Comparison of image registration methods in patients with non-melanoma skin cancer treated with superficial brachytherapy.

The accumulated dose from sequential treatments of metachronous non-melanoma skin cancer can be assessed using image registration, although guidelines for selecting the appropriate algorithm are lacking. This study shows the impact of rigid (RIR), deformable (DIR) and deformable structure-based (SDIR) algorithms on the skin dose. DIR increased: the maximum dose (39.2 Gy vs 9.4 Gy), the dose to 0.1 cm3 (16.4 Gy vs 7.8 Gy) and the dose to 2 cm3 (7.6 Gy vs 5.7 Gy). RIR only affected the maximum dose, which increased to 17.0 Gy. SDIR correctly translated the dose maps, as none of the parameters changed significantly.

Graph-based cell pattern recognition for merging the multi-modal optical microscopic image of neurons.

A deep understanding of neuron structure and function is crucial for elucidating brain mechanisms, diagnosing and treating diseases. Optical microscopy, pivotal in neuroscience, illuminates neuronal shapes, projections, and electrical activities. To explore the projection of specific functional neurons, scientists have been developing optical-based multimodal imaging strategies to simultaneously capture dynamic in vivo signals and static ex vivo structures from the same neuron. However, the original position of neurons is highly susceptible to displacement during ex vivo imaging, presenting a significant challenge for integrating multimodal information at the single-neuron level. This study introduces a graph-model-based approach for cell image matching, facilitating precise and automated pairing of sparsely labeled neurons across different optical microscopic images. It has been shown that utilizing neuron distribution as a matching feature can mitigate modal differences, the high-order graph model can address scale inconsistency, and the nonlinear iteration can resolve discrepancies in neuron density. This strategy was applied to the connectivity study of the mouse visual cortex, performing cell matching between the two-photon calcium image and the HD-fMOST brain-wide anatomical image sets. Experimental results demonstrate 96.67% precision, 85.29% recall rate, and 90.63% F1 Score, comparable to expert technicians. This study builds a bridge between functional and structural imaging, offering crucial technical support for neuron classification and circuitry analysis.

Longitudinal registration of thoracic CT images with radiation-induced lung diseases: A divide-and-conquer approach based on component structure wise registration using coherent point drift.

BACKGROUND AND OBJECTIVE: Registration of pulmonary computed tomography (CT) images with radiation-induced lung diseases (RILD) was essential to investigate the voxel-wise relationship between the formation of RILD and the radiation dose received by different tissues. Although various approaches had been developed for the registration of lung CTs, their performances remained clinically unsatisfactory for registration of lung CT images with RILD. The main difficulties arose from the longitudinal change in lung parenchyma, including RILD and volumetric change of lung cancers, after radiation therapy, leading to inaccurate registration and artifacts caused by erroneous matching of the RILD tissues. METHODS: To overcome the influence of the parenchymal changes, a divide-and-conquer approach rooted in the coherent point drift (CPD) paradigm was proposed. The proposed method was based on two kernel ideas. One was the idea of component structure wise registration. Specifically, the proposed method relaxed the intrinsic assumption of equal isotropic covariances in CPD by decomposing a lung and its surrounding tissues into component structures and independently registering the component structures pairwise by CPD. The other was the idea of defining a vascular subtree centered at a matched branch point as a component structure. This idea could not only provide a sufficient number of matched feature points within a parenchyma, but avoid being corrupted by the false feature points resided in the RILD tissues due to globally and indiscriminately sampling using mathematical operators. The overall deformation model was built by using the Thin Plate Spline based on all matched points. RESULTS: This study recruited 30 pairs of lung CT images with RILD, 15 of which were used for internal validation (leave-one-out cross-validation) and the other 15 for external validation. The experimental results showed that the proposed algorithm achieved a mean and a mean of maximum 1 % of average surface distances <2 and 8 mm, respectively, and a mean and a maximum target registration error <2 mm and 5 mm on both internal and external validation datasets. The paired two-sample t-tests corroborated that the proposed algorithm outperformed a recent method, the Stavropoulou's method, on the external validation dataset (p < 0.05). CONCLUSIONS: The proposed algorithm effectively reduced the influence of parenchymal changes, resulting in a reasonably accurate and artifact-free registration.

