Neuronavigation in endoscopic skull base surgery and the accuracy of different MRI sequences.

OBJECTIVE: Neuronavigation is common technology used by skull base teams when performing endoscopic endonasal surgery. A common practice of MRI imagining is to obtain 3D isotopic gadolinium enhanced T1W magnetisation prepared rapid gradient echo (MPRAGE) sequences. These are prone to distortion when undertaken on 3 T magnets. The aim of this project is to compare the in vivo accuracy of MRI sequences between current and new high resolution 3D sequences. The goal is to determine if geometric distortion significantly affects neuronavigation accuracy. METHODS: Patients were scanned with a 3D T1 MPRAGE sequence, 3D T1 SPACE sequence and a CT stereotactic localisation. Following general anaesthesia, patients were registered on the Stealth Station (Medtronic, USA) using a side mount emitter for Electromagnetic navigation. A variety of surgically relevant anatomical landmarks in the sagittal and coronal plane were selected with real and virtual data points measured. RESULTS: A total of 10 patients agreed be enrolled in the study with datapoints collected during surgery. The distance between real and virtual datapoints trended to be lower in SPACE sequences compared to MPRAGE. Paired t test did not demonstrate a significant difference. CONCLUSION: We have demonstrated that navigational accuracy is not significantly affected by the type of MRI sequence selected and that current corrective algorithms are sufficient. Navigational accuracy is affected by many factors, with registration error likely playing the most significant role. Further research involving real time imaging such as endoscopic ultrasound may hopefully address this potential error.

3D printing of an artificial intelligence-generated patient-specific coronary artery segmentation in a support bath.

Accurate segmentation of coronary artery tree and personalized 3D printing from medical images is essential for CAD diagnosis and treatment. The current literature on 3D printing relies solely on generic models created with different software or 3D coronary artery models manually segmented from medical images. Moreover, there are not many studies examining the bioprintability of a 3D model generated by artificial intelligence (AI) segmentation for complex and branched structures. In this study, deep learning algorithms with transfer learning have been employed for accurate segmentation of the coronary artery tree from medical images to generate printable segmentations. We propose a combination of deep learning and 3D printing, which accurately segments and prints complex vascular patterns in coronary arteries. Then, we performed the 3D printing of the AI-generated coronary artery segmentation for the fabrication of bifurcated hollow vascular structure. Our results indicate improved performance of segmentation with the aid of transfer learning with a Dice overlap score of 0.86 on a test set of 10 coronary tomography angiography images. Then, bifurcated regions from 3D models were printed into the Pluronic F-127 support bath using alginate + glucomannan hydrogel. We successfully fabricated the bifurcated coronary artery structures with high length and wall thickness accuracy, however, the outer diameters of the vessels and length of the bifurcation point differ from the 3D models. The extrusion of unnecessary material, primarily observed when the nozzle moves from left to the right vessel during 3D printing, can be mitigated by adjusting the nozzle speed. Moreover, the shape accuracy can also be improved by designing a multi-axis printhead that can change the printing angle in three dimensions. Thus, this study demonstrates the potential of the use of AI-segmented 3D models in the 3D printing of coronary artery structures and, when further improved, can be used for the fabrication of patient-specific vascular implants.

[Fully Automatic Glioma Segmentation Algorithm of Magnetic Resonance Imaging Based on 3D-UNet With More Global Contextual Feature Extraction: An Improvement on Insufficient Extraction of Global Features]. / 3DGE-UNetç£å ±æŒ¯æˆåƒçš„è„‘èƒ¶è´¨ç˜¤å ¨è‡ªåŠ¨åˆ†å‰²ç®—æ³•ï¼šå¯¹ä¸å åˆ†å ¨å±€ç‰¹å¾æå–çš„æ”¹è¿›.

