Head Motion Correction Based on Filtered Backprojection in Helical CT Scanning.

Head motion may unexpectedly occur during a CT scan. It thereby results in motion artifacts in a reconstructed image and may lead to a false diagnosis or a failure of diagnosis. To alleviate this motion problem, as a hardware approach, increasing the gantry rotation speed or using an immobilization device is usually considered. These approaches, however, cannot completely resolve the motion problem. Hence, motion estimation (ME) and compensation for it have been explored as a software approach instead. In this paper, adopting the latter approach, we propose a head motion correction algorithm in helical CT scanning, based on filtered backprojection (FBP). For the motion correction, we first introduce a new motion-compensated (MC) reconstruction scheme based on FBP, which is applicable to helical scanning. We then estimate the head motion parameters by using an iterative nonlinear optimization algorithm, or the L-BFGS. Note here that an objective function for the optimization is defined on reconstructed images in each iteration, which are obtained by using the proposed MC reconstruction scheme. Using the estimated motion parameters, we then obtain the final MC reconstructed image. Using numerical and physical phantom datasets along with simulated head motions, we demonstrate that the proposed algorithm can provide significantly improved quality to MC reconstructed images by alleviating motion artifacts.

Dynamic focal plane estimation for dental panoramic radiography.

PURPOSE: The digital panoramic radiography is widely used in dental clinics and provides the anatomical information of the intraoral structure along the predefined arc-shaped path. Since the intraoral structure varies depending on the patient, however, it is nearly impossible to design a common and static focal path or plane fitted to the dentition of all patients. In response, we introduce an imaging algorithm for digital panoramic radiography that can provide a focused panoramic radiographic image for all patients, by automatically estimating the best focal plane for each patient. METHODS: The aim of this study is to improve the image quality of dental panoramic radiography based on a three-dimensional (3D) dynamic focal plane. The plane is newly introduced to represent the arbitrary 3D intraoral structure of each patient. The proposed algorithm consists of three steps: preprocessing, focal plane estimation, and image reconstruction. We first perform preprocessing to improve the accuracy of focal plane estimation. The 3D dynamic focal plane is then estimated by adjusting the position of the image plane so that object boundaries in the neighboring projection data are aligned or focused on the plane. Finally, a panoramic radiographic image is reconstructed using the estimated dynamic focal plane. RESULTS: The proposed algorithm is evaluated using a numerical phantom dataset and four clinical human datasets. In order to examine the image quality improvement owing to the proposed algorithm, we generate panoramic radiographic images based on a conventional static focal plane and estimated 3D dynamic focal planes, respectively. Experimental results show that the image quality is dramatically improved for all datasets using the 3D dynamic focal planes that are estimated from the proposed algorithm. CONCLUSIONS: We propose an imaging algorithm for digital panoramic radiography that provides improved image quality by estimating dynamic focal planes fitted to each individual patient's intraoral structure.

Development of Positron Emission Tomography With Wobbling and Zooming for High Sensitivity and High-Resolution Molecular Imaging.

Demands for in-vivo human molecular imaging with high resolution and high sensitivity in positron emission tomography (PET) require several new design formulae. A classical problem of the PET design, however, was the trade-off between sensitivity and resolution. To satisfy both requirements, the brain-body convertible PET with wobbling and zooming is proposed. The features of this new proposed system are wobble sampling for high-resolution imaging and zooming mode for high sensitivity, especially for the brain dedicated imaging. For the high resolution, wobbling with a linear interpolation and line spread function (LSF) deconvolution reconstruction algorithm was introduced. The result of the proposed system provided resolution up to 1.56 mm full width at half maximum (FWHM) in the brain mode and resulting in the detector-to-resolution ratio (DRR) was 2.47. For both brain phantom and in-vivo rat brain imaging, the proposed system demonstrated superior image quality compared with the commercial PET systems. The newly designed PET with wobbling and zooming also demonstrated the possibility of developing practically usable high-resolution human brain PET-MRI fusion system, especially for the neuroscience research.

Cardiac Motion Correction for Helical CT Scan With an Ordinary Pitch.

