AIMC Topic: Phantoms, Imaging

Clear Filters Showing 141 to 150 of 825 articles

Unsupervised Learning-Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical b...

The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers.

BMC medical imaging
BACKGROUND: This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning...

A Deep-Learning-Based Partial-Volume Correction Method for Quantitative Lu SPECT/CT Imaging.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
With the development of new radiopharmaceutical therapies, quantitative SPECT/CT has progressively emerged as a crucial tool for dosimetry. One major obstacle of SPECT is its poor resolution, which results in blurring of the activity distribution. Es...

Deep Learning With Physics-Embedded Neural Network for Full Waveform Ultrasonic Brain Imaging.

IEEE transactions on medical imaging
The convenience, safety, and affordability of ultrasound imaging make it a vital non-invasive diagnostic technique for examining soft tissues. However, significant differences in acoustic impedance between the skull and soft tissues hinder the succes...

Anatomically Guided PET Image Reconstruction Using Conditional Weakly-Supervised Multi-Task Learning Integrating Self-Attention.

IEEE transactions on medical imaging
To address the lack of high-quality training labels in positron emission tomography (PET) imaging, weakly-supervised reconstruction methods that generate network-based mappings between prior images and noisy targets have been developed. However, the ...

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

Physics in medicine and biology
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 spectru...

Liver respiratory-induced motion estimation using abdominal surface displacement as a surrogate: robotic phantom and clinical validation with varied correspondence models.

International journal of computer assisted radiology and surgery
PURPOSE: This work presents the implementation of an RGB-D camera as a surrogate signal for liver respiratory-induced motion estimation. This study aims to validate the feasibility of RGB-D cameras as a surrogate in a human subject experiment and to ...

Development of Convolutional Neural Network to Segment Ultrasound Images of Histotripsy Ablation.

IEEE transactions on bio-medical engineering
OBJECTIVE: Histotripsy is a focused ultrasound therapy that ablates tissue via the action of bubble clouds. It is under investigation to treat a number of ailments, including renal tumors. Ultrasound imaging is used to monitor histotripsy, though the...

Multimodality quantitative ultrasound envelope statistics imaging based support vector machines for characterizing tissue scatterer distribution patterns: Methods and application in detecting microwave-induced thermal lesions.

Ultrasonics sonochemistry
Ultrasound envelope statistics imaging, including ultrasound Nakagami imaging, homodyned-K imaging, and information entropy imaging, is an important group of quantitative ultrasound techniques for characterizing tissue scatterer distribution patterns...

[New Method of Paired Comparison for Improved Observer Shortage Using Deep Learning Models].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: The aim of this study was to validate the potential of substituting an observer in a paired comparison with a deep-learning observer.