Objective: The fully automatic segmentation of glioma and its subregions is fundamental for computer-aided clinical diagnosis of tumors. In the segmentation process of brain magnetic resonance imaging (MRI), convolutional neural networks with small convolutional kernels can only capture local features and are ineffective at integrating global features, which narrows the receptive field and leads to insufficient segmentation accuracy. This study aims to use dilated convolution to address the problem of inadequate global feature extraction in 3D-UNet. Methods: 1) Algorithm construction: A 3D-UNet model with three pathways for more global contextual feature extraction, or 3DGE-UNet, was proposed in the paper. By using publicly available datasets from the Brain Tumor Segmentation Challenge (BraTS) of 2019 (335 patient cases), a global contextual feature extraction (GE) module was designed. This module was integrated at the first, second, and third skip connections of the 3D UNet network. The module was utilized to fully extract global features at different scales from the images. The global features thus extracted were then overlaid with the upsampled feature maps to expand the model's receptive field and achieve deep fusion of features at different scales, thereby facilitating end-to-end automatic segmentation of brain tumors. 2) Algorithm validation: The image data were sourced from the BraTs 2019 dataset, which included the preoperative MRI images of 335 patients across four modalities (T1, T1ce, T2, and FLAIR) and a tumor image with annotations made by physicians. The dataset was divided into the training, the validation, and the testing sets at an 8∶1∶1 ratio. Physician-labelled tumor images were used as the gold standard. Then, the algorithm's segmentation performance on the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) was evaluated in the test set using the Dice coefficient (for overall effectiveness evaluation), sensitivity (detection rate of lesion areas), and 95% Hausdorff distance (segmentation accuracy of tumor boundaries). The performance was tested using both the 3D-UNet model without the GE module and the 3DGE-UNet model with the GE module to internally validate the effectiveness of the GE module setup. Additionally, the performance indicators were evaluated using the 3DGE-UNet model, ResUNet, UNet++, nnUNet, and UNETR, and the convergence of these five algorithm models was compared to externally validate the effectiveness of the 3DGE-UNet model. Results: 1) In internal validation, the enhanced 3DGE-UNet model achieved Dice mean values of 91.47%, 87.14%, and 83.35% for segmenting the WT, TC, and ET regions in the test set, respectively, producing the optimal values for comprehensive evaluation. These scores were superior to the corresponding scores of the traditional 3D-UNet model, which were 89.79%, 85.13%, and 80.90%, indicating a significant improvement in segmentation accuracy across all three regions (P<0.05). Compared with the 3D-UNet model, the 3DGE-UNet model demonstrated higher sensitivity for ET (86.46% vs. 80.77%) (P<0.05) , demonstrating better performance in the detection of all the lesion areas. When dealing with lesion areas, the 3DGE-UNet model tended to correctly identify and capture the positive areas in a more comprehensive way, thereby effectively reducing the likelihood of missed diagnoses. The 3DGE-UNet model also exhibited exceptional performance in segmenting the edges of WT, producing a mean 95% Hausdorff distance superior to that of the 3D-UNet model (8.17 mm vs. 13.61 mm, P<0.05). However, its performance for TC (8.73 mm vs. 7.47 mm) and ET (6.21 mm vs. 5.45 mm) was similar to that of the 3D-UNet model. 2) In the external validation, the other four algorithms outperformed the 3DGE-UNet model only in the mean Dice for TC (87.25%), the mean sensitivity for WT (94.59%), the mean sensitivity for TC (86.98%), and the mean 95% Hausdorff distance for ET (5.37 mm). Nonetheless, these differences were not statistically significant (P>0.05). The 3DGE-UNet model demonstrated rapid convergence during the training phase, outpacing the other external models. Conclusion: The 3DGE-UNet model can effectively extract and fuse feature information on different scales, improving the accuracy of brain tumor segmentation.

Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring.

Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs. Our approach utilizes a multi-task deep convolutional neural network (CNN) with a topology-aware loss function to learn voxel-wise segmentation of the cerebrovascular tree. We use Decorrelation Loss to achieve domain regularization for the encoder network and auxiliary tasks to provide additional regularization and enable the encoder to learn higher-level intermediate representations for improved performance. We compare our method to six state-of-the-art 3D vessel segmentation methods using retrospective TOF-MRA datasets from multiple private and public data sources scanned at six hospitals, with and without vascular pathologies. The proposed model achieved the best scores in all the qualitative performance measures. Furthermore, we have developed an AI-assisted Graphical User Interface (GUI) based on our research to assist radiologists in their daily work and establish a more efficient work process that saves time.

