ラシド イサム

Transcranial magnetic stimulation (TMS) is a non-invasive clinical technique used for treatment of several neurological diseases such as depression, Alzheimer's disease and Parkinson's disease. However, it is always challenging to accurately adjust the electric field on different specific brain regions due to the requirement of several stimulation parameters' optimizations. A major factor of brain induced electric field is the inter-subject variability, therefore a computer simulation is frequently used to simulate different TMS setups using anatomical models generated from MR images of the examined subject. Human head models are generated by segmentation of MR images into different anatomical tissues with a uniform electric conductivity value for each tissue. This process is time-consuming and requires a special experience to segment a relatively large number of tissues.In this paper, we propose a deep convolution network for human head segmentation that is convenient for simulation of electrical field distribution, such as TMS. The proposed network is used to generate head models and is evaluated using TMS simulation studies. Results indicate that the head models generated using the proposed network demonstrate strong matching results with those achieved from manually segmented ones.

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International guidelines/standards have been published for human protection from electromagnetic field exposure. The research in the intermediate frequencies (IF: 300 Hz-10 MHz) is scattered unlike for other frequencies, and thus the limit prescribed in the guidelines/standards are different by a factor of 10. The IEEE International Committee on Electromagnetic Safety has published a research agenda for exploring the electrostimulation thresholds. However, the consistency of the excitation models for specific target tissue needs to be revised. For this purpose, we present the first intercomparison study using multiphysics modelling to investigate stimulation thresholds during transcranial magnetic stimulation (TMS). To define the stimulation threshold, a noninvasive technique for brain stimulation has been used. In this study, by incorporating individual neurons into electromagnetic computation in realistic head models, stimulation thresholds can be determined. The study case of one subject showed that the allowable external magnetic field strength in the current guidelines/standard is conservative.

Breast cancer is one of the leading fatal disease worldwide with high risk control if early discovered. Conventional method for breast screening is x-ray mammography, which is known to be challenging for early detection of cancer lesions. The dense breast structure produced due to the compression process during imaging lead to difficulties to recognize small size abnormalities. Also, inter- and intra-variations of breast tissues lead to significant difficulties to achieve high diagnosis accuracy using hand-crafted features. Deep learning is an emerging machine learning technology that requires a relatively high computation power. Yet, it proved to be very effective in several difficult tasks that requires decision making at the level of human intelligence. In this paper, we develop a new network architecture inspired by the U-net structure that can be used for effective and early detection of breast cancer. Results indicate a high rate of sensitivity and specificity that indicate potential usefulness of the proposed approach in clinical use.

Aim: Glioblastoma (GB) is one notable example of miRNA-modulated neoplasms. Given its unique expression signature, proper miRNA profiling can help discriminate between GB and other types of brain tumors. The current work aimed to develop a more GB-specific and applicable custom designed quantitative real-Time reverse transcription polymerase chain reaction (qRT-PCR) miRNA assay. Materials & methods: A comprehensive data analysis of bioinformatics databases, previous literature and commercially available pre-designed miRNA PCR arrays within the market. Results: A highly enriched panel of 84 deregulated and GB-specific miRNAs has been developed. Conclusion: After validation of this newly developed array, it can not only save the researcher's time and effort, but can also have a potential diagnostic and/or prognostic role in GB, paving the road toward personalized medicine.

The contradiction between the great benefits of computed tomography (CT) in diagnosis and the risk of redundant CT scan on the patient health, make the researchers compete developing image reconstruction methods for low-dose CT. Sparse-view CT is a common technique in radiation dose minimization. Due to the streak artifacts that result while using the analytical reconstruction method with sparse-view CT, several iterative reconstruction methods have presented to produce high image quality. In this work, we introduce extracting the prior information incorporated in the reconstruction method during the process of reconstruction itself, in contrast to the other related methods that prepare the prior information in advance. The proposed technique is divided into two main steps. The first step is the construction of self-prior information. The second step is incorporating this produced information into the reconstruction process. The performance of the proposed method is evaluated using simulation and synthetic real data. Results show that the proposed technique produce high image quality.

ICNIRP and IEEE publish standards/guidelines for exposures to low-frequency electromagnetic fields and their associated in situ electric fields. Two methods are prescribed for spatially averaging the in situ electric field to evaluate compliance: averaging (1) over a 2 mm times 2 mm times 2 mm volume (ICNIRP) and (2) along a 5 mm linear segment of neural tissue (IEEE). However, detailed calculation procedures for these two schemes are not provided, particularly when the averaging volume/line straddles a tissue/air or tissue/tissue interface. This study proposes detailed schemes for implementing the volume- and line- averaging in such cases, applying them to both a spherical model of layered tissues and a human anatomical model. To extend the applicability of the proposed averaging schemes to the voxels at the tissue boundaries, a parameter, p_{mathrm {max } } , is introduced and defined as the maximum permissible percentage of air/other tissues in the averaging volume/line. For most inner-tissue voxels results show good agreement between the two averaging schemes, in general. Excluding skin, the relative differences between the two averaging schemes were less than 9% for the 99 percentile in situ electric field, and these differences decrease as p_{mathrm {max } } increases. Results indicate that around 20-30% inclusion of air or other tissues for volume averaging of internal tissues provides stable percentile values; less stability is observed across p_{mathrm {max } } for linear averaging. Invoking the suggestion of ICNIRP (2010) that the averaging cube for skin 'may extend to subcutaneous tissue,' ≥10% inclusion of air results in stable averaged induced electric fields. th

