History of Changes in the Protocol of Clinical Trial of Zinc Supplementation in Treatment of COVID-19 by Hydroxychloroquine

An article published in this journal used a randomized controlled trial to evaluate the efficacy of combining chloroquine/hydroxychloroquine (CQ/HCQ) and zinc in the treatment of COVID-19 patients. Findings from this study indicate that zinc supplements did not enhance the clinical efficacy of hydroxychloroquine in improving COVID-19 treatment. Although this finding is consistent with many previous studies, several concerns regarding study protocol and trial registration, including interventions and primary outcomes, have been raised in which the protocol has been changed after the completion of the recruitment.

Classification, registration and segmentation of ear canal impressions using convolutional neural networks

Today, fitting bespoke hearing aids involves injecting silicone into patients' ears to produce ear canal molds. These are subsequently 3D scanned to create digital ear canal impressions. However, before digital impressions can be used they require a substantial amount of effort in manual 3D editing. In this article, we present computational methods to pre-process ear canal impressions. The aim is to create automation tools to assist the hearing aid design, manufacturing and fitting processes as well as normalizing anatomical data to assist the study of the outer ear canal's morphology. The methods include classifying the handedness of the impression into left and right ear types, orienting the geometries onto the same coordinate system sense, and removing extraneous artifacts introduced by the silicone mold. We investigate the use of convolutional neural networks for performing these semantic tasks and evaluate their accuracy using a dataset of 3000 ear canal impressions. The neural networks proved highly effective at performing these tasks with 95.8% adjusted accuracy in classification, 92.3% within 20° angular error in registration and 93.4% intersection over union in segmentation.

An automated algorithm for stereoelectroencephalography electrode localization and labelling

PURPOSE

Stereoelectroencephalography (sEEG) is increasingly utilized for localization of seizure foci, functional mapping, and neurocognitive research due to its ability to target deep and difficult to reach anatomical locations and to study in vivo brain function with a high signal-to-noise ratio. The research potential of sEEG is constrained by the need for accurate localization of the implanted electrodes in a common template space for group analyses.

METHODS

We present an algorithm to automate the grouping of sEEG electrodes by trajectories, labelled by target and insertion point. This algorithm forms the core of a pipeline that fully automates the entire process of electrode localization in standard space, using raw CT and MRI images to produce atlas labelled MNI coordinates.

RESULTS

Across 196 trajectories from 20 patients, the pipeline successfully processed 190 trajectories with localizations within 0.25±0.55 mm of the manual annotation by two reviewers. Six electrode trajectories were not directly identified due to metal artifacts and locations were interpolated based on the first and last contact location and the number of contacts in that electrode as listed in the surgical record.

CONCLUSION

We introduce our algorithm and pipeline for automatically localizing, grouping, and classifying sEEG electrodes from raw CT and MRI. Our algorithm adds to existing pipelines and toolboxes for electrode localization by automating the manual step of marking and grouping electrodes, thereby expedites the analyses of sEEG data, particularly in large datasets.

Industry Perceptions and Experiences with the Access Consortium New Active Substance Work-Sharing Initiative (NASWSI): Survey Results and Recommendations

The Access Consortium New Active Substance Work-Sharing Initiative, or "Access" for simplicity, allows regulatory authorities (RAs) of the Access Consortium countries to jointly review applications for the registration of new active substances or for new indications. Using a survey developed by the pharmaceutical industry trade associations of the five Access Consortium countries-Australia, Canada, Singapore, Switzerland, and the United Kingdom (UK)-this study gathered insights into the perceptions and experiences of the Access pathway held by affiliates of pharmaceutical companies. Understanding industry perceptions of Access is important for the success of the initiative, as participation is voluntary. Findings indicate that affiliates who participated in Access had mostly positive experiences with this pathway; most affiliates were satisfied with their interactions with the Access RAs and appeared willing to continue to participate in the initiative. Affiliates' reasons for not having yet participated in Access included a lack of opportunity to do so and perceived barriers, such as the Access pathway being too complicated to manage. Recommendations to improve Access cover six key areas: ensure predictability, increase guidance and transparency, streamline processes, maintain flexibility, increase harmonization, and advance RA-industry cooperation. This study should facilitate informed discussions among relevant stakeholders on how to improve Access to maximize efficiencies, accelerate approvals, and improve patient access to innovative medicines.

Catheter navigation support for mechanical thrombectomy guidance: 3D/2D multimodal catheter-based registration with no contrast dye fluoroscopy

PURPOSE

The fusion of pre-operative imaging and intra-operative fluoroscopy may support physicians during mechanical thrombectomy for catheter navigation from the aortic arch to carotids. Nevertheless, the aortic arch volume is too important for intra-operative contrast dye injection leading to a lack of common anatomical structure of interest that results in a challenging 3D/2D registration. The objective of this work is to propose a registration method between pre-operative 3D image and no contrast dye intra-operative fluoroscopy.

METHODS

The registration method exploits successive 2D fluoroscopic images of the catheter navigating in the aortic arch. The similarity measure is defined as the normalized cross-correlation between a binary combination of catheter images and a pseudo-DRR resulting from the 2D binary projection of the pre-operative 3D image (MRA or CTA). The 3D/2D transformation is decomposed in out-plane and in-plane transformations to reduce computational complexity. The 3D/2D transformation is then obtained by maximizing the similarity measure through multiresolution exhaustive search.

RESULTS

We evaluated the registration performance through dice score and mean landmark error. We evaluated the influence of parameters setting, aortic arch type and 2D navigation sequence duration. Results on a physical phantom and data from a patient who underwent a mechanical thrombectomy showed good registration accuracy with a dice score higher than 92% and a mean landmark error lower than the quarter of a carotid diameter (8-10 mm).

CONCLUSION

A new registration method compatible with no contrast dye fluoroscopy has been proposed to guide the crossing from aortic arch to a carotid in mechanical thrombectomy. First evaluation showed the feasibility and accuracy of the method as well as its compatibility with clinical routine practice.

Rib region detection for scanning path planning for fully automated robotic abdominal ultrasonography

PURPOSE

Scanning path planning is an essential technology for fully automated ultrasound (US) robotics. During biliary scanning, the subcostal boundary is critical body surface landmarks for scanning path planning but are often invisible, depending on the individual. This study developed a method of estimating the rib region for scanning path planning toward fully automated robotic US systems.

METHODS

We proposed a method for determining the rib region using RGB-D images and respiratory variation. We hypothesized that detecting the rib region would be possible based on changes in body surface position due to breathing. We generated a depth difference image by finding the difference between the depth image taken at the resting inspiratory position and the depth image taken at the maximum inspiratory position, which clearly shows the rib region. The boundary position of the subcostal was then determined by applying training using the YOLOv5 object detection model to this depth difference image.

RESULTS

In the experiments with healthy subjects, the proposed method of rib detection using the depth difference image marked an intersection over union (IoU) of 0.951 and average confidence of 0.77. The average error between the ground truth and predicted positions was 16.5 mm in 3D space. The results were superior to rib detection using only the RGB image.

CONCLUSION

The proposed depth difference imaging method, which measures respiratory variation, was able to accurately estimate the rib region without contact and physician intervention. It will be useful for planning the scan path during the biliary imaging.

[Evaluation of a coding guide on social determinants of health in primary care consultations: A mixed study]

OBJECTIVE

To evaluate a coding guide for social determinants of health in primary care consultations as an effective tool in the professional's daily workflow.

DESIGN

Mixed sequential explanatory study. Formed by a quantitative part (experimental) and a qualitative part (descriptive-evaluative).

LOCATION

All the primary care teams of the Central Catalonia Management (32 teams).

PARTICIPANTS AND SETTING

All nursing, social work and medical professionals working in the 32 primary care teams of the Catalan Institute of Health in Central Catalonia from February 2023 to July 2023.

METHODS

A social determinants of health coding guide was developed. This guide was created in a multidisciplinary and multicenter manner. Purposive sampling. Quantitatively, the number of diagnoses recorded by the experimental group versus the control group was counted. Qualitatively, a thematic analysis was carried out from a socio-constructivist perspective.

RESULTS

The results were significant and satisfactory. Using a quantitative methodology, the effectiveness of the use of the guide was assessed. A significant increase in the use of the social determinants was observed in the intervention group vs. the control group, with a percentage of post-intervention use of 19.53% in the control group and 32.26% in the intervention group (P < .001). The number of diagnoses recorded increased from 312 to 1322 in the intervention group, while it remained the same in the control group. The main factors identified through qualitative methodology that may explain the effectiveness of the guideline were: 1) the effectiveness of the guideline among primary care professionals, 2) the appropriateness of the guideline by assessing its usefulness and practicality, 3) feasibility and 4) specific contributions to the improvement of the guideline.

CONCLUSIONS

The social determinants of health coding guide is effective, appropriate and can be implemented in the workflow of primary health care professionals for good recording of the social determinants of health.