Comparison of vestibular aqueduct visualization on computed tomography and magnetic resonance imaging in patients with Ménière's disease.

BACKGROUND: The vestibular aqueduct (VA) serves an essential role in homeostasis of the inner ear and pathogenesis of Ménière's disease (MD). The bony VA can be clearly depicted by high-resolution computed tomography (HRCT), whereas the optimal sequences and parameters for magnetic resonance imaging (MRI) are not yet established. We investigated VA characteristics and potential factors influencing MRI-VA visibility in unilateral MD patients. METHODS: One hundred patients with unilateral MD underwent MRI with three-dimensional sampling perfection with application optimized contrasts using different flip angle evolutions (3D-SPACE) sequence and HRCT evaluation. The imaging variables included MRI-VA and CT-VA visibility, CT-VA morphology and CT-peri-VA pneumatization. RESULTS: The most frequent type of MRI-VA and CT-VA visualization was invisible VA and continuous VA, respectively. The MRI-VA visibility was significantly lower than CT-VA visibility. MRI-VA visibility had a weak positive correlation with ipsilateral CT-VA visualization. For the affected side, the MRI-VA visualization was negatively correlated with the incidence of obliterated-shaped CT-VA and positively with that of tubular-shaped CT-VA. MRI-VA visualization was not affected by CT-peri-VA pneumatization. CONCLUSION: In patients with MD, the VA visualization on 3D-SPACE MRI is poorer than that observed on CT and may be affected by its osseous configuration. These findings may provide a basis for further characterization of VA demonstrated by MRI and its clinical significance.

Anatomy of Maxillary Sinus: Focus on Vascularization and Underwood Septa via 3D Imaging.

The study of the maxillary sinus anatomy should consider the presence of two features of clinical importance. The arterial supply course and the presence of the so-called Underwood septa are two important factors to consider when planning surgical treatment to reduce the risk of surgical complications such as excessive bleeding and Schneiderian membrane perforations. This study aimed to investigate the above-mentioned anatomical structures to improve the management of eventual vascular and surgical complications in this area. This study included a total of 200 cone-beam computed topographies (CBCTs) divided into two groups of 100 CBCTs to evaluate the arterial supply (AAa) course through the lateral sinus wall and Underwood's septa, respectively. The main parameters considered on 3D imaging were the presence of the AAa in the antral wall, the length of the arterial pathway, the height of the maxillary bone crest, the branch sizes of the artery in the first group, and the position of the septa, the length of the septa, and their gender associations in the second group. The CBCT analysis showed the presence of the arterial supply through the bone wall in 100% of the examined patients, with an average size of 1.07 mm. With regard to the septa, 19% of patients presented variations, and no gender difference was found to be statistically significant. The findings add to the current understanding of the clinical structure of the maxillary sinus, equipping medical professionals with vital details for surgical preparation and prevention of possible complications.

Three-Dimensional Visualization of Shunt Valves with Photon Counting CT and Comparison to Traditional X-ray in a Simple Phantom Model.

This study introduces an application of innovative medical technology, Photon Counting Computer Tomography (PC CT) with novel detectors, for the assessment of shunt valves. PC CT technology offers enhanced visualization capabilities, especially for small structures, and opens up new possibilities for detailed three-dimensional imaging. Shunt valves are implanted under the skin and redirect excess cerebrospinal fluid, for example, to the abdominal cavity through a catheter. They play a vital role in regulating cerebrospinal fluid drainage in various pathologies, which can lead to hydrocephalus. Accurate imaging of shunt valves is essential to assess the rate of drainage, as their precise adjustment is a requirement for optimal patient care. This study focused on two adjustable shunt valves, the proGAV 2.0® and M. blue® (manufactured by Miethke, Potsdam, Germany). A comprehensive comparative analysis of PC CT and traditional X-ray techniques was conducted to explore this cutting-edge technology and it demonstrated that routine PC CT can efficiently assess shunt valves' adjustments. This technology shows promise in enhancing the accurate management of shunt valves used in settings where head scans are already frequently required, such as in the treatment of hydrocephalus.

Laparoscopy training of novices with complex curved instruments using 2D- and 3D-visualization.

