Image Sequences Research Articles

An algorithm for large-span flexible bridge pose estimation and multi-keypoint vibration displacement measurement

Traditional visual vibration measurement methods face limitations in dynamic outdoor environments, making accurate multi-point vibration measurements challenging for the targeted objects. In this paper, a visual vibration measurement algorithm for large-span bridges is proposed by anthropomorphizing the bridge target and introducing key point detection at the same time. Firstly, the algorithm utilizes a convolutional neural network to extract multi-scale feature information from the image sequence of the bridge model. Secondly, to enhance target detection accuracy, a coordinate attention (CA) mechanism is incorporated, and the shape intersection over union (SIoU) loss function replaces the original loss function. Finally, the algorithm integrates the density-based spatial clustering of applications with noise (DBSCAN) and tracking algorithms to achieve precise localization of bridge targets. Vibration displacement data were extracted from a large-span suspension bridge structure in an outdoor environment and analyzed quantitatively and qualitatively. The vibration displacement curves obtained in this study show the highest level of agreement with the standard displacement signals, with a mean absolute percentage error of 0.5170% for the cable-stayed bridge model, 1.3822% for the Humen Bridge, and 3.2263% and 1.6982% for the Longjiang Bridge.

Comparison of clinical efficacy between autologous partially demineralized dentin matrix and deproteinized bovine bone mineral for bone augmentation in orthodontic patients with alveolar bone deficiency: a randomized controlled clinical trial

BackgroundIt is common to see patients who need orthodontic treatment but with insufficient alveolar bone volume. However, safe and effective tooth movement requires sufficient alveolar bone width and height. The aim of this study is to compare the bone augmentation efficacy of Autologous Partially Demineralized Dentin Matrix (APDDM) and Deproteinized Bovine Bone Mineral (DBBM) in orthodontic patients with insufficient bone by using a randomized controlled clinical trial approach.Materials and methodsTwenty-seven orthodontic patients involving 40 posterior teeth alveolar sites (n = 40) with insufficient alveolar bone volume were randomly divided into a control group (n = 20) and an experimental group (n = 20). The patients in the experimental group were treated with APDDM, and those in the control group were treated with DBBM. After surgery, the adjacent teeth are moved toward the bone grafting sites according to the orthodontic treatment plan. Patients completed a postoperative response questionnaire by the Visual Analogue Scale (VAS) score to indicate pain and swelling in the bone grafted area at the time of suture removal; and CBCT scans were conducted before surgery, 6 months and 2 years after surgery to assess changes in buccal and central alveolar heights, as well as widths at the alveolar ridge apex and 3 mm, 5 mm below the apex, respectively. The CBCT image sequences were imported into Mimics 21.0 software in DICOM format. The data of the patients in both groups were collected and analyzed by SPSS 25.0.ResultsThe VAS scores were significantly lower in the APDDM group than in the DBBM group (p < 0.05). Significant increases were observed in alveolar bone height and width at 6 months and 2 years postoperative (p < 0.05); At 2 years, the APDDM group exhibited a reduction in buccal crest height and in 3 mm, 5 mm width below alveolar ridge apex, relative to 6 months (p < 0.05), while the DBBM group showed a decrease only in the central height of the alveolar bone (p < 0.05). There was a significant bone augmentation increase found only 3 mm below the alveolar ridge apex in the APDDM group compared with the DBBM group among all 6 months group comparison (p < 0.05). At 2 years, the augmentation effects were similar across both groups (p > 0.05).ConclusionRadiomics analysis indicates that APDDM serves as a viable bone augmentation material for orthodontic patients with insufficient alveolar bone volume, achieving comparable clinical efficacy to DBBM. Additionally, APDDM is associated with a milder postoperative response than DBBM.The registration number (TRN)ChiCTR2400084607.

Inter-visit and inter-reader reproducibility of multi-parametric diffusion-weighted MR imaging in longitudinally imaged patients with metabolic dysfunction-associated fatty liver disease and healthy volunteers.