The Accuracy of Intraoral Registration for Dynamic Surgical Navigation in the Edentulous Maxilla

PURPOSE

Despite the high clinical accuracy of dynamic navigation, inherent sources of error exist. The purpose of this study was to improve the accuracy of dynamic navigated surgical procedures in the edentulous maxilla by identifying the optimal configuration of intra-oral points that results in the lowest possible registration error for direct clinical implementation.

MATERIALS AND METHODS

Six different 4-area configurations were tested by 3 operators against positive and negative controls (8-areas and 3-areas, respectively) using a skull model. The two dynamic navigation systems (X-Guide® and NaviDent®) and the two registration methods (bone surface tracing and fiducial markers) produced four registration groups. The accuracy of the registration was checked at the frontal process of the zygoma. Intra- and inter- operator reliability for each registration group were reported. Multiple comparisons were conducted to find the best configuration with the minimum registration error.

RESULTS

Ranking revealed one configuration in the tracing groups (Conf.3) and two configurations in the fiducial groups (Conf.3 and Conf.5) that had the best accuracy. When the inferior surfaces of the zygomatic buttress were excluded, fiducial registration produced better accuracy with both systems (p 0.006 and <0.0001). However, tracing 1 cm areas at these surfaces bilaterally resulted in similar registration accuracy as placing fiducial markers there (p 0.430 and 0.237). NaviDent® performed generally better (p 0.049, 0.001 and 0.002) albeit having a wider margin of uncertainty in the obtained values. Changing the distribution of the 4 tracing areas or fiducial markers had a less pronounced effect with X-Guide® than with the NaviDent® system.

CONCLUSION

For edentulous maxillary surgeries, 4 fiducial markers placed according to configuration 3 or 5 result in the lowest registration error. Where implants are being placed bilaterally, an additional 2 sites may reduce the error further. For bilateral zygomatic implant placement, it is optimal to place 2 fiducials on the inferior surfaces of the maxillary tuberosities, other 2 on their buccal surfaces, and 2 on the anterior labial surface of the alveolar bone. Utilising the inferior zygomatic buttress is recommended over the inferior maxillary tuberosities in other types of maxillary surgeries.

The completeness of routine registration of the counterpart in deliberate interpersonal violence in an urban emergency department

Most studies of violence from the health care system lack reliable information about the counterpart, which is important for distinguishing between different types of violence. Since 2014, the emergency department at Odense University Hospital in Denmark has routinely registered information about the counterpart. The purpose of this study was to evaluate the completeness of registering information about the counterpart during routine registration of victims of interpersonal violence in the emergency department. We included 11,200 victims treated at the emergency department 2014-2021. Using the patient registration data, we estimated the proportion of missing information on the counterpart, stratified by age group and gender of the victim as well as type of incident and severity of injury. Information about the counterpart was registered in 91.5 % of all cases. In 43.1 % (CI: 42.2-44.0) of the cases, the counterpart was unknown to the victim, in 24.3 % (CI: 23.5-25.1) the counterpart was an acquaintance, in 10.5 % (CI: 10.0-11.1) the counterpart was a partner, and in 4.2 % (CI: 3.8-4.5) the counterpart was another family member. The proportion of cases with no information about the counterpart varied with gender, age group, time of violence, place of violence, weapon use, and severity of injury. Half of the victims injured with firearms (46.2 %, CI: 30.1-62.8) and one-fourth of the victims injured with knives (25.9 %, CI: 21.9-30.2) did not reveal information about the counterpart. The majority of the victims revealed information about the counterpart, making it possible to analyse different types of violence separately.

Deep learning-based 3D brain multimodal medical image registration

Medical image registration is a critical preprocessing step in medical image analysis. While traditional medical image registration techniques have matured, their registration speed and accuracy still fall short of clinical requirements. In this paper, we propose an improved VoxelMorph network incorporating ResNet modules and CBAM (RCV-Net), for 3D multimodal unsupervised registration. Unlike popular convolution-based U-shaped registration networks like VoxelMorph, RCV-Net incorporates the convolutional block attention module (CBAM) during the convolution process. This inclusion enhances the feature map information extraction capabilities during training and effectively prevents information loss. Additionally, we introduce a lightweight and residual network module at the network's base, which enhances learning ability without significantly increasing training parameters. To evaluate the superiority of our registration model, we utilize evaluation metrics such as structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE). Experimental results demonstrate that our proposed network structure outperforms current state-of-the-art methods, yielding better performance in multimodal registration tasks. Furthermore, generalization testing on databases outside of the training set has confirmed the optimal registration effectiveness of our model.

Single scanning of CBCT and intraoral scanning for guided implantation in terminal dentitions with multi-unit metal restorations: technical note

When anatomical landmarks are missing or obstructed by metal artefacts, it is challenging to accurately merge cone beam computed tomography (CBCT) and intraoral scanning (IOS) information, and the accuracy of the implant surgical guides would be compromised. This article describes a novel technical note using oral wound dressings and flowable resin as additional new radiopaque fiducial landmarks to design surgical guides for full-arch immediate implant placement. This technical note provided an accurate, convenient, and cost-effective option for the clinician.

Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks

PURPOSE

Patient-to-image registration is a preliminary step required in surgical navigation based on preoperative images. Human intervention and fiducial markers hamper this task as they are time-consuming and introduce potential errors. We aimed to develop a fully automatic 2D registration system for augmented reality in ear surgery.

METHODS

CT-scans and corresponding oto-endoscopic videos were collected from 41 patients (58 ears) undergoing ear examination (vestibular schwannoma before surgery, profound hearing loss requiring cochlear implant, suspicion of perilymphatic fistula, contralateral ears in cases of unilateral chronic otitis media). Two to four images were selected from each case. For the training phase, data from patients (75% of the dataset) and 11 cadaveric specimens were used. Tympanic membranes and malleus handles were contoured on both video images and CT-scans by expert surgeons. The algorithm used a U-Net network for detecting the contours of the tympanic membrane and the malleus on both preoperative CT-scans and endoscopic video frames. Then, contours were processed and registered through an iterative closest point algorithm. Validation was performed on 4 cases and testing on 6 cases. Registration error was measured by overlaying both images and measuring the average and Hausdorff distances.

RESULTS

The proposed registration method yielded a precision compatible with ear surgery with a 2D mean overlay error of [Formula: see text] mm for the incus and [Formula: see text] mm for the round window. The average Hausdorff distance for these 2 targets was [Formula: see text] mm and [Formula: see text] mm respectively. An outlier case with higher errors (2.3 mm and 1.5 mm average Hausdorff distance for incus and round window respectively) was observed in relation to a high discrepancy between the projection angle of the reconstructed CT-scan and the video image. The maximum duration for the overall process was 18 s.

CONCLUSIONS

A fully automatic 2D registration method based on a convolutional neural network and applied to ear surgery was developed. The method did not rely on any external fiducial markers nor human intervention for landmark recognition. The method was fast and its precision was compatible with ear surgery.

Brain Imaging and Registration in Larval Zebrafish

Registration of larval zebrafish brain scans to a common reference brain enables comparison of transgene and gene expression patterns, neuroanatomy, and morphometry. Here we describe methods for staining and mounting larval zebrafish to facilitate whole-brain fluorescence imaging. Following image acquisition, we provide a template for aligning brain images to a reference atlas using nonlinear registration with the ANTs software package.

Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery

Established surgical navigation systems for pedicle screw placement have been proven to be accurate, but still reveal limitations in registration or surgical guidance. Registration of preoperative data to the intraoperative anatomy remains a time-consuming, error-prone task that includes exposure to harmful radiation. Surgical guidance through conventional displays has well-known drawbacks, as information cannot be presented in-situ and from the surgeon's perspective. Consequently, radiation-free and more automatic registration methods with subsequent surgeon-centric navigation feedback are desirable. In this work, we present a marker-less approach that automatically solves the registration problem for lumbar spinal fusion surgery in a radiation-free manner. A deep neural network was trained to segment the lumbar spine and simultaneously predict its orientation, yielding an initial pose for preoperative models, which then is refined for each vertebra individually and updated in real-time with GPU acceleration while handling surgeon occlusions. An intuitive surgical guidance is provided thanks to the integration into an augmented reality based navigation system. The registration method was verified on a public dataset with a median of 100% successful registrations, a median target registration error of 2.7 mm, a median screw trajectory error of 1.6°and a median screw entry point error of 2.3 mm. Additionally, the whole pipeline was validated in an ex-vivo surgery, yielding a 100% screw accuracy and a median target registration error of 1.0 mm. Our results meet clinical demands and emphasize the potential of RGB-D data for fully automatic registration approaches in combination with augmented reality guidance.

