Formaldehyde initiates memory and motor impairments under weightlessness condition.

During space flight, prolonged weightlessness stress exerts a range of detrimental impacts on the physiology and psychology of astronauts. These manifestations encompass depressive symptoms, anxiety, and impairments in both short-term memory and motor functions, albeit the precise underlying mechanisms remain elusive. Recent studies have revealed that hindlimb unloading (HU) animal models, which simulate space weightlessness, exhibited a disorder in memory and motor function associated with endogenous formaldehyde (FA) accumulation in the hippocampus and cerebellum, disruption of brain extracellular space (ECS), and blockage of interstitial fluid (ISF) drainage.

Adaptive prior image constrained total generalized variation for low-dose dynamic cerebral perfusion CT reconstruction.

Background: Dynamic cerebral perfusion CT (DCPCT) can provide valuable insight into cerebral hemodynamics by visualizing changes in blood within the brain. However, the associated high radiation dose of the standard DCPCT scanning protocol has been a great concern for the patient and radiation physics. Minimizing the x-ray exposure to patients has been a major effort in the DCPCT examination.

DeCoGAN: MVCT image denoising via coupled generative adversarial network.

In helical tomotherapy, image-guided radiotherapy employs megavoltage computed tomography (MVCT) for precise targeting. However, the high voltage of megavoltage radiation introduces substantial noise, significantly compromising MVCT image clarity. This study aims to enhance MVCT image quality using a deep learning-based denoising method.

Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network.

Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and critical to study AD through multi-modal data. Existing learning methods, however, usually ignore the influence of feature heterogeneity and directly fuse features in the last stages.

Exploring the biological basis of CT imaging features in pancreatic neuroendocrine tumors: a two-center study.

Medical imaging offered a non-invasive window to visualize tumors, with radiomics transforming these images into quantitative data for tumor phenotyping. However, the intricate web linking imaging features, clinical endpoints, and tumor biology was mostly uncharted. This study aimed to unravel the connections between CT imaging features and clinical characteristics, including tumor histopathological grading, clinical stage, and endocrine symptoms, alongside immunohistochemical markers of tumor cell growth, such as the Ki-67 index and nuclear mitosis rate.

Novel methods to diagnose rotator cuff tear and predict post-operative Re-tear: Radiomics models.

Objective: To validated a classifier to distinguish the status of rotator cuff tear and predict post-operative re-tear by utilizing magnetic resonance imaging (MRI) markers.

Methods: This retrospective study included patients with healthy rotator cuff and patients diagnosed as rotator cuff tear (RCT) by MRI. Radiomics features were identified from the pre-operative shoulder MRI and selected by using maximum relevance minimum redundancy (MRMR) methods.

An indirect estimation of x-ray spectrum via convolutional neural network and transmission measurement.

In this work, we aim to propose an accurate and robust spectrum estimation method by synergistically combining x-ray imaging physics with a convolutional neural network (CNN).The approach relies on transmission measurements, and the estimated spectrum is formulated as a convolutional summation of a few model spectra generated using Monte Carlo simulation. The difference between the actual and estimated projections is utilized as the loss function to train the network.

Evaluation of low-dose computed tomography reconstruction using spatial-radon domain total generalized variation regularization.

The x-ray radiation dose in computed tomography (CT) examination has been a major concern for patients. Lowing the tube current and exposure time in data acquisition is a straightforward and cost-effective strategy to reduce the x-ray radiation dose. However, this will inevitably increase the noise fluctuations in measured projection data, and the corresponding CT image quality will be severely degraded if noise suppression is not performed during image reconstruction.

PNMC: Four-dimensional conebeam CT reconstruction combining prior network and motion compensation.

Four-dimensional conebeam computed tomography (4D CBCT) is an efficient technique to overcome motion artifacts caused by organ motion during breathing. 4D CBCT reconstruction in a single scan usually divides projections into different groups of sparsely sampled data based on the respiratory phases. The reconstructed images within each group present poor image quality due to the limited number of projections.

UBES: Unified scatter correction using ultrafast Boltzmann equation solver for conebeam CT.

A semi-analytical solution to the unified Boltzmann equation is constructed to exactly describe the scatter distribution on a flat-panel detector for high-quality conebeam CT (CBCT) imaging. The solver consists of three parts, including the phase space distribution estimator, the effective source constructor and the detector signal extractor. Instead of the tedious Monte Carlo solution, the derived Boltzmann equation solver achieves ultrafast computational capability for scatter signal estimation by combining direct analytical derivation and time-efficient one-dimensional numerical integration over the trajectory along each momentum of the photon phase space distribution.

Prediction of SBRT response in liver cancer by combining original and delta cone-beam CT radiomics: a pilot study.

This study aims to explore the feasibility of utilizing a combination of original and delta cone-beam CT (CBCT) radiomics for predicting treatment response in liver tumors undergoing stereotactic body radiation therapy (SBRT). A total of 49 patients are included in this study, with 36 receiving 5-fraction SBRT, 3 receiving 4-fraction SBRT, and 10 receiving 3-fraction SBRT. The CBCT and planning CT images from liver cancer patients who underwent SBRT are collected to extract overall 547 radiomics features.

Virtual differential phase-contrast and dark-field imaging of x-ray absorption images via deep learning.

Weak absorption contrast in biological tissues has hindered x-ray computed tomography from accessing biological structures. Recently, grating-based imaging has emerged as a promising solution to biological low-contrast imaging, providing complementary and previously unavailable structural information of the specimen. Although it has been successfully applied to work with conventional x-ray sources, grating-based imaging is time-consuming and requires a sophisticated experimental setup.

4D-CT deformable image registration using unsupervised recursive cascaded full-resolution residual networks.