PURPOSE: Beside many advantages, disadvantages such as reduced degrees of freedom and poorer depth perception are still apparent in laparoscopic surgery. 3D visualization and the development of complex instruments are intended to counteract the disadvantages. We want to find out whether the use of complex instruments and 3D visualization has an influence on the performance of novices. METHODS: 48 medical students with no experience in laparoscopic surgery or simulator-based laparoscopy training were included. They were randomized in four groups according to a stratification assessment. During a structured training period they completed the FLS-Tasks "PEG Transfer", "Pattern Cut" and "Intracorporeal Suture" and a transfer task based on these three. Two groups used conventional laparoscopic instruments with 3D or 2D visualization, two groups used complex curved instruments. The groups were compared in terms of their performance. RESULTS: In 2D laparoscopy there was a better performance with straight instruments vs. curved instruments in PEG Transfer and Intracorporeal Suture. In the transfer task, fewer errors were made with straight instruments. In 2D vs. 3D laparoscopy when using complex curved instruments there was an advantage in Intracorporeal Suture and PEG Transfer for 3D visualization. Regarding the transfer exercise, a better performance was observed and fewer errors were made in 3D group. CONCLUSION: We could show that learning laparoscopic techniques with complex curved instruments is more difficult with standard 2D visualization and can be overcome using 3D optics. The use of curved instruments under 3D vision seems to be advantageous when working on more difficult tasks.

CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.

Significance: Three-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. It has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw three-dimensional (3D) tomograms is not well-developed. We focus on a critical, yet often underappreciated, step of the analysis pipeline that of 3D cell segmentation from the acquired tomograms. Aim: We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the 3D segmentation of QPI images. Approach: The cell segmentation algorithm mimics the gemstone extraction process, initiating with a coarse 3D extrusion from a two-dimensional (2D) segmented mask to outline the cell structure. A 2D image is generated, and a segmentation algorithm identifies the boundary in the x-y plane. Leveraging cell continuity in consecutive z-stacks, a refined 3D segmentation, akin to fine chiseling in gemstone carving, completes the process. Results: The CellSNAP algorithm outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 s per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused AI-based segmentation tools. Conclusion: Our proposed method is less memory intensive and significantly faster than existing methods. The method can be easily implemented on a student laptop. Since the approach is rule-based, there is no need to collect a lot of imaging data and manually annotate them to perform machine learning based training of the model. We envision our work will lead to broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.

RT-SRTS: Angle-agnostic real-time simultaneous 3D reconstruction and tumor segmentation from single X-ray projection.

Radiotherapy is one of the primary treatment methods for tumors, but the organ movement caused by respiration limits its accuracy. Recently, 3D imaging from a single X-ray projection has received extensive attention as a promising approach to address this issue. However, current methods can only reconstruct 3D images without directly locating the tumor and are only validated for fixed-angle imaging, which fails to fully meet the requirements of motion control in radiotherapy. In this study, a novel imaging method RT-SRTS is proposed which integrates 3D imaging and tumor segmentation into one network based on multi-task learning (MTL) and achieves real-time simultaneous 3D reconstruction and tumor segmentation from a single X-ray projection at any angle. Furthermore, the attention enhanced calibrator (AEC) and uncertain-region elaboration (URE) modules have been proposed to aid feature extraction and improve segmentation accuracy. The proposed method was evaluated on fifteen patient cases and compared with three state-of-the-art methods. It not only delivers superior 3D reconstruction but also demonstrates commendable tumor segmentation results. Simultaneous reconstruction and segmentation can be completed in approximately 70 ms, significantly faster than the required time threshold for real-time tumor tracking. The efficacies of both AEC and URE have also been validated in ablation studies. The code of work is available at https://github.com/ZywooSimple/RT-SRTS.

Comparison of weighting algorithms to mitigate respiratory motion in free-breathing neonatal pulmonary radial UTE-MRI.