BackgroundDespite the widespread use of diffusion-weighted imaging (DWI) in metabolic dysfunction-associated fatty liver disease (MAFLD), MRI acquisition and quantification techniques vary in the literature suggesting the need for established and reproducible protocols. The goal of this study was to assess inter-visit and inter-reader reproducibility of DWI- and IVIM-derived parameters in patients with MAFLD and healthy volunteers using extensive sampling of the “fast” compartment, non-rigid registration, and exclusion voxels with poor fit quality. MethodsFrom June 2019 to April 2023, 31 subjects (20 patients with biopsy-proven MAFLD and 11 healthy volunteers) were included in this IRB-approved study. Subjects underwent MRI examinations twice within 40 days. 3.0 T DWI was acquired using a respiratory-triggered spin-echo diffusion-weighted echo-planar imaging sequence (b-values of 0, 10, 20, 30, 40, 50, 100, 200, 400, 800 s/mm2). DWI series were co-registered prior to voxel-wise non-linear regression of the IVIM model and voxels with poor fit quality were excluded (normalized root mean squared error ≥ 0.05). IVIM parameters (perfusion fraction, f; diffusion coefficient, D; and pseudo-diffusion coefficient, D*), and apparent diffusion coefficients (ADC) were computed from manual segmentation of the right liver lobe performed by two analysts on two MRI examinations. ResultsAll results are reported for f, D, D*, and ADC respectively. For inter-reader agreement on the first visit, ICC were of 0.985, 0.994, 0.986, and 0.993 respectively. For intra-reader agreement of analyst 1 assessed on both imaging examinations, ICC between visits were of 0.805, 0.759, 0.511, and 0.850 respectively. For inter-reader agreement on the first visit, mean bias and 95 % limits of agreement were (0.00 ± 0.03), (−0.01 ± 0.03) × 10−3 mm2/s, (0.70 ± 10.40) × 10−3 mm2/s, and (−0.02 ± 0.04) × 10−3 mm2/s respectively. For intra-reader agreement of analyst 1, mean bias and 95 % limits of agreement were (0.01 ± 0.09) × 10−3 mm2/s, (−0.01 ± 0.21) × 10−3 mm2/s, (−13.37 ± 56.19) × 10−3 mm2/s, and (−0.01 ± 0.16) × 10−3 mm2/s respectively. Except for parameter D* that was associated with between-subjects parameter variability (P = 0.009), there was no significant variability between subjects, examinations, or readers. ConclusionWith our approach, IVIM parameters f, D, D*, and ADC provided excellent inter-reader agreement and good to very good inter-visit or intra-reader agreement, thus showing the reproducibility of IVIM-DWI of the liver in MAFLD patients and volunteers.

A novel 3D reconstruction method of blast furnace burden surface based on virtual camera array

Accurate three-dimensional (3D) blast furnace burden surface topography is crucial for optimizing material distribution and furnace operation. However, harsh conditions within the furnace pose challenges to this 3D reconstruction, such as difficulties in matching feature points and sparse 3D point clouds. To overcome these challenges, we propose a 3D reconstruction method for the blast furnace burden surface using virtual camera array. Firstly, an ORB feature extraction and matching method based on bidirectional optical flow tracking is proposed to solve the impact of illumination changes and noise. Then, the virtual camera array is constructed to generate a sparse 3D point cloud from an image sequence. Finally, dense surface reconstruction of the burden surface is achieved based on the improved PMVS and the Poisson surface reconstruction method. Experimental results demonstrate that the proposed method can extract accurate feature points and reconstruct the accurate 3D topography of the burden surface.

Contrast-free visualization of distal trigeminal nerve segments using MR neurography.

The 3-dimensional cranial nerve imaging (CRANI) sequence may assist visualization of anatomical details of extraforaminal cranial nerves and aid in clinical diagnosis and preoperative planning. In this study, we investigated the feasibility of using a combined CRANI and magnetization-prepared rapid-acquisition gradient-echo (MPRAGE) imaging protocol to comprehensively identify trigeminal nerve projections. We evaluated the detection of distal regions of three branches of the ophthalmic nerve (V1), three branches of the maxillary nerve (V2), and five branches of the mandibular nerve (V3) in seven healthy adult subjects, with and without contrast injection. Nerve branches were rated on a 5-point scale by three observers. Interobserver reliability was studied using weighted kappa statistics and percentage agreement. Among V1 and V2 branches, the frontal nerve and infraorbital nerve were most successfully identified (average rating of 3.9, agreement >80%) in precontrast MPRAGE images. In V3 branches, lingual and inferior alveolar nerves were most successfully identified (average rating of 3.9, agreement >80%) in precontrast CRANI images, with an excellent average rating. In all cases except one, interobserver reliability was rated good to excellent. The buccal nerve was the only branch with a low average interobserver rating. Gadolinium contrast did not improve nerve segment visualization in our study. This may relate to the specific anatomic regions assessed, gadolinium dose, postcontrast image timing, and lack of pathology. A combined CRANI and MPRAGE protocol can be combined to visualize distal branches of V1, V2, and V3 and has potential for clinical use.