A novel recursive cascaded full-resolution residual network (RCFRR-Net) for abdominal four-dimensional computed tomography (4D-CT) image registration was proposed. The entire network was end-to-end and trained in the unsupervised approach, which meant that the deformation vector field, which presented the ground truth, was not needed during training. The network was designed by cascading three full-resolution residual subnetworks with different architectures.

Phase-Tailoring W V O Meta-Nanofiber Enables Temperature-Editing Energy Control.

Room-temperature phase change materials (RTPCMs) exhibit promise to address challenges in thermal energy storage and release, greatly aiding in numerous domains of human existence and productivity. The conventional RTPCMs undergo inevitable volume expansion, structural collapse, and diffusion of active ingredients while maintaining desirable phase change enthalpy and ideal phase change temperature. Here, a sol-gel 1D-induced growth approach is presented to fabricate meta nanofibers (Meta-NFs) comprised of vanadium dioxide with monoclinic crystal structure, and further achieve the editable phase change temperature from 68 to 37 °C through W-doping, which allowed for tailored length variation of the zigzag V-V bond.

A generalizable new figure of merit for dose optimization in dual energy cone beam CT scanning protocols.

. This study proposes and evaluates a new figure of merit (FOMn) for dose optimization of Dual-energy cone-beam CT (DE-CBCT) scanning protocols based on size-dependent modeling of radiation dose and multi-scale image quality..

The dose-related plateau effect of surviving fraction in normal tissue during the ultra-high-dose-rate radiotherapy.

Ultra-high-dose-rate radiotherapy, referred to as FLASH therapy, has been demonstrated to reduce the damage of normal tissue as well as inhibiting tumor growth compared with conventional dose-rate radiotherapy. The transient hypoxia may be a vital explanation for sparing the normal tissue. The heterogeneity of oxygen distribution for different doses and dose rates in the different radiotherapy schemes are analyzed.

Application of multi-modality MRI-based radiomics in the pre-treatment prediction of RPS6K expression in hepatocellular carcinoma.

In this study, we aim to develop and validate a radiomics model for pretreatment prediction of RPS6K expression in hepatocellular carcinoma (HCC) patients, thus helping clinical decision-making of mTOR-inhibitor (mTORi) therapy. We retrospectively enrolled 147 HCC patients, who underwent curative hepatic resection at First Affiliated Hospital Zhejiang University School of Medicine. RPS6K expression was determined with immunohistochemistry staining.

Preoperative radiomic signature based on CT images for noninvasive evaluation of localized nephroblastoma in pediatric patients.

Background: Nephron sparing nephrectomy may not reduce the prognosis of nephroblastoma in the absence of involvement of the renal capsule, sinus vessels, and lymph nodes, However, there is no accurate preoperative noninvasive evaluation method at present.

Materials And Methods: 105 nephroblastoma patients underwent contrast-enhanced CT scan between 2013 and 2020 in our hospital were retrospectively collected, including 59 cases with localized stage and 46 cases with non-localized stage, and then were divided into training cohort (n= 73) and validation cohort (n= 32) according to the order of CT scanning time. After lesion segmentation and data preprocessing, radiomic features were extracted from each volume of interest.

Four-Dimensional Cone Beam CT Imaging Using a Single Routine Scan via Deep Learning.

A novel method is proposed to obtain four-dimensional (4D) cone-beam computed tomography (CBCT) images from a routine scan in patients with upper abdominal cancer. The projections are sorted according to the location of the lung diaphragm before being reconstructed to phase-sorted data. A multiscale-discriminator generative adversarial network (MSD-GAN) is proposed to alleviate the severe streaking artifacts in the original images.

CT-based radiomics for the identification of colorectal cancer liver metastases sensitive to first-line irinotecan-based chemotherapy.

Background: Chemosensitivity prediction in colorectal cancer patients with liver metastases has remained a research hotspot. Radiomics can extract features from patient imaging, and deep learning or machine learning can be used to build models to predict patient outcomes prior to chemotherapy.

Purpose: In this study, the radiomics features and clinical data of colorectal cancer patients with liver metastases were used to predict their sensitivity to irinotecan-based chemotherapy.

Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network.

Background: Cone beam computed tomography (CBCT) plays an increasingly important role in image-guided radiation therapy. However, the image quality of CBCT is severely degraded by excessive scatter contamination, especially in the abdominal region, hindering its further applications in radiation therapy.

Purpose: To restore low-quality CBCT images contaminated by scatter signals, a scatter correction algorithm combining the advantages of convolutional neural networks (CNN) and Swin Transformer is proposed.

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.

The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography-guided adaptive radiation therapy. A deep unsupervised learning model was introduced to map breast's tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord from planning computed tomography to cone-beam CT. To improve the traditional image registration method's performance, we used a regional deformable framework based on the narrow-band mapping, which can mitigate the effect of the image artifacts on the cone-beam CT.

A temporo-spatial description of moving tumor and organs by the probability of presence time proportion: concept and implementation for 4D dose calculation and optimization.

. The binary definition of the internal target volume (ITV) artificially separates tumor from healthy organs at motion overlapping area for dose evaluation and optimization, bringing confusion about taking partial organs as tumor or adversely. In this work, the probability of presence time (PPT) proportion of a moving anatomic voxel at a geometric voxel is defined to construct a temporo-spatial description of moving objects.

AI-Augmented Images for X-Ray Guiding Radiation Therapy Delivery.

Cancer accounts for nearly 10 million deaths in 2020 and is a leading cause of death worldwide. Radiation therapy is an effective modality to cure cancer. The ultimate goal of successful radiation therapy is to accurately deliver a safe and therapeutic dose of radiation to cancerous target cells while limiting radiation to the healthy tissue in the beam pathway and the surrounding normal tissue.