Research related to the diagnosis of prostate cancer based on machine learning medical images: A review

BACKGROUND

Prostate cancer is currently the second most prevalent cancer among men. Accurate diagnosis of prostate cancer can provide effective treatment for patients and greatly reduce mortality. The current medical imaging tools for screening prostate cancer are mainly MRI, CT and ultrasound. In the past 20 years, these medical imaging methods have made great progress with machine learning, especially the rise of deep learning has led to a wider application of artificial intelligence in the use of image-assisted diagnosis of prostate cancer.

METHOD

This review collected medical image processing methods, prostate and prostate cancer on MR images, CT images, and ultrasound images through search engines such as web of science, PubMed, and Google Scholar, including image pre-processing methods, segmentation of prostate gland on medical images, registration between prostate gland on different modal images, detection of prostate cancer lesions on the prostate.

CONCLUSION

Through these collated papers, it is found that the current research on the diagnosis and staging of prostate cancer using machine learning and deep learning is in its infancy, and most of the existing studies are on the diagnosis of prostate cancer and classification of lesions, and the accuracy is low, with the best results having an accuracy of less than 0.95. There are fewer studies on staging. The research is mainly focused on MR images and much less on CT images, ultrasound images.

DISCUSSION

Machine learning and deep learning combined with medical imaging have a broad application prospect for the diagnosis and staging of prostate cancer, but the research in this area still has more room for development.

Breast simulation pipeline: From medical imaging to patient-specific simulations

BACKGROUND

Breast-conserving surgery is the most acceptable operation for breast cancer removal from an invasive and psychological point of view. Before the surgical procedure, a preoperative MRI is performed in the prone configuration, while the surgery is achieved in the supine position. This leads to a considerable movement of the breast, including the tumor, between the two poses, complicating the surgeon's task.

METHODS

In this work, a simulation pipeline allowing the computation of patient-specific geometry and the prediction of personalized breast material properties was put forward. Through image segmentation, a finite element model including the subject-specific geometry is established. By first computing an undeformed state of the breast, the geometrico-material model is calibrated by surface acquisition in the intra-operative stance.

FINDINGS

Using an elastic corotational formulation, the patient-specific mechanical properties of the breast and skin were identified to obtain the best estimates of the supine configuration. The final results are a shape-fitting closest point residual of 4.00 mm for the mechanical parameters Ebreast=0.32 kPa and Eskin=22.72 kPa, congruent with the current state-of-the-art. The Covariance Matrix Adaptation Evolution Strategy optimizer converges on average between 5 to 30 min depending on the initial parameters, reaching a simulation speed of 20 s. To our knowledge, our model offers one of the best compromises between accuracy and speed.

INTERPRETATION

Satisfactory results were obtained for the estimation of breast deformation from preoperative to intra-operative configuration. Furthermore, we have demonstrated the clinical feasibility of such applications using a simulation framework that aims at the smallest disturbance of the actual surgical pipeline.

Coarse-to-fine registration and time-intensity curves constraint for liver DCE-MRI synthesis

Image registration plays a crucial role in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), used as a fundamental step for the subsequent diagnosis of benign and malignant tumors. However, the registration process encounters significant challenges due to the substantial intensity changes observed among different time points, resulting from the injection of contrast agents. Furthermore, previous studies have often overlooked the alignment of small structures, such as tumors and vessels. In this work, we propose a novel DCE-MRI registration framework that can effectively align the DCE-MRI time series. Specifically, our DCE-MRI registration framework consists of two steps, i.e., a de-enhancement synthesis step and a coarse-to-fine registration step. In the de-enhancement synthesis step, a disentanglement network separates DCE-MRI images into a content component representing the anatomical structures and a style component indicating the presence or absence of contrast agents. This step generates synthetic images where the contrast agents are removed from the original images, alleviating the negative effects of intensity changes on the subsequent registration process. In the registration step, we utilize a coarse registration network followed by a refined registration network. These two networks facilitate the estimation of both the coarse and refined displacement vector fields (DVFs) in a pairwise and groupwise registration manner, respectively. In addition, to enhance the alignment accuracy for small structures, a voxel-wise constraint is further conducted by assessing the smoothness of the time-intensity curves (TICs). Experimental results on liver DCE-MRI demonstrate that our proposed method outperforms state-of-the-art approaches, offering more robust and accurate alignment results.

Preserving Derivative Information while Transforming Neuronal Curves

The international neuroscience community is building the first comprehensive atlases of brain cell types to understand how the brain functions from a higher resolution, and more integrated perspective than ever before. In order to build these atlases, subsets of neurons (e.g. serotonergic neurons, prefrontal cortical neurons etc.) are traced in individual brain samples by placing points along dendrites and axons. Then, the traces are mapped to common coordinate systems by transforming the positions of their points, which neglects how the transformation bends the line segments in between. In this work, we apply the theory of jets to describe how to preserve derivatives of neuron traces up to any order. We provide a framework to compute possible error introduced by standard mapping methods, which involves the Jacobian of the mapping transformation. We show how our first order method improves mapping accuracy in both simulated and real neuron traces under random diffeomorphisms. Our method is freely available in our open-source Python package brainlit.

A deformable patch-based transformer for 3D medical image registration

PURPOSE

Medical image registration is of great importance in clinical medicine. However, medical image registration algorithms are still in the development stage due to the challenges posed by the related complex physiological structures. The objective of this study was to design a 3D medical image registration algorithm that satisfies the need for high accuracy and speed of complex physiological structures.

METHODS

We present a new unsupervised learning algorithm, "DIT-IVNet," for 3D medical image registration. Unlike the more popular convolution-based U-shaped registration network architectures like VoxelMorph, DIT-IVNet uses a combined convolution and transformer network architecture. To better extract image information features and reduce the heavy training parameters, we improved the 2D_Depatch module to a 3D_Depatch module, thus replacing the patch embedding in the original Vision Transformer which adaptively performs patch embedding based on 3D image structure information. We also designed inception blocks in the down-sampling part of the network to help coordinate feature learning from images to different scales.

RESULTS

Dice score, Negative Jacobian determinant, Hausdorff distance, and Structural Similarity evaluation metrics were used to evaluate the registration effects. The results showed that our proposed network had the best metric results compared with some state-of-the-art methods. Moreover, our network obtained the highest Dice score in the generalization experiments which indicated better generalizability of our model.

CONCLUSION

We proposed an unsupervised registration network and evaluated its performance in deformable medical image registration. The results of the evaluation metrics showed that the network structure outperformed state-of-the-art methods for the registration of brain datasets.

A multi-view assisted registration network for MRI registration pre- and post-therapy

Image registration of magnetic resonance imaging (MRI) pre- and post-therapy is an important part of evaluating the effect of therapy in tumor patients. The accuracy of evaluation results heavily relies on the alignment of the MRI image after registration. Although recent advancements have been made in medical image registration, applying these methods to MRI registration pre- and post-therapy remains challenging. Existing methods typically utilize single-view data for registration. However, when applied to MRI data where some slices are clear while others are blurred, these methods can be misled by erroneous spatial information in the blurred regions, leading to poor registration outcomes. To mitigate the interference caused by erroneous spatial information in single-view data, this paper proposes a multi-stream fusion-assisted registration network that incorporates different-view MRIs of the same patient at the same site. Additionally, a cross-attention guided fusion module is designed within the network to effectively utilize accurate spatial information from multi-view data. The proposed approach was evaluated on clinical data, and the experimental results demonstrated that incorporating multiple view data as auxiliary information significantly enhances the accuracy of MRI image registration before and after radiotherapy.

Safety profile of herbal medicines submitted for marketing authorization in Tanzania: a cross-sectional retrospective study

BACKGROUND

The popular use of herbal medicines necessitates national regulatory authorities to have efficient mechanisms for the control of these products including marketing authorization (MA) and safety follow-up. Herbal medicines like conventional medicines require assessment of efficacy, safety and quality information before MA can be granted. However, the complete proof of safety is mainly based on the history of the long-term traditional use. Herbal medicines can cause adverse reactions due to various factors and thus require clinical trials to ensure their safety. Herbal medicines treatment practices involve combinations of different plants to achieve the desired effect while multiple herbal components have been known to cause herbal-herbal toxicity and interactions due to variety of complex active ingredients in plants. Compliance with regulatory requirements on herbal medicines has been shown to be difficult for manufacturers since different countries have different regulatory requirements with wide variations which results in the MA of very few herbal medicines. Limited studies on dossiers of marketing authorization of herbal medicines have been performed in other countries, with no studies in African regulatory system settings. The aim of this study is to determine the type of safety documentation that is submitted on herbal medicines application dossiers to support MA in Tanzania.

METHODS

A cross-sectional retrospective study of herbal medicines dossiers submitted at the Tanzania Medicines and Medical Devices Authority from 2009 to 2020 was conducted.