Visualization of patient's anatomy is the most important preoperation process in surgeries; minimally invasive surgeries are among these types of medical operations that counts totally on medical visualization before operating on a patient. However, medicine has a problem in visualizing patients' through looking through multiple slices of scans, trying to understand the three-dimensional (3D) anatomical structure of patients. With Mixed Reality (MR) the developments in medicine visualization will become much easier andcreates a better environment for surgeries. This will help reduce the excessive effort and time spent by surgeons to locate where the problem lies with patients without looking through multiple of two-dimensional (2D) slices, but to see patients' bodies in 3D in front of them augmented in their reality, and to interact with it whatever pleases them. Moreover, this will reduce the number of scans that doctors will ask their patient's for, which will result in less harmful x-ray dosages for both the patient and the radiologist. Biomedical development in medical visualization is an active research topic as it provides the physicians with required devices for clinically feasible way for diagnosis, follow-up and take decisions in different disease lifeline. Current clinical imaging facility can provide a 3D imaging that can be used to guide different interventional procedures. The main challenge is how to map the information presented in the digital image with the real object. This is commonly implemented by mental processing that requires skills from the medical doctor. This paper contributes to this problem by providing a mixed reality system to merge the digital image of the patient anatomy with the patient visual image. Anatomical image obtained from Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) is mapped over the patient body using virtual reality (VR) head-mounted device (HMD).

In several medical applications as well as human safety evaluation, accurate electromagnetic field exposure assessments are required to identify potential side/adverse effects on humans. Computational human models representing anatomy are commonly used to conduct computational dosimetry studies to assess the in situ electric field for quantitative evaluation due to a limitation in conventional human models. The limitation in conventional human models was due to a limited model resolution (typically a few millimeters), which is attributable to the original resolution of medical images. In particular, the importance of the skin layer is suggested in the research agenda of the international standardization body for human electromagnetic exposure. In this paper, we propose a novel method to improve the accuracy of human head skin modeling, which is applicable even to conventional models. To demonstrate the effect of skin modeling on the computed in situ electric field, computational dosimetry is conducted for uniform magnetic field exposure as well as transcranial magnetic stimulation. Computational results indicate that the in situ electric field for uniform exposure is marginally influenced by the skin thickness and model resolution (up to 5%) for different evaluation metrics used in international safety standards. However, the in situ electric field in the skin during transcranial magnetic stimulation and a simulated electrical shaver (non-uniform field exposure) was affected by 11%, which may be worth discussing for optimal brain stimulation considering the side effects of unintended exposure.

Visualization of patient's anatomy is the most important pre-operation process in surgeries; minimally invasive surgeries are among these types of medical operations that counts totally on medical visualization before operating on a patient. However, medicine has a problem in visualizing patients' through looking through multiple slices of scans, trying to understand the threedimensional (3D) anatomical structure of patients. With Mixed Reality (MR) the developments in medicine visualization will become much easier and creates a better environment for surgeries. This will help reduce the excessive effort and time spent by surgeons to locate where the problem lies with patients without looking through multiple of two-dimensional (2D) slices, but to see patients' bodies in 3D in front of them augmented in their reality, and to interact with it whatever pleases them. Moreover, this will reduce the number of scans that doctors will ask their patient's for, which will result in less harmful x-ray dosages for both the patient and the radiologist. Biomedical development in medical visualization is an active research topic as it provides the physicians with required devices for clinically feasible way for diagnosis, follow-up and take decisions in different disease life line. Current clinical imaging facility can provide a 3D imaging that can be used to guide different interventional procedures. The main challenge is how to map the information presented in the digital image with the real object. This is commonly implemented by mental processing that requires skills from the medical doctor. This paper contributes to this problem by providing a mixed reality system to merge the digital image of the patient anatomy with the patient visual image. Anatomical image obtained from Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) is mapped over the patient body using virtual reality (VR) head-mounted device (HMD).

In computed tomography, image reconstructed from limited projection data is subject to strong noise and artifacts. Reducing the number of projection views in synchrotron radiation imaging has a great benefits in decreasing the computation cost for image formation. Moreover, it also prevents the damage of biological specimen caused by the x-ray radiation exposure. In this paper, a new procedure for image reconstruction from limited projection views with the assist of deep learning approach is proposed. A deep learning approach is employed to estimate an initial guess of the tomographic image followed by a fast row-action reconstruction to guarantee the restoration of any pattern missed in the first stage. Most of previous attempts that use deep learning in image reconstruction are implemented as a post-processing techniques applied on the reconstructed image. This may lead to anomalies in the final result due to the lack of consistency between the acquired projection data and the reconstructed image. The proposed procedure solves this problem by keeping the data consistency up to the final stage of image formation. This would reduce the possibility of losing image abnormal structures due to insufficient training in deep learning. Experimental results using synchrotron radiation data demonstrate the usefulness of the proposed framework.

This work introduces a computer-aided diagnosis (CAD) system for diagnosing liver cirrhosis in ultrasound (US) images. The proposed system uses a set of features obtained from different feature extraction methods. These features are the first order statistics (FOS), the fractal dimension (FD), the gray level co-occurrence matrix (GLCM), the Gabor filter (GF), the wavelet (WT) and the curvelet (CT) features. The measured features are presented in two different classifiers such as support vector machine (SVM) and k-nearest neighbors (K-NN). The proposed system is applied on dataset consists of 72 cirrhosis and 75 normal regions each of 128128 pixels. The classification accuracy rates are calculated using a 10-fold cross validation. A correlation-based feature selection (CFS) is used resulting in better accuracy predictions. The results showed that SVM and K-NN classifiers achieved higher performance with the combination of the wavelet and curvelet feature vectors than other feature extraction methods.