Enhancing severe weather predictions with the I-ConvGRU model: An iterative approach for radar echo time series through ConvGRU and RainNet integration

ABSTRACT Precipitation nowcasting plays a crucial role in disaster prevention and mitigation. Existing forecasting models often underutilize output data, leading to suboptimal forecasting performance. To tackle this issue, we introduce the I-ConvGRU model, a novel radar echo timing prediction model that synergizes the temporal dynamics optimization of ConvGRU with the spatial feature enhancement capabilities of RainNet. The model forecasts future scenarios by processing 10 sequential time-series images as input while employing skip connections to boost its spatial feature representation further. Evaluation of the radar echo data set from the Hong Kong Hydrological and Meteorological Bureau spanning from 2009 to 2015 demonstrates the I-ConvGRU model's superiority, with reductions of 17(3.8%) and 49(3.2%) in MSE and MAE metrics, respectively, compared with the TrajGRU model; meanwhile, the I-ConvGRU model had 52(5.8%) and 144(3.8%) lower values on the B-MSE and B-MAE metrics, respectively, than the slightly better performing TrajGRU model. Notably, it significantly improves the prediction of severe precipitation events, with the CSI and HSS metrics increasing by 0.0251(9.6%) and 0.0277(6.8%). These results affirm the model's enhanced effectiveness in radar echo forecasting, particularly in predicting heavy rainfall events.

GGC repeat expansions in NOTCH2NLC cause uN2CpolyG cerebral amyloid angiopathy.

The expansion of GGC repeats within NOTCH2NLC leads to the translation of the uN2CpolyG protein, the primary pathogenic factor in neuronal intranuclear inclusion disease (NIID). This study aims to explore the deposition of uN2CpolyG as an amyloid in the vessel wall, leading to uN2CpolyG cerebral amyloid angiopathy (CAA)-related cerebral microbleeds (CMBs). A total of 97 patients with genetically confirmed NIID were enrolled in this study. We analyzed the presence of CMBs using susceptibility-weighted imaging sequences and compared general clinical information, cerebrovascular risk factors, stroke history, antiplatelet medication use, and MRI features between NIID patients with and without CMBs. We further performed hematoxylin and eosin (H&E), Perl's, Congo red, and Thioflavin S staining, ubiquitin, p62 and uN2CpolyG immunostaining on brain tissue obtained from four NIID patients. A total of 354 CMBs were detected among 41 patients with NIID, with nearly half located in the deep brain, one-third in the lobes, and approximately 20% in the infratentorial area. No significant differences in cerebrovascular disease risk factors or history of antiplatelet drug use were observed between patients with and without CMBs. However, patients with CMBs suffered a higher incidence of previous ischemic and hemorrhagic stroke events. This group also had a higher incidence of recent subcortical infarcts and a higher proportion of white matter lesions in the external capsule and temporal pole. Conversely, patients without CMBs showed higher detection of high signals at the corticomedullary junction on diffusion-weighted imaging and more pronounced brain atrophy. H&E staining showed blood vessel leakage and hemosiderin-laden macrophage clusters, and Prussian blue staining revealed brain tissue iron deposition. CMBs occurred more frequently in small vessels lacking intranuclear inclusions, and extensive degeneration of endothelial cells and smooth muscle fibres was observed mainly in vessels lacking inclusions. Congo red-positive amyloid deposition was observed in the cerebral vessels of NIID patients, with disordered filamentous fibres appearing under an electron microscope. Additionally, the co-localization of Thioflavin S-labeled amyloid and uN2CpolyG protein in the cerebral vascular walls of NIID patients further suggested that uN2CpolyG is the main pathogenic protein in this form of amyloid angiopathy. In conclusion, we reviewed patients with GGC repeat expansion of NOTCH2NLC from a novel perspective, providing initial clinical, neuroimaging, and pathological evidence suggesting that uN2CpolyG may contribute to a distinct type of CAA.