RESULTS

As many as 75% of the herbal products applications were combination products made by more than one herbal substance or plant. Out of 84 dossiers subjected to analysis the majority did not provide evidence of preclinical (55%) and clinical safety data (68%). Evidence of safety data in humans was mostly from the literature (70%) and not manufacturers' clinical studies. Quality parameters with safety implications were not included in 48% and 23% of the active herbal substance and finished product specifications, respectively.

CONCLUSION

Analysis of the herbal medicine dossiers submitted showed major deficiencies of safety data to support MA. Manufactures need to provide evidence to support the safety of their products for evidence-based regulatory decisions and to avoid multiple reviews of the applications.

Improved fMRI-based pain prediction using Bayesian group-wise functional registration

In recent years, the field of neuroimaging has undergone a paradigm shift, moving away from the traditional brain mapping approach towards the development of integrated, multivariate brain models that can predict categories of mental events. However, large interindividual differences in both brain anatomy and functional localization after standard anatomical alignment remain a major limitation in performing this type of analysis, as it leads to feature misalignment across subjects in subsequent predictive models. This article addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subject's functional data to a common latent template map. Our proposed Bayesian functional group-wise registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. We achieve the probabilistic registration with inverse-consistency by utilizing the generalized Bayes framework with a loss function for the symmetric group-wise registration. It models the latent template with a Gaussian process, which helps capture spatial features in the template, producing a more precise estimation. We evaluate the method in simulation studies and apply it to data from an fMRI study of thermal pain, with the goal of using functional brain activity to predict physical pain. We find that the proposed approach allows for improved prediction of reported pain scores over conventional approaches. Received on 2 January 2017. Editorial decision on 8 June 2021.

Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning

Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires labor-intensive pixel-wise manual annotation from experienced domain experts (e.g., pathologists). Moreover, such annotation is error-prone when differentiating fine-grained cell types (e.g., podocyte and mesangial cells) via the naked human eye. In this study, we assess the feasibility of democratizing pathological AI deployment by only using lay annotators (annotators without medical domain knowledge). The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data. From the experimental results, our learning method achieved F1 = 0.8496 using molecular-informed annotations from lay annotators, which is better than conventional morphology-based annotations (F1 = 0.7015) from experienced pathologists. Our method democratizes the development of a pathological segmentation deep model to the lay annotator level, which consequently scales up the learning process similar to a non-medical computer vision task. The official implementation and cell annotations are publicly available at https://github.com/hrlblab/MolecularEL.

Fast fetal head compounding from multi-view 3D ultrasound

The diagnostic value of ultrasound images may be limited by the presence of artefacts, notably acoustic shadows, lack of contrast and localised signal dropout. Some of these artefacts are dependent on probe orientation and scan technique, with each image giving a distinct, partial view of the imaged anatomy. In this work, we propose a novel method to fuse the partially imaged fetal head anatomy, acquired from numerous views, into a single coherent 3D volume of the full anatomy. Firstly, a stream of freehand 3D US images is acquired using a single probe, capturing as many different views of the head as possible. The imaged anatomy at each time-point is then independently aligned to a canonical pose using a recurrent spatial transformer network, making our approach robust to fast fetal and probe motion. Secondly, images are fused by averaging only the most consistent and salient features from all images, producing a more detailed compounding, while minimising artefacts. We evaluated our method quantitatively and qualitatively, using image quality metrics and expert ratings, yielding state of the art performance in terms of image quality and robustness to misalignments. Being online, fast and fully automated, our method shows promise for clinical use and deployment as a real-time tool in the fetal screening clinic, where it may enable unparallelled insight into the shape and structure of the face, skull and brain.

Quantifying maxillary anterior tooth movement in digital orthodontics: Does the choice of the superimposition software matter?

BACKGROUND

To compare the agreement between predetermined angular and linear tooth movement measurements processed with three digital model registration software packages.

METHODS

Twenty maxillary intraoral pretreatment scans of patients undergoing clear aligner therapy were randomly selected. Digital setups were generated using OrthoAnalyzer Clear Aligner Studio software to serve as the reference standard. Both pretreatment scans and setups were converted to STL files and exported to Geomagic, OrthoAnalyzer-Model Set Compare, and Compare model registration software packages. The amount of tooth movement of the maxillary incisors and canines was calculated in six degrees of freedom.

RESULTS

Statistical significance of the obtained results was expressed at P < 0.01 to account for multiple comparisons. The maxillary central incisors showed the highest agreement for torque and rotation as measured by all software programs. Lateral incisors showed the least agreement in linear movements as measured by Geomagic and Compare, and for tip as measured by Geomagic and OrthoAnalyzer. Maxillary canines had the highest agreement for all linear movements as measured by Geomagic and Compare, and tip as measured by Geomagic and OrthoAnalyzer. Geomagic showed excellent agreement for all measurements except for torque, whereas Compare showed excellent agreement only for rotation and linear measurements. OrthoAnalyzer showed moderate agreement for all measurements except for rotation, which showed good agreement.

CONCLUSIONS

Maxillary central incisor measurements showed higher agreement compared with measurements of the maxillary lateral incisors and canines. Although none of the software showed poor agreement, Geomagic seemed to have the highest accuracy.

Increasing the transparency of systematic reviews: presenting a generalized registration form

This paper presents a generalized registration form for systematic reviews that can be used when currently available forms are not adequate. The form is designed to be applicable across disciplines (i.e., psychology, economics, law, physics, or any other field) and across review types (i.e., scoping review, review of qualitative studies, meta-analysis, or any other type of review). That means that the reviewed records may include research reports as well as archive documents, case law, books, poems, etc. Items were selected and formulated to optimize broad applicability instead of specificity, forgoing some benefits afforded by a tighter focus. This PRISMA 2020 compliant form is a fallback for more specialized forms and can be used if no specialized form or registration platform is available. When accessing this form on the Open Science Framework website, users will therefore first be guided to specialized forms when they exist. In addition to this use case, the form can also serve as a starting point for creating registration forms that cater to specific fields or review types.

Feasibility of tracked ultrasound registration for pelvic-abdominal tumor navigation: a patient study

PURPOSE

Surgical navigation techniques can guide surgeons in localizing pelvic-abdominal malignancies. For abdominal navigation, accurate patient registration is crucial and is generally performed using an intra-operative cone-beam CT (CBCT). However, this method causes 15-min surgical preparation workflow interruption and radiation exposure, and more importantly, it cannot be repeated during surgery to compensate for large patient movement. As an alternative, the accuracy and feasibility of tracked ultrasound (US) registration are assessed in this patient study.

METHODS

Patients scheduled for surgical navigation during laparotomy of pelvic-abdominal malignancies were prospectively included. In the operating room, two percutaneous tracked US scans of the pelvic bone were acquired: one in supine and one in Trendelenburg patient position. Postoperatively, the bone surface was semiautomatically segmented from US images and registered to the bone surface on the preoperative CT scan. The US registration accuracy was computed using the CBCT registration as a reference and acquisition times were compared. Additionally, both US measurements were compared to quantify the registration error caused by patient movement into Trendelenburg.

RESULTS

In total, 18 patients were included and analyzed. US registration resulted in a mean surface registration error of 1.2 ± 0.2 mm and a mean target registration error of 3.3 ± 1.4 mm. US acquisitions were 4 × faster than the CBCT scans (two-sample t-test P < 0.05) and could even be performed during standard patient preparation before skin incision. Patient repositioning in Trendelenburg caused a mean target registration error of 7.7 ± 3.3 mm, mainly in cranial direction.

CONCLUSION

US registration based on the pelvic bone is accurate, fast and feasible for surgical navigation. Further optimization of the bone segmentation algorithm will allow for real-time registration in the clinical workflow. In the end, this would allow intra-operative US registration to correct for large patient movement.

TRIAL REGISTRATION

This study is registered in ClinicalTrials.gov (NCT05637359).

The use of robot-assisted surgery for the unstable traumatic spine: A retrospective cohort study

Background

Robotic assistance has been shown to increase instrumentation placement accuracy in open and minimally invasive spinal fusion. These gains have been achieved without increases in operative times, blood loss, or hospitalization duration. However, most work has been done in the degenerative population and little is known of the utility of robotic assistance when applied to spinal trauma. This is largely due to the uncertainty stemming from the disruption of normal anatomy by the traumatic injury. Since the robot depends upon registration for instrumentation guidance according to the fiducials it uses, trauma can introduce unique challenges. The present study sought to evaluate the safety and efficacy of robotic assistance in a consecutive cohort of spine trauma patients.

Methods

All patients with Thoracolumbar Injury Classification and Severity Scale (TLICS) >4 who underwent robot-assisted spinal fusion using the Globus ExcelsiusGPS at a single tertiary care center for trauma between 2020 and 2022 were identified. Demographic, clinical, and surgical data were collected and analyzed; the primary endpoints were operative time, fluoroscopy time, estimated blood loss, postoperative complications, admission time, and 90-day readmission rate. The paired t-test was used to compare differences between mean values when looking at the number of surgical levels.

