A histological and diceCT-derived 3D reconstruction of the avian visual thalamofugal pathway.

Amniotes feature two principal visual processing systems: the tectofugal and thalamofugal pathways. In most mammals, the thalamofugal pathway predominates, routing retinal afferents through the dorsolateral geniculate complex to the visual cortex. In most birds, the thalamofugal pathway often plays the lesser role with retinal afferents projecting to the principal optic thalami, a complex of several nuclei that resides in the dorsal thalamus. This thalamic complex sends projections to a forebrain structure called the Wulst, the terminus of the thalamofugal visual system. The thalamofugal pathway in birds serves many functions such as pattern discrimination, spatial memory, and navigation/migration. A comprehensive analysis of avian species has unveiled diverse subdivisions within the thalamic and forebrain structures, contingent on species, age, and techniques utilized. In this study, we documented the thalamofugal system in three dimensions by integrating histological and contrast-enhanced computed tomography imaging of the avian brain. Sections of two-week-old chick brains were cut in either coronal, sagittal, or horizontal planes and stained with Nissl and either Gallyas silver or Luxol Fast Blue. The thalamic principal optic complex and pallial Wulst were subdivided on the basis of cell and fiber density. Additionally, we utilized the technique of diffusible iodine-based contrast-enhanced computed tomography (diceCT) on a 5-week-old chick brain, and right eyeball. By merging diceCT data, stained histological sections, and information from the existing literature, a comprehensive three-dimensional model of the avian thalamofugal pathway was constructed. The use of a 3D model provides a clearer understanding of the structural and spatial organization of the thalamofugal system. The ability to integrate histochemical sections with diceCT 3D modeling is critical to better understanding the anatomical and physiologic organization of complex pathways such as the thalamofugal visual system.

Trueness evaluation of three intraoral scanners for the recording of maximal intercuspal position.

OBJECTIVES: This clinical study aimed to assess the trueness of three intraoral scanners for the recor-ding of the maximal intercuspal position (MIP) to provide a reference for clinical practice. METHODS: Ten participants with good occlusal relationship and healthy temporomandibular joint were recruited. For the control group, facebow transferring procedures were performed, and bite registrations at the MIP were used to transfer maxillary and mandibular casts to a mechanical articulator, which were then scanned with a laboratory scanner to obtain digital cast data. For the experimental groups, three intraoral scanners (Trios 3, Carestream 3600, and Aoralscan 3) were used to obtain digital casts of the participants at the MIP following the scanning workflows endorsed by the corresponding manufacturers. Subsequently, measurement points were marked on the control group's digital casts at the central incisors, canines, and first molars, and corresponding distances between these points on the maxillary and mandibular casts were measured to calculate the sum of measured distances (DA). Distances between measurement points in the incisor (DI), canine (DC), and first molar (DM) regions were also calculated. The control group's maxillary and mandibular digital casts with the added measurement points were aligned with the experimental group's casts, and DA, DI, DC, and DM values of the aligned control casts were determined. Statistical analysis was performed on DA, DI, DC, and DM obtained from both the control and experimental groups to evaluate the trueness of the three intraoral scanners for the recording of MIP. RESULTS: In the control group, DA, DI, DC, and DM values were (39.58±6.40), (13.64±3.58), (14.91±2.85), and (11.03±1.56) mm. The Trios 3 group had values of (38.99±6.60), (13.42±3.66), (14.55±2.87), and (11.03±1.69) mm. The Carestream 3600 group showed values of (38.57±6.36), (13.56±3.68), (14.45±2.85), and (10.55±1.41) mm, while the Aoralscan 3 group had values of (38.16±5.69), (13.03±3.54), (14.23±2.59), and (10.90±1.54) mm. Analysis of variance revealed no statistically significant differences between the experimental and control groups for overall deviation DA (P=0.96), as well as local deviations DI (P=0.98), DC (P=0.96), and DM (P=0.89). CONCLUSIONS: With standardized scanning protocols, the three intraoral scanners demonstrated comparable trueness to traditional methods in recording MIP, fulfilling clinical requirements.

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.

3D auto-segmentation of biliary structure of living liver donors using magnetic resonance cholangiopancreatography for enhanced preoperative planning.

BACKGROUND: This study aimed to develop an automated segmentation system for biliary structures using a deep learning model, based on data from magnetic resonance cholangiopancreatography (MRCP). MATERIALS AND METHODS: Living liver donors who underwent MRCP using the gradient and spin echo technique followed by three-dimensional modeling were eligible for this study. A three-dimensional residual U-Net model was implemented for the deep learning process. Data were divided into training and test sets at a 9:1 ratio. Performance was assessed using the dice similarity coefficient to compare the model's segmentation with the manually labeled ground truth. RESULTS: The study incorporated 250 cases. There was no difference in the baseline characteristics between the train set (n=225) and test set (n=25). The overall mean Dice Similarity Coefficient was 0.80±0.20 between the ground truth and inference result. The qualitative assessment of the model showed relatively high accuracy especially for the common bile duct (88%), common hepatic duct (92%), hilum (96%), right hepatic duct (100%), and left hepatic duct (96%), while the third-order branch of the right hepatic duct (18.2%) showed low accuracy. CONCLUSION: The developed automated segmentation model for biliary structures, utilizing MRCP data and deep learning techniques, demonstrated robust performance and holds potential for further advancements in automation.

