirical mode decomposition (BEMD) method to reduce speckle noise in digital speckle pattern interferometry (DSPI) fringes. The BEMD method is based on a sifting process that decomposes the DSPI fringes in a finite set of subimages represented by high and low frequency oscillations, which are named modes. The sifting process assigns the high frequency information to the first modes, so that it is possible to discriminate speckle noise from fringe information, which is contained in the remaining modes. The proposed method is a fully data-driven technique, therefore neither fixed basis functions nor operator intervention are required. The performance of the BEMD method to denoise DSPI fringes is analyzed using computer-simulated data, and the results are also compared with those obtained by means of a previously developed one-dimensional empirical mode decomposition approach. An application of the proposed BEMD method to denoise experimental fringes is also presented.     

Improving closely spaced dim object detection through multiframe blind deconvolution of near stellar neighbourhoods

A new iterative algorithm is proposed to improve the detection of dim stellar objects that are in the neighbourhood of a bright object, using short-exposure images. This method separates data functions into the primary bright object function, the neighbourhood system function, and the background function. This approach uses the principles of the Expectation-Maximization algorithm with the Gerchberg-Saxton phase retrieval algorithm to overcome the image degradation caused by the photon counting noise from the charge-coupled devices and the turbulent atmospheric conditions. The performance of this new neighbourhood system algorithm is compared with that of the multiframe blind deconvolution algorithm, using laboratory data and computer-simulated data. This paper provides an improved technique to image closely spaced dim objects.