Results

Forty-two patients undergoing robot-assisted spinal surgery were included (mean age 61.3±17.1 year; 47% female. Patients were stratified by the number of operative levels, 2 (n = 10), 3-4 (n = 11), 5 to 6 (n = 13), or >6 (n = 8). There appeared to be a positive correlation between number of levels instrumented and odds of postoperative complications, admission duration, fluoroscopy time, and estimated blood loss. There were no instances of screw malposition or breach.

Conclusions

This initial experience suggests robotic assistance can be safely employed in the spine trauma population. Additional experiences in larger patient populations are necessary to delineate those traumatic pathologies most amenable to robotic assistance.

Estimating cancer incidence in Uganda: a feasibility study for periodic cancer surveillance research in resource limited settings

BACKGROUND

Population based cancer registries (PBCRs) are accepted as the gold standard for estimating cancer incidence in any population. However, only 15% of the world's population is covered by high quality cancer registries with coverage as low as 1.9% in settings such as Africa. This study was conducted to assess the operational feasibility of estimating cancer incidence using a retrospective "catchment population" approach in Uganda.

METHODS

A retrospective population study was conducted in 2018 to identify all newly diagnosed cancer cases between 2013 and 2017 in Mbarara district. Data were extracted from the medical records of health facilities within Mbarara and from national and regional centres that provide cancer care services. Cases were coded according to the International Classification of Diseases for Oncology (ICD-0-03). Data was analysed using CanReg5 and Excel.

RESULTS

We sought to collect data from 30 health facilities serving Mbarara district, southwestern Uganda. Twenty-eight sources (93%) provided approval within the set period of two months. Among the twenty-eight sources, two were excluded, as they did not record addresses for cancer cases, leaving 26 sources (87%) valid for data collection. While 13% of the sources charged a fee, ranging from $30 to $100, administrative clearance and approval was at no cost in most (87%) data sources. This study registered 1,258 new cancer cases in Mbarara district. Of the registered cases, 65.4% had a morphologically verified diagnosis indicating relatively good quality of data. The Age-Standardised Incidence Rates for all cancers combined were 109.9 and 91.9 per 100,000 in males and females, respectively. In males, the most commonly diagnosed cancers were prostate, oesophagus, stomach, Kaposi's sarcoma and liver. In females, the most common malignancies were cervix uteri, breast, stomach, liver and ovary. Approximately, 1 in 8 males and 1 in 10 females would develop cancer in Mbarara before the age of 75 years.

CONCLUSION

Estimating cancer incidence using a retrospective cohort design and a "catchment population approach" is feasible in Uganda. Periodic studies using this approach are potentially a precious resource for producing quality cancer data in settings where PBCRs are scarce. This could supplement PBCR data to provide a detailed and comprehensive picture of the cancer burden over time, facilitating the direction of cancer control efforts in resource-limited countries.

Knowledge about stem cell sources and obstacles in donation of bone marrow and peripheral blood stem cells: a cross-sectional survey from Ha'il city to track the prospects of regenerative medicine in Saudi Arabia

BACKGROUND

Promoting stem cell donation behaviors could be crucial in advancing stem cell-based treatment, research and improving public health in Saudi Arabia. Donation of stem cells can be considered an act of social welfare just like blood donation because stem-cell-based therapies are emerging as a hope for those suffering from chronic health conditions and/or terminal illnesses.

AIM

This study aims at assessing levels of awareness about sources of stem-cells, donor organizations and predictors of stem cell donation behavior in target population.

METHODS

The study employed a cross-sectional online survey method. The study sample comprises 1325 educated Saudi people living in Ha'il city. The survey questionnaire collected data about respondents' demographic background, awareness about various sources of stem cells and stem-cell donor registries, willingness to donate stem cells, registration status and obstacles in stem cell donation registration. Percentages, Chi-square analysis and Odd Ratios were computed to analyze the data.

RESULTS

In this sample, (n = 696; 52%) were males and (n = 629; 48%) were females. Although (n = 1308; 98%) percent of respondents reported willingness to donate stem cell, less than one percent (n = 6; 0.5) were registered with Saudi Stem Cell Registry. Over 50% of respondents hold inaccurate perceptions about sources of stem cell. Odd Ratio (OR) values from binary logistic regression model identified four factors as significant predictors of non-registration status. These included (i) unaware about donor agencies and procedures (OR = 10.07; p < 0.05), (ii) unaware about possibility to donate stem cells (OR = 8.08; p < 0.05) (iii) concerns about impact on health (OR = 10.01; p < 0.05) and (iv) have health issues that does not permit donation (OR = 10.50; p < 0.05).

CONCLUSION

Stem cell registrations can be enhanced through appropriate health education programs that focus on increasing awareness about donation procedures, trustworthy donor organizations and reducing people apprehensions related to donation.

Multi-modality cardiac image computing: A survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future.

Vaccines development in India: advances, regulation, and challenges

One of the most significant medical advancements in human history is the development of vaccines. Progress in vaccine development has always been greatly influenced by scientific human innovation. The main objective of vaccine development would be to acquire sufficient evidence of vaccine effectiveness, immunogenicity, safety, and/or quality to support requests for marketing approval. Vaccines are biological products that enhance the body's defenses against infectious diseases. From the first smallpox vaccine to the latest notable coronavirus disease 2019 nasal vaccine, India has come a long way. The development of numerous vaccines, driven by scientific innovation and advancement, combined with researcher's knowledge, has helped to reduce the global burden of disease and mortality rates. The Drugs and Cosmetics Rules of 1945 and the New Drugs and Clinical Trials Rules of 2019 specify the requirements and guidelines for CMC (chemistry, manufacturing, and controls) for all manufactured and imported vaccines, including those against coronavirus infections. This article provides an overview of the regulation pertaining to the development process, registration, and approval procedures for vaccines, particularly in India, along with their brief history.

SPECT and CT misregistration reduction in [99mTc]Tc-MAA SPECT/CT for precision liver radioembolization treatment planning

PURPOSE

Respiration and body movement induce misregistration between static [99mTc]Tc-MAA SPECT and CT, causing lung shunting fraction (LSF) and tumor-to-normal liver ratio (TNR) errors for 90Y radioembolization planning. We aim to alleviate the misregistration between [99mTc]Tc-MAA SPECT and CT using two registration schemes on simulation and clinical data.

METHODS

In the simulation study, 70 XCAT phantoms were modeled. The SIMIND Monte Carlo program and OS-EM algorithm were used for projection generation and reconstruction, respectively. Low-dose CT (LDCT) at end-inspiration was simulated for attenuation correction (AC), lungs and liver segmentation, while contrast-enhanced CT (CECT) was simulated for tumor and perfused liver segmentation. In the clinical study, 16 patient data including [99mTc]Tc-MAA SPECT/LDCT and CECT with observed SPECT and CT mismatch were analyzed. Two liver-based registration schemes were studied: SPECT registered to LDCT/CECT and vice versa. Mean count density (MCD) of different volumes-of-interest (VOIs), normalized mutual information (NMI), LSF, TNR, and maximum injected activity (MIA) based on the partition model before and after registration were compared. Wilcoxon signed-rank test was performed.

RESULTS

In the simulation study, compared to before registration, registrations significantly reduced estimation errors of MCD of all VOIs, LSF (Scheme 1: - 100.28%, Scheme 2: - 101.59%), and TNR (Scheme 1: - 7.00%, Scheme 2: - 5.67%), as well as MIA (Scheme 1: - 3.22%, Scheme 2: - 2.40%). In the clinical study, Scheme 1 reduced 33.68% LSF and increased 14.75% TNR, while Scheme 2 reduced 38.88% LSF and increased 6.28% TNR compared to before registration. One patient may change from 90Y radioembolization untreatable to treatable and other patients may change the MIA up to 25% after registration. NMI between SPECT and CT was significantly increased after registrations in both studies.

CONCLUSION

Registration between static [99mTc]Tc-MAA SPECT and corresponding CTs is feasible to reduce their spatial mismatch and improve dosimetric estimation. The improvement of LSF is larger than TNR. Our method can potentially improve patient selection and personalized treatment planning for liver radioembolization.

Quantifying the Variability Associated with Postoperative Localization of Deep Brain Stimulation Electrodes

INTRODUCTION

Computational models of deep brain stimulation (DBS) have become common tools in clinical research studies that attempt to establish correlations between stimulation locations in the brain and behavioral outcome measures. However, the accuracy of any patient-specific DBS model depends heavily upon accurate localization of the DBS electrodes within the anatomy, which is typically defined via co-registration of clinical CT and MRI datasets. Several different approaches exist for this challenging registration problem, and each approach will result in a slightly different electrode localization. The goal of this study was to better understand how different processing steps (e.g., cost-function masking, brain extraction, intensity remapping) affect the estimate of the DBS electrode location in the brain.