Cardiac X-ray computed tomography (CT) imaging is still challenging due to the cardiac motion during CT scanning, which leads to the presence of motion artifacts in the reconstructed image. In response, many cardiac X-ray CT imaging algorithms have been proposed, based on motion estimation (ME) and motion compensation (MC), to improve the image quality by alleviating the motion artifacts in the reconstructed image. However, these ME/MC algorithms are mainly based on an axial scan or a low-pitch helical scan. In this paper, we propose a ME/MC-based cardiac imaging algorithm for the data set acquired from a helical scan with an ordinary pitch of around 1.0 so as to obtain the whole cardiac image within a single scan of short time without ECG gating. In the proposed algorithm, a sequence of partial angle reconstructed (PAR) images is generated by using consecutive parts of the sinogram, each of which has a small angular span. Subsequently, an initial 4-D motion vector field (MVF) is obtained using multiple pairs of conjugate PAR images. The 4-D MVF is then refined based on an image quality metric so as to improve the quality of the motion-compensated image. Finally, a time-resolved cardiac image is obtained by performing motion-compensated image reconstruction by using the refined 4-D MVF. Using digital XCAT phantom data sets and a human data set commonly obtained via a helical scan with a pitch of 1.0, we demonstrate that the proposed algorithm significantly improves the image quality by alleviating motion artifacts.

Subject-specific Brain Tumor Growth Modelling via An Efficient Bayesian Inference Framework.

An accurate prediction of brain tumor progression is crucial for optimized treatment of the tumors. Gliomas are primarily treated by combining surgery, external beam radiotherapy, and chemotherapy. Among them, radiotherapy is a non-invasive and effective therapy, and an understanding of tumor growth will allow better therapy planning. In particular, estimating parameters associated with tumor growth, such as the diffusion coefficient and proliferation rate, is crucial to accurately characterize physiology of tumor growth and to develop predictive models of tumor infiltration and recurrence. Accurate parameter estimation, however, is a challenging task due to inaccurate tumor boundaries and the approximation of the tumor growth model. Here, we introduce a Bayesian framework for a subject-specific tumor growth model that estimates the tumor parameters effectively. This is achieved by using an improved elliptical slice sampling method based on an adaptive sample region. Experimental results on clinical data demonstrate that the proposed method provides a higher acceptance rate, while preserving the parameter estimation accuracy, compared with other state-of-the-art methods such as Metropolis-Hastings and elliptical slice sampling without any modification. Our approach has the potential to provide a method to individualize therapy, thereby offering an optimized treatment.

Head motion correction based on filtered backprojection for x-ray CT imaging.

PURPOSE: For head x-ray CT imaging, the head needs to remain motionless during the scan. In clinical practice, however, head motion is sometimes unavoidable depending on the patient. The motion can occur abruptly during the scan and can be unpredictable. It thereby causes motion artifacts such as tissue blurring or doubled edges around the skull area. To mitigate this problem, we propose a 3D head motion estimation (ME) and compensation algorithm based on filtered backprojection. METHODS: If a patient moves his or her head during the scan, a motion-corrupted sinogram is obtained. Modeling the head motion as a 3D rigid transformation, we develop a motion-compensated (MC) reconstruction algorithm based on the FDK algorithm. To determine the head motion of a rigid transformation, we propose two optimization-based ME schemes depending on the degree of head motion, both of which are performed by updating motion parameters and the corresponding MC reconstructed image alternatively until the proposed cost function is minimized for the MC reconstructed image. In particular, to improve the robustness in the case of large motion, we propose attaching a fiducial marker to the head so that more reliable motion parameters can be initialized by determining the marker position, before the optimization. To evaluate the proposed algorithm, a numerical phantom with realistic, continuous, and smoothly varying motion, and a moving physical phantom are used with a gantry rotation time of 1 s. RESULTS: In the simulation using a numerical phantom and in the experiment using a physical phantom, the proposed algorithm provides well-restored 3D motion-compensated images in both cases of small and large motion. In particular, in the case of large motion of the physical phantom, using a fiducial marker, we obtain remarkable improvement of image quality in cerebral arteries and a lesion as well as the skull. Quantitative evaluations using the image sharpness and root-mean-square error also show noticeable improvement of image quality in both simulations and experiments. CONCLUSIONS: We propose a framework for head motion correction in an axial CT scan, which consists of motion estimation and compensation steps. Two image-based ME algorithms for rigid motion tracking are developed according to the degree of head motion. The estimated motion information is then used for MC image reconstruction. Both motion estimation and compensation algorithms are based on computationally efficient filtered backprojection. Excellent performance of the proposed framework is illustrated by means of simulations using a numerical phantom and experiments using a physical phantom.

