Development of a thermotaxis and rheotaxis microfluidic device for motile spermatozoa sorting.

Male infertility is a pervasive global reproductive challenge, primarily attributed to a decline in semen quality. Addressing this concern, there has been a growing focus on spermatozoa sorting in assisted reproductive technology. This study introduces a groundbreaking development in the form of a thermotaxis and rheotaxis microfluidic (TRMC) device designed for efficient motile spermatozoa sorting within a short 15-min timeframe. The TRMC device mimics the natural sperm sorting mechanism of the oviduct, selecting spermatozoa with superior motility and DNA integrity. The experimental outcomes demonstrate a remarkable enhancement in the percentage of progressive spermatozoa following sorting, soaring from 3.90% to an impressive 96.11% when subjected to a temperature decrease from 38 °C to 35 °C. Notably, sperm motility exhibited a substantial 69% improvement. The TRMC device exhibited a commendable recovery rate of 60.93%, surpassing current clinical requirements. Furthermore, the sorted spermatozoa displayed a notable reduction in the DNA fragmentation index to 6.94%, signifying a substantial 90% enhancement in DNA integrity. This remarkable advancement positions the TRMC device as highly suitable for applications in in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI), offering a promising solution to male infertility challenges.

Design of a gradient-rheotaxis microfluidic chip for sorting of high-quality Sperm with progressive motility.

Assisted reproductive technology (ART) is an important invention for the treatment of human infertility, and the isolation of high-quality sperm with progressive motility is one of the most critical steps that eventually affect the fertilization rate. Conventional sperm separation approaches include the swim-up method and density gradient centrifugation. However, the quality of isolated sperm obtained from both approaches can still be improved by improving sorted sperm motility, minimizing the DNA fragmentation rate, and removing abnormal phenotypes. Here, we report a Progressive Sperm Sorting Chip (PSSC) for high-quality sperm isolation. Based on the rheotaxis behavior of sperm, a gradient flow field is created in the chip for progressive sperm sorting. Clinical experiment results for 10 volunteers showed that greater than 90% of isolated sperm exhibit high motility (> 25 µm/s), high linearity (0.8), and a very low DNA fragmentation rate (< 5%). In addition, the whole process is label and chemical free. These features aid in gentle sperm sorting to obtain healthy sperm. This device uniquely enables the selection of high-quality sperm with progressive motility and might be clinically applied for infertility treatment in the near future.

Discrimination between Alzheimer's disease and mild cognitive impairment using SOM and PSO-SVM.

In this study, an MRI-based classification framework was proposed to distinguish the patients with AD and MCI from normal participants by using multiple features and different classifiers. First, we extracted features (volume and shape) from MRI data by using a series of image processing steps. Subsequently, we applied principal component analysis (PCA) to convert a set of features of possibly correlated variables into a smaller set of values of linearly uncorrelated variables, decreasing the dimensions of feature space. Finally, we developed a novel data mining framework in combination with support vector machine (SVM) and particle swarm optimization (PSO) for the AD/MCI classification. In order to compare the hybrid method with traditional classifier, two kinds of classifiers, that is, SVM and a self-organizing map (SOM), were trained for patient classification. With the proposed framework, the classification accuracy is improved up to 82.35% and 77.78% in patients with AD and MCI. The result achieved up to 94.12% and 88.89% in AD and MCI by combining the volumetric features and shape features and using PCA. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

MRI/SPECT-based diagnosis and CT-guided high-intensity focused-ultrasound treatment system in MPTP mouse model of Parkinson's disease.

Single-photon emission computed tomography (SPECT) of dopamine transporters with (99m)Tc-TRODAT-1 has recently been proposed to offer valuable information for the diagnosis of Parkinson's disease (PD). Furthermore, High-intensity focused ultrasound (HIFU) is a newly developed technique in which the energy of ultrasound wave is directed to a focused spot for the purpose treatment of PD. This study presents a diagnosis and image-guided system using HIFU to treat the mouse with PD under a designed stereotactic frame. The system comprises two key components: an automatic atlas-based SPECT/MRI image registration module for diagnosis and a stereotactic CT-guided module for HIFU treatment. The SPECT/MR image registration here is important in the non-invasive examination of the dopamine concentration in vivo. From the experimental results, the image registration module proves to have comparable performance to that derived from manual drawing by experts. In addition, the stereotactic CT-guided module achieved a positioning accuracy to within 2mm on the average, which is acceptable for the purpose of HIFU treatment.