METHODS

No "gold standard" exists for this kind of analysis, as the exact location of the electrode in the living human brain cannot be determined with existing clinical imaging approaches. However, we can estimate the uncertainty associated with the electrode position, which can be used to guide statistical analyses in DBS mapping studies. Therefore, we used high-quality clinical datasets from 10 subthalamic DBS subjects and co-registered their long-term postoperative CT with their preoperative surgical targeting MRI using 9 different approaches. The distances separating all of the electrode location estimates were calculated for each subject.

RESULTS

On average, electrodes were located within a median distance of 0.57 mm (0.49-0.74) of one another across the different registration approaches. However, when considering electrode location estimates from short-term postoperative CTs, the median distance increased to 2.01 mm (1.55-2.78).

CONCLUSIONS

The results of this study suggest that electrode location uncertainty needs to be factored into statistical analyses that attempt to define correlations between stimulation locations and clinical outcomes.

Deep learning-based affine medical image registration for multimodal minimal-invasive image-guided interventions - A comparative study on generalizability

Multimodal image registration is applied in medical image analysis as it allows the integration of complementary data from multiple imaging modalities. In recent years, various neural network-based approaches for medical image registration have been presented in papers, but due to the use of different datasets, a fair comparison is not possible. In this research 20 different neural networks for an affine registration of medical images were implemented. The networks' performance and the networks' generalizability to new datasets were evaluated using two multimodal datasets - a synthetic and a real patient dataset - of three-dimensional CT and MR images of the liver. The networks were first trained semi-supervised using the synthetic dataset and then evaluated on the synthetic dataset and the unseen patient dataset. Afterwards, the networks were finetuned on the patient dataset and subsequently evaluated on the patient dataset. The networks were compared using our own developed CNN as benchmark and a conventional affine registration with SimpleElastix as baseline. Six networks improved the pre-registration Dice coefficient of the synthetic dataset significantly (p-value < 0.05) and nine networks improved the pre-registration Dice coefficient of the patient dataset significantly and are therefore able to generalize to the new datasets used in our experiments. Many different machine learning-based methods have been proposed for affine multimodal medical image registration, but few are generalizable to new data and applications. It is therefore necessary to conduct further research in order to develop medical image registration techniques that can be applied more widely.

RegiSTORM: channel registration for multi-color stochastic optical reconstruction microscopy

BACKGROUND

Stochastic optical reconstruction microscopy (STORM), a super-resolution microscopy technique based on single-molecule localizations, has become popular to characterize sub-diffraction limit targets. However, due to lengthy image acquisition, STORM recordings are prone to sample drift. Existing cross-correlation or fiducial marker-based algorithms allow correcting the drift within each channel, but misalignment between channels remains due to interchannel drift accumulating during sequential channel acquisition. This is a major drawback in multi-color STORM, a technique of utmost importance for the characterization of various biological interactions.

RESULTS

We developed RegiSTORM, a software for reducing channel misalignment by accurately registering STORM channels utilizing fiducial markers in the sample. RegiSTORM identifies fiducials from the STORM localization data based on their non-blinking nature and uses them as landmarks for channel registration. We first demonstrated accurate registration on recordings of fiducials only, as evidenced by significantly reduced target registration error with all the tested channel combinations. Next, we validated the performance in a more practically relevant setup on cells multi-stained for tubulin. Finally, we showed that RegiSTORM successfully registers two-color STORM recordings of cargo-loaded lipid nanoparticles without fiducials, demonstrating the broader applicability of this software.

CONCLUSIONS

The developed RegiSTORM software was demonstrated to be able to accurately register multiple STORM channels and is freely available as open-source (MIT license) at https://github.com/oystein676/RegiSTORM.git and https://doi.org/10.5281/zenodo.5509861 (archived), and runs as a standalone executable (Windows) or via Python (Mac OS, Linux).

Geometric Deep Learning for Unsupervised Registration of Diffusion Magnetic Resonance Images

Deep learning based models for registration predict a transformation directly from moving and fixed image appearances. These models have revolutionized the field of medical image registration, achieving accuracy on-par with classical registration methods at a fraction of the computation time. Unfortunately, most deep learning based registration methods have focused on scalar imaging modalities such as T1/T2 MRI and CT, with less attention given to more complex modalities such as diffusion MRI. In this paper, to the best of our knowledge, we present the first end-to-end geometric deep learning based model for the non-rigid registration of fiber orientation distribution fields (fODF) derived from diffusion MRI (dMRI). Our method can be trained in a fully-unsupervised fashion using only input fODF image pairs, i.e. without ground truth deformation fields. Our model introduces several novel differentiable layers for local Jacobian estimation and reorientation that can be seamlessly integrated into the recently introduced manifold-valued convolutional network in literature. The results of this work are accurate deformable registration algorithms for dMRI data that can execute in the order of seconds, as opposed to dozens of minutes to hours consumed by their classical counterparts.

COVID-19 trials were not more likely to report intent to share individual data than non-COVID-19 trials in ClinicalTrials.gov

OBJECTIVES

To compare intent to share individual participant data (IPD) between COVID-19 and non-COVID-19 trials registered at ClinicalTrials.gov between 01/09/2020, and 01/03/2021. We also evaluated factors independently associated with intent to share IPD and whether intent to share IPD has improved as compared with the prepandemic period.

METHODS

We searched ClinicalTrials.gov for all interventional phase 3 studies registered between 01/09/2020, and 01/03/2021. Then, we identified COVID-19 trials and selected a random sample of non-COVID-19 trials with a ratio 2:1. We compared the intent to share IPD between these trials and with 292 trials registered between 01/12/2019, and 01/03/2020 (prepandemic period).

RESULTS

We included 148 COVID-19 trials and 296 non-COVID-19 trials. Intent to share IPD did not significantly differ between COVID-19 and non-COVID-19 trials (22.3% vs. 27.0%, P = 0.3). Intent to share IPD was independently associated with industry-sponsorship (odds ratio [OR] = 2.92; 95% confidence interval [CI]: 1.65-5.27) and location in the United States (OR = 2.93; 95% CI: 1.64-5.41) or the European Union (OR = 2.06; 95% CI: 1.03-4.19). The intent to share IPD has not significantly improved compared with the prepandemic period (P = 0.16).

CONCLUSION

Data-sharing intent at registration does not seem better for COVID-19 trials.

Real-time continuous image guidance for endoscopic retrograde cholangiopancreatography based on 3D/2D registration and respiratory compensation

BACKGROUND

It has been confirmed that three-dimensional (3D) imaging allows easier identification of bile duct anatomy and intraoperative guidance of endoscopic retrograde cholangiopancreatography (ERCP), which reduces the radiation dose and procedure time with improved safety. However, current 3D biliary imaging does not have good real-time fusion with intraoperative imaging, a process meant to overcome the influence of intraoperative respiratory motion and guide navigation. The present study explored the feasibility of real-time continuous image-guided ERCP.

AIM

To explore the feasibility of real-time continuous image-guided ERCP.

METHODS

We selected 2 3D-printed abdominal biliary tract models with different structures to simulate different patients. The ERCP environment was simulated for the biliary phantom experiment to create a navigation system, which was further tested in patients. In addition, based on the estimation of the patient's respiratory motion, preoperative 3D biliary imaging from computed tomography of 18 patients with cholelithiasis was registered and fused in real-time with 2D fluoroscopic sequence generated by the C-arm unit during ERCP.

RESULTS

Continuous image-guided ERCP was applied in the biliary phantom with a registration error of 0.46 mm ± 0.13 mm and a tracking error of 0.64 mm ± 0.24 mm. After estimating the respiratory motion, 3D/2D registration accurately transformed preoperative 3D biliary images to each image in the X-ray image sequence in real-time in 18 patients, with an average fusion rate of 88%.

CONCLUSION

Continuous image-guided ERCP may be an effective approach to assist the operator and reduce the use of X-ray and contrast agents.

Nail it! vision-based drift correction for accurate mixed reality surgical guidance

PURPOSE

Mixed reality-guided surgery through head-mounted displays (HMDs) is gaining interest among surgeons. However, precise tracking of HMDs relative to the surgical environment is crucial for successful outcomes. Without fiducial markers, spatial tracking of the HMD suffers from millimeter- to centimeter-scale drift, resulting in misaligned visualization of registered overlays. Methods and workflows capable of automatically correcting for drift after patient registration are essential to assuring accurate execution of surgical plans.

METHODS

We present a mixed reality surgical navigation workflow that continuously corrects for drift after patient registration using only image-based methods. We demonstrate its feasibility and capabilities using the Microsoft HoloLens on glenoid pin placement in total shoulder arthroplasty. A phantom study was conducted involving five users with each user placing pins on six glenoids of different deformity, followed by a cadaver study by an attending surgeon.

