Research on path planning for clear aligner flexible manufacturing.

In recent years, clear aligner can enhance individual appearance with dental defects, so it used more and more widely. However, in manufacturing process, there are still some problems, such as low degree of automation and high equipment cost. The problem of coordinate system mismatch between gingival curve point cloud and dental CAD model is faced to. The PCA-ICP registration algorithm is proposed, which includes coarse match algorithm and improve-ICP registration algorithm. The principal component analysis (PCA) based method can roughly find the posture relationship between the two point clouds. Using z-level dynamic hierarchical, the ICP registration can accurately find the posture between these two clouds. The final registration maximum distance error is 0.03 mm, which is smaller than robot machining error. Secondly, the clear aligner machining process is conducted to verify the registration effectiveness. Before machining, the path is generated based on the well registered gingival curve. After full registration, the tool path is calculated by establishing a local coordinate system between the workpiece and the tool to avoid interference. This path is calculated and generated as an executable program for ABB industrial robots. Finally, the robot was used for flexible cutting of clear aligners and was able to extract products, ensuring the effectiveness of the proposed research. This method can effectively solve the limitations of traditional milling path planning under such complex conditions.

Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling.

PURPOSE: Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a 2D linear-combination model (2D-LCM) of individual transients ("model-based FPC"). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates. METHODS: We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the SD of those ground-truth errors, and amplitude Cramér Rao lower bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data. RESULTS: 2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of FPC and amplitudes performed substantially better at low-to-very-low SNR. CONCLUSION: Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, for example, long TEs or strong diffusion weighting.

The contribution of E3D imaging integrated with robotic navigation: analysis of the first 80 consecutive posterior spinal fusion cases.

Eighty consecutive complex spinal robotic cases utilizing intraoperative 3D CT imaging (E3D, Group 2) were compared to 80 age-matched controls using the Excelsius robot alone with C-arm Fluoroscopic registration (Robot Only, Group 1). The demographics between the two groups were similar-severity of deformity, ASA Score for general anesthesia, patient age, gender, number of spinal levels instrumented, number of patients with prior spinal surgery, and amount of neurologic compression. The intraoperative CT scanning added several objective factors improving patient safety. There were significantly fewer complications in the E3D group with only 3 of 80 (4%) patients requiring a return to the operating room compared to 11 of 80 (14%) patients in the Robot Only Group requiring repeat surgery for implant related problems (Chi squared analysis = 5.00, p = 0.025). There was a significant reduction the amount of fluoroscopy time in the E3D Group (36 s, range 4-102 s) compared to Robot only group (51 s, range 15-160 s) (p = 0.0001). There was also shorter mean operative time in the E3D group (257 ± 59.5 min) compared to the robot only group (306 ± 73.8 min) due to much faster registration time (45 s). A longer registration time was required in the Robot only group to register each vertebral level with AP and Lateral fluoroscopy shots. The estimated blood loss was also significantly lower in Group 2 (mean 345 ± 225 ml) vs Group 1 (474 ± 397 ml) (p = 0.012). The mean hospital length of stay was also significantly shorter for Group 2 (3.77 ± 1.86 days) compared to Group 1 (5.16 ± 3.40) (p = 0.022). There was no significant difference in the number of interbody implants nor corrective osteotomies in both groups-Robot only 52 cases vs. 42 cases in E3D group.Level of evidence: IV, Retrospective review.

The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue.

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods.

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment.

Head and neck cancer patients experience systematic anatomical changes as well as random day to day anatomical changes during fractionated radiotherapy treatment. Modelling the expected systematic anatomical changes could aid in creating treatment plans which are more robust against such changes. A patient specific (SM) and population average (AM) model are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. Inter- patient correspondence aligned all patients to a model space. Intra- patient correspondence between each planning CT scan and on treatment cone beam CT scans was obtained using diffeomorphic deformable image registration. The stationary velocity fields were then used to develop B-Spline based SMs and AMs. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models. Both SMs and AMs were able to capture systematic changes. The average surface distance between the registration propagated contours and the contours generated by the SM was less than 2mm, showing that the SM are able to capture the anatomical changes which a patient experiences during the course of radiotherapy. The testing accuracy was lower than the training accuracy of the SM, suggesting that the model overfits to the limited data available and therefore also captures some of the random day to day changes. For most patients the AMs were a better estimate of the anatomical changes than assuming there were no changes, but the AMs could not capture the variability in the anatomical changes seen in all patients. No difference was seen in the training and testing accuracy of the AMs. These observations were highlighted in both the geometric and dosimetric evaluations and comparisons. The large patient variability highlights the need for more complex, capable population models.

