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Phantoms, Imaging

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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.

MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study.

Scientific reports
Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provi...

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.

Physics in medicine and biology
. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-prin...

SPICER: Self-supervised learning for MRI with automatic coil sensitivity estimation and reconstruction.

Magnetic resonance in medicine
PURPOSE: To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation.

Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduc...

Investigation of a potential upstream harmonization based on image appearance matching to improve radiomics features robustness: a phantom study.

Biomedical physics & engineering express
. Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations in used image acquisition and reconstruction parameters...

Artificial neural network for enhancing signal-to-noise ratio and contrast in photothermal optical coherence tomography.

Scientific reports
Optical coherence tomography (OCT) is a medical imaging method that generates micron-resolution 3D volumetric images of tissues in-vivo. Photothermal (PT)-OCT is a functional extension of OCT with the potential to provide depth-resolved molecular inf...

A deep-learning-based scatter correction with water equivalent path length map for digital radiography.

Radiological physics and technology
We proposed a new deep learning (DL) model for accurate scatter correction in digital radiography. The proposed network featured a pixel-wise water equivalent path length (WEPL) map of subjects with diverse sizes and 3D inner structures. The proposed...

Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI.

IEEE transactions on medical imaging
Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications. Deep learning has demonstrated grea...