Real-time respiratory phase matching between 2D fluoroscopic images and 3D CT images for precise percutaneous lung biopsy.

PURPOSE: A 3D CT image is used along with real-time 2D fluoroscopic images in the state-of-the-art cone-beam CT system to guide percutaneous lung biopsy (PLB). To improve the guiding accuracy by compensating for respiratory motion, we propose an algorithm for real-time matching of 2D fluoroscopic images to multiple 3D CT images of different respiratory phases that is robust to the small movement and deformation due to cardiac motion. METHODS: Based on the transformations obtained from nonrigid registration between two 3D CT images acquired at expiratory and inspiratory phases, we first generate sequential 3D CT images (or a 4D CT image) and the corresponding 2D digitally reconstructed radiographs (DRRs) of vessels. We then determine 3D CT images corresponding to each real-time 2D fluoroscopic image, by matching the 2D fluoroscopic image to a 2D DRR. RESULTS: Quantitative evaluations performed with 20 clinical datasets show that registration errors of anatomical features between a 2D fluoroscopic image and its matched 2D DRR are less than 3 mm on average. Registration errors of a target lesion are determined to be roughly 3 mm on average for 10 datasets. CONCLUSIONS: We propose a real-time matching algorithm to compensate for respiratory motion between a 2D fluoroscopic image and 3D CT images of the lung, regardless of cardiac motion, based on a newly improved matching measure. The proposed algorithm can improve the accuracy of a guiding system for the PLB by providing 3D images precisely registered to 2D fluoroscopic images in real-time, without time-consuming respiratory-gated or cardiac-gated CT images.

Cardiac Image Reconstruction via Nonlinear Motion Correction Based on Partial Angle Reconstructed Images.

Even though the X-ray Computed Tomography (CT) scan is considered suitable for fast imaging, motion-artifact-free cardiac imaging is still an important issue, because the gantry rotation speed is not fast enough compared with the heart motion. To obtain a heart image with less motion artifacts, a motion estimation (ME) and motion compensation (MC) approach is usually adopted. In this paper, we propose an ME/MC algorithm that can estimate a nonlinear heart motion model from a sinogram with a rotation angle of less than 360°. In this algorithm, we first assume the heart motion to be nonrigid but linear, and thereby estimate an initial 4-D motion vector field (MVF) during a half rotation by using conjugate partial angle reconstructed images, as in our previous ME/MC algorithm. We then refine the MVF to determine a more accurate nonlinear MVF by maximizing the information potential of a motion-compensated image. Finally, MC is performed by incorporating the determined MVF into the image reconstruction process, and a time-resolved heart image is obtained. By using a numerical phantom, a physical cardiac phantom, and an animal data set, we demonstrate that the proposed algorithm can noticeably improve the image quality by reducing motion artifacts throughout the image.

Denoising of polychromatic CT images based on their own noise properties.

