Rapid and reversible adaptation of a clinical linear accelerator for electron FLASH radiotherapy.

Purpose: The aim of this work was to establish a procedure that allows the conversion of a standard clinical LINAC into a "FLASH" LINAC capable of delivering ultra-high dose rates above 40 Gy/s, with minimal, fully reversible modifications to the device. A dosimetric characterization of the resulting treatment beam is presented.

Methods: A LINAC was modified to emit a 10 MeV electron FLASH beam.

A multi-model deep learning approach for the identification of coronary artery calcifications within 2D coronary angiography images.

Purpose: Identifying and quantifying coronary artery calcification (CAC) is crucial for preoperative planning, as it helps to estimate both the complexity of the 2D coronary angiography (2DCA) procedure and the risk of developing intraoperative complications. Despite the relevance, the actual practice relies upon visual inspection of the 2DCA image frames by clinicians. This procedure is prone to inaccuracies due to the poor contrast and small size of the CAC; moreover, it is dependent on the physician's experience.

FedSynthCT-Brain: A federated learning framework for multi-institutional brain MRI-to-CT synthesis.

The generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) treatment planning. The use of sCT enables the calculation of doses, pushing towards Magnetic Resonance Imaging (MRI) guided radiotherapy treatments. Moreover, with the introduction of MRI-Positron Emission Tomography (PET) hybrid scanners, the derivation of sCT from MRI can improve the attenuation correction of PET images.

Repeatability of radiomic features from brain T1-W MRI after image intensity normalization: Implications for longitudinal studies on structural neurodegeneration.

Background And Objective: Radiomics extracts quantitative features from magnetic resonance images (MRI) and is especially useful in the presence of subtle pathological changes within human soft tissues. This scenario, however, may not cover the effects of intrinsic, e.g.

Nano-Topography Enhanced Topological-Cell-Analysis in Radiation-Therapy.

Radiotherapy (RT) is a cancer treatment technique that involves exposing cells to ionizing radiation, including X-rays, electrons, or protons. RT offers promise to treat cancer, however, some inherent limitations can hamper its performance. Radio-resistance, whether innate or acquired, refers to the ability of tumor cells to withstand treatment, making it a key factor in RT failure.

Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy.

In recent years, synthetic Computed Tomography (CT) images generated from Magnetic Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be comparable to real CT images in terms of dose computation for radiotherapy simulation. However, until now, there has been no independent strategy to assess the quality of each synthetic image in the absence of ground truth. In this work, we propose a Deep Learning (DL)-based framework to predict the accuracy of synthetic CT in terms of Mean Absolute Error (MAE) without the need for a ground truth (GT).

Combining simulation models and machine learning in healthcare management: strategies and applications.

Simulation models and artificial intelligence (AI) are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and AI could provide a strategy to further boost the quality of health services.

Automated pipeline for linear and volumetric assessment of facial swelling after third molar surgery.

Background: Extraction of mandibular third molars (M3Ms) is a routine procedure in oral and maxillofacial surgery, often associated with postoperative symptoms like pain, facial swelling, and trismus. This study aimed to introduce a standardized and automated protocol for swelling analysis following M3M surgery, presenting results regarding clinical conditions immediately and one-week after surgery.

Methods: In a prospective study, 35 patients were enrolled (mean age: 24.

Carotid stents reduce longitudinal movements within the vascular wall.

Background: Longitudinal Displacement (LD) is the relative motion of the intima-media upon adventitia of the arterial wall during the cardiac cycle, probably linked to atherosclerosis. It has a direction, physiologically first backward in its main components with respect to the arterial flow. Here, LD was investigated in various disease and in presence of a unilateral carotid stent.

Analysis of hydrogen peroxide production in pure water: Ultrahigh versus conventional dose-rate irradiation and mechanistic insights.

Background: Ultrahigh dose-rate radiation (UHDR) produces less hydrogen peroxide (HO) in pure water, as suggested by some experimental studies, and is used as an argument for the validity of the theory that FLASH spares the normal tissue due to less reactive oxygen species (ROS) production. In contrast, most Monte Carlo simulation studies suggest the opposite.

Purpose: We aim to unveil the effect of UHDR on HO production in pure water and its underlying mechanism, to serve as a benchmark for Monte Carlo simulation.

Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19) patients.

Background: The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists' assessments.

Design and Experimental Validation of a 3D-Printed Embedded-Sensing Continuum Robot for Neurosurgery.

A minimally-invasive manipulator characterized by hyper-redundant kinematics and embedded sensing modules is presented in this work. The bending angles (tilt and pan) of the robot tip are controlled through tendon-driven actuation; the transmission of the actuation forces to the tip is based on a Bowden-cable solution integrating some channels for optical fibers. The viability of the real-time measurement of the feedback control variables, through optoelectronic acquisition, is evaluated for automated bending of the flexible endoscope and trajectory tracking of the tip angles.

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases.

Background: The aim of the current study was to investigate the distribution and extent of lung involvement in patients with COVID-19 with AI-supported, automated computer analysis and to assess the relationship between lung involvement and the need for intensive care unit (ICU) admission. A secondary aim was to compare the performance of computer analysis with the judgment of radiological experts.

Methods: A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study.

Human Cancer Cell Radiation Response Investigated through Topological Analysis of 2D Cell Networks.

Clonogenic assays are routinely used to evaluate the response of cancer cells to external radiation fields, assess their radioresistance and radiosensitivity, estimate the performance of radiotherapy. However, classic clonogenic tests focus on the number of colonies forming on a substrate upon exposure to ionizing radiation, and disregard other important characteristics of cells such their ability to generate structures with a certain shape. The radioresistance and radiosensitivity of cancer cells may depend less on the number of cells in a colony and more on the way cells interact to form complex networks.

Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma.

Purpose: Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. Though radiomics-based machine learning (ML) has demonstrated the promising performance in PCNSL, it demands large amounts of manual feature extraction efforts from magnetic resonance images beforehand.

Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy.

Background: Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton dose calculations. Deep learning can be used to correct CT numbers and generate synthetic CTs (sCTs) that can enable CBCT-based proton dose calculations.

Human lung-cancer-cell radioresistance investigated through 2D network topology.

Radiation therapy (RT) is now considered to be a main component of cancer therapy, alongside surgery, chemotherapy and monoclonal antibody-based immunotherapy. In RT, cancer tissues are exposed to ionizing radiation causing the death of malignant cells and favoring cancer regression. However, the efficiency of RT may be hampered by cell-radioresistance (RR)-that is a feature of tumor cells of withstanding RT.

CoroFinder: A New Tool for Real Time Detection and Tracking of Coronary Arteries in Contrast-Free Cine-Angiography.

Coronary Angiography (CA) is the standard of reference to diagnose coronary artery disease. Yet, only a portion of the information it conveys is usually used. Quantitative Coronary Angiography (QCA) reliably contributes to improving the measurable assessment of CA.

Algorithms to Preprocess Microarray Image Data.

Microarray is a powerful technology that enables the monitoring of expression levels for thousands of genes simultaneously, providing scientists with a full overview about DNA and RNA investigation. The process is made of three main phases: interaction with biological samples, data extraction, and data analysis. In particular, the data extraction phase strongly relies on image processing algorithms, since the expression levels are revealed by the interaction of light with fluorescent markers.

Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer.

Purpose: Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone-beam CT (CBCT) can provide these daily images, but x-ray scattering limits CBCT-image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT-based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients.

Lipid Droplet Biosynthesis Impairment through DGAT2 Inhibition Sensitizes MCF7 Breast Cancer Cells to Radiation.

Breast cancer is the most frequent cancer in women worldwide and late diagnosis often adversely affects the prognosis of the disease. Radiotherapy is commonly used to treat breast cancer, reducing the risk of recurrence after surgery. However, the eradication of radioresistant cancer cells, including cancer stem cells, remains the main challenge of radiotherapy.

Deep learning based synthetic-CT generation in radiotherapy and PET: A review.

Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020.

SlicerArduino: A Bridge between Medical Imaging Platform and Microcontroller.

Interaction between medical image platform and external environment is a desirable feature in several clinical, research, and educational scenarios. In this work, the integration between 3D Slicer package and Arduino board is introduced, enabling a simple and useful communication between the two software/hardware platforms. The open source extension, programmed in Python language, manages the connection process and offers a communication layer accessible from any point of the medical image suite infrastructure.