Comparison of the influenza vaccination coverage among high-risk people between the online registration system and walk-in service system in Bangkok, Thailand.

Until recently, the Thai national program of seasonal influenza vaccination for high-risk people has been using a walk-in service system. However, in 2020, an online registration system was introduced in Bangkok to improve vaccine coverage. This study aimed to compare the coverage of influenza vaccination between the walk-in service and online registration systems. The study participants included 374,710 Thai individuals who obtained an influenza vaccination from the national program in the Bangkok health region in 2018 (n = 162,214) and in 2020 (n = 212,496). The registration systems that were examined were the walk-in service system in 2018 and the online registration system in 2020. The characteristics of vaccine recipients and the vaccine coverage in each risk group and health facility level were compared between the two systems. Coverage comparison in Bangkok between the years 2018 and 2020 showed an increase in coverage, particularly among individuals who had an influenza vaccination at health facilities of the primary level and in the elderly and obesity groups. The coverage among children was lowest among all high-risk groups. To improve coverage in Thailand, the online registration system should be introduced in all regions. Additionally, information about influenza vaccination for children should be disseminated to parents using handbooks or by word-of-mouth from healthcare workers.

Medical image registration via neural fields.

Image registration is an essential step in many medical image analysis tasks. Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images. Recent learning-based methods, trained to directly predict transformations between two images, run much faster, but suffer from performance deficiencies due to domain shift. Here we present a new neural network based image registration framework, called NIR (Neural Image Registration), which is based on optimization but utilizes deep neural networks to model deformations between image pairs. NIR represents the transformation between two images with a continuous function implemented via neural fields, receiving a 3D coordinate as input and outputting the corresponding deformation vector. NIR provides two ways of generating deformation field: directly output a displacement vector field for general deformable registration, or output a velocity vector field and integrate the velocity field to derive the deformation field for diffeomorphic image registration. The optimal registration is discovered by updating the parameters of the neural field via stochastic mini-batch gradient descent. We describe several design choices that facilitate model optimization, including coordinate encoding, sinusoidal activation, coordinate sampling, and intensity sampling. NIR is evaluated on two 3D MR brain scan datasets, demonstrating highly competitive performance in terms of both registration accuracy and regularity. Compared to traditional optimization-based methods, our approach achieves better results in shorter computation times. In addition, our methods exhibit performance on a cross-dataset registration task, compared to the pre-trained learning-based methods.

DeepSAP: A Novel Brain Image-Based Deep Learning Model for Predicting Stroke-Associated Pneumonia From Spontaneous Intracerebral Hemorrhage.

RATIONALE AND OBJECTIVE: Stroke-associated pneumonia (SAP) often appears as a complication following intracerebral hemorrhage (ICH), leading to poor prognosis and increased mortality rates. Previous studies have typically developed prediction models based on clinical data alone, without considering that ICH patients often undergo CT scans immediately upon admission. As a result, these models are subjective and lack real-time applicability, with low accuracy that does not meet clinical needs. Therefore, there is an urgent need for a quick and reliable model to timely predict SAP. METHODS: In this retrospective study, we developed an image-based model (DeepSAP) using brain CT scans from 244 ICH patients to classify the presence and severity of SAP. First, DeepSAP employs MRI-template-based image registration technology to eliminate structural differences between samples, achieving statistical quantification and spatial standardization of cerebral hemorrhage. Subsequently, the processed images and filtered clinical data were simultaneously input into a deep-learning neural network for training and analysis. The model was tested on a test set to evaluate diagnostic performance, including accuracy, specificity, and sensitivity. RESULTS: Brain CT scans from 244 ICH patients (mean age, 60.24; 66 female) were divided into a training set (n = 170) and a test set (n = 74). The cohort included 143 SAP patients, accounting for 58.6% of the total, with 66 cases classified as moderate or above, representing 27% of the total. Experimental results showed an AUC of 0.93, an accuracy of 0.84, a sensitivity of 0.79, and a precision of 0.95 for classifying the presence of SAP. In comparison, the model relying solely on clinical data showed an AUC of only 0.76, while the radiomics method had an AUC of 0.74. Additionally, DeepSAP achieved an optimal AUC of 0.84 for the SAP grading task. CONCLUSION: DeepSAP's accuracy in predicting SAP stems from its spatial normalization and statistical quantification of the ICH region. DeepSAP is expected to be an effective tool for predicting and grading SAP in clinic.

ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration.

Retinal image registration is of utmost importance due to its wide applications in medical practice. In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration. In contrast to current registration methods, our approach employs a novel multi-positive multi-negative contrastive learning strategy that enables the utilization of additional information from the available training samples. This makes it possible to learn high-quality descriptors from limited training data. To train and evaluate ConKeD, we combine these descriptors with domain-specific keypoints, particularly blood vessel bifurcations and crossovers, that are detected using a deep neural network. Our experimental results demonstrate the benefits of the novel multi-positive multi-negative strategy, as it outperforms the widely used triplet loss technique (single-positive and single-negative) as well as the single-positive multi-negative alternative. Additionally, the combination of ConKeD with the domain-specific keypoints produces comparable results to the state-of-the-art methods for retinal image registration, while offering important advantages such as avoiding pre-processing, utilizing fewer training samples, and requiring fewer detected keypoints, among others. Therefore, ConKeD shows a promising potential towards facilitating the development and application of deep learning-based methods for retinal image registration.

Australian researchers' perceptions and experiences with stem cell registration.

The recently issued ISSCR standards in stem cell research recommend registration of human pluripotent stem cell lines (hPSCs). Registration is critical to establishing stem cell provenance and connecting cell lines to data derived on those lines. In this study, we sought to understand common barriers to registration by conducting interviews with forty-eight Australian stem cell stakeholders, including researchers, clinicians, and industry professionals. Australian stem cell researchers do not routinely register their lines, and only a third of those Australian lines captured by an international registry have fully completed the registration process. Most registered Australian cell lines lack complete information about their ethical provenance or key pluripotency characteristics. Incomplete registration is poorly aligned with the goals of open science on which registries are founded. Users also expressed concerns about the quality of the incomplete information provided to the resource. Registration was considered negatively, for instance as a hurdle or barrier to publication, which impacted on user perceptions of usefulness of registration and lowered the likelihood that they would engage with registries to find resources. Broader adoption of registration by journals, and continued advocacy by stem cell societies, will be important levers for awareness and engagement with registration. Although the Australian community represents a small fraction of potential registry users, the results of this study suggest ways for journals, registries, funders, and the international stem cell community to improve registration compliance.

Augmented reality navigation method based on image segmentation and sensor tracking registration technology.

With the rapid development of modern science and technology, navigation technology provides great convenience for people's life, but the problem of inaccurate localization in complex environments has always been a challenge that navigation technology needs to be solved urgently. To address this challenge, this paper proposes an augmented reality navigation method that combines image segmentation and multi-sensor fusion tracking registration. The method optimizes the image processing process through the GA-OTSU-Canny algorithm and combines high-precision multi-sensor information in order to achieve accurate tracking of positioning and guidance in complex environments. Experimental results show that the GA-OTSU-Canny algorithm has a faster image edge segmentation rate, and the fastest start speed is only 1.8 s, and the fastest intersection selection time is 1.2 s. The navigation system combining the image segmentation and sensor tracking and registration techniques has a highly efficient performance in real-world navigation, and its building recognition rates are all above 99%. The augmented reality navigation system not only improves the navigation accuracy in high-rise and urban canyon environments, but also significantly outperforms traditional navigation solutions in terms of navigation startup time and target building recognition accuracy. In summary, this research not only provides a new framework for the theoretical integration of image processing and multi-sensor data, but also brings innovative technical solutions for the development and application of practical navigation systems.