PURPOSE: Because of high diagnostic accuracy and fast scan time, computed tomography (CT) has been widely used in various clinical applications. Since the CT scan introduces radiation exposure to patients, however, dose reduction has recently been recognized as an important issue in CT imaging. However, low-dose CT causes an increase of noise in the image and thereby deteriorates the accuracy of diagnosis. In this paper, the authors develop an efficient denoising algorithm for low-dose CT images obtained using a polychromatic x-ray source. The algorithm is based on two steps: (i) estimation of space variant noise statistics, which are uniquely determined according to the system geometry and scanned object, and (ii) subsequent novel conversion of the estimated noise to Gaussian noise so that an existing high performance Gaussian noise filtering algorithm can be directly applied to CT images with non-Gaussian noise. METHODS: For efficient polychromatic CT image denoising, the authors first reconstruct an image with the iterative maximum-likelihood polychromatic algorithm for CT to alleviate the beam-hardening problem. We then estimate the space-variant noise variance distribution on the image domain. Since there are many high performance denoising algorithms available for the Gaussian noise, image denoising can become much more efficient if they can be used. Hence, the authors propose a novel conversion scheme to transform the estimated space-variant noise to near Gaussian noise. In the suggested scheme, the authors first convert the image so that its mean and variance can have a linear relationship, and then produce a Gaussian image via variance stabilizing transform. The authors then apply a block matching 4D algorithm that is optimized for noise reduction of the Gaussian image, and reconvert the result to obtain a final denoised image. To examine the performance of the proposed method, an XCAT phantom simulation and a physical phantom experiment were conducted. RESULTS: Both simulation and experimental results show that, unlike the existing denoising algorithms, the proposed algorithm can effectively reduce the noise over the whole region of CT images while preventing degradation of image resolution. CONCLUSIONS: To effectively denoise polychromatic low-dose CT images, a novel denoising algorithm is proposed. Because this algorithm is based on the noise statistics of a reconstructed polychromatic CT image, the spatially varying noise on the image is effectively reduced so that the denoised image will have homogeneous quality over the image domain. Through a simulation and a real experiment, it is verified that the proposed algorithm can deliver considerably better performance compared to the existing denoising algorithms.

Cardiac motion correction based on partial angle reconstructed images in x-ray CT.

PURPOSE: Cardiac x-ray CT imaging is still challenging due to heart motion, which cannot be ignored even with the current rotation speed of the equipment. In response, many algorithms have been developed to compensate remaining motion artifacts by estimating the motion using projection data or reconstructed images. In these algorithms, accurate motion estimation is critical to the compensated image quality. In addition, since the scan range is directly related to the radiation dose, it is preferable to minimize the scan range in motion estimation. In this paper, the authors propose a novel motion estimation and compensation algorithm using a sinogram with a rotation angle of less than 360°. The algorithm estimates the motion of the whole heart area using two opposite 3D partial angle reconstructed (PAR) images and compensates the motion in the reconstruction process. METHODS: A CT system scans the thoracic area including the heart over an angular range of 180° + α + ß, where α and ß denote the detector fan angle and an additional partial angle, respectively. The obtained cone-beam projection data are converted into cone-parallel geometry via row-wise fan-to-parallel rebinning. Two conjugate 3D PAR images, whose center projection angles are separated by 180°, are then reconstructed with an angular range of ß, which is considerably smaller than a short scan range of 180° + α. Although these images include limited view angle artifacts that disturb accurate motion estimation, they have considerably better temporal resolution than a short scan image. Hence, after preprocessing these artifacts, the authors estimate a motion model during a half rotation for a whole field of view via nonrigid registration between the images. Finally, motion-compensated image reconstruction is performed at a target phase by incorporating the estimated motion model. The target phase is selected as that corresponding to a view angle that is orthogonal to the center view angles of two conjugate PAR images. To evaluate the proposed algorithm, digital XCAT and physical dynamic cardiac phantom datasets are used. The XCAT phantom datasets were generated with heart rates of 70 and 100 bpm, respectively, by assuming a system rotation time of 300 ms. A physical dynamic cardiac phantom was scanned using a slowly rotating XCT system so that the effective heart rate will be 70 bpm for a system rotation speed of 300 ms. RESULTS: In the XCAT phantom experiment, motion-compensated 3D images obtained from the proposed algorithm show coronary arteries with fewer motion artifacts for all phases. Moreover, object boundaries contaminated by motion are well restored. Even though object positions and boundary shapes are still somewhat different from the ground truth in some cases, the authors see that visibilities of coronary arteries are improved noticeably and motion artifacts are reduced considerably. The physical phantom study also shows that the visual quality of motion-compensated images is greatly improved. CONCLUSIONS: The authors propose a novel PAR image-based cardiac motion estimation and compensation algorithm. The algorithm requires an angular scan range of less than 360°. The excellent performance of the proposed algorithm is illustrated by using digital XCAT and physical dynamic cardiac phantom datasets.

Dynamic PET reconstruction using temporal patch-based low rank penalty for ROI-based brain kinetic analysis.