Fast-MICP for frameless image-guided surgery.

PURPOSE: In image-guided surgery (IGS) systems, image-to-physical registration is critical for reliable anatomical information mapping and spatial guidance. Conventional stereotactic frame-based or fiducial-based approaches provide accurate registration but are not patient-friendly. This study proposes a frameless cranial IGS system that uses computer vision techniques to replace the frame or fiducials with the natural features of the patient. METHODS: To perform a cranial surgery with the proposed system, the facial surface of the patient is first reconstructed by stereo vision. Accuracy is ensured by capturing parallel-line patterns projected from a calibrated LCD projector. Meanwhile, another facial surface is reconstructed from preoperative computed tomography (CT) images of the patient. The proposed iterative closest point (ICP)-based algorithm [fast marker-added ICP (Fast-MICP)] is then used to register the two facial data sets, which transfers the anatomical information from the CT images to the physical space. RESULTS: Experimental results reveal that the Fast-MICP algorithm reduces the computational cost of marker-added ICP (J.-D. Lee et al., "A coarse-to-fine surface registration algorithm for frameless brain surgery," in Proceedings of International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 836-839) to 10% and achieves comparable registration accuracy, which is under 3 mm target registration error (TRE). Moreover, two types of optical-based spatial digitizing devices can be integrated for further surgical navigation. Anatomical information or image-guided surgical landmarks can be projected onto the patient to obtain an immersive augmented reality environment. CONCLUSION: The proposed frameless IGS system with stereo vision obtains TRE of less than 3 mm. The proposed Fast-MICP registration algorithm reduces registration time by 90% without compromising accuracy.

A navigation system of cerebral endovascular surgery integrating multiple space-guiding trackers.

In this paper, a navigation system combining multiple space-guiding trackers is proposed for cerebral endovascular surgery. This system not only provides 2-D and 3-D visualization of medical images but also integrates an ICP-based algorithm to increase the registration accuracy during cerebral endovascular navigation. This tracking system integrates a mechanical digitizer, an optical tracker and an electromagnetic sensor to increase the flexibility in clinical application. An ICP-based registration algorithm is proposed to dynamically correct the registration error according to the current observation of the cerebral endovascular. To evaluate the system performance, a plastic phantom with cerebral endovascular structures is used and the experimental results show that the localization error is significantly reduced from 5.21 approximately 6.47mm to 2.29 approximately 2.87mm using the proposed scheme.

A coarse-to-fine surface registration algorithm for frameless brain surgery.

In this paper, we present a coarse-to-fine approach for surface registration used in frameless brain surgery. The reference point data are extracted from patient's pre-stored CT images; the floating point data are captured from the patient's head by using an electromagnetic tracker. The registration algorithm takes the information of global coarse markers (or feature points) and local fine point cloud into account to avoid the local optimum problem of the iterative closest point (ICP) algorithm. Experimental results show that the registration accuracy of using the proposed registration algorithm is improved than of using other ICP-related algorithms. More specifically, it shows that the target registration error (TRE) is decreased by 50% by using the proposed method.

Computer-aided evaluation system for Parkinson's disease using image registration and labeling.

Single photon emission computed tomography image (SPECT) of dopamine transporter with 99mTc-TRODAT-1 has recently been proposed to be a valuable and feasible means of assessing the integrity of dopamine neurons. In order to measure the specific-to-nonspecific binding ratio of the nuclear medicine within the specific-binding tissues, i.e. putamens and caudate nuclei, the corresponding MRI is needed to be registered to SPECT for bounding the regions of interest. Therefore, an automatic labeling algorithm which enables contouring the putamens and caudate nuclei is necessary because segmenting these tissues manually from MRI costs tons of time and energy of physicians. We have built a computer-aided clinical diagnosis system which integrates MRI/SPECT registration and MRI labeling for the evaluation of Parkinson's disease. Clinical MRI and SPECT data including eighteen healthy subjects and thirteen patients were involved to validate the performance of the proposed system.

Improved accuracy of brain MRI/SPECT registration using a two-cluster SPECT normalization algorithm and a combinative similarity measure: application to the evaluation of Parkinson's disease.

