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Perfusion

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Development of attenuation correction methods using deep learning in brain-perfusion single-photon emission computed tomography.

Medical physics
PURPOSE: Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation corre...

Improving detection accuracy of perfusion defect in standard dose SPECT-myocardial perfusion imaging by deep-learning denoising.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: We previously developed a deep-learning (DL) network for image denoising in SPECT-myocardial perfusion imaging (MPI). Here we investigate whether this DL network can be utilized for improving detection of perfusion defects in standard-dos...

Optical tissue clearing and machine learning can precisely characterize extravasation and blood vessel architecture in brain tumors.

Communications biology
Precise methods for quantifying drug accumulation in brain tissue are currently very limited, challenging the development of new therapeutics for brain disorders. Transcardial perfusion is instrumental for removing the intravascular fraction of an in...

Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net.

Magnetic resonance imaging
PURPOSE: To develop an end-to-end deep learning solution for quickly reconstructing radial simultaneous multi-slice (SMS) myocardial perfusion datasets with comparable quality to the pixel tracking spatiotemporal constrained reconstruction (PT-STCR) ...

AIFNet: Automatic vascular function estimation for perfusion analysis using deep learning.

Medical image analysis
Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the optimal reperfusion treatment. In perfusion CT imaging, deconvolution ...

Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning.

Physics in medicine and biology
To develop and evaluate the performance of a deep learning model to generate synthetic pulmonary perfusion images from clinical 4DCT images for patients undergoing radiotherapy for lung cancer.. A clinical data set of 58 pre- and post-radiotherapyTc-...

Workflow for automatic renal perfusion quantification using ASL-MRI and machine learning.

Magnetic resonance in medicine
PURPOSE: Clinical applicability of renal arterial spin labeling (ASL) MRI is hampered because of time consuming and observer dependent post-processing, including manual segmentation of the cortex to obtain cortical renal blood flow (RBF). Machine lea...

Forward Model and Deep Learning Based Iterative Deconvolution for Robust Dynamic CT Perfusion.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Perfusion maps obtained from low-dose computed tomography (CT) data suffer from poor signal to noise ratio. To enhance the quality of the perfusion maps, several works rely on denoising the low-dose CT (LD-CT) images followed by conventional regulari...

Deep learning-based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance.

European journal of nuclear medicine and molecular imaging
PURPOSE: This work was set out to investigate the feasibility of dose reduction in SPECT myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning approach was proposed to synthesize full-dose images from the corresp...

Improving Sensitivity of Arterial Spin Labeling Perfusion MRI in Alzheimer's Disease Using Transfer Learning of Deep Learning-Based ASL Denoising.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) denoising through deep learning (DL) often faces insufficient training data from patients. One solution is to train DL models using healthy subjects' data which are m...