Dynamic positron emission tomography (PET) is widely used to measure changes in the bio-distribution of radiopharmaceuticals within particular organs of interest over time. However, to retain sufficient temporal resolution, the number of photon counts in each time frame must be limited. Therefore, conventional reconstruction algorithms such as the ordered subset expectation maximization (OSEM) produce noisy reconstruction images, thus degrading the quality of the extracted time activity curves (TACs). To address this issue, many advanced reconstruction algorithms have been developed using various spatio-temporal regularizations. In this paper, we extend earlier results and develop a novel temporal regularization, which exploits the self-similarity of patches that are collected in dynamic images. The main contribution of this paper is to demonstrate that the correlation of patches can be exploited using a low-rank constraint that is insensitive to global intensity variations. The resulting optimization framework is, however, non-Lipschitz and nonconvex due to the Poisson log-likelihood and low-rank penalty terms. Direct application of the conventional Poisson image deconvolution by an augmented Lagrangian (PIDAL) algorithm is, however, problematic due to its large memory requirements, which prevents its parallelization. Thus, we propose a novel optimization framework using the concave-convex procedure (CCCP)

Position tracking of moving liver lesion based on real-time registration between 2D ultrasound and 3D preoperative images.

PURPOSE: Registration between 2D ultrasound (US) and 3D preoperative magnetic resonance (MR) (or computed tomography, CT) images has been studied recently for US-guided intervention. However, the existing techniques have some limits, either in the registration speed or the performance. The purpose of this work is to develop a real-time and fully automatic registration system between two intermodal images of the liver, and subsequently an indirect lesion positioning/tracking algorithm based on the registration result, for image-guided interventions. METHODS: The proposed position tracking system consists of three stages. In the preoperative stage, the authors acquire several 3D preoperative MR (or CT) images at different respiratory phases. Based on the transformations obtained from nonrigid registration of the acquired 3D images, they then generate a 4D preoperative image along the respiratory phase. In the intraoperative preparatory stage, they properly attach a 3D US transducer to the patient's body and fix its pose using a holding mechanism. They then acquire a couple of respiratory-controlled 3D US images. Via the rigid registration of these US images to the 3D preoperative images in the 4D image, the pose information of the fixed-pose 3D US transducer is determined with respect to the preoperative image coordinates. As feature(s) to use for the rigid registration, they may choose either internal liver vessels or the inferior vena cava. Since the latter is especially useful in patients with a diffuse liver disease, the authors newly propose using it. In the intraoperative real-time stage, they acquire 2D US images in real-time from the fixed-pose transducer. For each US image, they select candidates for its corresponding 2D preoperative slice from the 4D preoperative MR (or CT) image, based on the predetermined pose information of the transducer. The correct corresponding image is then found among those candidates via real-time 2D registration based on a gradient-based similarity measure. Finally, if needed, they obtain the position information of the liver lesion using the 3D preoperative image to which the registered 2D preoperative slice belongs. RESULTS: The proposed method was applied to 23 clinical datasets and quantitative evaluations were conducted. With the exception of one clinical dataset that included US images of extremely low quality, 22 datasets of various liver status were successfully applied in the evaluation. Experimental results showed that the registration error between the anatomical features of US and preoperative MR images is less than 3 mm on average. The lesion tracking error was also found to be less than 5 mm at maximum. CONCLUSIONS: A new system has been proposed for real-time registration between 2D US and successive multiple 3D preoperative MR/CT images of the liver and was applied for indirect lesion tracking for image-guided intervention. The system is fully automatic and robust even with images that had low quality due to patient status. Through visual examinations and quantitative evaluations, it was verified that the proposed system can provide high lesion tracking accuracy as well as high registration accuracy, at performance levels which were acceptable for various clinical applications.

Ultra-fast hybrid CPU-GPU multiple scatter simulation for 3-D PET.