Background. Thoracoabdominal MRI is limited by respiratory motion, especially in populations who cannot perform breath-holds. One approach for reducing motion blurring in radially-acquired MRI is respiratory gating. Straightforward 'hard-gating' uses only data from a specified respiratory window and suffers from reduced SNR. Proposed 'soft-gating' reconstructions may improve scan efficiency but reduce motion correction by incorporating data with nonzero weight acquired outside the specified window. However, previous studies report conflicting benefits, and importantly the choice of soft-gated weighting algorithm and effect on image quality has not previously been explored. The purpose of this study is to map how variable soft-gated weighting functions and parameters affect signal and motion blurring in respiratory-gated reconstructions of radial lung MRI, using neonates as a model population.Methods. Ten neonatal inpatients with respiratory abnormalities were imaged using a 1.5 T neonatal-sized scanner and 3D radial ultrashort echo-time (UTE) sequence. Images were reconstructed using ungated, hard-gated, and several soft-gating weighting algorithms (exponential, sigmoid, inverse, and linear weighting decay outside the period of interest), with %Nprojrepresenting the relative amount of data included. The apparent SNR (aSNR) and motion blurring (measured by the maximum derivative of image intensity at the diaphragm, MDD) were compared between reconstructions.Results. Soft-gating functions produced higher aSNR and lower MDD than hard-gated images using equivalent %Nproj, as expected. aSNR was not identical between different gating schemes for given %Nproj. While aSNR was approximately linear with %Nprojfor each algorithm, MDD performance diverged between functions as %Nprojdecreased. Algorithm performance was relatively consistent between subjects, except in images with high noise.Conclusion. The algorithm selection for soft-gating has a notable effect on image quality of respiratory-gated MRI; the timing of included data across the respiratory phase, and not simply the amount of data, plays an important role in aSNR. The specific soft-gating function and parameters should be considered for a given imaging application's requirements of signal and sharpness.

Evaluating the precision and accuracy of digital dental models with a low-cost structured light device.

OBJECTIVE: This study evaluated the accuracy and precision of digital models acquisition using a home-built, low-cost scanning system based on the structured light method. METHODS: a plaster model (PM) was scanned using the experimental device (SL) and a dental desktop scanner (DS). The teeth dimensions of PM and SL models were measured in triplicate, with a caliper and digitally, respectively. The agreement of the measurements of each model was evaluated using the intraclass correlation coefficient, and the validity between the different measurement techniques was assessed using the Bland-Altman analysis. The accuracy and precision of the models were qualitatively investigated using the mesh superposition of the SL and DS models. RESULTS: A high intraclass correlation coefficient was observed in all models (PM=0.964; SL1=0.998; SL2=0.995; SL3=0.998), and there was no statistical difference between the measurements of the SL models (p>0.05). PM and SL model measurements were found to be in good agreement, with only 3.57% of the observed differences between the same measurement being located outside 95% limits of agreement according to Bland and Altman (0.43 and -0.40 mm). In the superimpositions of SL-SL and SL-DS models, areas of discrepancy greater than 0.5 mm were observed mainly in interproximal, occlusal, and cervical sites. CONCLUSION: These results indicate that the home-built SL scanning system did not possess sufficient accuracy and precision for many clinical applications. However, the consistency in preserving the dental proportions suggests that the equipment can be used for planning, storage, and simple clinical purposes.

Volumetric Analysis of Peri-implant Tissue Change Over Time: Validating an Innovative 3D Method.

PURPOSE: To validate an innovative 3D volumetric method of evaluating tissue changes proposed by Lee et al in 2020 by comparing the results of this method-in which the scanned peri-implant surfaces were transformed, visualized, and analyzed as 3D objects-to the results reported by an existing method based on calculation of the mean distance between measured surfaces. The null hypothesis was that there was no statistically significant difference between the two methods. Additionally, the present study evaluated peri-implant tissue changes 5 years after single implant placement in the esthetic zone. MATERIALS AND METHODS: Both methods were applied to 11 oral implant site casts (6 maxillary central incisor sites, 5 maxillary lateral incisor sites) taken from 11 patients at crown placement and at follow-up examinations 5 years later. The methods are based on digital workflows in which the reference and 5-year casts are scanned and the resulting STL files are superimposed and analyzed for three regions of interest (mesial papilla, central area, and distal papilla). The volumetric changes reported by the Lee et al method and the mean distance method were calculated and compared using the Spearman rank correlation coefficient (P < .01) and the Wilcoxon signed-rank test (P < .05). RESULTS: The correlation between the two sets of measurements was very high (Spearman rank correlation coefficient = 0.885). The new volumetric method indicated a mean volume loss of 2.82 mm3 (SD: 5.06), while the method based on the measurement of mean distance showed a mean volume loss of 2.92 mm3 (SD: 4.43; Wilcoxon signed-rank test result: P = .77). No statistically significant difference was found. The two methods gave equivalent results, and the null hypothesis was accepted. CONCLUSIONS: The new volumetric method was validated and can be considered a trustworthy tool.

