Metin Ertas

Digital breast tomosynthesis image reconstruction using 2D and 3D total variation minimization

BackgroundDigital breast tomosynthesis (DBT) is an emerging imaging modality which produces three-dimensional radiographic images of breast. DBT reconstructs tomographic images from a limited view angle, thus data acquired from DBT is not sufficient enough to reconstruct an exact image. It was proven that a sparse image from a highly undersampled data can be reconstructed via compressed sensing (CS) techniques. This can be done by minimizing the l1 norm of the gradient of the image which can also be defined as total variation (TV) minimization. In tomosynthesis imaging problem, this idea was utilized by minimizing total variation of image reconstructed by algebraic reconstruction technique (ART). Previous studies have largely addressed 2-dimensional (2D) TV minimization and only few of them have mentioned 3-dimensional (3D) TV minimization. However, quantitative analysis of 2D and 3D TV minimization with ART in DBT imaging has not been studied.MethodsIn this paper two different DBT image reconstruction algorithms with total variation minimization have been developed and a comprehensive quantitative analysis of these two methods and ART has been carried out: The first method is ART + TV2D where TV is applied to each slice independently. The other method is ART + TV3D in which TV is applied by formulating the minimization problem 3D considering all slices.ResultsA 3D phantom which roughly simulates a breast tomosynthesis image was designed to evaluate the performance of the methods both quantitatively and qualitatively in the sense of visual assessment, structural similarity (SSIM), root means square error (RMSE) of a specific layer of interest (LOI) and total error values. Both methods show superior results in reducing out-of-focus slice blur compared to ART.ConclusionsComputer simulations show that ART + TV3D method substantially enhances the reconstructed image with fewer artifacts and smaller error rates than the other two algorithms under the same configuration and parameters and it provides faster convergence rate.

An iterative tomosynthesis reconstruction using total variation combined with non-local means filtering

BackgroundAfter the release of compressed sensing (CS) theory, reconstruction algorithms from sparse and incomplete data have shown great improvements in diminishing artifacts of missing data. Following this progress, both local and non-local regularization induced iterative reconstructions have been actively used in limited view angle imaging problems.MethodsIn this study, a 3D iterative image reconstruction method (ART + TV)NLM was introduced by combining local total variation (TV) with non-local means (NLM) filter. In the first step, TV minimization was applied to the image obtained by algebraic reconstruction technique (ART) for background noise removal with preserving edges. In the second step, NLM is used in order to suppress the out of focus slice blur which is the most existent image artifact in tomosynthesis imaging. NLM exploits the similar structures to increase the smoothness in the image reconstructed by ART + TV.ResultsA tomosynthesis system and a 3D phantom were designed to perform simulations to show the superior performance of our proposed (ART + TV)NLM over ART and widely used ART + TV methods. Visual inspections show a significant improvement in image quality compared to ART and ART + TV.ConclusionsRMSE, Structure SIMilarity (SSIM) value and SNR of a specific layer of interest (LOI) showed that by proper selection of NLM parameters, significant improvements can be achieved in terms of convergence rate and image quality.

Iterative image reconstruction using non-local means with total variation from insufficient projection data

In this work, algebraic reconstruction technique (ART) is extended by using non-local means (NLM) and total variation (TV) for reduction of artifacts that are due to insufficient projection data. TV and NLM algorithms use different image models and their application in tandem becomes a powerful denoising method that reduces erroneous variations in the image while preserving edges and details. Simulations were performed on a widely used 2D Shepp-Logan phantom to demonstrate performance of the introduced method (ART + TV) NLM and compare it to TV based ART (ART + TV) and ART. The results indicate that (ART + TV) NLM achieves better reconstructions compared to (ART + TV) and ART.

Image denoising by using non-local means and Total Variation

Recently, medical modalities such as low dose CT, MRI and tomosynthesis have focused on generating noise-free images by using fewer measurements. However acquiring or using less data to reconstruct an image increases the noise level in the image. Thus, image denoising has been one of the most active research areas due to the noise existence in most medical imaging modalities. Due to its virtue of edge preserving, Total Variation (TV) has been actively used in medical imaging. Non-Local Means has recently been proposed as a filtering to suppress the Gaussian noise and preserve fine details in the image. In this study, the total variation (TV) minimization, is combined with Non-Local Means (NLM) filtering to increase the noise reduction. Visual and numerical results show that an important improvement in image denoising has been achieved in the sense of Structure Similarity (SSIM) and RMSE. The optimum NLM filtering parameters selection has also been studied to increase the performance the proposed method.