Multiscale unsupervised network for deformable image registration.

BACKGROUND: Deformable image registration (DIR) plays an important part in many clinical tasks, and deep learning has made significant progress in DIR over the past few years. OBJECTIVE: To propose a fast multiscale unsupervised deformable image registration (referred to as FMIRNet) method for monomodal image registration. METHODS: We designed a multiscale fusion module to estimate the large displacement field by combining and refining the deformation fields of three scales. The spatial attention mechanism was employed in our fusion module to weight the displacement field pixel by pixel. Except mean square error (MSE), we additionally added structural similarity (ssim) measure during the training phase to enhance the structural consistency between the deformed images and the fixed images. RESULTS: Our registration method was evaluated on EchoNet, CHAOS and SLIVER, and had indeed performance improvement in terms of SSIM, NCC and NMI scores. Furthermore, we integrated the FMIRNet into the segmentation network (FCN, UNet) to boost the segmentation task on a dataset with few manual annotations in our joint leaning frameworks. The experimental results indicated that the joint segmentation methods had performance improvement in terms of Dice, HD and ASSD scores. CONCLUSIONS: Our proposed FMIRNet is effective for large deformation estimation, and its registration capability is generalizable and robust in joint registration and segmentation frameworks to generate reliable labels for training segmentation tasks.

CCMNet: Cross-scale correlation-aware mapping network for 3D lung CT image registration.

The lung is characterized by high elasticity and complex structure, which implies that the lung is capable of undergoing complex deformation and the shape variable is substantial. Large deformation estimation poses significant challenges to lung image registration. The traditional U-Net architecture is difficult to cover complex deformation due to its limited receptive field. Moreover, the relationship between voxels weakens as the number of downsampling times increases, that is, the long-range dependence issue. In this paper, we propose a novel multilevel registration framework which enhances the correspondence between voxels to improve the ability of estimating large deformations. Our approach consists of a convolutional neural network (CNN) with a two-stream registration structure and a cross-scale mapping attention (CSMA) mechanism. The former extracts the robust features of image pairs within layers, while the latter establishes frequent connections between layers to maintain the correlation of image pairs. This method fully utilizes the context information of different scales to establish the mapping relationship between low-resolution and high-resolution feature maps. We have achieved remarkable results on DIRLAB (TRE 1.56 ± 1.60) and POPI (NCC 99.72% SSIM 91.42%) dataset, demonstrating that this strategy can effectively address the large deformation issues, mitigate long-range dependence, and ultimately achieve more robust lung CT image registration.

Characterizing Low-cost Registration for Photographic Images to Computed Tomography.

Mapping information from photographic images to volumetric medical imaging scans is essential for linking spaces with physical environments, such as in image-guided surgery. Current methods of accurate photographic image to computed tomography (CT) image mapping can be computationally intensive and/or require specialized hardware. For general purpose 3-D mapping of bulk specimens in histological processing, a cost-effective solution is necessary. Here, we compare the integration of a commercial 3-D camera and cell phone imaging with a surface registration pipeline. Using surgical implants and chuck-eye steak as phantom tests, we obtain 3-D CT reconstruction and sets of photographic images from two sources: Canfield Imaging's H1 camera and an iPhone 14 Pro. We perform surface reconstruction from the photographic images using commercial tools and open-source code for Neural Radiance Fields (NeRF) respectively. We complete surface registration of the reconstructed surfaces with the iterative closest point (ICP) method. Manually placed landmarks were identified at three locations on each of the surfaces. Registration of the Canfield surfaces for three objects yields landmark distance errors of 1.747, 3.932, and 1.692 mm, while registration of the respective iPhone camera surfaces yields errors of 1.222, 2.061, and 5.155 mm. Photographic imaging of an organ sample prior to tissue sectioning provides a low-cost alternative to establish correspondence between histological samples and 3-D anatomical samples.

PelviNet: A Collaborative Multi-agent Convolutional Network for Enhanced Pelvic Image Registration.

