AIMC Topic: Respiration

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Multifrequency Time-Dependent Deep Image Prior for Real-Time Free-Breathing Cardiac Imaging.

NMR in biomedicine
The aim of this study is to enable high temporal resolution functional cardiac imaging without breathholds or electrocardiogram (ECG) gating. Real-time MRI is essential for assessing heart function in patients with limited breathhold capacity or arrh...

Dynamic abdominal MRI image generation using cGANs: A generalized model for various breathing patterns with extensive evaluation.

Computers in biology and medicine
Organ motion is a limiting factor during the treatment of abdominal tumors. During abdominal interventions, medical images are acquired to provide guidance, however, this increases operative time and radiation exposure. In this paper, conditional gen...

Real-time respiratory motion forecasting with online learning of recurrent neural networks for accurate targeting in externally guided radiotherapy.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In lung radiotherapy, infrared cameras can track reflective objects on the chest to estimate tumor motion due to breathing. However, treatment system latencies hinder radiation beam precision. Real-time recurrent learning (R...

Homeostasis: Stay cool but keep on breathing.

Current biology : CB
Homeostatic control of neural networks is based on monitoring the activity level of neurons and adjusting excitability. A recent study demonstrates that the physical environment per se (temperature) can directly regulate the neural circuits underlyin...

Non-invasive diagnosis of lung diseases via multimodal feature extraction from breathing audio and chest dynamics.

Computers in biology and medicine
Early and accurate diagnosis of lung diseases is crucial for effective treatment. While traditional methods have limitations, audio analysis offers a promising non-invasive approach. However, existing studies often rely solely on acoustic features, n...

Prediction of Spontaneous Breathing Trial Outcome in Critically Ill-Ventilated Patients Using Deep Learning: Development and Verification Study.

JMIR medical informatics
BACKGROUND: Long-term ventilator-dependent patients often face problems such as decreased quality of life, increased mortality, and increased medical costs. Respiratory therapists must perform complex and time-consuming ventilator weaning assessments...

Free-breathing, Highly Accelerated, Single-beat, Multisection Cardiac Cine MRI with Generative Artificial Intelligence.

Radiology. Cardiothoracic imaging
Purpose To develop and evaluate a free-breathing, highly accelerated, multisection, single-beat cine sequence for cardiac MRI. Materials and Methods This prospective study, conducted from July 2022 to December 2023, included participants with various...

Deep learning reconstruction of free-breathing, diffusion-weighted imaging of the liver: A comparison with conventional free-breathing acquisition.

PloS one
This study aimed to compare image quality and solid focal liver lesion (FLL) assessments between free-breathing, diffusion-weighted imaging using deep learning reconstruction (FB-DL-DWI) and conventional DWI (FB-C-DWI) in patients undergoing clinical...

A Respiratory Signal Monitoring Method Based on Dual-Pathway Deep Learning Networks in Image-Guided Robotic-Assisted Intervention System.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Percutaneous puncture procedures, guided by image-guided robotic-assisted intervention (IGRI) systems, are susceptible to disruptions in patients' respiratory rhythm due to factors such as pain and psychological distress.

A back propagation neural network based respiratory motion modelling method.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: This study presents the development of a backpropagation neural network-based respiratory motion modelling method (BP-RMM) for precisely tracking arbitrary points within lung tissue throughout free respiration, encompassing deep inspirati...