Three-dimensional computed tomographic reconstruction using a C-arm mounted XRII: image-based correction of gantry motion nonidealities

The image quality of 3D reconstructions produced using a C-arm mounted XRII depends on precise determination of the geometric parameters that describe the detector system in the laboratory frame of reference. We have designed a simplified calibration system that depends on images of a metal sphere, acquired during rotation of the gantry through 200 degrees. Angle-dependent shift corrections are obtained, accounting for nonideal motion in two directions: perpendicular to the axis of rotation and tangential to the circular trajectory (tau), and parallel to the axis of rotation (xi). Projection images are corrected prior to reconstruction using a simple shift-interpolation algorithm. We show that the motion of the gantry is highly reproducible during acquisitions within one day (mean standard deviation in tau and xi is 0.11 mm and 0.08 mm, respectively), and over 21 months (mean standard deviation in tau and xi is 0.10 mm and 0.06 mm, respectively). Reconstruction of a small-bead phantom demonstrates uniformity of the correction algorithm over the full volume of the reconstruction [standard deviation of full-width-half-maximum of the beads is approximately 0.25 pixels (0.13 mm) over the volume of reconstruction]. Our approach provides a simple correction technique that can be applied when trajectory deviations are significant relative to the pixel size of the detector but small relative to the detector field of view, and when the fan angle of the acquisition geometry is small (<20 degrees). A comparison with other calibration techniques in the literature is provided.     

Interslice coding for medical three-dimensional images using an adaptive mode selection technique in wavelet transform domain

In this article the authors propose a novel interslice coding algorithm especially appropriate for medical 3-dimensional (3D) images. The proposed algorithm is based on a video coding algorithm using motion estimation/compensation and transform coding. In the algorithm, warping is adopted for motion compensation. Then, by using adaptive mode selection, an MC residual image and original image are mixed up in the wavelet transform domain for improvement in coding performance. The mixed image is then compressed by the zerotree coding method. It is proven that the adaptive mode selection technique in the wavelet transform domain is very useful for medical 3D image coding. Simulation results show that the proposed scheme provides good performance, regardless of interslice distance, and is prospective for medical 3D image compression.     

Real-time 3D surface-image-guided beam setup in radiotherapy of breast cancer

We describe an approach for external beam radiotherapy of breast cancer that utilizes the three-dimensional (3D) surface information of the breast. The surface data of the breast are obtained from a 3D optical camera that is rigidly mounted on the ceiling of the treatment vault. This 3D camera utilizes light in the visible range therefore it introduces no ionization radiation to the patient. In addition to the surface topographical information of the treated area, the camera also captures gray-scale information that is overlaid on the 3D surface image. This allows us to visualize the skin markers and automatically determine the isocenter position and the beam angles in the breast tangential fields. The field sizes and shapes of the tangential, supraclavicular, and internal mammary gland fields can all be determined according to the 3D surface image of the target. A least-squares method is first introduced for the tangential-field setup that is useful for compensation of the target shape changes. The entire process of capturing the 3D surface data and subsequent calculation of beam parameters typically requires less than 1 min. Our tests on phantom experiments and patient images have achieved the accuracy of 1 mm in shift and 0.5 degrees in rotation. Importantly, the target shape and position changes in each treatment session can both be corrected through this real-time image-guided system.     

Motion estimation for cardiac emission tomography by optical flow methods

This paper describes a new method for estimating the 3D, non-rigid object motion in a time sequence of images. The method is a generalization of a standard optical flow algorithm that is incorporated into a successive quadratic approximation framework. The method was evaluated for gated cardiac emission tomography using images obtained from a mathematical, 4D phantom and a physical, dynamic phantom. The results showed that the proposed method offers improved motion estimation accuracy relative to the standard optical flow method. Convergence of the proposed algorithm was evidenced with a monotonically decreasing objective function value with iteration. Practical application of the motion estimation method in cardiac emission tomography includes quantitative myocardial motion estimation and 4D, motion-compensated image reconstruction.     