RESULTS

In both studies, all users were satisfied with the registration overlay before drilling the pin. Postoperative CT scans showed 1.5 mm error in entry point deviation and 2.4[Formula: see text] error in pin orientation on average in the phantom study and 2.5 mm and 1.5[Formula: see text] in the cadaver study. A trained user takes around 90 s to complete the workflow. Our method also outperformed HoloLens native tracking in drift correction.

CONCLUSION

Our findings suggest that image-based drift correction can provide mixed reality environments precisely aligned with patient anatomy, enabling pin placement with consistently high accuracy. These techniques constitute a next step toward purely image-based mixed reality surgical guidance, without requiring patient markers or external tracking hardware.

Automatic registration of dental CT and 3D scanned model using deep split jaw and surface curvature

BACKGROUND AND OBJECTIVES

In the medical field, various image registration applications have been studied. In dentistry, the registration of computed tomography (CT) volume data and 3D optically scanned models is essential for various clinical applications, including orthognathic surgery, implant surgical planning, and augmented reality. Our purpose was to present a fully automatic registration method of dental CT data and 3D scanned models.

METHODS

We use a 2D convolutional neural network to regress a curve splitting the maxilla (i.e., upper jaw) and mandible (i.e., lower jaw) and the points specifying the front and back ends of the crown from the CT data. Using this regressed information, we extract the point cloud and vertices corresponding to the tooth crown from the CT and scanned data, respectively. We introduce a novel metric, called curvature variance of neighbor (CVN), to discriminate between highly fluctuating and smoothly varying regions of the tooth crown. The registration based on CVN enables more accurate fine registration while reducing the effects of metal artifacts. Moreover, the proposed method does not require any preprocessing such as extracting the iso-surface for the tooth crown from the CT data, thereby significantly reducing the computation time.

RESULTS

We evaluated the proposed method with the comparison to several promising registration techniques. Our experimental results using three datasets demonstrated that the proposed method exhibited higher registration accuracy (i.e., 2.85, 1.92, and 7.73 times smaller distance errors for individual datasets) and smaller computation time (i.e., 4.12 times faster registration) than one of the state-of-the-art methods. Moreover, the proposed method worked considerably well for partially scanned data, whereas other methods suffered from the unbalancing of information between the CT and scanned data.

CONCLUSIONS

The proposed method was able to perform fully automatic and highly accurate registration of dental CT data and 3D scanned models, even with severe metal artifacts. In addition, it could achieve fast registration because it did not require any preprocessing for iso-surface reconstruction from the CT data.

Quantification of liver-Lung shunt fraction on 3D SPECT/CT images for selective internal radiation therapy of liver cancer using CNN-based segmentations and non-rigid registration

PURPOSE

Selective internal radiation therapy (SIRT) has been proven to be an effective treatment for hepatocellular carcinoma (HCC) patients. In clinical practice, the treatment planning for SIRT using ⁹⁰Y microspheres requires estimation of the liver-lung shunt fraction (LSF) to avoid radiation pneumonitis. Currently, the manual segmentation method to draw a region of interest (ROI) of the liver and lung in 2D planar imaging of ^99mTc-MAA and 3D SPECT/CT images is inconvenient, time-consuming and observer-dependent. In this study, we propose and evaluate a nearly automatic method for LSF quantification using 3D SPECT/CT images, offering improved performance compared with the current manual segmentation method.

METHODS

We retrospectively acquired 3D SPECT with non-contrast-enhanced CT images (nCECT) of 60 HCC patients from a SPECT/CT scanning machine, along with the corresponding diagnostic contrast-enhanced CT images (CECT). Our approach for LSF quantification is to use CNN-based methods for liver and lung segmentations in the nCECT image. We first apply 3D ResUnet to coarsely segment the liver. If the liver segmentation contains a large error, we dilate the coarse liver segmentation into the liver mask as a ROI in the nCECT image. Subsequently, non-rigid registration is applied to deform the liver in the CECT image to fit that obtained in the nCECT image. The final liver segmentation is obtained by segmenting the liver in the deformed CECT image using nnU-Net. In addition, the lung segmentations are obtained using 2D ResUnet. Finally, LSF quantitation is performed based on the number of counts in the SPECT image inside the segmentations. Evaluations and Results: To evaluate the liver segmentation accuracy, we used Dice similarity coefficient (DSC), asymmetric surface distance (ASSD), and max surface distance (MSD) and compared the proposed method to five well-known CNN-based methods for liver segmentation. Furthermore, the LSF error obtained by the proposed method was compared to a state-of-the-art method, modified Deepmedic, and the LSF quantifications obtained by manual segmentation. The results show that the proposed method achieved a DSC score for the liver segmentation that is comparable to other state-of-the-art methods, with an average of 0.93, and the highest consistency in segmentation accuracy, yielding a standard deviation of the DSC score of 0.01. The proposed method also obtains the lowest ASSD and MSD scores on average (2.6 mm and 31.5 mm, respectively). Moreover, for the proposed method, a median LSF error of 0.14% is obtained, which is a statically significant improvement to the state-of-the-art-method (p=0.004), and is much smaller than the median error in LSF manual determination by the medical experts using 2D planar image (1.74% and p<0.001).

CONCLUSIONS

A method for LSF quantification using 3D SPECT/CT images based on CNNs and non-rigid registration was proposed, evaluated and compared to state-of-the-art techniques. The proposed method can quantitatively determine the LSF with high accuracy and has the potential to be applied in clinical practice.

[Multiresolution discrete optimization registration method of ultrasound and magnetic resonance images based on key points]

The registration of preoperative magnetic resonance (MR) images and intraoperative ultrasound (US) images is very important in the planning of brain tumor surgery and during surgery. Considering that the two-modality images have different intensity range and resolution, and the US images are degraded by lots of speckle noises, a self-similarity context (SSC) descriptor based on local neighborhood information was adopted to define the similarity measure. The ultrasound images were considered as the reference, the corners were extracted as the key points using three-dimensional differential operators, and the dense displacement sampling discrete optimization algorithm was adopted for registration. The whole registration process was divided into two stages including the affine registration and the elastic registration. In the affine registration stage, the image was decomposed using multi-resolution scheme, and in the elastic registration stage, the displacement vectors of key points were regularized using the minimum convolution and mean field reasoning strategies. The registration experiment was performed on the preoperative MR images and intraoperative US images of 22 patients. The overall error after affine registration was (1.57 ± 0.30) mm, and the average computation time of each pair of images was only 1.36 s; while the overall error after elastic registration was further reduced to (1.40 ± 0.28) mm, and the average registration time was 1.53 s. The experimental results show that the proposed method has prominent registration accuracy and high computational efficiency.

A novel method for spine ultrasound and X-ray radiograph registration

Ultrasound is a promising imaging method for scoliosis evaluation because it is radiation free and provide real-time images. However, it cannot provide bony details because ultrasound cannot penetrate the bony structure. Therefore, registration of real-time ultrasound images with the previous X-ray radiograph can help physicians understand the spinal deformity of patients. In this study, an improved free-from deformation registration method based on mutual registration and hierarchical adaptive grid (MRHA-FFD) was developed. The method first performed registration grid preprocessing and then optimized control points and conducted mutual registration. Finally, a Blur-aware Attention Network was adopted for image deblurring. The performance of each step was verified by ablation experiments. Comparison experiment between the proposed method and traditional registration methods was also conducted. The qualitative and quantitative results suggested that MRHA-FFD is a promising approach for registering spine ultrasound image and X-ray radiograph.

Incorporating heterogeneity and anisotropy for surgical applications in breast deformation modeling

BACKGROUND

Simulating soft-tissue breast deformations is of interest for many applications including image fusion, longitudinal registration, and image-guided surgery. For the surgical use case, positional changes cause breast deformations that compromise the use of preoperative imaging to inform tumor excision. Even when acquiring imaging in the supine position, which better reflects surgical presentation, deformations still occur due to arm motion and orientation changes. A biomechanical modeling approach to simulate supine breast deformations for surgical applications must be both accurate and compatible with the clinical workflow.

METHODS

A supine MR breast imaging dataset from n = 11 healthy volunteers was used to simulate surgical deformations by acquiring images in arm-down and arm-up positions. Three linear-elastic modeling approaches with varying levels of complexity were used to predict deformations caused by this arm motion: a homogeneous isotropic model, a heterogeneous isotropic model, and a heterogeneous anisotropic model using a transverse-isotropic constitutive model.

FINDINGS

The average target registration errors for subsurface anatomical features were 5.4 ± 1.5 mm for the homogeneous isotropic model, 5.3 ± 1.5 mm for the heterogeneous isotropic model, and 4.7 ± 1.4 mm for the heterogeneous anisotropic model. A statistically significant improvement in target registration error was observed between the heterogeneous anisotropic model and both the homogeneous and the heterogeneous isotropic models (P < 0.01).