A method for calculating vector forces at human-mattress interface during sleeping positions utilizing image registration.

The vector forces at the human-mattress interface are not only crucial for understanding the distribution of vertical and shear forces exerted on the human body during sleep but also serves as a significant input for biomechanical models of sleeping positions, whose accuracy determines the credibility of predicting musculoskeletal system loads. In this study, we introduce a novel method for calculating the interface vector forces. By recording indentations after supine and lateral positions using a vacuum mattress and 3D scanner, we utilize image registration techniques to align body pressure distribution with the mattress deformation scanning images, thereby calculating the vector force values for each unit area (36.25 mm × 36.25 mm). This method was validated through five participants attendance from two perspectives, revealing that (1) the mean summation of the vertical force components is 98.67% ± 7.21% body weight, exhibiting good consistency, and mean ratio of horizontal component force to body weight is 2.18% ± 1.77%. (2) the predicted muscle activity using the vector forces as input to the sleep position model aligns with the measured muscle activity (%MVC), with correlation coefficient over 0.7. The proposed method contributes to the vector force distribution understanding and the analysis of musculoskeletal loads during sleep, providing valuable insights for mattress design and evaluation.

Assessing the transparency in reporting of clinical trials investigating manual therapy interventions for low back pain: A methodological review.

RATIONALE: Low back pain (LBP) is a common condition with a significant societal burden. Manual therapy is an effective treatment for LBP and recommended in clinical practice guidelines. While the quantity of literature supporting the use of manual therapy is large, the methodological quality and transparency of this collective work are unclear. AIMS AND OBJECTIVES: Explore the transparency in reporting of clinical trials assessing manual therapy interventions in patients with LBP by comparing planned components in the trial registration with what was reported in the published manuscript. METHODS: Three databases were searched to identify trials assessing the treatment effect of manual therapy for LBP from January 2005 to May 2023. Studies were included if the manual therapy consisted of thrust manipulations, mobilizations or muscle energy techniques. RESULTS: From 4462 studies initially identified, 167 studies remained in the final review after title, abstract and full-text review. Only 87 (52.1%) of the 167 studies were registered (n = 57 prospectively and n = 30 retrospectively). Primary outcomes in the publications were identical to the registration in 54 (62.1%) of the registered trials. Secondary outcomes in the publication were identical to the registration in 27 (31.0%) of the registered trials. The CONSORT reporting guideline was referenced in only 19 (21.8%) trials. Multiple discrepancies between registration and publication were noted for primary and secondary outcomes. All trials had eligibility criteria in the registration that matched their corresponding manuscript, while only four (4.6%) trial registrations addressed any type of statistical analysis plan. CONCLUSION: Approximately half of the trials were not registered. Of those registered, only half were registered prospectively. Substantial discrepancies existed between registered and published outcomes that were never addressed by the authors, raising questions about potential bias. Transparency can be improved through more stringent requirements during manuscript submission to journals, and better reporting of the rationale for discrepancies between registration and publication.

Applications of phase-separating multi-bilayers in protein-membrane domain interactions.

Synthetic model membranes are important tools to elucidate lipid domain and protein interactions due to predefined lipid compositions and characterizable biophysical properties. Here, we introduce a model membrane with multiple lipid bilayers (multi-bilayers) stacked on a mica substrate that is prepared through a spin-coating technique. The spin-coated multi-bilayers are useful in the study of phase separated membranes with a high cholesterol content, mobile lipids, microscopic and reversible phase separation, and easy conjugation with proteins, which make them a good model to study interactions between proteins and membrane domains.

Development of a software system for surgical robots based on multimodal image fusion: study protocol.