PelviNet introduces a groundbreaking multi-agent convolutional network architecture tailored for enhancing pelvic image registration. This innovative framework leverages shared convolutional layers, enabling synchronized learning among agents and ensuring an exhaustive analysis of intricate 3D pelvic structures. The architecture combines max pooling, parametric ReLU activations, and agent-specific layers to optimize both individual and collective decision-making processes. A communication mechanism efficiently aggregates outputs from these shared layers, enabling agents to make well-informed decisions by harnessing combined intelligence. PelviNet's evaluation centers on both quantitative accuracy metrics and visual representations to elucidate agents' performance in pinpointing optimal landmarks. Empirical results demonstrate PelviNet's superiority over traditional methods, achieving an average image-wise error of 2.8 mm, a subject-wise error of 3.2 mm, and a mean Euclidean distance error of 3.0 mm. These quantitative results highlight the model's efficiency and precision in landmark identification, crucial for medical contexts such as radiation therapy, where exact landmark identification significantly influences treatment outcomes. By reliably identifying critical structures, PelviNet advances pelvic image analysis and offers potential enhancements for broader medical imaging applications, marking a significant step forward in computational healthcare.

Boundary-based registration improves sensitivity for detecting hypoperfusion in sporadic frontotemporal lobar degeneration.

Introduction: Frontotemporal lobar degeneration (FTLD) is associated with FTLD due to tau (FTLD-tau) or TDP (FTLD-TDP) inclusions found at autopsy. Arterial Spin Labeling (ASL) MRI is often acquired in the same session as a structural T1-weighted image (T1w), enabling detection of regional changes in cerebral blood flow (CBF). We hypothesize that ASL-T1w registration with more degrees of freedom using boundary-based registration (BBR) will better align ASL and T1w images and show increased sensitivity to regional hypoperfusion differences compared to manual registration in patient participants. We hypothesize that hypoperfusion will be associated with a clinical measure of disease severity, the FTLD-modified clinical dementia rating scale sum-of-boxes (FTLD-CDR). Materials and methods: Patients with sporadic likely FTLD-tau (sFTLD-tau; N = 21), with sporadic likely FTLD-TDP (sFTLD-TDP; N = 14), and controls (N = 50) were recruited from the Connectomic Imaging in Familial and Sporadic Frontotemporal Degeneration project (FTDHCP). Pearson's Correlation Coefficients (CC) were calculated on cortical vertex-wise CBF between each participant for each of 3 registration methods: (1) manual registration, (2) BBR initialized with manual registration (manual+BBR), (3) and BBR initialized using FLIRT (FLIRT+BBR). Mean CBF was calculated in the same regions of interest (ROIs) for each registration method after image alignment. Paired t-tests of CC values for each registration method were performed to compare alignment. Mean CBF in each ROI was compared between groups using t-tests. Differences were considered significant at p < 0.05 (Bonferroni-corrected). We performed linear regression to relate FTLD-CDR to mean CBF in patients with sFTLD-tau and sFTLD-TDP, separately (p < 0.05, uncorrected). Results: All registration methods demonstrated significant hypoperfusion in frontal and temporal regions in each patient group relative to controls. All registration methods detected hypoperfusion in the left insular cortex, middle temporal gyrus, and temporal pole in sFTLD-TDP relative to sFTLD-tau. FTLD-CDR had an inverse association with CBF in right temporal and orbitofrontal ROIs in sFTLD-TDP. Manual+BBR performed similarly to FLIRT+BBR. Discussion: ASL is sensitive to distinct regions of hypoperfusion in patient participants relative to controls, and in patients with sFTLD-TDP relative to sFTLD-tau, and decreasing perfusion is associated with increasing disease severity, at least in sFTLD-TDP. BBR can register ASL-T1w images adequately for controls and patients.

Atlas-based assessment of hypomyelination: Quantitative MRI in Pelizaeus-Merzbacher disease.