OBJECTIVE: Single-photon emission computed tomography (SPECT) of dopamine transporters with technetium-99-labeled tropane derivative (99m)Tc-TRODAT-1 has recently been suggested to offer valuable information in assessing the functionality of dopaminergic systems. To facilitate the non-invasive examination of the dopamine concentration in vivo, registering magnetic resonance imaging (MRI) and SPECT image is important. This article proposes a new similarity measure for MRI/SPECT registration. METHODS: The proposed similarity measure combines anatomic features that are characterized by specific binding of nuclear medicine and the distribution of image intensity that are characterized by the normalized mutual information (NMI). A preprocess, a novel two-cluster SPECT normalization algorithm, is also proposed. RESULTS: Compared with the conventional NMI-based registration algorithm, the proposed registration framework reduces the target of registration error from >7 mm to approximately 4 mm. The error of the specific-to-non-specific (99m)Tc-TRODAT-1 binding ratio (BR), a quantitative measure of TRODAT receptor binding, is also reduced from 0.45 to 0.08 in the healthy subjects and from 0.28 to 0.12 in Parkinson's disease patients. CONCLUSIONS: A suitable color map, such as "rainbow," for image display enables the two-cluster SPECT normalization algorithm to provide clinically meaningful visual contrast. In addition, registering MRI/SPECT based on the proposed similarity measure improves the accuracy compared with the conventional NMI-based algorithm.

An automatic MRI/SPECT registration algorithm using image intensity and anatomical feature as matching characters: application on the evaluation of Parkinson's disease.

Single-photon emission computed tomography (SPECT) of dopamine transporters with (99m)Tc-TRODAT-1 has recently been proposed to offer valuable information in assessing the functionality of dopaminergic systems. Magnetic resonance imaging (MRI) and SPECT imaging are important in the noninvasive examination of dopamine concentration in vivo. Therefore, this investigation presents an automated MRI/SPECT image registration algorithm based on a new similarity metric. This similarity metric combines anatomical features that are characterized by specific binding, the mean count per voxel in putamens and caudate nuclei, and the distribution of image intensity that is characterized by normalized mutual information (NMI). A preprocess, a novel two-cluster SPECT normalization algorithm, is also presented for MRI/SPECT registration. Clinical MRI/SPECT data from 18 healthy subjects and 13 Parkinson's disease (PD) patients are involved to validate the performance of the proposed algorithms. An appropriate color map, such as "rainbow," for image display enables the two-cluster SPECT normalization algorithm to provide clinically meaningful visual contrast. The proposed registration scheme reduces target registration error from >7 mm for conventional registration algorithm based on NMI to approximately 4 mm. The error in the specific/nonspecific (99m)Tc-TRODAT-1 binding ratio, which is employed as a quantitative measure of TRODAT receptor binding, is also reduced from 0.45+/-0.22 to 0.08+/-0.06 among healthy subjects and from 0.28+/-0.18 to 0.12+/-0.09 among PD patients.

An Adaptive-Tabu GA for Registration of CT and Surface Laser Scan.

An adaptive-tabu GA (Genetic Algorithm) method is proposed to improve some traditional GA methods in the registration of computer tomography (CT) and surface laser scan. In this method, the adaptive memory structure and search strategy of Tabu Search (TS) with the modified chromosome crossover and adaptive mutation are proposed to increase the convergence speed and accuracy of the fitness function. This registration method can be used on non-fiducial stereo-tactic brain surgeries to assist surgeons to diagnose and treat brain diseases.

A Segmentation Scheme of Brainstem and Cerebellum using Scale-Based Fuzzy Connectedness and Deformable Contour Model.

A semi-automatic multiple-stage segmentation scheme of brainstem and cerebellum is proposed in this paper. This proposed scheme contains a modified Scale-based Fuzzy Connectedness (FC) algorithm, a morphology operator, Chain code, and Active Contour Mode (ACM). A modified Scale-based FC is utilized to delivery a rough segmentation, a morphology operator and Chain code are used to compensate the uncompleted small regions and derive an initial contour, and ACM is employed to obtain the final object boundary with the aid of the initial contour from the previous stages. The experimental results verify that the segmentation results using the proposed scheme are more accurate than using a single algorithm based on a Scale-Based FC method.