Acto3D: an open-source user-friendly volume rendering software for high-resolution 3D fluorescence imaging in biology.

Advances in fluorescence microscopy and tissue-clearing have revolutionised 3D imaging of fluorescently labelled tissues, organs and embryos. However, the complexity and high cost of existing software and computing solutions limit their widespread adoption, especially by researchers with limited resources. Here, we present Acto3D, an open-source software, designed to streamline the generation and analysis of high-resolution 3D images of targets labelled with multiple fluorescent probes. Acto3D provides an intuitive interface for easy 3D data import and visualisation. Although Acto3D offers straightforward 3D viewing, it performs all computations explicitly, giving users detailed control over the displayed images. Leveraging an integrated graphics processing unit, Acto3D deploys all pixel data to system memory, reducing visualisation latency. This approach facilitates accurate image reconstruction and efficient data processing in 3D, eliminating the need for expensive high-performance computers and dedicated graphics processing units. We have also introduced a method for efficiently extracting lumen structures in 3D. We have validated Acto3D by imaging mouse embryonic structures and by performing 3D reconstruction of pharyngeal arch arteries while preserving fluorescence information. Acto3D is a cost-effective and efficient platform for biological research.

Decreased Cerebral Perfusion in Chronic Migraine: A Voxel-based Cerebral Blood Flow Analysis Using 3D Pseudo-continuous Arterial Spin Labeling.

BACKGROUND: A contrast agent-free approach would be preferable to the frequently used invasive approaches for evaluating cerebral perfusion in chronic migraineurs (CM). In this work, non-invasive quantitative volumetric perfusion imaging was used to evaluate alterations in cerebral perfusion in CM. METHODS: We used conventional brain structural imaging sequences and 3D pseudo-continuous arterial spin labeling (3D PCASL) to examine thirteen CM patients and fifteen normal controls (NCs). The entire brain gray matter underwent voxel-based analysis, and the cerebral blood flow (CBF) values of the altered positive areas were retrieved to look into the clinical variables' significant correlation. RESULTS: Brain regions with the decreased perfusion were located in the left postcentral gyrus, bilateral middle frontal gyrus, left middle occipital gyrus, left superior parietal lobule, left medial segment of superior frontal gyrus, and right orbital part of the inferior frontal gyrus. White matter fibers with decreased perfusion were located in bilateral superior longitudinal tracts, superior corona radiata, external capsules, anterior and posterior limbs of the internal capsule, anterior corona radiata, inferior longitudinal fasciculus, and right corticospinal tract. However, the correlation analysis showed no significant correlation between the CBF value of the above positive brain regions with clinical variables (p > 0.05). CONCLUSION: The current study provided more useful information to comprehend the pathophysiology of CM and revealed a new insight into the neural mechanism of CM from the pattern of cerebral hypoperfusion.

Label-free Brillouin endo-microscopy for the quantitative 3D imaging of sub-micrometre biology.

This report presents an optical fibre-based endo-microscopic imaging tool that simultaneously measures the topographic profile and 3D viscoelastic properties of biological specimens through the phenomenon of time-resolved Brillouin scattering. This uses the intrinsic viscoelasticity of the specimen as a contrast mechanism without fluorescent tags or photoacoustic contrast mechanisms. We demonstrate 2 µm lateral resolution and 320 nm axial resolution for the 3D imaging of biological cells and Caenorhabditis elegans larvae. This has enabled the first ever 3D stiffness imaging and characterisation of the C. elegans larva cuticle in-situ. A label-free, subcellular resolution, and endoscopic compatible technique that reveals structural biologically-relevant material properties of tissue could pave the way toward in-vivo elasticity-based diagnostics down to the single cell level.