Qualitative and Quantitative MR Imaging of the Cartilaginous Endplate: A Review.

The cartilaginous endplate (CEP) plays a pivotal role in facilitating the supply of nutrients and, transport of metabolic waste, as well as providing mechanical support for the intervertebral disc (IVD). Recent technological advances have led to a surge in MR imaging studies focused on the CEP. This article describes the anatomy and functions of the CEP as well as MRI techniques for both qualitative and quantitative assessment of the CEP. Effective CEP MR imaging sequences require two key features: high spatial resolution and relatively short echo time. High spatial resolution spoiled gradient echo (SPGR) and ultrashort echo time (UTE) sequences, fulfilling these requirements, are the basis for most of the sequences employed in CEP imaging. This article reviews existing sequences for qualitative CEP imaging, such as the fat-suppressed SPGR and UTE, dual-echo subtraction UTE, inversion recovery prepared and fat-suppressed UTE, and dual inversion recovery prepared UTE sequences. These sequences are employed together with other techniques for quantitative CEP imaging, including measurements of T2*, T2, T1, T1ρ, magnetization transfer, perfusion, and diffusion tensor parameters. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Beyond the Conventional Structural MRI: Clinical Application of Deep Learning Image Reconstruction and Synthetic MRI of the Brain.

Recent technological advancements have revolutionized routine brain magnetic resonance imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease evaluation. This review explores 2 pivotal breakthrough areas: deep learning reconstruction (DLR) and quantitative MRI techniques beyond conventional structural imaging. DLR using deep neural networks facilitates accelerated imaging with improved signal-to-noise ratio and spatial resolution, enhancing image quality with short scan times. DLR focuses on supervised learning applied to clinical implementation and applications. Quantitative MRI techniques, exemplified by 2D multidynamic multiecho, 3D quantification using interleaved Look-Locker acquisition sequences with T2 preparation pulses, and magnetic resonance fingerprinting, enable precise calculation of brain-tissue parameters and further advance diagnostic accuracy and efficiency. Potential DLR instabilities and quantification and bias limitations will be discussed. This review underscores the synergistic potential of DLR and quantitative MRI, offering prospects for improved brain imaging beyond conventional methods.

Ensemble learning-based pretreatment MRI radiomic model for distinguishing intracranial extraventricular ependymoma from glioblastoma multiforme.

This study aims to develop an ensemble learning (EL) method based on magnetic resonance (MR) radiomic features to preoperatively differentiate intracranial extraventricular ependymoma (IEE) from glioblastoma (GBM). This retrospective study enrolled patients with histopathologically confirmed IEE and GBM from June 2016 to June 2021. Radiomics features were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) sequence images, and classification models were constructed using EL methods and logistic regression (LR). The efficiency of the models was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. The combined EL model, based on clinical parameters and radiomic features from T1WI and T2WI images, demonstrated good discriminative ability, achieving an area under the receiver operating characteristics curve (AUC) of 0.96 (95% CI 0.94-0.98), a specificity of 0.84, an accuracy of 0.92, and a sensitivity of 0.95 in the training set, and an AUC of 0.89 (95% CI 0.83-0.94), a specificity of 0.83, an accuracy of 0.81, and a sensitivity of 0.74 in the validation set. The discriminative efficacy of the EL model was significantly higher than that of the LR model. Favorable calibration performance and clinical applicability for the EL model were observed. The EL model combining preoperative MR-based tumor radiomics and clinical data showed high accuracy and sensitivity in differentiating IEE from GBM preoperatively, which may potentially assist in clinical management of these brain tumors.

Cerebral microbleeds in acute ischemic stroke after intravenous thrombolysis and their impact on short term outcome of stroke