Low-dose computed tomography (LDCT) imaging is considerably recommended for use in clinical CT scanning because of growing fears over excessive radiation exposure. Automatic exposure control (AEC) is one of the methods used in dose reduction techniques that have been implemented clinically comprise showing significant decrease scan range. The quality of some images may be roughly degraded with noise and streak artifacts due to x-ray flux, based on modulating radiation dose in the angular and slice directions. In 2005, the nonlocal means (NLM) algorithm showed high performance in denoising images corrupted by LDCT. The proposed method incorporates a prior knowledge obtained from previous high-quality CT slices to improve low-quality CT slice during the filtering process because of the anatomical similarity between the arranged image slices of the scans. The proposed method is evaluated using real data and CT image quality is notably improved.

In orthopedic surgery, it is important for physicians to completely understand the three-dimensional (3D) anatomical structures for several procedures. With the current revolution in technology in every aspect of our life, mixed reality in the medical field is going to be very useful. However, medicine has a visualization problem hindering how surgeons operate. The surgeons are required to imagine the actual 3D structure of the patient by looking at multiple 2D slices of the patients' body. This process is time consuming, exhausting and requires special skill and experience. Moreover, patients and surgeons are exposed to extra x-ray doses. Therefore, it is important to provide the surgeon with a better way to diagnose the patient; a way that is more accurate and locates where the problem is in a faster and more efficient manner. Medical imaging systems usually provide 3D images that can guide interventional clinical procedures. However, it is difficult to map the 3D anatomical structure with real objects. This project investigates and solves this problem by providing a mixed reality technology solution that merges the 3D image with real objects to facilitate the work progress of the surgeon. The proposed solution is an interactive mixed reality (MR) system for minimally invasive surgeries. The system is based on mapping the patient volume scan using computed tomography (CT) or Magnetic Resonance Imaging (MRI) to a 3D model of the patient's body. The rendered model can be used in MR system to view 3D human structures through a set of wearable glasses.

Low-dose computed tomography (LDCT) is usually performed by reducing the power of the x-ray tube in clinical CT scanners. However, images acquired through LDCT are known to be of low-quality due to the presence of statistical noise and other related artifacts. Effective denoising techniques are required to improve the quality of LDCT images towards green and safe CT imaging. In this paper, a new method is presented to improve the so-called, non-local means (NLM) filtering for effective LDCT imaging. The proposed method incorporates a prior knowledge obtained from probabilistic atlas during the filtering process. Additional anatomical information obtained through the atlas is likely to be useful in improving the image quality using NLM filtering. The proposed method is evaluated using real data and a notable improvement in image quality improvement is achieved.

In orthopedic surgery, it is important for physicians to completely understand the three-dimensional bone structure for several procedures. To achieve this goal, it is required to image the patient several times using C-arm scanner from different positions during the surgery. This procedure is time consuming and increase the x-ray dose given to both patient and physician. In this paper, we propose an augmented reality imaging system for minimally invasive orthopedic surgery. The system is based on mapping the x-ray image to the real object such that the number of x-ray shots during the surgery can be significantly reduced. We consider two imaging scenarios that can fit with different cases. Results obtained through clinical data indicate that the proposed approach has a potential usefulness in real applications.

Interventional pain management surgery is a clinical application performed though the guidance of x-ray fluoroscopy. Several protocols requires the injection of a needle close to the spinal cord to deliver a medication directly to the nerve system. The needle position information in the 3D space is important to avoid possible damage to the nerve system. It is common to perform the pain management surgery using C-arm scanner to follow up the treatment procedure. In many cases, it is difficult to observe the exact position of the injected needle through 2D images acquired using conventional C-arm scanner especially with complicated bone structures. It requires several attempts to image the patient from different positions and physician requires a mental process to imagine how the 3D structure looks like before starting the interventional procedure. This process may be repeated several times during a single interventional session, which cause a significant increase of radiation dose given to both patient and surgeon. In this paper, we introduce a method for needle detection in interventional pain management surgery using a clinical C-arm scanner. First, an in-house made gantry control unit (GCU) is mounted to the C-arm gantry to control the scanner orbital rotation. Second, the gantry rotation is traced using inertial measurement unit (IMU) sensor attached. A single cine loop is acquired by automatically rotate the C-arm gantry around the patient using GCU. Geometry information obtained from the IMU sensor is used to define the gantry position in the 3D space and synchronized with detector measurements in cine loop frames. The SCAN algorithm is then adopted for the 3D reconstruction of bone structures and injected needle.

Interior tomography is image reconstruction of a region-of-interest (ROI) located inside a scanned object. It has major effect in reducing the radiation dose. It was believed previously that the solution of interior problem was not unique. In the last decade, it has been learned that the interior problem can be solvable if sub-region inside the ROI is recognized or if the ROI is piecewise constant. Eventhough, such cases are not always available and the existence of recognized/currently known subregion inside the ROI is not confirmed in many situations. In this paper, we introduce the use of prior knowledge from probabilistic atlas in solving the interior problem. The atlas is constructed by using a set of computed tomography (CT) images which are scanned in advance for different patients and fitted by Gaussian mixture model (GMM). Then, expectation maximization (EM) algorithm is used to estimate the mixture parameters. Whereas the probabilistic atlas is generated from advance scan of various patients, the prior information is always available and confirmed for all applications. We develop a statistical reconstruction method where the cost function is designed to incorporate the information of the probabilistic atlas. Both experimental results of the simulation and real data indicate that the proposed method remarkably improve the image quality and minimize the effect of DC-shift. Surprisingly, we observe a high quality reconstruction concerning the region outside the ROI.