RFID based hand hygiene compliance monitoring station

Hand hygiene (HH) compliance is known as the most significant factor to reduce the transmission of infection to patients in health care institutions. Thus, monitoring hand hygiene compliance has become an important tool to control the infection transmission. Most of the currently used HH monitoring devices and systems use battery powered badges or wristbands to control users. In this study, a user friendly, passive RF-ID based electronic system with monitoring capabilities was introduced. The system includes a wearable, passive RF-ID wristband, wall mounted dispenser and a software to measure the HH compliance rate. The technology laying behind the HH station is introduced and the advantages and drawbacks of the system is discussed.

An iterative reconstruction for tomosynthesis imaging using Non-Local Means

This paper proposes a new 3D iterative reconstruction method for reducing out-of-focus slice blur in tomosynthesis imaging by combining two powerful denoising methods. The methods used in the reconstruction are based on Total Variation (TV) minimization and Non-Local Means (NLM) filtering. A new method (ART+TV)NLM is introduced by adapting both methods to Algebraic reconstruction technique (ART) which is one of the most commonly used iterative method in limited angle tomographic imaging. Simulations were performed on a designed 3D phantom. The method is shown to be more effective by keeping SNR higher and RMSE values lower compared to the ART and ART+TV reconstructions.

Fractal dimension analysis of Uroflowmetry signals

The Uroflowmetry test is a commonly used method, which evaluates urine flow rates and volumetric analysis of patients. Flow rates and volumetric analysis are employed for assessing various urinary disorders such as Urethral Stricture, Bladder control problems, prostate etc. In this study, ten different Uroflowmetry signals of both abnormal and normal patients are processed. The Higuchis fractal dimension computation is proposed for analyzing and investigating the characteristics of these signals. The Higuchis method is an effective tool for measurement of fractal dimension of self-affine signals which is proposed for online monitoring of Uroflowmetry signals of patients and hence accelerated diagnose of urinary disorders.

Personal tracking in hospitals by using active RF-ID

The applications of wireless communication system dramatically increase day by day. Nowadays, these technologies have been widely used in personal tracking. RFID is one of the most actively used technologies among them. Along with its effective use in daily life, it allows localization and personal tracking by using location detection algorithms. In this study, tracking of personal and patients in the hospitals has been performed by using this feature of active RF-ID system. Thus, by tracking personal within the hospital it is aimed to take mandatory actions for hygiene control at special locations where hygiene rules must be strictly followed. Within this context, a system has been designed for personal to fulfill his responsibilities at this regard. The tracking of movement of personal inside the hospital building and rooms has been performed by using this system and problems have been investigated.

The effect of human body in location tracking by using passive RFID

RFID (Radio Frequency Identification) is a technique which is used to identify object singular and automatically. Nowadays, RFID technology is actively used in different areas such as personal, products and animals tracking to increase the efficiency in production and the quality of daily life. On the other hand, localization is another area wherein RFID is used and this provides applicable solution to staff tracking and patient treatment control. In hospitals, regular tracking of patients who need special care for their treatment is crucial. In this study, in this study, a control mechanism was created to track staff by using their personal RFID badges for special care units and the problems which has been encountered in passive RFID tags was shown.

2D versus 3D total variation minimization in digital breast tomosynthesis

Total variation (TV) minimization has become an important tool for the sparse image reconstruction. In this study a realistic 3D digital breast tomosynthesis (DBT) data was reconstructed and compared using two different forms of TV: (i) 2D reconstruction by applying TV layer by layer (ii) 3D reconstruction of the entire data. It can be assumed that a 3D reconstruction should perform better. However, in real DBT the resolution in z direction is about 10-15 times lower than the resolution in x-y plane. This study investigates the performance of the reconstruction when a TV term in z direction is added into the cost function of TV minimization.