Objectives Strong evidence suggests the occurrence of cerebral microbleeds (CMBs) in 5–13% of stroke patients within the first week after stroke onset. The aim of this work was to study risk factors associated with occurrence of CMBs in patients with stroke who received intravenous thrombolysis, and to clarify their impact on the clinical outcome. Methods This prospective observational study was conducted on 61 acute ischemic stroke patients eligible for treatment with recombinant tissue plasminogen activator (rt-PA). Assessment of stroke-related neurologic deficit was done using National Institute of Health Stroke Scale (NIHSS). Assessment of stroke related disability after 3 months from stroke onset was done using Modified Rankin Scale (mRS). CMBs were detected by T2*-weighed gradient-recalled echo (T2*-GRE) and susceptibility-weighted imaging (SWI) magnetic resonance imaging (MRI) sequences. Results There was a statistically significant impact of age, mean arterial pressure (MAP) at stroke onset, history of hypertension (HTN), and white matter changes assessed by Fazekas scale on the occurrence of CMBs in the included stroke patients (P-value= 0.002, <0.001, <0.001, 0.008 respectively). There was no statistically significant difference between patients with favorable and those with unfavorable outcome regarding the total number of CMBs (P-value =0.542). There was also no statistically significant difference between patients who developed complications from rt-PA and those who didn’t develop regarding the total number of CMBs (P-value =0.186). Conclusion Cerebral microbleeds are more likely to occur in older stroke patients and in those who had high MAP at stroke onset, history of HTN, and white matter changes.

Niraparib plays synergistic antitumor effects with NRT in a mouse ovarian cancer model with HRP

ObjectivePARPi offers less clinical benefit for HRP patients compared to HRD patients. PARPi has an immunomodulatory function. NRT therapy targets tumor neoantigens without off-target immune toxicity. We explored the synergy between Niraparib and NRT in enhancing antitumor activity in an HRP ovarian cancer mouse model. MethodsIn the C57BL/6 mouse ID8 ovarian cancer model, the effect of Niraparib on reshaping TIME was evaluated by immune cell infiltration analysis of transcriptomic data. The antitumor effects of Niraparib, NRT, and their combined use were systematically evaluated. To corroborate alterations in TILs, TAMs, and chemokine profiles within the TIME, we employed immunofluorescence imaging and transcriptome sequencing analysis. ResultsNiraparib increased the M1-TAMs and activated CD8+ T cells in tumor tissues of C57BL/6 mice with ID8 ovarian cancer. GSEA showed that gene set associated with immature DC and INFα, cytokines and chemokines were significantly enriched in immune feature, KEGG and GO gene sets, meanwhile CCL5, CXCL9 and CXCL10 play dominant roles together. In the animal trials, combined group had a tumor growth delay compared with Niraparib group (P < 0.01) and control group (P < 0.001), and longer survival compared with the single agent group (P<0.01) . ConclusionsNiraparib could exert immune-reshaping effects, then acts synergistic antitumor effects with NRT in HRP ovarian cancer model. Our findings provide new ideas and rationale for combined immunotherapy in HRP ovarian cancer.

Rolling up 2D WSe2 Nanosheets to 1D Anisotropic Nanoscrolls for Polarization-Sensitive Photodetectors.

The intrinsic low-symmetry crystal structures or external geometries of low-dimensional materials are crucial for polarization-sensitive photodetection. However, these inherently anisotropic materials are limited in variety, and their anisotropy is confined to specific crystal directions. Transforming 2D semiconductors, such as WSe2, from isotropic 2D nanosheets into anisotropic 1D nanoscrolls expands their application in polarization photodetection. Despite this considerable potential, research on polarization photodetection based on nanoscrolls remains scarce. Here, the uniform crystalline orientation of WSe2 nanoscrolls is achieved conveniently and efficiently by applying ethanol droplets to vapor deposition-grown bilayer WSe2 nanosheets. Angle-resolved polarized Raman spectroscopy of WSe2 nanoscrolls demonstrates vibrational anisotropy. Photodetectors based on these nanoscrolls show competitive overall performance with a broadband detection range from 405 to 808nm, a competitive on/off ratio of ≈900, a high detectivity of 3.4×108 Jones, and a fast response speed of ≈30ms. Additionally, WSe2 nanoscroll-based photodetectors exhibit strong polarization-sensitive detection with a maximum dichroic ratio of 1.5. More interestingly, due to high photosensitivity, the WSe2 nanoscroll detectors can easily record sequential puppy images. This work reveals the potential of WSe2 nanoscrolls as excellent polarization-sensitive photodetectors and provides new insights into the development of high-performance optoelectronic devices.

Trajectory Tracking of a Robot Arm Using Image Sequences

Trajectory Tracking of a Robot Arm Using Image Sequences

Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classification approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA significantly outperformed a single-image baseline in 19/20 head-to-head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.