The identification of blood vessels is an important image processing technique in several medical imaging applications. Detection of blood vessels in computed tomography (CT) images is essential in diagnosis and surgical planning. Computed tomography (CT) is a common imaging modality used for blood vessels imaging. It can provide a high quality images for both diagnosis and treatment purposes. However, it is known that CT is a source of high radiation dose especially for cases required a follow up scans in short time. Reducing the dose is knows to produce statistical noise and artifacts that significantly reduce the image quality. In this paper, we propose a method for three-dimensional (3D) blood vessel detection from CT images reconstructed from small number of projection views. The proposed method is implemented in two stages. First, is a preprocessing stage to reduce noise. In the second stage, we use skeletonization method to extract the centerline of 3D object. Then, we use the blood vessels skeleton to construct a graph model for 3D vascular network. Finally, we use the graph model to detect blood vessels. The proposed method is tested using 3D blood vessel-like phantom and good results are achieved.

Cone-beam computed tomography (CBCT) is known to provide high-quality three-dimensional (3D) images for accurate diagnosis of breast cancer compared to conventional two-dimensional (2D) mammography. Several CBCT techniques have been developed for breast imaging with different imaging setup and geometry configurations. In interventional procedures such as biopsy, it is likely that a 3D image with depth information is required. This motivates us to study the use of a generic purpose non-isocentric C-arm scanner in 3D breast imaging. The imaging equipment used in this study is known to have several design configurations so that its use in 3D tomographic imaging is limited. Recently, we have developed an image reconstruction method, named PA-MAP algorithm, that incorporates prior information obtained from probabilistic atlas to improve the image quality in low-dose CT. The atlas can be generated from a set of earlier scans of different patients to provide a prior information in the form of probabilistic atlas. It is proved that atlas prior can significantly reduce image artifacts in several imaging scenarios with limited data. In this paper, we study the use of PA-MAP algorithm in CBCT exploiting non-isocentric Carm scanner for breast imaging.

Accurate and efficient reconstruction of blood vessels is a common problem in several medical imaging applications. Detection of blood vessels in computed tomography (CT) images is essential in diagnosis and surgical planning. In low-dose CT seniors, the detectability of blood vessels is challenging due to the existence of statistical noise and/or streak artifacts. In this paper, we present a method for blood vessel reconstruction and detection in low-dose CT. Prior information concerning the shape of the blood vessels is incorporated into the image reconstruction process to improve the image quality and vessels detectability. The proposed method is implemented through a simultaneous reconstruction and detection of the vessels, which is the original contribution of this paper. The proposed method is tested using real data and experimental results demonstrate that the proposed method is potentially useful in clinical use.

Sparse-view CT image reconstruction is becoming a potential strategy for radiation dose reduction of CT scans. Compressed sensing (CS) has been utilized to address this problem. Total Variation (TV) minimization, a method which can reduce streak artifacts and preserve object boundaries well, is treated as the most standard approach of CS. However, TV minimization cannot be solved by using classical differentiable optimization techniques such as the gradient method, because the expression of TV (TV norm) is non-differentiable. In early stages, approximated solving methods were proposed by changing TV norm to be differentiable in the way of adding a small constant in TV norm to enable the usage of gradient methods. But this reduces the power of TV in preserving accuracy object boundaries. Subsequently, approaches which can optimize TV norm exactly were proposed based on the convex optimization theory, such as generalizations of the iterative soft-thresholding (GIST) algorithm and Chambolle-Pock algorithm. However, these methods are simultaneous-iterative-type algorithms. It means that their convergence is rather slower compared with row-action-type algorithms. The proposed method, called sparsity-constrained total variation (SCTV), is developed by using the alternating direction method of multipliers (ADMM). On the method we succeeded in solving the main optimization problem by iteratively splitting the problem into processes of row-action-type algebraic reconstruction technique (ART) procedure and TV minimization procedure which can be processed using Chambolle's projection algorithm. Experimental results show that the convergence speed of the proposed method is much faster than the conventional simultaneous iterative methods.

Three-dimensional (3D) computed tomography (CT) imaging is becoming an essential demand in several clinical procedures. Mobile C-arm is a useful imaging tool for image-guided interventional radiology. C-arm systems are provided with X-ray image intensifier (XRII) or flat-panel detectors. Essentially, C-arm CT systems requires scanners with flat-panel detectors for its ability to provide homogenous image quality and improve the resolution of low-contrast subjects compared to those equipped with XRII. However, C-arm systems with XRIIs are widely used in several interventional procedures. Such systems can provide a high quality two-dimensional (2D) fluoroscopic images that facilitates minimal invasive surgery. However, it is unable to provide depth information for 3D imaging due to several factors. First, the gantry of XRII-based C-arms is usually operated manually, where the rotation angle is determined using printed angle scale attached to the scanner gantry. Second, the gantry orbital rotation is normally limited to angular range less than theoretically required for exact 3D reconstruction. Third, considering the offset-scan geometry, which is common configuration in mobile C-arm with XRII, the number of rays passing through field-of-view (FOV) is limited. In this paper, we develop a 3D angiographic imaging system using commercial C-arm system equipped with XRII. First, an in-house made gantry rotation unit is developed to control the scanner orbital rotation. Second, the gantry rotation is traced using inertial measurement unit (IMU) sensor attached to the scanner gantry. Geometry information obtained from IMU sensor are used to define the gantry position in the 3D space and synchronized with detector measurements. The SCAN algorithm is used for the 3D reconstruction and achieved results are of high quality.