Preoperative three-dimensional modelling and virtual reality planning aids nephron sparing surgery in a child with bilateral Wilms tumour.

Bilateral Wilms tumour (BWT) is a surgically challenging condition. Virtual reality (VR) reconstruction aids surgeons to foresee the anatomy ahead of Nephron Sparing Surgery (NSS). Three-dimensional (3D) visualisation improves the anatomical orientation of surgeons performing NSS. We herewith report a case of BWT where VR planning and 3D printing were used to aid NSS. Conventional imaging is often found to be inadequate while assessing the tumour-organ-vascular anatomy. Advances like VR and 3D printing help surgeons plan better for complex surgeries like bilateral NSS. Next-generation extended reality tools will likely aid robotic-assisted precision NSS and improve patient outcomes.

Automatic pterygopalatine fossa segmentation and localisation based on DenseASPP.

BACKGROUND: Allergic rhinitis constitutes a widespread health concern, with traditional treatments often proving to be painful and ineffective. Acupuncture targeting the pterygopalatine fossa proves effective but is complicated due to the intricate nearby anatomy. METHODS: To enhance the safety and precision in targeting the pterygopalatine fossa, we introduce a deep learning-based model to refine the segmentation of the pterygopalatine fossa. Our model expands the U-Net framework with DenseASPP and integrates an attention mechanism for enhanced precision in the localisation and segmentation of the pterygopalatine fossa. RESULTS: The model achieves Dice Similarity Coefficient of 93.89% and 95% Hausdorff Distance of 2.53 mm with significant precision. Remarkably, it only uses 1.98 M parameters. CONCLUSIONS: Our deep learning approach yields significant advancements in localising and segmenting the pterygopalatine fossa, providing a reliable basis for guiding pterygopalatine fossa-assisted punctures.

Atomic force microscopy 3D structural reconstruction of individual particles in the study of amyloid protein assemblies.

Recent developments in atomic force microscopy (AFM) image analysis have made three-dimensional (3D) structural reconstruction of individual particles observed on 2D AFM height images a reality. Here, we review the emerging contact point reconstruction AFM (CPR-AFM) methodology and its application in 3D reconstruction of individual helical amyloid filaments in the context of the challenges presented by the structural analysis of highly polymorphous and heterogeneous amyloid protein structures. How individual particle-level structural analysis can contribute to resolving the amyloid polymorph structure-function relationships, the environmental triggers leading to protein misfolding and aggregation into amyloid species, the influences by the conditions or minor fluctuations in the initial monomeric protein structure on the speed of amyloid fibril formation, and the extent of the different types of amyloid species that can be formed, are discussed. Future perspectives in the capabilities of AFM-based 3D structural reconstruction methodology exploiting synergies with other recent AFM technology advances are also discussed to highlight the potential of AFM as an emergent general, accessible and multimodal structural biology tool for the analysis of individual biomolecules.

Improving ultrasound-based bone registration using the iterative closest point algorithm paired with a complex optimization solver.

The Iterative Close Point (ICP) algorithm is used for bone registrations based on ultrasound measurements. However, the ICP has been shown to suffer from local minima. The Complex optimization, as a more robust routine compared to the commonly used gradient-based algorithms, could be an alternative for solving the ICP problem. In this study, we investigated the effect of the initial estimate and the number of registration points on bone registrations achieved using the ICP and a Complex optimization routine and we compared it against using Quadratic Sequential Programming (SQP). Ultrasound measurements were performed with an A-mode probe on a bovine humerus and an ovine femur embedded into ballistic gel. Simultaneously, the bones and the probe were tracked in 3D space using retroreflective markers. Kinematic, ultrasound and geometrical data obtained from scans of the specimens and the probe served as input to a bone registrations routine. Registrations were performed using two ICP solvers for different initial estimates and number of registration points. On average, 68 % of the Complex optimization registrations had less than 1 mm translation error and less than 1° rotational error for perturbations of the initial estimate from the reference measurements compared to the 35 % of the SQP ones. Similar medians of registration errors were observed between the two methods for variations of the number of the employed registration points. Although the Complex optimization provided accurate bone registrations for all cases, the objective function could not always determine the registrations with the smallest registration error. Future research should explore methodologies to overcome this challenge.