An x-ray energy spectrum plays an essential role in computed tomography (CT) imaging and related tasks. Because of the high photon flux of clinical CT scanners, most of the spectrum estimation methods are indirect and usually suffer from various limitations. In this study, we aim to provide a segmentation-free, indirect transmission measurement–based energy spectrum estimation method using dual-energy material decomposition. The general principle of this method is to minimize the quadratic error between the polychromatic forward projection and the raw projection to calibrate a set of unknown weights, which are used to express the unknown spectrum together with a set of model spectra. The polychromatic forward projection is performed using material-specific images, which are obtained using dual-energy material decomposition. The algorithm was evaluated using numerical simulations, experimental phantom data, and realistic patient data. The results show that the estimated spectrum matches the reference spectrum quite well and the method is robust. Extensive studies suggest that the method provides an accurate estimate of the CT spectrum without dedicated physical phantom and prolonged workflow. This paper may be attractive for CT dose calculation, artifacts reduction, polychromatic image reconstruction, and other spectrum-involved CT applications.
Recently, we have developed a digital whole-body PET scanner based on multi-voltage threshold (MVT) digitizers. To mitigate the impact of resolution degrading factors, an accurate system response is calculated by
Monte Carlo simulation, which is computationally expensive. To address the problem, here we improve the
method of using symmetries by simulating an axial wedge region. This approach takes full advantage of intrinsic
symmetries in the cylindrical PET system without significantly increasing the computation cost in the process
of symmetries. A total of 4224 symmetries are exploited. It took 17 days to generate the system maxtrix on
160 cores of Xeon 2.5 GHz. Both simulation and experimental data are used to evaluate the accuracy of system
response modeling. The simulation studies show the full-width-half-maximum of a line source being 2.1 mm and
3.8 mm at the center of FOV and 200 mm at the center of FOV. Experimental results show the 2.4 mm rods in
the Derenzo phantom image, which can be well distinguished.
We report the development of a modularized compact positron emission tomography (PET) detector that outputs serial streams of digital samples of PET event pulses via an Ethernet interface using the UDP/IP protocol to enable rapid configuration of a PET system by connecting multiple such detectors via a network switch to a computer. Presently, the detector is 76 mm×50 mm×55 mm in extent (excluding I/O connectors) and contains an 18×12 array of 4.2×4.2×20 mm3 one-to-one coupled lutetium-yttrium oxyorthosilicate/silicon photomultiplier pixels. It employs cross-wire and stripline readouts to merge the outputs of the 216 detector pixels to 24 channels. Signals at these channels are sampled using a built-in 24-ch, 4-level field programmable gate arrays-only multivoltage threshold digitizer. In the computer, software programs are implemented to analyze the digital samples to extract event information and to perform energy qualification and coincidence filtering. We have developed two such detectors. We show that all their pixels can be accurately discriminated and measure a crystal-level energy resolution of 14.4% to 19.4% and a detector-level coincidence time resolution of 1.67 ns FWHM. Preliminary imaging results suggests that a PET system based on the detectors can achieve an image resolution of ∼1.6 mm.
At present, computer aided systems for liver surgery design and risk evaluation are widely used in clinical all over the world. However, most systems are local applications that run on high-performance workstations, and the images have to processed offline. Compared with local applications, a web-based system is accessible anywhere and for a range of regardless of relative processing power or operating system. RayPlus (http://rayplus.life.hust.edu.cn), a B/S platform for medical image processing, was developed to give a jump start on web-based medical image processing. In this paper, we implement a computer aided system for liver surgery planning on the architecture of RayPlus. The system consists of a series of processing to CT images including filtering, segmentation, visualization and analyzing. Each processing is packaged into an executable program and runs on the server side. CT images in DICOM format are processed step by to interactive modeling on browser with zero-installation and server-side computing. The system supports users to semi-automatically segment the liver, intrahepatic vessel and tumor from the pre-processed images. Then, surface and volume models are built to analyze the vessel structure and the relative position between adjacent organs. The results show that the initial implementation meets satisfactorily its first-order objectives and provide an accurate 3D delineation of the liver anatomy. Vessel labeling and resection simulation are planned to add in the future. The system is available on Internet at the link mentioned above and an open username for testing is offered.
In order to build high quality geometric models for liver containing vascular system, multi-phase CT series used in a computer–aided diagnosis and surgical planning system aims at liver diseases have to be accurately registered. In this paper we model the segmented liver containing vascular system as a complex shape and propose a two-step registration method. Without any tree modeling for vessel this method can carry out a simultaneous registration for both liver tissue and vascular system inside. Firstly a rigid aligning using vessel as feature is applied on the complex shape model while genetic algorithm is used as the optimization method. Secondly we achieve the elastic shape registration by combine the incremental free form deformation (IFFD) with a modified iterative closest point (ICP) algorithm. Inspired by the concept of demons method, we propose to calculate a fastest diffusion vector (FDV) for each control point on the IFFD lattice to replace the points correspondence needed in ICP iterations. Under the iterative framework of the modified ICP, the optimal solution of control points’ displacement in every IFFD level can be obtained efficiently. The method has been quantitatively evaluated on clinical multi-phase CT series.