This work investigates the problem of image reconstruction from low-dose x-ray computed tomography (CT). Statistical iterative reconstruction is known to provide higher image quality due to the ability to incorporate prior knowledge to the reconstruction method and accurately model the photon statistics. In this paper, we develop a statistical reconstruction method using prior knowledge extracted from probabilistic atlas. First, we use a set of CT images previously scanned of various patients to generate a probabilistic atlas using Gaussian mixture model (GMM). Then, expectation maximization (EM) clustering algorithm is used to estimate the mixture parameters. Probabilistic atlas and mixture model parameters are then used to formulate the image reconstruction cost function. By merging the atlas information and smoothing penalty into the reconstruction procedure, image quality has been remarkably improved.

In computed tomography (CT), statistical iterative reconstruction (SIR) approaches can produce images of higher quality compared to the conventional analytical methods such as filtered backprojection (FBP) algorithm. Effective noise modeling and possibilities to incorporate priors in the image reconstruction problem are the main advantages that lead to continuous development of SIR methods. Oriented by low-dose CT requirements, several methods are recently developed to obtain a high-quality image reconstruction from down-sampled or noisy projection data. In this paper, a new prior information obtained from probabilistic atlas is proposed for low-dose CT image reconstruction.

The main challenge of creating large interactive displays in the operating rooms (ORs) is in the definition of ways that are efficient and easy to learn for the physician. Apart from traditional input methods such as mouse and keyboard, we have developed a multimodal system with two different vision based human-computer interaction (HCI) systems that can simplify the way surgeons interact with the medical images shown on the LCD display. The purpose of this work is to construct a gesture recognition system with a fast, accurate, and easily attainable method. The first system is a laser pointer interaction framework that supports a 2D stroke gesture interface. The recorded laser gestures are recognized using two different algorithms: dynamic time warping (DTW) and one dollar (1$) recognizer. Our experimental results showed that the DTW algorithm performs better with an overall accuracy of 90 %. The second prototype presents an intuitive HCI to manipulate images using freehand gestures. In order to strengthen the gesture recognition process, the system incorporates contextual information to determine the intent of the user of interacting with the large display. Two cameras are used to observe the surgeon’s hand movements to continuously determine and monitor what the surgeon intends to perform. Experimental results showed that the system accuracy is 95 % for recognition with the effect of contextual integration.

Multiphase abdominal CT is an imaging protocol in which the patient is scanned at different phases before and after the injection of a contrast agent. Reconstructed images with different concentrations of contrast material provide useful information for effective detection of abnormalities. However, several scanning during a short period of time eventually increase the patient radiation dose to a remarkable value up to a risky level. Reducing the patient dose by modulating the x-ray tube current or acquiring the projection data through a small number of views are known to degrade the image quality and reduce the possibility to be useful for diagnosis purpose. In this work, we propose a novel multiphase abdominal CT imaging protocol with patient dose reduction and high image quality. The image reconstruction cost function consists of two terms, namely the data fidelity term and penalty term to enforce the anatomical similarity in successive contrast phase reconstruction. The prior information, named phase-induced swap prior (PISP) is computed using total variation minimization of image acquired from different contrast phases. The new method is evaluated through a simulation study using digital abdominal phantom and real data and results are promising.

X-ray C-arm is an important imaging tool in interventional surgery, road-mapping and radiation therapy. It provides accurate description of vascular anatomy and therapy end point. The C-arm scanner produces two-dimensional (2D) x-ray projection data obtained with flat-panel detector by rotating the source around the patient. The number of 2D projections acquired is several hundreds, which results in significant amount of radiation dose. Unlike the conventional fluoroscopic imaging, three-dimensional (3D) C-arm computed tomography (CT) provides more accurate cross-sectional images which are valuable for therapy planning, guidance and evaluation in interventional radiology. However, 3D vascular imaging using the conventional C-arm fluoroscopy is a challenging task. First, the rotation orbit of the C-arm gantry is usually limited to a range less than those of CT scanners. Second, in several commercial models (including the one of consideration in this study), the x-ray source and detector are shifted from the gantry isocenter to enlarge the scanner field-of-view (FOV), which is so-called the offset scan. Finally, it is difficult to acquire sufficient projection views required for stable 3D reconstruction using manually controlled gantry motion. Inspired by the theory of compressed sensing, we developed an image reconstruction algorithm for the conventional angiography C-arm scanners. The main challenge in this image reconstruction problem is the projection data limitations. We consider a small number of views (less than 10 views) acquired from a short orbit with the offset scan geometry. The proposed method is developed using the alternating direction method of multipliers (ADMM) and results obtained from simulated data and real data are encouraging. The proposed method can significantly contribute to the reduction of patient dose and provides a framework to generate 3D vascular images using the conventional C-arm scanners.