Pelizaeus-Merzbacher disease (PMD) is a rare childhood hypomyelinating leukodystrophy. Quantification of the pronounced myelin deficit and delineation of subtle myelination processes are of high clinical interest. Quantitative magnetic resonance imaging (qMRI) techniques can provide in vivo insights into myelination status, its spatial distribution, and dynamics during brain maturation. They may serve as potential biomarkers to assess the efficacy of myelin-modulating therapies. However, registration techniques for image quantification and statistical comparison of affected pediatric brains, especially those of low or deviant image tissue contrast, with healthy controls are not yet established. This study aimed first to develop and compare postprocessing pipelines for atlas-based quantification of qMRI data in pediatric patients with PMD and evaluate their registration accuracy. Second, to apply an optimized pipeline to investigate spatial myelin deficiency using myelin water imaging (MWI) data from patients with PMD and healthy controls. This retrospective single-center study included five patients with PMD (mean age, 6 years ± 3.8) who underwent conventional brain MRI and diffusion tensor imaging (DTI), with MWI data available for a subset of patients. Three methods of registering PMD images to a pediatric template were investigated. These were based on (a) T1-weighted (T1w) images, (b) fractional anisotropy (FA) maps, and (c) a combination of T1w, T2-weighted, and FA images in a multimodal approach. Registration accuracy was determined by visual inspection and calculated using the structural similarity index method (SSIM). SSIM values for the registration approaches were compared using a t test. Myelin water fraction (MWF) was quantified from MWI data as an assessment of relative myelination. Mean MWF was obtained from two PMDs (mean age, 3.1 years ± 0.3) within four major white matter (WM) pathways of a pediatric atlas and compared to seven healthy controls (mean age, 3 years ± 0.2) using a Mann-Whitney U test. Our results show that visual registration accuracy estimation and computed SSIM were highest for FA-based registration, followed by multimodal, and T1w-based registration (SSIMFA = 0.67 ± 0.04 vs. SSIMmultimodal = 0.60 ± 0.03 vs. SSIMT1 = 0.40 ± 0.14). Mean MWF of patients with PMD within the WM pathways was significantly lower than in healthy controls MWFPMD = 0.0267 ± 0.021 vs. MWFcontrols = 0.1299 ± 0.039. Specifically, MWF was measurable in brain structures known to be myelinated at birth (brainstem) or postnatally (projection fibers) but was scarcely detectable in other brain regions (commissural and association fibers). Taken together, our results indicate that registration accuracy was highest with an FA-based registration pipeline, providing an alternative to conventional T1w-based registration approaches in the case of hypomyelinating leukodystrophies missing normative intrinsic tissue contrasts. The applied atlas-based analysis of MWF data revealed that the extent of spatial myelin deficiency in patients with PMD was most pronounced in commissural and association and to a lesser degree in brainstem and projection pathways.

A 12-year comparison of patients with Alzheimer's dementia with their informants in eight Asian countries.

BACKGROUND: The number of patients with Alzheimer's disease (AD) has increased dramatically in Asia. OBJECTIVE: To update the demographic characteristics of patients with AD and their informants in eight Asian countries and compare them from 12 years prior. METHODS: The A1-A3 components of the Uniform Dataset (UDS), version 3.0, were administered in Taiwan, Beijing, Hong Kong, Korea, Japan, Philippines, Thailand, and Indonesia. Data were compared with patients with AD in the first registration using the UDS version 1.0 from 2010-2014 in the same regions. RESULTS: A total of 1885 patients with AD and their informants were recruited from 2022 to 2024 and were compared with 2042 patients recruited a decade prior. Each country had its own unique characteristics that changed between both eras. The mean age of the patients and informants was 79.8±8.2 years and 56.5±12.1 years, respectively. Compared with the first registration, the patients were older (79.8 vs 79.0, p=0.002) and had worse global function (mean CDR-SB scores 6.1 vs 5.8, p<0.001); more informants were children (56 % vs. 48 %, p<0.001), and their frequency of in-person visits increased significantly if not living together. A total of 11 %, 4.5 %, 11 %, and 0.4 % of the patients had a reported history of cognitive impairment in their mothers, fathers, siblings, and children, respectively; all percentages, except children, increased significantly over the past decade. CONCLUSION: The present study reports the heterogeneous characteristics of patients with AD and their informants in Asian countries, and the distinct changes in the past decade. The differences in dementia evaluation and care between developing and developed countries warrant further investigation.

CvTMorph: Improving Local Feature Extraction in Medical Image Registration for Respiratory Motion Modeling with Convolutional Vision Transformer.

