45 research outputs found
Organ-focused mutual information for nonrigid multimodal registration of liver CT and GdâEOBâDTPA-enhanced MRI
Accurate detection of liver lesions is of great importance in hepatic surgery planning. Recent studies have shown that the detection rate of liver lesions is significantly higher in gadoxetic acid-enhanced magnetic resonance imaging (GdâEOBâDTPA-enhanced MRI) than in contrast-enhanced portal-phase computed tomography (CT); however, the latter remains essential because of its high specificity, good performance in estimating liver volumes and better vessel visibility. To characterize liver lesions using both the above image modalities, we propose a multimodal nonrigid registration framework using organ-focused mutual information (OF-MI). This proposal tries to improve mutual information (MI) based registration by adding spatial information, benefiting from the availability of expert liver segmentation in clinical protocols. The incorporation of an additional information channel containing liver segmentation information was studied. A dataset of real clinical images and simulated images was used in the validation process. A GdâEOBâDTPA-enhanced MRI simulation framework is presented. To evaluate results, warping index errors were calculated for the simulated data, and landmark-based and surface-based errors were calculated for the real data. An improvement of the registration accuracy for OF-MI as compared with MI was found for both simulated and real datasets. Statistical significance of the difference was tested and confirmed in the simulated dataset (p < 0.01)
Development of an anthropomorphic breast phantom for combined PET, B-mode ultrasound and elastographic imaging
International audienceCombining the advantages of different imaging modalities leads to improved clinical results. For example, ultrasound provides good real-time structural information without any radiation and PET provides sensitive functional information. For the ongoing ClearPEM-Sonic project combining ultrasound and PET for breast imaging, we developed a dual-modality PET/Ultrasound (US) phantom. The phantom reproduces the acoustic and elastic properties of human breast tissue and allows labeling the different tissues in the phantom with different concentrations of FDG. The phantom was imaged with a whole-body PET/CT and with the Supersonic Imagine Aixplorer system. This system allows both B-mode US and shear wave elastographic imaging. US elastography is a new imaging method for displaying the tissue elasticity distribution. It was shown to be useful in breast imaging. We also tested the phantom with static elastography. A 6D magnetic positioning system allows fusing the images obtained with the two modalities. ClearPEM-Sonic is a project of the Crystal Clear Collaboration and the European Centre for Research on Medical Imaging (CERIMED)
Registration of Brain Images using Fast Walsh Hadamard Transform
A lot of image registration techniques have been developed with great
significance for data analysis in medicine, astrophotography, satellite imaging
and few other areas. This work proposes a method for medical image registration
using Fast Walsh Hadamard transform. This algorithm registers images of the
same or different modalities. Each image bit is lengthened in terms of Fast
Walsh Hadamard basis functions. Each basis function is a notion of determining
various aspects of local structure, e.g., horizontal edge, corner, etc. These
coefficients are normalized and used as numerals in a chosen number system
which allows one to form a unique number for each type of local structure. The
experimental results show that Fast Walsh Hadamard transform accomplished
better results than the conventional Walsh transform in the time domain. Also
Fast Walsh Hadamard transform is more reliable in medical image registration
consuming less time.Comment: 10 pages, 37 figures, 12 table
Wavelet-Based Image Registration and Segmentation Framework for the Quantitative Evaluation of Hydrocephalus
Hydrocephalus, characterized by increased fluid in the cerebral ventricles, is traditionally evaluated by a visual
assessment of serial CT scans. The complex shape of the ventricular system makes accurate visual comparison
of CT scans difficult. The current research developed a quantitative method to measure the change in cerebral
ventricular volume over time. Key elements of the developed framework are: adaptive image registration based
on mutual information and wavelet multiresolution analysis; adaptive segmentation with novel feature extraction
based on the Dual-Tree Complex Wavelet Transform; volume calculation. The framework, when tested on
physical phantoms, had an error of 2.3%. When validated on clinical cases, results showed that cases deemed to be
normal/stable had a calculated volume change less than 5%. Those with progressive/treated hydrocephalus had a
calculated change greater than 20%. These findings indicate that the framework is reasonable and has potential for
development as a tool in the evaluation of hydrocephalus