Basal and dentoalveolar transverse parameters in different sagittal and vertical malocclusions in adults: a comparative study.

OBJECTIVE: This study sought to three-dimensionally (3D) evaluate the maxillomandibular basal bone and dentoalveolar widths using cone-beam computed tomography (CBCT) scans in adult Chinese populations with different vertical and sagittal facial skeletal patterns whilst no apparent posterior dental crossbite. MATERIALS AND METHODS: The retrospective cross-sectional comparative study enrolled CBCT images of 259 adult patients (125 males and 134 females). The subjects were divided into the hyperdivergent(n = 82), hypodivergent(n = 88), and normodivergent(n = 89) groups based on the Jarabak ratio (S-GO/N-Me), which were further divided into three subgroups of skeletal Class I, II and III, based on both the ANB angle and AF-BF parameters. ANOVA was used to analyze the extracted data of the studied groups. The intra- and inter-observer reliability was analyzed using the intra-class correlation coefficient (ICC). RESULTS: In all three vertical facial skeletal patterns, the skeletal Class II had significantly smaller mandibular basal bone width compared to skeletal Class I and Class III, both at the first molar and first premolar levels. The skeletal Class III seemed to have smaller maxillary basal bone width compared to skeletal Class I and Class II malocclusions; however, a significant difference was found only in the normodivergent pattern. As for the dentoalveolar compensation, it was most notable that in the hypodivergent growth pattern, the skeletal Class II had significantly smaller maxillary dentoalveolar width compared to the Class I and Class III groups, both at the first molar and first premolar levels. CONCLUSIONS: Based on the sample in the present study, skeletal Class II has the narrowest mandibular basal bone regardless of the vertical facial skeletal pattern. CLINICAL RELEVANCE: For Chinese adults with no apparent transverse discrepancy, the maxillomandibular basal bone and dentoalveolar widths are revealed in specific categories based on different vertical and sagittal facial skeletal patterns. In diagnosis and treatment planning, particular attention should be paid to skeletal Class II for possibly existing mandibular narrowing.

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.

Computed tomography multi-planar and 3D image assessment protocol for detailed analysis of inner ear malformations in patients undergoing cochlear implantation counseling.

<b><br>Introduction:</b> Congenital inner ear malformations resulting from embryogenesis may be visualized in radiological scans. Many attempts have been made to describe and classify the defects of the inner ear based on anatomical and radiological findings.</br> <b><br>Aim:</b> The aim was to propose and discuss computed tomography multi-planar and 3D image assessment protocols for detailed analysis of inner ear malformations in patients undergoing cochlear implantation counseling.</br> <b><br>Material and methods:</b> A retrospective analysis of 22 malformed inner ears. CT scans were analyzed using the Multi-Planar Reconstruction (MPR) option and 3D reconstruction.</br> <b><br>Results:</b> The protocol of image interpretation was developed to allow reproducibility for evaluating each set of images. The following malformations were identified: common cavity, cochlear hypoplasia type II, III, and IV, incomplete partition type II and III, and various combinations of vestibule labyrinth malformations. All anomalies have been presented and highlighted in figures with appropriate descriptions for easier identification. Figures of normal inner ears were also included for comparison. 3D reconstructions for each malformation were presented, adding clinical value to the detailed analysis.</br> <b><br>Conclusions:</b> Properly analyzing CT scans in cochlear implantation counseling is a necessary and beneficial tool for appropriate candidate selection and preparation for surgery. As proposed in this study, the unified scans evaluation scheme simplifies the identification of malformations and reduces the risk of omitting particular anomalies. Multi-planar assessment of scans provides most of the necessary details. The 3D reconstruction technique is valuable in addition to diagnostics influencing the decision-making process. It can minimize the risk of misdiagnosis. Disclosure of the inner ear defect and its precise imaging provides detailed anatomical knowledge of each ear, enabling the selection of the appropriate cochlear implant electrode and the optimal surgical technique.</br>.

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.

Experimental validation and clinical feasibility of 3D reconstruction of coronary artery bifurcation stents using intravascular ultrasound.

The structural morphology of coronary stents and the local hemodynamic environment following stent deployment in coronary arteries are crucial determinants of procedural success and subsequent clinical outcomes. High-resolution intracoronary imaging has the potential to facilitate geometrically accurate three-dimensional (3D) reconstruction of coronary stents. This work presents an innovative algorithm for the 3D reconstruction of coronary artery stents, leveraging intravascular ultrasound (IVUS) and angiography. The accuracy and reproducibility of our method were tested in stented patient-specific silicone models, with micro-computed tomography serving as a reference standard. We also evaluated the clinical feasibility and ability to perform computational fluid dynamics (CFD) studies in a clinically stented coronary bifurcation. Our experimental and clinical studies demonstrated that our proposed algorithm could reproduce the complex 3D stent configuration with a high degree of precision and reproducibility. Moreover, the algorithm was proved clinically feasible in cases with stents deployed in a diseased coronary artery bifurcation, enabling CFD studies to assess the hemodynamic environment. In combination with patient-specific CFD studies, our method can be applied to stenting optimization, training in stenting techniques, and advancements in stent research and development.

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.