An enhanced block matching algorithm for fast elastic registration in adaptive radiotherapy

Image registration has many medical applications in diagnosis, therapy planning and therapy. Especially for time-adaptive radiotherapy, an efficient and accurate elastic registration of images acquired for treatment planning, and at the time of the actual treatment, is highly desirable. Therefore, we developed a fully automatic and fast block matching algorithm which identifies a set of anatomical landmarks in a 3D CT dataset and relocates them in another CT dataset by maximization of local correlation coefficients in the frequency domain. To transform the complete dataset, a smooth interpolation between the landmarks is calculated by modified thin-plate splines with local impact. The concept of the algorithm allows separate processing of image discontinuities like temporally changing air cavities in the intestinal track or rectum. The result is a fully transformed 3D planning dataset (planning CT as well as delineations of tumour and organs at risk) to a verification CT, allowing evaluation and, if necessary, changes of the treatment plan based on the current patient anatomy without time-consuming manual re-contouring. Typically the total calculation time is less than 5 min, which allows the use of the registration tool between acquiring the verification images and delivering the dose fraction for online corrections. We present verifications of the algorithm for five different patient datasets with different tumour locations (prostate, paraspinal and head-and-neck) by comparing the results with manually selected landmarks, visual assessment and consistency testing. It turns out that the mean error of the registration is better than the voxel resolution (2 x 2 x 3 mm(3)). In conclusion, we present an algorithm for fully automatic elastic image registration that is precise and fast enough for online corrections in an adaptive fractionated radiation treatment course.     

Real-time 3D-surface-guided head refixation useful for fractionated stereotactic radiotherapy

Accurate and precise head refixation in fractionated stereotactic radiotherapy has been achieved through alignment of real-time 3D-surface images with a reference surface image. The reference surface image is either a 3D optical surface image taken at simulation with the desired treatment position, or a CT/MRI-surface rendering in the treatment plan with corrections for patient motion during CT/MRI scans and partial volume effects. The real-time 3D surface images are rapidly captured by using a 3D video camera mounted on the ceiling of the treatment vault. Any facial expression such as mouth opening that affects surface shape and location can be avoided using a new facial monitoring technique. The image artifacts on the real-time surface can generally be removed by setting a threshold of jumps at the neighboring points while preserving detailed features of the surface of interest. Such a real-time surface image, registered in the treatment machine coordinate system, provides a reliable representation of the patient head position during the treatment. A fast automatic alignment between the real-time surface and the reference surface using a modified iterative-closest-point method leads to an efficient and robust surface-guided target refixation. Experimental and clinical results demonstrate the excellent efficacy of <2 min set-up time, the desired accuracy and precision of <1 mm in isocenter shifts, and <1 degree in rotation.     

Multichannel algorithm for fast 3D reconstruction

Some recent medical imaging applications such as functional imaging (PET and SPECT) or interventional imaging (CT fluoroscopy) involve increasing amounts of data. In order to reduce the image reconstruction time, we develop a new fast 3D reconstruction algorithm based on a divide and conquer approach. The proposed multichannel algorithm performs an indirect frequential subband decomposition of the image f to be reconstructed (f = sigma fj) through the filtering of the projections Rf. The subband images fj are reconstructed on a downsampled grid without information suppression. In order to reduce the computation time, we do not backproject the null filtered projections and we downsample the number of projections according to the Shannon conditions associated with the subband image. Our algorithm is based on filtering and backprojection operators. Using the same algorithms for these basic operators, our approach is three and a half times faster than a classical FBP algorithm for a 2D image 512 x 512 and six times faster for a 3D image 32 x 512 x 512.     

基于边界逼近的肺实质分割方法

肺部CT图像的分割是计算机辅助诊断系统处理的一个重要环节,其分割的结果影响到医生的诊断与进一步的分析。由于胸膜结节的灰度与肺实质外围的灰度相近,运用传统的分割方法无法正确分割出此类病灶。将胸膜结节包含肺实质一起分割出来,使计算机辅助诊断系统能够对此类病灶做进一步的分析。提出一种结合Graham算法以及边界逼近的方法,对肺实质的轮廓进行修正,进而得到修正的二值模板;将该模板与原图像做乘运算,得到包含胸膜结节的肺实质。运用所提出的方法,对公开数据库LIDC中68张含病灶的CT样本图像做处理,通过与传统方法的对比以及对算法的过分割比例、欠分割比例以及准确性的分析,得到准确率为98.5%,平均过分割比例为1.35%,平均欠分割比例为0.51%,证明了该方法的有效性。     