Scatter correction is very important in 3-D PET reconstruction due to a large scatter contribution in measurements. Currently, one of the most popular methods is the so-called single scatter simulation (SSS), which considers single Compton scattering contributions from many randomly distributed scatter points. The SSS enables a fast calculation of scattering with a relatively high accuracy; however, the accuracy of SSS is dependent on the accuracy of tail fitting to find a correct scaling factor, which is often difficult in low photon count measurements. To overcome this drawback as well as to improve accuracy of scatter estimation by incorporating multiple scattering contribution, we propose a multiple scatter simulation (MSS) based on a simplified Monte Carlo (MC) simulation that considers photon migration and interactions due to photoelectric absorption and Compton scattering. Unlike the SSS, the MSS calculates a scaling factor by comparing simulated prompt data with the measured data in the whole volume, which enables a more robust estimation of a scaling factor. Even though the proposed MSS is based on MC, a significant acceleration of the computational time is possible by using a virtual detector array with a larger pitch by exploiting that the scatter distribution varies slowly in spatial domain. Furthermore, our MSS implementation is nicely fit to a parallel implementation using graphic processor unit (GPU). In particular, we exploit a hybrid CPU-GPU technique using the open multiprocessing and the compute unified device architecture, which results in 128.3 times faster than using a single CPU. Overall, the computational time of MSS is 9.4 s for a high-resolution research tomograph (HRRT) system. The performance of the proposed MSS is validated through actual experiments using an HRRT.

Frame rate up conversion based on variational image fusion.

This paper presents a new framework for motion compensated frame rate up conversion (FRUC) based on variational image fusion. The proposed algorithm consists of two steps: 1) generation of multiple intermediate interpolated frames and 2) fusion of those intermediate frames. In the first step, we determine four different sets of the motion vector field using four neighboring frames. We then generate intermediate interpolated frames corresponding to the determined four sets of the motion vector field, respectively. Multiple sets of the motion vector field are used to solve the occlusion problem in motion estimation. In the second step, the four intermediate interpolated frames are fused into a single frame via a variational image fusion process. For effective fusion, we determine fusion weights for each intermediate interpolated frame by minimizing the energy, which consists of a weighted-L1-norm based data energy and gradient-driven smoothness energy. Experimental results demonstrate that the proposed algorithm improves the performance of FRUC compared with the existing algorithms.

Motion-compensated PET image reconstruction with respiratory-matched attenuation correction using two low-dose inhale and exhale CT images.

Positron emission tomography (PET) is widely used for diagnosis and follow up assessment of radiotherapy. However, thoracic and abdominal PET suffers from false staging and incorrect quantification of the radioactive uptake of lesion(s) due to respiratory motion. Furthermore, respiratory motion-induced mismatch between a computed tomography (CT) attenuation map and PET data often leads to significant artifacts in the reconstructed PET image. To solve these problems, we propose a unified framework for respiratory-matched attenuation correction and motion compensation of respiratory-gated PET. For the attenuation correction, the proposed algorithm manipulates a 4D CT image virtually generated from two low-dose inhale and exhale CT images, rather than a real 4D CT image which significantly increases the radiation burden on a patient. It also utilizes CT-driven motion fields for motion compensation. To realize the proposed algorithm, we propose an improved region-based approach for non-rigid registration between body CT images, and we suggest a selection scheme of 3D CT images that are respiratory-matched to each respiratory-gated sinogram. In this work, the proposed algorithm was evaluated qualitatively and quantitatively by using patient datasets including lung and/or liver lesion(s). Experimental results show that the method can provide much clearer organ boundaries and more accurate lesion information than existing algorithms by utilizing two low-dose CT images.

Contrast-enhanced fusion of multi-sensor images using subband-decomposed multiscale retinex.

In this paper, we propose a novel pixel-level multi-sensor image fusion algorithm with simultaneous contrast enhancement. In order to accomplish both image fusion and contrast enhancement simultaneously, we suggest a modified framework of the subband-decomposed multiscale retinex (SDMSR), our previous contrast enhancement algorithm. This framework is based on a fusion strategy that reflects the multiscale characteristics of the SDMSR well. We first apply two complementary intensity transfer functions to source images in order to effectively utilize hidden information in both shadows and highlights in the fusion process. We then decompose retinex outputs into nearly nonoverlapping spectral subbands. The decomposed retinex outputs are then fused subband-by-subband, by using global weighting as well as local weighting to overcome the limitations of the pixel-based fusion approach. After the fusion process, we apply a space-varying subband gain to each fused subband-decomposed retinex output according to the subband characteristic so that the contrast of the fused image can be effectively enhanced. In addition, in order to effectively manage artifacts and noise, we make the degree of enhancement of fused details adjustable by improving a detail adjustment function. From experiments with various multi-sensor image pairs, the results clearly demonstrate that even if source images have poor contrast, the proposed algorithm makes it possible to generate a fused image with highly enhanced contrast while preserving visually salient information contained in the source images.