Background: Surgical robots are gaining increasing popularity because of their capability to improve the precision of pedicle screw placement. However, current surgical robots rely on unimodal computed tomography (CT) images as baseline images, limiting their visualization to vertebral bone structures and excluding soft tissue structures such as intervertebral discs and nerves. This inherent limitation significantly restricts the applicability of surgical robots. To address this issue and further enhance the safety and accuracy of robot-assisted pedicle screw placement, this study will develop a software system for surgical robots based on multimodal image fusion. Such a system can extend the application range of surgical robots, such as surgical channel establishment, nerve decompression, and other related operations. Methods: Initially, imaging data of the patients included in the study are collected. Professional workstations are employed to establish, train, validate, and optimize algorithms for vertebral bone segmentation in CT and magnetic resonance (MR) images, intervertebral disc segmentation in MR images, nerve segmentation in MR images, and registration fusion of CT and MR images. Subsequently, a spine application model containing independent modules for vertebrae, intervertebral discs, and nerves is constructed, and a software system for surgical robots based on multimodal image fusion is designed. Finally, the software system is clinically validated. Discussion: We will develop a software system based on multimodal image fusion for surgical robots, which can be applied to surgical access establishment, nerve decompression, and other operations not only for robot-assisted nail placement. The development of this software system is important. First, it can improve the accuracy of pedicle screw placement, percutaneous vertebroplasty, percutaneous kyphoplasty, and other surgeries. Second, it can reduce the number of fluoroscopies, shorten the operation time, and reduce surgical complications. In addition, it would be helpful to expand the application range of surgical robots by providing key imaging data for surgical robots to realize surgical channel establishment, nerve decompression, and other operations.

MACG-Net: Multi-axis cross gating network for deformable medical image registration.

Deformable Image registration is a fundamental yet vital task for preoperative planning, intraoperative information fusion, disease diagnosis and follow-ups. It solves the non-rigid deformation field to align an image pair. Latest approaches such as VoxelMorph and TransMorph compute features from a simple concatenation of moving and fixed images. However, this often leads to weak alignment. Moreover, the convolutional neural network (CNN) or the hybrid CNN-Transformer based backbones are constrained to have limited sizes of receptive field and cannot capture long range relations while full Transformer based approaches are computational expensive. In this paper, we propose a novel multi-axis cross grating network (MACG-Net) for deformable medical image registration, which combats these limitations. MACG-Net uses a dual stream multi-axis feature fusion module to capture both long-range and local context relationships from the moving and fixed images. Cross gate blocks are integrated with the dual stream backbone to consider both independent feature extractions in the moving-fixed image pair and the relationship between features from the image pair. We benchmark our method on several different datasets including 3D atlas-based brain MRI, inter-patient brain MRI and 2D cardiac MRI. The results demonstrate that the proposed method has achieved state-of-the-art performance. The source code has been released at https://github.com/Valeyards/MACG.

Development of a Subtraction Processing Technology for Assistance in the Comparative Interpretation of Mammograms.

A comparative interpretation of mammograms has become increasingly important, and it is crucial to develop subtraction processing and registration methods for mammograms. However, nonrigid image registration has seldom been applied to subjects constructed with soft tissue only, such as mammograms. We examined whether subtraction processing for the comparative interpretation of mammograms can be performed using nonrigid image registration. As a preliminary study, we evaluated the results of subtraction processing by applying nonrigid image registration to normal mammograms, assuming a comparative interpretation between the left and right breasts. Mediolateral-oblique-view mammograms were taken from noncancer patients and divided into 1000 cases for training, 100 cases for validation, and 500 cases for testing. Nonrigid image registration was applied to align the horizontally flipped left-breast mammogram with the right one. We compared the sum of absolute differences (SAD) of the difference of bilateral images (Difference Image) with and without the application of nonrigid image registration. Statistically, the average SAD was significantly lower with the application of nonrigid image registration than without it (without: 0.0692; with: 0.0549 (p < 0.001)). In four subgroups using the breast area, breast density, compressed breast thickness, and Difference Image without nonrigid image registration, the average SAD of the Difference Image was also significantly lower with nonrigid image registration than without it (p < 0.001). Nonrigid image registration was found to be sufficiently useful in aligning bilateral mammograms, and it is expected to be an important tool in the development of a support system for the comparative interpretation of mammograms.

Robust Point Cloud Registration for Aircraft Engine Pipeline Systems.

Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using 3D scanning techniques from multiple viewpoints. To obtain a complete and accurate representation of the aircraft pipeline system, it is necessary to register and align the individual point clouds acquired from different views. However, the structures of aircraft pipelines often appear similar from different viewpoints, and the scanning process is prone to occlusions, resulting in incomplete point cloud data. The occlusions pose a challenge for existing registration methods, as they can lead to missing or wrong correspondences. To this end, we present a novel registration framework specifically designed for aircraft pipeline scenes. The proposed framework consists of two main steps. First, we extract the point feature structure of the pipeline axis by leveraging the cylindrical characteristics observed between adjacent blocks. Then, we design a new 3D descriptor called PL-PPFs (Point Line-Point Pair Features), which combines information from both the pipeline features and the engine assembly line features within the aircraft pipeline point cloud. By incorporating these relevant features, our descriptor enables accurate identification of the structure of the engine's piping system. Experimental results demonstrate the effectiveness of our approach on aircraft engine pipeline point cloud data.

Retinal image registration method for myopia development.

OBJECTIVE: The development of myopia is usually accompanied by changes in retinal vessels, optic disc, optic cup, fovea, and other retinal structures as well as the length of the ocular axis. And the accurate registration of retinal images is very important for the extraction and analysis of retinal structural changes. However, the registration of retinal images with myopia development faces a series of challenges, due to the unique curved surface of the retina, as well as the changes in fundus curvature caused by ocular axis elongation. Therefore, our goal is to improve the registration accuracy of the retinal images with myopia development. METHOD: In this study, we propose a 3D spatial model for the pair of retinal images with myopia development. In this model, we introduce a novel myopia development model that simulates the changes in the length of ocular axis and fundus curvature due to the development of myopia. We also consider the distortion model of the fundus camera during the imaging process. Based on the 3D spatial model, we further implement a registration framework, which utilizes corresponding points in the pair of retinal images to achieve registration in the way of 3D pose estimation. RESULTS: The proposed method is quantitatively evaluated on the publicly available dataset without myopia development and our Fundus Image Myopia Development (FIMD) dataset. The proposed method is shown to perform more accurate and stable registration than state-of-the-art methods, especially for retinal images with myopia development. SIGNIFICANCE: To the best of our knowledge, this is the first retinal image registration method for the study of myopia development. This method significantly improves the registration accuracy of retinal images which have myopia development. The FIMD dataset we constructed has been made publicly available to promote the study in related fields.

Evaluating normalized registration and preprocessing methodologies for the analysis of brain MRI in pediatric patients with shunt-treated hydrocephalus.

Introduction: Registration to a standardized template (i.e. "normalization") is a critical step when performing neuroimaging studies. We present a comparative study involving the evaluation of general-purpose registration algorithms for pediatric patients with shunt treated hydrocephalus. Our sample dataset presents a number of intersecting challenges for registration, representing the potentially large deformations to both brain structures and overall brain shape, artifacts from shunts, and morphological differences corresponding to age. The current study assesses the normalization accuracy of shunt-treated hydrocephalus patients using freely available neuroimaging registration tools. Methods: Anatomical neuroimages from eight pediatric patients with shunt-treated hydrocephalus were normalized. Four non-linear registration algorithms were assessed in addition to the preprocessing steps of skull-stripping and bias-correction. Registration accuracy was assessed using the Dice Coefficient (DC) and Hausdorff Distance (HD) in subcortical and cortical regions. Results: A total of 592 registrations were performed. On average, normalizations performed using the brain extracted and bias-corrected images had a higher DC and lower HD compared to full head/ non-biased corrected images. The most accurate registration was achieved using SyN by ANTs with skull-stripped and bias corrected images. Without preprocessing, the DARTEL Toolbox was able to produce normalized images with comparable accuracy. The use of a pediatric template as an intermediate registration did not improve normalization. Discussion: Using structural neuroimages from patients with shunt-treated pediatric hydrocephalus, it was demonstrated that there are tools which perform well after specified pre-processing steps were taken. Overall, these results provide insight to the performance of registration programs that can be used for normalization of brains with complex pathologies.