New designs of future computed tomography (CT) scanners called sparse-view CT and interior CT have been considered in the CT community. Since these CTs measure only incomplete projection data, a key to put these CT scanners to practical use is a development of advanced image reconstruction methods. After 2000, there was a large progress in this research area briefly summarized as follows. In the sparse-view CT, various image reconstruction methods using the compressed sensing (CS) framework have been developed towards reconstructing clinically feasible images from a reduced number of projection data. In the interior CT, several novel theoretical results on solution uniqueness and solution stability have been obtained thanks to the discovery of a new class of reconstruction methods called differentiated backprojection (DBP). In this paper, we mainly review this progress including mathematical principles of the CS image reconstruction and the DBP image reconstruction for readers unfamiliar with this area. We also show some experimental results from our past research to demonstrate that this progress is not only theoretically elegant but also works in practical imaging situations.

Egypt is witnessing over the next decade many challenges in the field of healthcare, especially with regard to the spread of hepatitis and coincides with the spread of liver hepatocellular carcinoma. Because most cases of liver cancer in Egypt are detected in very late stages, the use of surgical resection, liver transplantation and percutaneous ablative therapies constitutes unsuitable therapeutic options either due to high recurrence rate or unfeasibility. Therapy sessions can be made through the introduction of chemotherapy using a catheter directly into the hepatic artery supplying the tumor guided by angiography imaging system. This method of treatment known to prevent the patient from different problems associated with surgical treatment, but it is still needs to be further improved to maximize the benefits and minimize the risks. Hepatic angiography is an x-ray study of the blood vessels that supply the liver. The procedure uses a catheter that is placed into a blood vessel through a small incision. The catheter is guided using the xray images obtained through the interventional session. During angiography, hepatic arterial supply is usually displayed in one, two or three projections. Mental 3D interpretation of the anatomy is not an easy task. Reaching the target supply artery by the catheter tip is mandatory to obtain satisfactory tumor response and reduce complications and recurrence. This work aims to develop an interactive 3D imaging system of hepatic angiography. The developed system uses a set of 2D images measured over few view angles to reconstruct a full 3D volume of the hepatic arteries. The problem can be thought as a combination of three main approaches. (1) Image reconstruction of 3D artery volume from few number of projections (each is presented as 2D image), (2) automatic detection of the catheter roailmap to the labeled artery which feed the tumor, and (3) interactive system to control and display images using simple gestures of the physician.

This paper provides an overview of the compressed sensing approach and its contribution in the development of reconstruction algorithms in x-ray computed tomography. We briefly review the recent advances in the topic and refer to the related applications. Finally, we discuss the challenges related to the development of the compressed sensing based reconstruction algorithms and possibilities to be used in clinical scanners.

Egypt is witnessing over the next decade many challenges in the field of healthcare, especially with regard to the spread of hepatitis and coincides with the spread of liver hepatocellular carcinoma. Because most cases of liver cancer in Egypt are detected in very late stages, the use of surgical resection, liver transplantation and percutaneous ablative therapies constitutes unsuitable therapeutic options either due to high recurrence rate or unfeasibility. Therapy sessions can be made through the introduction of chemotherapy using a catheter directly into the hepatic artery supplying the tumor guided by angiography imaging system. This method of treatment known to prevent the patient from different problems associated with surgical treatment, but it is still needs to be further improved to maximize the benefits and minimize the risks. Hepatic angiography is an x-ray study of the blood vessels that supply the liver. The procedure uses a catheter that is placed into a blood vessel through a small incision. The catheter is guided using the xray images obtained through the interventional session. During angiography, hepatic arterial supply is usually displayed in one, two or three projections. Mental 3D interpretation of the anatomy is not an easy task. Reaching the target supply artery by the catheter tip is mandatory to obtain satisfactory tumor response and reduce complications and recurrence. This work aims to develop an interactive 3D imaging system of hepatic angiography. The developed system uses a set of 2D images measured over few view angles to reconstruct a full 3D volume of the hepatic arteries. The problem can be thought as a combination of three main approaches. (1) Image reconstruction of 3D artery volume from few number of projections (each is presented as 2D image), (2) automatic detection of the catheter roadmap to the labeled artery which feed the tumor, and (3) interactive system to control and display images using simple gestures of the physician. © 2013 IEEE.

Synchrotron radiation (SR) X-ray micro-computed tomography (CT) is an effective imaging modality for high-resolution investigation of small objects, with several applications in medicine, biology and industry. However, the limited size of the detector field of view (FOV) restricts the sample dimensions to only a few millimeters. When the sample size is larger than the FOV, images reconstructed using conventional methods suffer from DC-shift and low-frequency artifacts. This classical problem is known as the local tomography or the interior problem. In this paper, a statistical iterative reconstruction method is introduced to eliminate image artifacts resulting from the local tomography. The proposed method, which can be used in several SR imaging applications, enables high-resolution SR imaging with superior image quality compared with conventional methods. Real data obtained from different SR micro-CT applications are used to evaluate the proposed method. Results indicate a noteworthy quality improvement in the image reconstructed from the local tomography measurements.

