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Multimodal Imaging

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Noninvasive diagnostic imaging for endometriosis part 1: a systematic review of recent developments in ultrasound, combination imaging, and artificial intelligence.

Fertility and sterility
Endometriosis affects 1 in 9 women and those assigned female at birth. However, it takes 6.4 years to diagnose using the conventional standard of laparoscopy. Noninvasive imaging enables a timelier diagnosis, reducing diagnostic delay as well as the ...

Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions.

Nuklearmedizin. Nuclear medicine
Positron emission tomography (PET) is vital for diagnosing diseases and monitoring treatments. Conventional image reconstruction (IR) techniques like filtered backprojection and iterative algorithms are powerful but face limitations. PET IR can be se...

Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space.

Computer methods and programs in biomedicine
Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabili...

Artificial Intelligence in Oncological Hybrid Imaging.

Nuklearmedizin. Nuclear medicine
BACKGROUND:  Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications ...

Multimodal imaging-based material mass density estimation for proton therapy using supervised deep learning.

The British journal of radiology
OBJECTIVE: Mapping CT number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, MRI, and advanced d...

Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study.

Physics in medicine and biology
. Breast cancer is the most prevalent cancer diagnosed in women worldwide. Accurately and efficiently stratifying the risk is an essential step in achieving precision medicine prior to treatment. This study aimed to construct and validate a nomogram ...

The Clinical Added Value of Breast Cancer Imaging Using Hybrid PET/MR Imaging.

Magnetic resonance imaging clinics of North America
Dedicated MR imaging is highly performant for the evaluation of the primary lesion and should regularly be added to whole-body PET/MR imaging for the initial staging. PET/MR imaging is highly sensitive for the detection of nodal involvement and could...

Deep learning-based affine medical image registration for multimodal minimal-invasive image-guided interventions - A comparative study on generalizability.

Zeitschrift fur medizinische Physik
Multimodal image registration is applied in medical image analysis as it allows the integration of complementary data from multiple imaging modalities. In recent years, various neural network-based approaches for medical image registration have been ...