INTERPRETATION

While a model that fully incorporates all constitutive complexities of anatomical structure likely achieves the best accuracy, a computationally tractable heterogeneous anisotropic model provided significant improvement and may be applicable for image-guided breast surgeries.

Using synthetic MR images for distortion correction

Functional MRI (fMRI) data acquired using echo-planar imaging (EPI) are highly distorted by magnetic field inhomogeneities. Distortion and differences in image contrast between EPI and T1-weighted and T2-weighted (T1w/T2w) images makes their alignment a challenge. Typically, field map data are used to correct EPI distortions. Alignments achieved with field maps can vary greatly and depends on the quality of field map data. However, many public datasets lack field map data entirely. Additionally, reliable field map data is often difficult to acquire in high-motion pediatric or developmental cohorts. To address this, we developed Synth, a software package for distortion correction and cross-modal image registration that does not require field map data. Synth combines information from T1w and T2w anatomical images to construct an idealized undistorted synthetic image with similar contrast properties to EPI data. This synthetic image acts as an effective reference for individual-specific distortion correction. Using pediatric (ABCD: Adolescent Brain Cognitive Development) and adult (MSC: Midnight Scan Club; HCP: Human Connectome Project) data, we demonstrate that Synth performs comparably to field map distortion correction approaches, and often outperforms them. Field map-less distortion correction with Synth allows accurate and precise registration of fMRI data with missing or corrupted field map information.

Roundtable on Deregistration and Gender Law Reform Internationally

In this roundtable discussion, early-career researchers working in the field of law, gender, and sexuality discuss international and trans-national developments to legal gender. 'The Future of Legal Gender' research project focused on the legislative framework of England and Wales to develop a prototype for decertification. The domestic legislation, however, was situated within a wider international context throughout the project. This roundtable discussion, therefore, provided an opportunity for reflection on the transnational issues raised by decertification, with a particular focus on developments arising in the jurisdiction(s) studied by the early career researchers. The roundtable began with a brief outline of these recent developments before moving to an open discussion on key themes including the value of reform on wider society, changes on-the-ground by non-state actors, and alternative processes for tackling gender inequalities without certifying legal gender. The online conversation took place on 28 June 2021 and has been transcribed and edited for continuity, clarity, and referencing.

The Consistency between Registered Acupuncture-Moxibustion Clinical Studies and Their Published Studies and Update Status of Registered Information

BACKGROUND

Few studies have analyzed the consistency between registered acupuncture-moxibustion clinical studies and their published research results as well as their update status of registered information.

METHODS

We searched for acupuncture-moxibustion clinical studies that were registered at the World Health Organization International Clinical Trials Registry Platform between 2013 and 2015 and collected data regarding their characteristics and update status. Published results of these registered studies were identified and compared with registered information.

RESULTS

A total of 425 registered acupuncture-moxibustion clinical studies were included; 379 (89.2%) of them were interventional studies, and the remaining 46 (10.8%) were observational studies. Forty-six studies (10.8%) were found to have published results, and 51 published articles were identified. Overall, 73.2% (311) of registered studies did not update the research status in time; 46.6% (198) stopped updating before recruiting; 21.6% (92) stopped updating after recruiting; and 4.9% (21) stopped updating after completion. Regarding the 46 studies with published results, 29 (63.0%) were considered to be affected by reporting bias. These reporting biases predominantly involved the omission of some predefined outcomes or endpoints (16 studies), contradictions regarding descriptions of sample sizes (9 studies), discrepancies in treatment measurements or group distribution (7 studies), and inconsistent treatment durations (4 studies). When compared with other studies, significant and various reporting biases could also be commonly found in fields other than acupuncture-moxibustion.

CONCLUSIONS

There were many discrepancies between registered information and published reports on acupuncture-moxibustion, which could also be commonly observed in other fields. Moreover, a large proportion of registered studies did not update their research status in time. Efforts should be made to improve the reporting quality and timely updates.

Hintergrund

Es gibt nur wenige Studien, in denen die Übereinstimmung zwischen den registrierten klinischen Studien zur Akupunktur und Moxibustion mit den veröffentlichten Studienergebnissen und dem Aktualisierungsstand der Informationen im Register untersucht wurde.

Methoden

Wir suchten nach klinischen Studien zur Akupunktur und Moxibustion, die zwischen 2013 und 2015 auf der International Clinical Trials Registry Platform der Weltgesundheitsorganisation registriert wurden, und erhoben Daten zu ihren Merkmalen und ihrem Aktualisierungsstand. Die veröffentlichten Ergebnisse der registrierten Studien wurden identifiziert und mit den Informationen im Register verglichen.

Ergebnisse

Insgesamt wurden 425 registrierte klinische Studien zur Akupunktur und Moxibustion eingeschlossen, davon waren 379 (89,2 %) Interventionsstudien und die restlichen 46 (10,8 %) waren Beobachtungsstudien. Es wurden 46 Studien (10,8 %) mit veröffentlichten Ergebnissen gefunden und 51 veröffentlichte Artikel identifiziert. Insgesamt wurde bei 73,2 % (311) der registrierten Studien der Forschungsstand nicht zeitnah aktualisiert; bei 46,6 % (198) wurde die Aktualisierung vor der Rekrutierung eingestellt; bei 21,6 % (92) wurde die Aktualisierung nach der Rekrutierung eingestellt und bei 4,9 % (21) wurde die Aktualisierung nach Abschluss der Studie eingestellt. Von den 46 Studien mit veröffentlichten Ergebnissen wurden 29 (63,0 %) als von Publikationsverzerrung betroffen angesehen. Diese Publikationsverzerrung betraf vor allem die Auslassung einiger vordefinierter Zielkriterien oder Endpunkte (16 Studien), Widersprüche bei der Beschreibung des Stichprobenumfangs (9 Studien), Diskrepanzen bei den Behandlungsmessungen oder der Gruppenverteilung (7 Studien) und Inkonsistenzen bei der Behandlungsdauer (4 Studien). Beim Vergleich mit anderen Studien wurden auch in anderen Bereichen als Akupunktur und Moxibustion häufig signifikante und unterschiedliche Publikationsverzerrungen festgestellt.

Schlussfolgerungen

Es bestanden zahlreiche Diskrepanzen zwischen den Informationen im Register und den veröffentlichten Berichten über Akupunktur und Moxibustion, die auch in anderen Bereichen häufig zu beobachten waren. Darüber hinaus wurde bei einem Großteil der registrierten Studien der Forschungsstand nicht zeitnah aktualisiert. Es sollten Anstrengungen unternommen werden, um die Qualität der Berichterstattung und die zeitnahe Aktualisierung zu verbessern.

Regulatory registration timelines of generic medicines in South Africa: Assessment of the performance of SAHPRA between 2011 and 2022

BACKGROUND

Various regulatory authorities are experiencing backlogs of applications which result in delayed access to medicines for patients. The objective of this study is to critically assess the registration process utilised by SAHPRA between 2011 and 2022 and determine the fundamental root causes for the formation of a backlog. The study also aims to detail the remedial actions that were undertaken which resulted in the development of a new review pathway termed the risk-based assessment approach for regulatory authorities experiencing backlogs to implement.

METHODS

A sample of 325 applications was used to evaluate the end-to-end registration process employed for the Medicine Control Council (MCC) process between 2011 and 2017; 129 applications were used for the backlog clearance project (BCP) between 2019 and 2022; 63 and 156 applications were used for the risk-based assessment (RBA) pilot studies in 2021 and 2022, respectively. The three processes are compared, and the timelines are discussed in detail.

RESULTS

The longest median value of 2092 calendar days was obtained for the approval times between 2011 and 2017 using the MCC process. Continuous process optimisation and refinement are crucial to prevent recurring backlogs and hence implementation of the RBA process. Implementation of the RBA process resulted in a shorter median approval time of 511 calendar days. The finalisation timeline by the Pharmaceutical and Analytical (P&A) pre-registration Unit, which conducts the majority of the evaluations, is used as a tool for the direct comparison of the processes. The finalisation timeline for the MCC process was a median value of 1470 calendar days, the BCP was 501 calendar days and the RBA process phases 1 and 2 were 68 and 73 calendar days, respectively. The median values of the various stages of the end-to-end registration processes are also analysed in order to build efficiency within the process.

CONCLUSIONS

The observations from the study have identified the RBA process which can be implemented to reduce regulatory assessment times while assuring the timeous approval of safe and effective, quality medicines. The continuous monitoring of a process remains one of the critical tools required to ensure the effectiveness of a registration process. The RBA process also becomes a better alternative for generic applications that do not qualify to undergo the reliance approach due to its drawbacks. This robust procedure can therefore be utilised by other regulatory agencies that may have a backlog or want to optimise their registration process.