The radiation dose generated from x-ray computed tomography (CT) scans and its responsibility for increasing the risk of malignancy became a major concern in the medical imaging community. Accordingly, investigating possible approaches for image reconstruction from low-dose imaging protocols, which minimize the patient radiation exposure without affecting image quality, has become an issue of interest. Statistical reconstruction (SR) methods are known to achieve a superior image quality compared with conventional analytical methods. Effective physical noise modeling and possibilities of incorporating priors in the image reconstruction problem are the main advantages of the SR methods. Nevertheless, the high computation cost limits its wide use in clinical scanners. This paper presents a framework for SR in x-ray CT when the angular sampling rate of the projection data is low. The proposed framework is based on the fact that, in many CT imaging applications, some physical and anatomical structures and the corresponding attenuation information of the scanned object can be a priori known. Therefore, the x-ray attenuation distribution in some regions of the object can be expected prior to the reconstruction. Under this assumption, the proposed method is developed by incorporating this prior information into the image reconstruction objective function to suppress streak artifacts. We limit the prior information to only a set of intensity values that represent the average intensity of the normal and expected homogeneous regions within the scanned object. This prior information can be easily computed in several x-ray CT applications. Considering the theory of compressed sensing, the objective function is formulated using the ℓ1 norm distance between the reconstructed image and the available intensity priors. Experimental comparative studies applied to simulated data and real data are used to evaluate the proposed method. The comparison indicates a significant improvement in image quality when the proposed method is used.

This paper focuses on fan-beam image reconstruction from free-form X-ray source trajectory in computed tomography (CT). As in major standard image reconstruction methods, the weighting function of the redundant projection data should be carefully considered. The data redundancy reduction principle aims to compute the projection data only one time during image reconstruction. Usually, an X-ray line that passes through the object point intersects with the radiation source trajectory more than once time. For the symmetric closed circular trajectory, because each X-ray line passing through an object point would intersect with the source trajectory twice, the weighting function of 0.5 is used to handle this problem. However, normally there is no known symmetry property for the free-form X-ray source trajectory. In order to estimate the weighting function we have to calculate the number N of intersection between each X-ray line and every object points, then the weighting function is set to be 1/N such that the summation of weighting operator corresponding to a single X-ray line is the unity. However, the calculation of N is difficult and computationally expensive. In this paper we proposed a new scheme for a robust control of the redundant projection data for both closed and open trajectories. Instead of calculation of N, we assign each intersection point a plus or minus sign according to the proposed weighting function. As a result of summation of successive signs, they cancel out each other and finally equal to the unity. Numerical study was performed to evaluate the proposed weighting scheme using standard Shepp-Logan phantom with butterfly trajectory. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

This work investigates the problem of image reconstruction from small number of projection views in x-ray computed tomography (CT) imaging. The number of acquired projection views has a large influence on accuracy and stability of the image reconstruction problem. However, measuring the projection data over small number of views leads to a patient dose reduction and/or minimization of imaging time which become a principal target in many clinical applications. The presented work aims to develop a row-action type reconstruction algorithm that include a priori known information extracted from a reference image. The proposed method is based on the fact that, in many CT imaging applications, some physical and anatomical structures and the corresponding attenuation information of the scanned object can be a priori known. The main idea is to include a distance function consisting of l(p) norm of the reconstructed image into the cost function for image reconstruction. The constrained minimization problem is then transferred to the corresponding non-constrained maximization dual problem using Lagrangian duality. Experimental results indicate that the proposed reconstruction algorithms can effectively reduce the streak artifacts when a simple reference image is used.

This paper presents a framework for row-action type iterative reconstruction from projection data measured over small number of projection views, or sparse projections. Image reconstruction from sparse projections usually suffers from streak artifacts that degrade the image quality. However, this imaging scenario becomes a hot topic of research due its possibilities to reduce patient dose and other benefits in several imaging applications. The motivation behind this work is the use of l(1)/l(0) norm distance to a reference image to select the sparse solution corresponding to the difference between the reconstructed image and the reference image. The concept of Lagrangian duality is used to derive the iterative thresholding framework which can be classified as an ART-like reconstruction method. The work presented here can be thought as a generalization of the previous work in (Li et al., 2004) which mainly focuses on the reconstruction of sparse objects, such as blood vessels. However, most of clinical applications consider imaging of non-sparse objects. The extension to the general case of x-ray computed tomography (CT) imaging, where the target object is non-sparse, is presented here. Experimental data indicates the power of the proposed method in reducing streak artifacts produced from data down-sampling.

X-ray computed tomography (CT) is generally obtained through a reconstruction of object attenuation function from its projection data acquired through many angle views. However, in some cases the number of projection views is limited to only a small number that is theoretically not enough for stable reconstruction. The theory of compressed sensing ( CS) introduce a new framework for solving this problem. In this paper, we present a simple iterative thresholding image reconstruction algorithm that include a minimization of cost function that include Log-likelihood and l(1) norm distance to a reference image. The resulting thresholding function in the reconstruction algorithm is approximated with adaptive function that can preserve low-contrast regions from incorrect thresholding. Moreover, we present a simple approach to handle registration error between the reconstructed image and the reference. This approach is based on using dynamic reference image that generated from previous estimation of the reference image and the current image estimate. The proposed method proved to be robust in image reconstruction from small number of projection views through a simulation study.

Synchrotron radiation (SR) x-ray micro-CT is an effective method for high-resolution imaging of small objects with several applications in biology and industry. However, the detector field of view is tiny, which limits the sample size to a few millimeters. When the sample size is larger than the limited field of view, reconstructed images, using conventional methods, known to suffer from DC-shift and low-frequency artifacts. This problem is known as local tomography or interior problem. In this paper we introduce a statistical iterative image reconstruction method to eliminate image artifacts produced from local tomography. The proposed method can be used in several SR imaging applications to enable a high resolution of the scanned object while preserving the image quality from artifacts produced due to the local tomography problem.