The Impact of MRI-Based Advanced Neuroimaging on Neurooncologists' Clinical Decision-Making in Patients With Posttreatment High-Grade Glioma: A Prospective Survey-Based Study.

Background: Advanced MRI-based neuroimaging techniques, such as perfusion and spectroscopy, have been increasingly incorporated into routine follow-up protocols in patients treated for high-grade glioma (HGG), to help differentiate tumor progression from treatment effect. However, these techniques' influence on clinical management remains poorly understood. Objective: To evaluate the impact of MRI-based advanced neuroimaging on clinical decision-making in patients with HGG in the posttreatment setting. Methods: This prospective study, performed at a comprehensive cancer center from March 1, 2017, to October 31, 2020, included adult patients treated by chemoradiation for WHO grade 4 diffuse glioma who underwent MRIbased advanced neuroimaging (comprising multiple perfusion imaging sequences and spectroscopy) to further evaluate findings on conventional MRI equivocal for tumor progression versus treatment effect. The ordering neuro-oncologists completed surveys before and after each advanced neuroimaging session. The percent of care episodes with a change between the intended and actual management plan on the surveys conducted before and after advanced neuroimaging, respectively, was computed and compared with a previously published percent using the Wald test for independent samples proportions. Results: The study included 63 patients (mean age, 55±13 years; 36 women, 27 men) who underwent 70 advanced neuroimaging sessions. Ordering neuro-oncologists' intended and actual management plans on the surveys completed before and after advanced neuroimaging, respectively, differed in 44% (31/70, [95% CI: 33-56%]) of episodes, which differed from the previously published frequency of 8.5% (5/59) (p<.001). These management plan changes included selection of a different plan for 6/8 episodes with an intended plan to enroll patients in a clinical trial, 12/19 episodes with an intended plan to change chemotherapeutic agents, 4/8 episodes with an intended plan of surgical intervention, and 1/2 episodes with an intended plan of re-irradiation. The ordering neuro-oncologists found advanced neuroimaging to be helpful in 93% (95% CI: 87%-99%) (65/70) of episodes. Conclusion: Neuro-oncologists' management plans changed in a substantial fraction of adult patients with HGG who underwent advanced neuroimaging to further evaluate conventional MRI findings equivocal for tumor progression versus treatment effect. Clinical Impact: The findings support incorporation of advanced neuroimaging into HGG posttreatment monitoring protocols.

Interlayer information fusion-based and dual-attention improved U-Net for ABVS image sequence intelligent tumor segmentation

Breast cancer is the most common cancer among women. Accurate and intelligent ultrasound image tumor segmentation can help physicians efficiently determine the location, shape and features of tumors and then develop appropriate treatment plans. To this end, we propose a deep learning method combining interlayer information and dual attention improvement for accurate and intelligent segmentation of breast tumors in ABVS image sequences. We extracted the slices containing tumors in ABVS images and formed a three-channel image input network with the current frame and the corresponding before and after frames. The network combines interlayer information during computation, and the before and after frames can guide the current frame image for more accurate tumor segmentation. In addition, we improved the channel attention module by replacing the full connection with a one-dimensional convolution and applying global max pooling on the basis of the original channel attention module, which improved the segmentation performance of the module while reducing the number of parameters. The improved channel attention module was added to the U-Net skip-connection section to assign different weight coefficients to each feature channel according to the characteristics of the input three-channel image. Our method outperformed other mainstream tumor segmentation methods on the dataset in both centers, achieving a Dice similarity coefficient (DSC) of 84.97 %, a Jaccard coefficient (JC) of 74.63 %, a 95 % Hausdorff distance (95HD) of 6.22 mm on the inner center, and DSC of 80.79 %, JC of 69.74 %, 95HD of 9.82 mm on the outer dataset.

Primary sclerosing cholangitis: Is qualitative and quantitative 3 T MR imaging useful for the evaluation of disease severity?