基于射野影像的剂量重建方法之可行性初探

利用治疗时得到的影像信息重建病人体内所接受的真实剂量是剂量引导下放射治疗（DGRT）技术临床应用的重要内容之一。治疗剂最重建方法之一是：通过匹对模拟的射野影像和治疗时获得的射野影像来反推射野注量分布，然后计算出治疗时病人体内实际接受的剂量：由此重建出的射野注量是否精确受诸多因素的影响．因素之一是是否能真实地模拟射野影像。本文用傅立叶卷积积分法模拟射野影像，采用不同积分半径得剑不同程度散射近似下的模拟射野影像。通过用不同程度散射近似下的模拟射野影像来反推射野注量分布．考察了散射因素对反推射野注量分布的影响。发现：即便射野影像在没有噪音和分辨率足够高的理想情况下，要精确地反推射野注量，必须在模拟射野影像中充分考虑散射因素。     

Identification of the optic nerve head with genetic algorithms

OBJECTIVE: This work proposes creating an automatic system to locate and segment the optic nerve head (ONH) in eye fundus photographic images using genetic algorithms. METHODS AND MATERIAL: Domain knowledge is used to create a set of heuristics that guide the various steps involved in the process. Initially, using an eye fundus colour image as input, a set of hypothesis points was obtained that exhibited geometric properties and intensity levels similar to the ONH contour pixels. Next, a genetic algorithm was used to find an ellipse containing the maximum number of hypothesis points in an offset of its perimeter, considering some constraints. The ellipse thus obtained is the approximation to the ONH. The segmentation method is tested in a sample of 110 eye fundus images, belonging to 55 patients with glaucoma (23.1%) and eye hypertension (76.9%) and random selected from an eye fundus image base belonging to the Ophthalmology Service at Miguel Servet Hospital, Saragossa (Spain). RESULTS AND CONCLUSIONS: The results obtained are competitive with those in the literature. The method's generalization capability is reinforced when it is applied to a different image base from the one used in our study and a discrepancy curve is obtained very similar to the one obtained in our image base. In addition, the robustness of the method proposed can be seen in the high percentage of images obtained with a discrepancy delta<5 (96% and 99% in our and a different image base, respectively). The results also confirm the hypothesis that the ONH contour can be properly approached with a non-deformable ellipse. Another important aspect of the method is that it directly provides the parameters characterising the shape of the papilla: lengths of its major and minor axes, its centre of location and its orientation with regard to the horizontal position.     

A high-resolution three-dimensional far-infrared thermal and true-color imaging system for medical applications

As the needs for various kinds of body surface information are wide-ranging, we developed an imaging-sensor integrated system that can synchronously acquire high-resolution three-dimensional (3D) far-infrared (FIR) thermal and true-color images of the body surface. The proposed system integrates one FIR camera and one color camera with a 3D structured light binocular profilometer. To eliminate the emotion disturbance of the inspector caused by the intensive light projection directly into the eye from the LCD projector, we have developed a gray encoding strategy based on the optimum fringe projection layout. A self-heated checkerboard has been employed to perform the calibration of different types of cameras. Then, we have calibrated the structured light emitted by the LCD projector, which is based on the stereo-vision idea and the least-squares quadric surface-fitting algorithm. Afterwards, the precise 3D surface can fuse with undistorted thermal and color images. To enhance medical applications, the region-of-interest (ROI) in the temperature or color image representing the surface area of clinical interest can be located in the corresponding position in the other images through coordinate system transformation. System evaluation demonstrated a mapping error between FIR and visual images of three pixels or less. Experiments show that this work is significantly useful in certain disease diagnoses.     

Three-dimensional reconstruction of human femur using consecutive computer tomography images and simulated implantation system

This study is concerned with the construction of a simulated implantation system (SIS) for a human femur using various algorithms of image processing and computer graphics. The SIS is the software which performs 3D visualization as well as various numeric analyses between the patient's femur and the artificial hip joint through certain stages of processing on consecutive CT images and projected images. We present the concrete methods that were to be implemented into the suggested prototype of SIS and the corresponding experimental results will also be shown.     

