Image-guided navigation system for minimally invasive total hip arthroplasty (MITHA) using an improved position-sensing marker.

PURPOSE: Minimally invasive total hip arthroplasty (MITHA) is a treatment for hip arthritis, and it causes less tissue trauma, blood loss, and recovery time. However, the limited incision makes it difficult for surgeons to perceive the instruments' location and orientation. Computer-assisted navigation systems can help improve the medical outcome of MITHA. Directly applying existing navigation systems for MITHA, however, suffers from problems of bulky fiducial marker, severe feature-loss, multiple instruments tracking confusion, and radiation exposure. To tackle these problems, we propose an image-guided navigation system for MITHA using a novel position-sensing marker. METHODS: A position-sensing marker is proposed to serve as the fiducial marker with high-density and multi-fold ID tags. It results in less feature span and enables the use of ID for each feature, overcoming the problem of bulky fiducial markers and multiple instruments tracking confusion. And the marker can be recognized even when a large part of locating features is obscured. As for the elimination of intraoperative radiation exposure, we propose a point-based method to achieve patient-image registration based on anatomical landmarks. RESULTS: Quantitative experiments are conducted to evaluate the feasibility of our system. The accuracy of instrument positioning is achieved at 0.33 ± 0.18 mm, and that of patient-image registration is achieved at 0.79 ± 0.15 mm. And qualitative experiments are also performed, verifying that our system can be used in compact surgical spatial volume and can address severe feature-loss and tracking confusion problems. In addition, our system does not require any intraoperative medical scans. CONCLUSION: Experimental results indicate that our proposed system can assist surgeons without larger space occupations, radiation exposure, and extra incision, showing its potential application value in MITHA.

An image super-resolution reconstruction model based on fractional-order anisotropic diffusion equation.

The image denoising model based on anisotropic diffusion equation often appears the staircase effect while image denoising, and the traditional super-resolution reconstruction algorithm can not effectively suppress the noise in the image in the case of blur and serious noise. To tackle this problem, a novel model is proposed in this paper. Based on the original diffusion equation, we propose a new method for calculating the adaptive fidelity term and its coefficients, which is based on the relationship between the image gradient and the diffusion function. It is realized that the diffusion speed can be slowed down by adaptively changing the coefficient of the fidelity term, and it is proved mathematically that the proposed fractional adaptive fidelity term will not change the existence and uniqueness of the solution of the original model. At the same time, washout filter is introduced as the control item of the model, and a new model of image super-resolution reconstruction and image denoising is constructed. In the proposed model, the order of fractional differential will be determined adaptively by the local variance of the image. And we give the numerical calculation method of the new model in the frequency domain by the method of Fourier transform. The experimental results show that the proposed algorithm can better prevent the staircase effect and achieve better visual effect. And by introducing washout filter to act as the control of the model, the stability of the system can be improved and the system can converge to a stable state quickly.

Exon sequencing mutation detection algorithm based on PCR matching.

BACKGROUND: With the development of second-generation sequencing technology, more and more DNA sequence variations have been detected. Exon sequencing is the first choice for sequencing many cancer genes, and it can be better used to identify disease status by detecting gene variants. PCR sequence is an effective method to capture that sequence of an exon in the process of sequencing. Exon sequencing sequence contains PCR primer sequence, the correct position of the sequence can be determined by PCR primer sequence, which can be found in SNP, Indel mutation point by comparing the sequence of PCR primer sequence. RESULTS: In this paper, a matching algorithm based on the PCR primer sequence is proposed, which can effectively sequence the position of PCR primer sequence and find out the key position sequence. Then the sequencing sequence is sorted and the number of the same sequence is counted to reduce the matching times. Then, the sequenced sequence was matched with PCR primer sequence, so that the DNA position could be accurately matched and the variation in the sequenced sequence could be found more quickly. CONCLUSIONS: Compared with the traditional sequence matching method, PCR primer sequence matching method can match many sequences and find more variation. It also showed a high recall rate in the recall rate.

A Novel Fractional-Order Chaotic Phase Synchronization Model for Visual Selection and Shifting.

Visual information processing is one of the fields of cognitive informatics. In this paper, a two-layer fractional-order chaotic network, which can simulate the mechanism of visual selection and shifting, is established. Unlike other object selection models, the proposed model introduces control units to select object. The first chaotic network layer of the model is used to implement image segmentation. A control layer is added as the second layer, consisting of a central neuron, which controls object selection and shifting. To implement visual selection and shifting, a strategy is proposed that can achieve different subnets corresponding to the objects in the first layer synchronizing with the central neuron at different time. The central unit acting as the central nervous system synchronizes with different subnets (hybrid systems), implementing the mechanism of visual selection and shifting in the human system. The proposed model corresponds better with the human visual system than the typical model of visual information encoding and transmission and provides new possibilities for further analysis of the mechanisms of the human cognitive system. The reasonability of the proposed model is verified by experiments using artificial and natural images.