PurposeTo analyze the role of qualitative and quantitative 3 T MR imaging assessment as a non-invasive method for the evaluation of disease severity in patients with primary sclerosing cholangitis (PSC). MethodsA series of 26 patients, with histological diagnosis of PSC undergoing 3 T MRI and hepatological evaluation, was retrospectively enrolled. All MR examinations included diffusion-weighted imaging (DWI), T2-weighted (T2w) and T1-weighted (T1w) sequences, before and after administration of Gd-EOB-DTPA with the acquisition of both dynamic and hepato-biliary phase (HBP). Qualitative analysis was performed by assessment of liver parenchyma and biliary tract changes, also including biliary excretion of gadoxetic acid on HBP. Quantitative evaluation was conducted on liver parenchyma by measurement of apparent diffusion coefficient (ADC) and relative enhancement (RE) on 3-minute delayed phase and on HBP. Results of blood tests (ALT, ALP, GGT, total and direct bilirubin, albumin, and platelets) and transient elastography-derived liver stiffness measurements (TE-LSM) were collected and correlated with qualitative and quantitative MRI findings. ResultsAmong qualitative and quantitative findings, fibrosis visual assessment and RE had the best performance in estimating disease severity, showing a statistically significant correlation with both biomarkers of cholestasis and TE-LSM. Statistical analysis also revealed a significant correlation of gadoxetic acid biliary excretion with ALT and direct bilirubin, as well as of ADC with total bilirubin. ConclusionQualitative and quantitative 3 T MR evaluation is a promising non-invasive method for the assessment of disease severity in patients with PSC.

An effective ultrasound fetal palate screening software based on the “sequential sector scan through the oral fissure” and three-dimensional ultrasound

BackgroundOrofacial clefts are one of the most common congenital malformations of the fetal face and ultrasound is mainly responsible for its diagnosis. It is difficult to view the fetal palate, so there is currently no unified standard for fetal palate screening, and the diagnosis of cleft palate is not included in the relevant prenatal ultrasound screening guidelines. Many prenatal diagnoses for cleft palate are missed due to the lack of effective screening methods. Therefore, it is imperative to increase the display rate of the fetal palate, which would improve the detection rate and diagnostic accuracy for cleft palate. We aim to introduce a fetal palate screening software based on the “sequential sector scan though the oral fissure”, an effective method for fetal palate screening which was verified by our follow up results and three-dimensional ultrasound and to evaluate its feasibility and clinical practicability.MethodsA software was designed and programmed based on “sequential sector scan through the oral fissure” and three-dimensional ultrasound. The three-dimensional ultrasound volume data of the fetal face were imported into the software. Then, the median sagittal plane was taken as the reference interface, the anterior upper margin of the mandibular alveolar bone was selected as the fulcrum, the interval angles, and the number of layers of the sector scan were set, after which the automatic scan was performed. Thus, the sector scan sequential planes of the mandibular alveolar bone, pharynx, soft palate, hard palate, and maxillary alveolar bone were obtained in sequence to display and evaluate the palate. In addition, the feasibility and accuracy of the software in fetal palate displaying and screening was evaluated by actual clinical cases.ResultsFull views of the normal fetal palates and the defective parts of the cleft palates were displayed, and relatively clear sequential tomographic images and continuous dynamic videos were formed after the three-dimensional volume data of 10 normal fetal palates and 10 cleft palates were imported into the software.ConclusionsThe software can display fetal palates more directly which might allow for a new method of fetal palate screening and cleft palate diagnosis.

A Diffusion Tensor Imaging White Matter Atlas of the Domestic Canine Brain

Abstract There is increasing reliance on magnetic resonance imaging (MRI) techniques in both research and clinical settings. However, few standardized methods exist to permit comparative studies of brain pathology and function. To help facilitate these studies, we have created a detailed, MRI-based white matter atlas of the canine brain using diffusion tensor imaging. This technique, which relies on the movement properties of water, permits the creation of a three-dimensional diffusivity map of white matter brain regions that can be used to predict major axonal tracts. To generate an atlas of white matter tracts, thirty neurologically and clinically normal dogs underwent MRI imaging under anesthesia. High-resolution, three-dimensional T1-weighted sequences were collected and averaged to create a population average template. Diffusion weighted imaging sequences were collected and used to generate diffusivity maps, which were then registered to the T1-weighted template. Using these diffusivity maps, individual white matter tracts—including association, projection, commissural, brainstem, olfactory and cerebellar tracts—were identified with reference to previous canine brain atlas sources. To enable the use of this atlas, we created downloadable overlay files for each white matter tract identified using manual segmentation software. In addition, using diffusion tensor imaging tractography, we created tract files to delineate major projection pathways. This comprehensive white matter atlas serves as a standard reference to aid in the interpretation of quantitative changes in brain structure and function in clinical and research settings.