A Projection-Based Image Registration Algorithm and Its Application

Summary: Proposed is a projection-based image registration technique where, by rearranging the projections of characteristic images, the image registration is implemented with two independent steps - rotation and translation, to perform the two-dimensional or three-dimensional rigid-body image registration addressing the head motion problem in functional magnetic resonance imaging (fMRI). For a 2D problem, the approach is based on a one-dimensional projection of a segmented two-dimensional characteristic image, in which the translation and rotation parameters are obtained with a one-dimensional cross-correlation-based estimator. This is then used to compute the cross-correlation between the projection of an image and a registration table that is created by rearranged projections of a selected two-dimensional image with various rotation angles. In this approach, the translation registration table may be created by rearranged projections of sub-voxel level two-dimensional images with various sub-voxel level parameters, and so it may be applied into a sub-voxel registration. Such an approach replaced the general multi-dimensional optimization procedure with a linear projection calculation and a finite cross-correlation with a registration table, thus the amount of computation is considerably reduced. The performance of this method was confirmed by simulation study different SNRs and applications to 2D and 3D actual functional MRI images. Supported by the 973 Project number 2003CB716106, NSFC 90208003, #30130180 and #30200059, TRAPOYT, Doctor training Fund of MOE, PRC, Fok Ying Tong Education Foundation (91041). The authors wish to thank the Wellcome Department of Imaging Neuroscience for the permission of using the fMRI experimental data.     

Implementation of an iterative scatter correction, the influence of attenuation map quality and their effect on absolute quantitation in SPECT

We investigated the accuracy of qSPECT, a quantitative SPECT reconstruction algorithm we have developed which employs corrections for collimator blurring, photon attenuation and scatter, and provides images in units of absolute radiotracer concentrations (kBq cm(-3)). Using simulated and experimental phantom data with characteristics similar to clinical cardiac perfusion data, we studied the implementation of a scatter correction (SC) as part of an iterative reconstruction protocol. Additionally, with experimental phantom studies we examined the influence of CT-based attenuation maps, relative to those obtained from conventional SPECT transmission scans, on SCs and quantitation. Our results indicate that the qSPECT estimated scatter corrections did not change appreciably after the third iteration of the reconstruction. For the simulated data, qSPECT concentrations agreed with images reconstructed using ideal, scatter-free, simulated data to within 6%. For the experimental data, we observed small systematic differences in the scatter fractions for data using different combinations of SCs and attenuation maps. The SCs were found to be significantly influenced by errors in image coregistration. The reconstructed concentrations using CT-based corrections were more quantitatively accurate than those using attenuation maps from conventional SPECT transmission scans. However, segmenting the attenuation maps from SPECT transmission scans could provide sufficient accuracy for most applications.     

Post-processing noise removal algorithm for magnetic resonance imaging based on edge detection and wavelet analysis

A post-processing noise suppression technique for biomedical MRI images is presented. The described procedure recovers both sharp edges and smooth surfaces from a given noisy MRI image; it does not blur the edges and does not introduce spikes or other artefacts. The fine details of the image are also preserved. The proposed algorithm first extracts the edges from the original image and then performs noise reduction by using a wavelet de-noise method. After the application of the wavelet method, the edges are restored to the filtered image. The result is the original image with less noise, fine detail and sharp edges. Edge extraction is performed by using an algorithm based on Sobel operators. The wavelet de-noise method is based on the calculation of the correlation factor between wavelet coefficients belonging to different scales. The algorithm was tested on several MRI images and, as an example of its application, we report the results obtained from a spin echo (multi echo) MRI image of a human wrist collected with a low field experimental scanner (the signal-to-noise ratio, SNR, of the experimental image was 12). Other filtering operations have been performed after the addition of white noise on both channels of the experimental image, before the magnitude calculation. The results at SNR = 7, SNR = 5 and SNR = 3 are also reported. For SNR values between 5 and 12, the improvement in SNR was substantial and the fine details were preserved, the edges were not blurred and no spikes or other artefacts were evident, demonstrating the good performances of our method. At very low SNR (SNR = 3) our result is worse than that obtained by a simpler filtering procedure.     