Liver segmentation in CT images has been acknowledged as a basic and indispensable part in systems of computer aided liver surgery for operation design and risk evaluation. In this paper, we will introduce and implement a web-based procedure for liver segmentation to help radiologists and surgeons get an accurate result efficiently and expediently. Several clinical datasets are used to evaluate the accessibility and the accuracy. This procedure seems a promising approach for extraction of liver volumetry of various shapes. Moreover, it is possible for user to access the segmentation wherever the Internet is available without any specific machine.
The Silicon Photomultiplier (SiPM) represents a new step in the development of the modern silicon based detection structures in the area of low photon flux detection. The high intrinsic gain obtained in the detecting structure is responsible of the digital signal nature of the SiPM at the elementary cell level. In this paper the study and development of SiPM detection structures is shown, for the specific application to the read out of scintillation light in high energy physics and Nuclear Medicine.
Liver segmentation is a basic and indispensable function in systems of computer aided liver surgery for volume calculation, operation designing and risk evaluation. Traditional manual segmentation is very time consuming because of the complicated contours of liver and the big amount of images. For increasing the efficiency of the clinical work, in this paper, a fully-automatic method was proposed to segment the liver from multi-phase contrast-enhanced computed tomography (CT) images. As an advanced region growing method, we applied various pre- and post-processing to get better segmentation from the different phases. Fifteen sets of clinical abdomens CT images of five patients were segmented by our algorithm, and the results were acceptable and evaluated by an experienced surgeon. The running-time is about 30 seconds for a single-phase data which includes more than 200 slices.
It is of vital importance that providing detailed and accurate information about hepatic vein (HV) for liver surgery
planning, such as pre-operative planning of living donor liver transplantation (LDLT). Due to the different blood flow
rate of intra-hepatic vascular systems and the restrictions of CT scan, it is common that HV and hepatic portal vein (HPV) are both filled with contrast medium during the scan and in high intensity in the hepatic venous phase images. As a result, the HV segmentation result obtained from the hepatic venous phase images is always contaminated by HPV which makes accurate HV modeling difficult. In this paper, we proposed a method for quick and accurate HV extraction. Based on the topological structure of intra-hepatic vessels, we analyzed the anatomical features of HV and HPV. According to the analysis, three conditions were presented to identify the nodes that connect HV with HPV in the topological structure, and thus to distinguish HV from HPV. The method costs less than one minute to extract HV and provides a correct and detailed HV model even with variations in vessels. Evaluated by two experienced radiologists, the accuracy of the HV model obtained from our method is over 97%. In the following work, we will extend our work to a comprehensive clinical evaluation and apply this method to actual LDLT surgical planning.
In this paper, we propose an improved fuzzy c-means (FCM) algorithm based on cluster height information to deal with
the sensitivity of unbalanced sized clusters in FCM. As we know, cluster size sensitivity is an major drawback of FCM,
which tends to balance the cluster sizes during iteration, so the center of smaller cluster might be drawn to the adjacent
larger one, which will lead to bad classification. To overcome this problem, the cluster height information is considered
and introduced to the distance function to adjust the conventional Euclidean distance, thus to control the effect on
classification from cluster size difference. Experimental results demonstrate that our algorithm can obtain good
clustering results in spite of great size difference, while traditional FCM cannot work well in such case. The improved
FCM has shown its potential for extracting small clusters, especially in medical image segmentation.
Designed for general purpose with nearly fixed performance, traditional PET systems are constructed with almost
identical and unmovable detectors. In this work, we are developing an application specific PET with detectors with
inhomogeneous performances, which can be adaptively rearranged for different objects and regions of interest (ROIs).
This article reports our initial investigation on a prototype system consisting of inhomogeneous detectors with two levels
of energy resolution. In this system, the high performance detectors and the normal performance detectors are arranged
in one scanner, and the high performance detectors are continuous distributed on the scanner. A liver phantom is
constructed as our object of detection. The coincidence data and image quality are analyzed with different distribution
schemes of the high performance detectors. Preliminary results indicate that the proposed prototype obtains higher true
counts and lower scatter counts than the system with normal performance detectors, resulting in lower scatter fractions
for every region and the whole object. The extent of the reduction of scatter fraction is varied with different distribution
schemes of the high performance detectors, which is related to the distribution of activity. Better signal-to-noise ratio for
every region in the object and better percent contrast are also obtained in some schemes of the high performance
Liver tumor, one of the most wide-spread diseases, has a very high mortality in China. To improve success rates of liver
surgeries and life qualities of such patients, we implement an interactive liver surgery planning system based on contrastenhanced
liver CT images. The system consists of five modules: pre-processing, segmentation, modeling, quantitative
analysis and surgery simulation. The Graph Cuts method is utilized to automatically segment the liver based on an
anatomical prior knowledge that liver is the biggest organ and has almost homogeneous gray value. The system supports
users to build patient-specific liver segment and sub-segment models using interactive portal vein branch labeling, and to
perform anatomical resection simulation. It also provides several tools to simulate atypical resection, including resection
plane, sphere and curved surface. To match actual surgery resections well and simulate the process flexibly, we extend
our work to develop a virtual scalpel model and simulate the scalpel movement in the hepatic tissue using multi-plane
continuous resection. In addition, the quantitative analysis module makes it possible to assess the risk of a liver surgery.
The preliminary results show that the system has the potential to offer an accurate 3D delineation of the liver anatomy, as
well as the tumors' location in relation to vessels, and to facilitate liver resection surgeries. Furthermore, we are testing
the system in a full-scale clinical trial.