BACKGROUND: Accurately modeling respiratory motion in medical images is crucial for various applications, including radiation therapy planning. However, existing registration methods often struggle to extract local features effectively, limiting their performance. OBJECTIVE: In this paper, we aimed to propose a new framework called CvTMorph, which utilizes a Convolutional vision Transformer (CvT) and Convolutional Neural Networks (CNN) to improve local feature extraction. METHODS: CvTMorph integrates CvT and CNN to construct a hybrid model that combines the strengths of both approaches. Additionally, scaling and square layers are added to enhance the registration performance. We have evaluated the performance of CvTMorph on the 4D-Lung and DIR-Lab datasets and compared it with state-of-the-art methods to demonstrate its effectiveness. RESULTS: The experimental results have demonstrated CvTMorph to outperform the existing methods in terms of accuracy and robustness for respiratory motion modeling in 4D images. The incorporation of the convolutional vision transformer has significantly improved the registration performance and enhanced the representation of local structures. CONCLUSION: CvTMorph offers a promising solution for accurately modeling respiratory motion in 4D medical images. The hybrid model, leveraging convolutional vision transformer and convolutional neural networks, has proven effective in extracting local features and improving registration performance. The results have highlighted the potential of CvTMorph for various applications, such as radiation therapy planning, and provided a basis for further research in this field.

6DoF assembly pose estimation dataset for robotic manipulation.

Robotic assembling is a challenging task that requires cognition and dexterity. In recent years, perception tools have achieved tremendous success in endowing the cognitive capabilities to robots. Although these tools have succeeded in tasks such as detection, scene segmentation, pose estimation and grasp manipulation, the associated datasets and the dataset contents lack crucial information that requires adapting them for assembling pose estimation. Furthermore, existing datasets of object 3D meshes and point clouds are presented in non-canonical view frames and therefore lack information to train perception models that infer on a visual scene. The dataset presents 2 simulated object assembly scenes with RGB-D images, 3D mesh files and ground truth assembly poses as an extension for the State-of-the-Art BOP format. This enables smooth expansion of existing perception models in computer vision as well as development of novel algorithms for estimating assembly pose in robotic assembly manipulation tasks.

Stillbirth rates following the change in definition of fetal mortality in Quebec.

OBJECTIVES: In 2019, Quebec changed its stillbirth definition to include fetal deaths at 20 weeks gestation or more. Previously, the criterion was a minimum birth weight of 500 g. We assessed the impact of the new definition on stillbirth rates. METHODS: We conducted a retrospective study of stillbirth rates between 2010 and 2021 in Quebec. The exposure consisted of the period during the new definition versus the preceding period. We assessed how the new definition affected stillbirth rates using interrupted time series regression, and compared the period during the new definition with the preceding period using prevalence differences and prevalence ratios with 95% confidence intervals (CI). We determined the extent to which fetuses at the limit of viability (under 500 g or 20‒23 weeks) accounted for any increase in rates. RESULTS: Stillbirth rates went from 4.11 before the new definition to 6.76 per 1000 total births immediately after. Overall, the change in definition led to an absolute increase of 2.58 stillbirths per 1000 total births, for a prevalence ratio of 1.76 (95% CI 1.61‒1.92) compared with the preceding period. Fetal deaths due to congenital anomalies increased by 6.82 per 10,000 (95% CI 4.85‒8.78), while deaths due to pregnancy termination increased by 10.47 per 10,000 (95% CI 8.04‒12.89). Once the definition changed, 37% of stillbirths were under 500 g and 42% were between 20 and 23 weeks, with around half of these caused by congenital anomalies and terminations. CONCLUSION: Stillbirth rates increased after the definition changed in Quebec, mainly due to congenital anomalies and pregnancy terminations.