Robust edge-directed interpolation of magnetic resonance images

Image interpolation is intrinsically a severely under-determined inverse problem. Traditional non-adaptive interpolation methods do not account for local image statistics around the edges of image structures. In practice, this results in artifacts such as jagged edges, blurring and/or edge halos. To overcome this shortcoming, edge-directed interpolation has been introduced in different forms. One variant, new edge-directed interpolation (NEDI), has successfully exploited the 'geometric duality' that links the low-resolution image to its corresponding high-resolution image. It has been demonstrated that for scalar images, NEDI is able to produce better results than non-adaptive traditional methods, both visually and quantitatively. In this work, we return to the root of NEDI as a least-squares estimation method of neighborhood patterns and propose a robust scheme to improve it. The improvement is twofold: firstly, a robust least-squares technique is used to improve NEDI's performance to outliers and noise; secondly, the NEDI algorithm is extended with the recently proposed non-local mean estimation scheme. Moreover, the edge-directed concept is applied to the interpolation of multi-valued diffusion-weighted images. The framework is tested on phantom scalar images and real diffusion images, and is shown to achieve better results than the non-adaptive methods as well as NEDI, in terms of visual quality as well as quantitative measures.     

Rotationally acquired four-dimensional optical coherence tomography of embryonic chick hearts using retrospective gating on the common central A-scan

We introduce a new method of rotational image acquisition for four-dimensional (4D) optical coherence tomography (OCT) of beating embryonic chick hearts. The rotational axis and the central A-scan of the OCT are identical. An out-of-phase image sequence covering multiple heartbeats is acquired at every angle of an incremental rotation of the deflection mirrors of the OCT system. Image acquisition is accomplished after a rotation of 180°. Comparison of a displayed live M-mode of the central A-scan with a reference M-mode allows instant detection of translational movements of the embryo. For calculation of 4D data sets, we apply an image-based retrospective gating algorithm using the phase information of the common central A-scan present in all acquired images. This leads to cylindrical three-dimensional data sets for every time step of the cardiac cycle that can be used for 4D visualization. We demonstrate this approach and provide a video of a beating Hamburger and Hamilton stage 16 embryonic chick heart generated from a 4D OCT data set using rotational image acquisition.     

A three-dimensional model-based partial volume correction strategy for gated cardiac mouse PET imaging

Quantification in cardiac mouse positron emission tomography (PET) imaging is limited by the imaging spatial resolution. Spillover of left ventricle (LV) myocardial activity into adjacent organs results in partial volume (PV) losses leading to underestimation of myocardial activity. A PV correction method was developed to restore accuracy of the activity distribution for FDG mouse imaging. The PV correction model was based on convolving an LV image estimate with a 3D point spread function. The LV model was described regionally by a five-parameter profile including myocardial, background and blood activities which were separated into three compartments by the endocardial radius and myocardium wall thickness. The PV correction was tested with digital simulations and a physical 3D mouse LV phantom. In vivo cardiac FDG mouse PET imaging was also performed. Following imaging, the mice were sacrificed and the tracer biodistribution in the LV and liver tissue was measured using a gamma-counter. The PV correction algorithm improved recovery from 50% to within 5% of the truth for the simulated and measured phantom data and image uniformity by 5-13%. The PV correction algorithm improved the mean myocardial LV recovery from 0.56 (0.54) to 1.13 (1.10) without (with) scatter and attenuation corrections. The mean image uniformity was improved from 26% (26%) to 17% (16%) without (with) scatter and attenuation corrections applied. Scatter and attenuation corrections were not observed to significantly impact PV-corrected myocardial recovery or image uniformity. Image-based PV correction algorithm can increase the accuracy of PET image activity and improve the uniformity of the activity distribution in normal mice. The algorithm may be applied using different tracers, in transgenic models that affect myocardial uptake, or in different species provided there is sufficient image quality and similar contrast between the myocardium and surrounding structures.     

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.