Automatic registration between 3D intra-operative ultrasound and pre-operative CT images of the liver based on robust edge matching.

The registration of a three-dimensional (3D) ultrasound (US) image with a computed tomography (CT) or magnetic resonance image is beneficial in various clinical applications such as diagnosis and image-guided intervention of the liver. However, conventional methods usually require a time-consuming and inconvenient manual process for pre-alignment, and the success of this process strongly depends on the proper selection of initial transformation parameters. In this paper, we present an automatic feature-based affine registration procedure of 3D intra-operative US and pre-operative CT images of the liver. In the registration procedure, we first segment vessel lumens and the liver surface from a 3D B-mode US image. We then automatically estimate an initial registration transformation by using the proposed edge matching algorithm. The algorithm finds the most likely correspondences between the vessel centerlines of both images in a non-iterative manner based on a modified Viterbi algorithm. Finally, the registration is iteratively refined on the basis of the global affine transformation by jointly using the vessel and liver surface information. The proposed registration algorithm is validated on synthesized datasets and 20 clinical datasets, through both qualitative and quantitative evaluations. Experimental results show that automatic registration can be successfully achieved between 3D B-mode US and CT images even with a large initial misalignment.

Sinogram-based super-resolution in PET.

Spatial resolution is intrinsically limited in positron emission tomography (PET) systems, mainly due to the crystal width. To increase the spatial resolution for a given crystal width, mechanical movements such as wobble and dichotomic motions are introduced to the PET systems. However, multiple sinograms obtained through such movements provide oversampled data. In this paper, to increase the spatial resolution, we present a novel super-resolution (SR) scheme that employs multiple sinograms. For SR, we first propose a blur kernel estimation scheme through a Monte Carlo simulation. Based on the estimated blur kernel, we adopt a maximum a posteriori expectation maximization method in estimating a high-resolution sinogram from multiple low-resolution sinograms. The proposed algorithm provides noticeable improvement of the spatial resolution in real PET images.

Non-rigid registration between 3D ultrasound and CT images of the liver based on intensity and gradient information.

In order to utilize both ultrasound (US) and computed tomography (CT) images of the liver concurrently for medical applications such as diagnosis and image-guided intervention, non-rigid registration between these two types of images is an essential step, as local deformation between US and CT images exists due to the different respiratory phases involved and due to the probe pressure that occurs in US imaging. This paper introduces a voxel-based non-rigid registration algorithm between the 3D B-mode US and CT images of the liver. In the proposed algorithm, to improve the registration accuracy, we utilize the surface information of the liver and gallbladder in addition to the information of the vessels inside the liver. For an effective correlation between US and CT images, we treat those anatomical regions separately according to their characteristics in US and CT images. Based on a novel objective function using a 3D joint histogram of the intensity and gradient information, vessel-based non-rigid registration is followed by surface-based non-rigid registration in sequence, which improves the registration accuracy. The proposed algorithm is tested for ten clinical datasets and quantitative evaluations are conducted. Experimental results show that the registration error between anatomical features of US and CT images is less than 2 mm on average, even with local deformation due to different respiratory phases and probe pressure. In addition, the lesion registration error is less than 3 mm on average with a maximum of 4.5 mm that is considered acceptable for clinical applications.

Image resolution improvement based on sinogram super-resolution in PET.

One of the limits of PET imaging is the low spatial resolution due to a predetermined detector width. To overcome this limit, we may increase the number of samples by using the wobbling motion. Since the line spread function (LSF) of the sinogram is determined by the detector width, however, the increase of the number of samples is not sufficient to improve the sinogram resolution. In this paper, based on oversampled data obtained from the wobbling motion, we propose a novel and efficient super-resolution (SR) scheme for the sinogram. Since the proposed SR scheme adopts the penalized expectation maximization (EM) algorithm, it guarantees non-negative values of the super-resolved sinogram data. Through the experiments, we demonstrate that the proposed SR scheme can noticeably improve the spatial image resolution.