RéSUMé: OBJECTIFS: En 2019, le Québec a modifié sa définition de mortinaissance pour inclure les morts fœtales à 20 semaines de gestation ou plus. Auparavant, le critère était un poids minimum de 500 g à la naissance. Nous avons évalué l'impact du changement de définition sur la mesure de mortinatalité. MéTHODES: Nous avons mené une étude rétrospective de la mortinatalité entre 2010 et 2021 au Québec. L'exposition était la période après l'introduction de la nouvelle définition par rapport à la période précédente. Nous avons évalué l'impact du changement de définition sur la prévalence de la mortinatalité en utilisant des régressions de séries temporelles interrompues, et en comparant la période suivant le changement de définition avec la période précédente à l'aide de différences de prévalences et de ratios de prévalences avec des intervalles de confiance à 95% (IC). Nous avons déterminé dans quelle mesure les fœtus à la limite de la viabilité (moins de 500 g ou 20 à 23 semaines) contribuaient à l'augmentation. RéSULTATS: La prévalence de la mortinatalité est passé de 4,11 avant la nouvelle définition à 6,76 pour 1 000 naissances immédiatement après le changement de définition. Il y a eu une augmentation absolue de 2,58 mortinaissances pour 1 000 naissances, pour un ratio de prévalences de 1,76 (IC à 95% 1,61‒1,92) comparativement à la période précédente. Les mortinaissances dues aux anomalies congénitales ont augmenté de 6,82 pour 10 000 (IC 95% 4,85‒8,78), tandis que les décès dus aux interruptions de grossesse ont augmenté de 10,47 pour 10 000 (IC 95% 8,04‒12,89). Une fois la définition modifiée, 37 % des mortinaissances survenaient chez des fœtus pesant moins de 500 g et 42 % avaient lieu entre 20 et 23 semaines, la moitié d'entre elles étant dues à des anomalies congénitales et interruptions de grossesse. CONCLUSION: La prévalence de la mortinatalité a augmenté après le changement de définition au Québec, principalement en raison des décès causés par des anomalies congénitales et des interruptions de grossesse.

Evaluating guideline and registration policies among neurology journals: a cross-sectional analysis.

BACKGROUND: Neurological disorders have had a substantial rise the last three decades, imposing substantial burdens on both patients and healthcare costs. Consequently, the demand for high-quality research has become crucial for exploring effective treatment options. However, current neurology research has some limitations in terms of transparency, reproducibility, and reporting bias. The adoption of reporting guidelines (RGs) and trial registration policies has been proven to address these issues and improve research quality in other medical disciplines. It is unclear the extent to which these policies are being endorsed by neurology journals. Therefore, our study aims to evaluate the publishing policies of top neurology journals regarding RGs and trial registration. METHODS: For this cross-sectional study, neurology journals were identified using the 2021 Scopus CiteScore Tool. The top 100 journals were listed and screened for eligibility for our study. In a masked, duplicate fashion, investigators extracted data on journal characteristics, policies on RGs, and policies on trial registration using information from each journal's Instruction for Authors webpage. Additionally, investigators contacted journal editors to ensure information was current and accurate. No human participants were involved in this study. Our data collection and analyses were performed from December 14, 2022, to January 9, 2023. RESULTS: Of the 356 neurology journals identified, the top 100 were included into our sample. The five-year impact of these journals ranged from 50.844 to 2.226 (mean [SD], 7.82 [7.01]). Twenty-five (25.0%) journals did not require or recommend a single RG within their Instructions for Authors webpage, and a third (33.0%) did not require or recommend clinical trial registration. The most frequently mentioned RGs were CONSORT (64.6%), PRISMA (52.5%), and ARRIVE (53.1%). The least mentioned RG was QUOROM (1.0%), followed by MOOSE (9.0%), and SQUIRE (17.9%). CONCLUSIONS: While many top neurology journals endorse the use of RGs and trial registries, there are still areas where their adoption can be improved. Addressing these shortcomings leads to further advancements in the field of neurology, resulting in higher-quality research and better outcomes for patients.

A review on organ deformation modeling approaches for reliable surgical navigation using augmented reality.

Augmented Reality (AR) holds the potential to revolutionize surgical procedures by allowing surgeons to visualize critical structures within the patient's body. This is achieved through superimposing preoperative organ models onto the actual anatomy. Challenges arise from dynamic deformations of organs during surgery, making preoperative models inadequate for faithfully representing intraoperative anatomy. To enable reliable navigation in augmented surgery, modeling of intraoperative deformation to obtain an accurate alignment of the preoperative organ model with the intraoperative anatomy is indispensable. Despite the existence of various methods proposed to model intraoperative organ deformation, there are still few literature reviews that systematically categorize and summarize these approaches. This review aims to fill this gap by providing a comprehensive and technical-oriented overview of modeling methods for intraoperative organ deformation in augmented reality in surgery. Through a systematic search and screening process, 112 closely relevant papers were included in this review. By presenting the current status of organ deformation modeling methods and their clinical applications, this review seeks to enhance the understanding of organ deformation modeling in AR-guided surgery, and discuss the potential topics for future advancements.