Synchrotron radiation (SR) x-ray micro-CT is an effective method for high-resolution imaging of small objects with several applications in biology and industry. However, the detector field of view is tiny, which limits the sample size to a few millimeters. When the sample size is larger than the limited field of view, reconstructed images, using conventional methods, known to suffer from DC-shift and low-frequency artifacts. This problem is known as local tomography or interior problem. In this paper we introduce a statistical iterative image reconstruction method to eliminate image artifacts produced from local tomography. The proposed method can be used in several SR imaging applications to enable a high resolution of the scanned object while preserving the image quality from artifacts produced due to the local tomography problem. © 2011 IEEE.

X-ray computed tomography (CT) is generally obtained through a reconstruction of object attenuation function from its projection data acquired through many angle views. However, in some cases the number of projection views is limited to only a small number that is theoretically not enough for stable reconstruction. The theory of compressed sensing (CS) introduce a new framework for solving this problem. In this paper, we present a simple iterative thresholding image reconstruction algorithm that include a minimization of cost function that include Log-likelihood and '1 norm distance to a reference image. The resulting thresholding function in the reconstruction algorithm is approximated with adaptive function that can preserve low-contrast regions from incorrect thresholding. Moreover, we present a simple approach to handle registration error between the reconstructed image and the reference. This approach is based on using dynamic reference image that generated from previous estimation of the reference image and the current image estimate. The proposed method proved to be robust in image reconstruction from small number of projection views through a simulation study. © 2011 IEEE.

This work investigates the problem of image reconstruction from small number of projection views in x-ray computed tomography (CT) imaging. The number of acquired projection views has a large influence on accuracy and stability of the image reconstruction problem. However, measuring the projection data over small number of views leads to a patient dose reduction and/or minimization of imaging time which become a principal target in many clinical applications. The presented work aims to develop a row-action type reconstruction algorithm that include a priori known information extracted from a reference image. The proposed method is based on the fact that, in many CT imaging applications, some physical and anatomical structures and the corresponding attenuation information of the scanned object can be a priori known. The main idea is to include a distance function consisting of ℓ norm of the reconstructed image into the cost function for image reconstruction. The constrained minimization problem is then transferred to the corresponding non-constrained maximization dual problem using Lagrangian duality. Experimental results indicate that the proposed reconstruction algorithms can effectively reduce the streak artifacts when a simple reference image is used. © 2011 IEEE. p

This paper presents a framework for row-action type iterative reconstruction from projection data measured over small number of projection views, or sparse projections. Image reconstruction from sparse projections usually suffers from streak artifacts that degrade the image quality. However, this imaging scenario becomes a hot topic of research due its possibilities to reduce patient dose and other benefits in several imaging applications. The motivation behind this work is the use of ℓ /ℓ norm distance to a reference image to select the sparse solution corresponding to the difference between the reconstructed image and the reference image. The concept of Lagrangian duality is used to derive the iterative thresholding framework which can be classified as an ART-like reconstruction method. The work presented here can be thought as a generalization of the previous work in (Li et al., 2004) which mainly focuses on the reconstruction of sparse objects, such as blood vessels. However, most of clinical applications consider imaging of non-sparse objects. The extension to the general case of x-ray computed tomography (CT) imaging, where the target object is non-sparse, is presented here. Experimental data indicates the power of the proposed method in reducing streak artifacts produced from data down-sampling. © 2011 IEEE. 1 0

Recently, the accurate Region-of-Interest (ROI) reconstruction from truncated projection data has been proved to be theoretically possible under few restrictions. One of these restrictions is the requirement of a priori knowledge of the object on a limited region inside the ROI. This region can be defined as a set of pixels having known intensity values inside or outside the object. In particular the knowledge of the background region outside the object is called the Object Support (OS). OS is defined as the region inside the scanner view where the object is certainly inside, therefore, the region outside the OS has zero intensity value. Using the OS constraint in image reconstruction when the projection data suffers from truncation has a large influence on image quality. The OS constraint is usually enforced by selecting a region slightly larger than the true object and set the pixel values outside this region to zero. However, the lack of the exact OS knowledge introduces DC-shift and lowfrequency artifacts in the reconstructed image. In many clinical imaging scenarios, the prior knowledge of the exact OS is not available or requires extra efforts for an accurate estimation. In this paper, we propose an iterative reconstruction algorithm that automatically detects the OS during the reconstruction. The cost function for image reconstruction is modified by including a thresholding function in the form of the ℓ norm, the OS is estimated by the thresholding function and the OS converges toward the accurate one. Simulation results with different ROI and object configurations indicate a significant reduction in the artifacts by using the proposed algorithm. ©2008 IEEE. o

This paper deals with reconstructing a small region-of-interest (ROI) of an object from non-truncated or truncated projection data by using statistical (iterative) methods. The imaging situations which we consider here can be classified into the following two scenarios. The first scenario is the case where non-truncated projection data is measured but only a small ROI needs to be reconstructed. The second scenario is the case where only truncated projection data passing through a ROE is measured and only the ROI needs to be reconstructed. When we blindly apply statistical methods to such cases, as described in the literature, the image matrix during the iteration must be large enough to contain the whole object (not only the ROI) even if the ROI to be reconstructed is small. This significantly increases the computational cost (approximately by a factor of the area of the whole object divided by the area of the ROI). Solutions to this problem have been investigated only very recently. We develop practical statistical models for the ROI reconstruction problem under the assumption that an initial estimate of the ROI image by an analytical method such as FBP or DBP is available. Thanks to a more rigorous treatment compared to the previous work, the proposed models are more accurate leading to significantly better noise properties as we demonstrate in the simulation study. Also, extending the proposed models to the 3D long object problem is discussed. © 2007 IEEE.