Reporting quality of scoping reviews in endodontics: A meta-research study.

OBJECTIVES: To evaluate the reporting quality of Scoping Reviews (ScRs) in endodontics according to the PRISMA Extension Checklist for Scoping Reviews (PRISMA-ScR) and to analyse their association with a range of publication and methodological/reporting characteristics. METHODS: Pubmed, Scopus, and Web of Science databases were searched up to 31 January 2024 to identify scoping reviews in the field of endodontics. An additional search was performed in three leading endodontic journals. Study selection and appraising the quality of the studies was carried out independently by two reviewers. Each of the 20 PRISMA-ScR items were allocated a score of either 0, 0.5 or 1 to reflect the completeness of the reporting. An item-specific and overall percentage reporting quality score was calculated and reported through descriptive statistics across a range of publication, as well as methodological/reporting characteristics. A univariable and multivariable quantile regression was performed to identify the effect of publication and methodological/reporting characteristics (year of publication, journal, inclusion of an appropriate reporting guideline, and study registration) on the overall percentage reporting quality score. Association of reporting quality score with publication characteristics was then investigated. RESULTS: A total of 40 ScRs were identified and included for appraisal. Most of the studies were published from 2021 onwards. The overall median reporting quality score was 86%. The most frequent items not included in the studies were: a priori protocol registration (22/40 compliant; 55%), and reporting of funding (16/40 compliant; 40%). Other key elements that were inadequately reported were the abstract (7/40 compliant; 18%), the rationale and justification of the ScR (21/40 compliant; 52%) and the objectives of the study (18/40 compliant; 45%). Studies that adhered to appropriate reporting guidelines were associated with greater reporting quality scores (ß-coefficient: 10; 95%CI: 1.1, 18.9; p = .03). ScRs with protocols registered a priori had significantly greater reporting quality scores (ß-coefficient: 12.5; 95%CI: 6.1, 18.9; p < .001), compared with non-registered reviews. CONCLUSIONS: The reporting quality of the ScRs in endodontics varied and was greater when the ScR protocols were registered a priori and when the authors adhered to reporting guidelines.

Global registration of kidneys in 3D ultrasound and CT images.

PURPOSE: Automatic registration between abdominal ultrasound (US) and computed tomography (CT) images is needed to enhance interventional guidance of renal procedures, but it remains an open research challenge. We propose a novel method that doesn't require an initial registration estimate (a global method) and also handles registration ambiguity caused by the organ's natural symmetry. Combined with a registration refinement algorithm, this method achieves robust and accurate kidney registration while avoiding manual initialization. METHODS: We propose solving global registration in a three-step approach: (1) Automatic anatomical landmark localization, where 2 deep neural networks (DNNs) localize a set of landmarks in each modality. (2) Registration hypothesis generation, where potential registrations are computed from the landmarks with a deterministic variant of RANSAC. Due to the Kidney's strong bilateral symmetry, there are usually 2 compatible solutions. Finally, in Step (3), the correct solution is determined automatically, using a DNN classifier that resolves the geometric ambiguity. The registration may then be iteratively improved with a registration refinement method. Results are presented with state-of-the-art surface-based refinement-Bayesian coherent point drift (BCPD). RESULTS: This automatic global registration approach gives better results than various competitive state-of-the-art methods, which, additionally, require organ segmentation. The results obtained on 59 pairs of 3D US/CT kidney images show that the proposed method, combined with BCPD refinement, achieves a target registration error (TRE) of an internal kidney landmark (the renal pelvis) of 5.78 mm and an average nearest neighbor surface distance (nndist) of 2.42 mm. CONCLUSION: This work presents the first approach for automatic kidney registration in US and CT images, which doesn't require an initial manual registration estimate to be known a priori. The results show a fully automatic registration approach with performances comparable to manual methods is feasible.

Improving deep learning-based automatic cranial defect reconstruction by heavy data augmentation: From image registration to latent diffusion models.

Modeling and manufacturing of personalized cranial implants are important research areas that may decrease the waiting time for patients suffering from cranial damage. The modeling of personalized implants may be partially automated by the use of deep learning-based methods. However, this task suffers from difficulties with generalizability into data from previously unseen distributions that make it difficult to use the research outcomes in real clinical settings. Due to difficulties with acquiring ground-truth annotations, different techniques to improve the heterogeneity of datasets used for training the deep networks have to be considered and introduced. In this work, we present a large-scale study of several augmentation techniques, varying from classical geometric transformations, image registration, variational autoencoders, and generative adversarial networks, to the most recent advances in latent diffusion models. We show that the use of heavy data augmentation significantly increases both the quantitative and qualitative outcomes, resulting in an average Dice Score above 0.94 for the SkullBreak and above 0.96 for the SkullFix datasets. The results show that latent diffusion models combined with vector quantized variational autoencoder outperform other generative augmentation strategies. Moreover, we show that the synthetically augmented network successfully reconstructs real clinical defects, without the need to acquire costly and time-consuming annotations. The findings of the work will lead to easier, faster, and less expensive modeling of personalized cranial implants. This is beneficial to numerous people suffering from cranial injuries. The work constitutes a considerable contribution to the field of artificial intelligence in the automatic modeling of personalized cranial implants.

Respiratory motion tracking of the thoracoabdominal surface based on defect-aware point cloud registration.

The performance of conventional lung puncture surgery is a complex undertaking due to the surgeon's reliance on visual assessment of respiratory conditions and the manual execution of the technique while the patient maintains breath-holding. However, the failure to correctly perform a puncture technique can lead to negative outcomes, such as the development of sores and pneumothorax. In this work, we proposed a novel approach for monitoring respiratory motion by utilizing defect-aware point cloud registration and descriptor computation. Through a thorough examination of the attributes of the inputs, we suggest the incorporation of a defect detection branch into the registration network. Additionally, we developed two modules with the aim of augmenting the quality of the extracted features. A coarse-to-fine respiratory phase recognition approach based on descriptor computation is devised for the respiratory motion tracking. The efficacy of the suggested registration method is demonstrated through experimental findings conducted on both publicly accessible datasets and thoracoabdominal point cloud datasets. We obtained state-of-the-art registration results on ModelNet40 datasets, with 1.584∘ on rotation mean absolute error and 0.016 mm on translation mean absolute error, respectively. The experimental findings conducted on a thoracoabdominal point cloud dataset indicate that our method exhibits efficacy and efficiency, achieving a frame matching rate of 2 frames per second and a phase recognition accuracy of 96.3%. This allows identifying matching frames from template point clouds that display different parts of a patient's thoracoabdominal surface while breathing regularly to distinguish breathing stages and track breathing.

A motion assessment method for reference stack selection in fetal brain MRI reconstruction based on tensor rank approximation.

Slice-to-volume registration and super-resolution reconstruction are commonly used to generate 3D volumes of the fetal brain from 2D stacks of slices acquired in multiple orientations. A critical initial step in this pipeline is to select one stack with the minimum motion among all input stacks as a reference for registration. An accurate and unbiased motion assessment (MA) is thus crucial for successful selection. Here, we presented an MA method that determines the minimum motion stack based on 3D low-rank approximation using CANDECOMP/PARAFAC (CP) decomposition. Compared to the current 2D singular value decomposition (SVD) based method that requires flattening stacks into matrices to obtain ranks, in which the spatial information is lost, the CP-based method can factorize 3D stack into low-rank and sparse components in a computationally efficient manner. The difference between the original stack and its low-rank approximation was proposed as the motion indicator. Experiments on linearly and randomly simulated motion illustrated that CP demonstrated higher sensitivity in detecting small motion with a lower baseline bias, and achieved a higher assessment accuracy of 95.45% in identifying the minimum motion stack, compared to the SVD-based method with 58.18%. CP also showed superior motion assessment capabilities in real-data evaluations. Additionally, combining CP with the existing SRR-SVR pipeline significantly improved 3D volume reconstruction. The results indicated that our proposed CP showed superior performance compared to SVD-based methods with higher sensitivity to motion, assessment accuracy, and lower baseline bias, and can be used as a prior step to improve fetal brain reconstruction.