AIMC Topic: Phantoms, Imaging

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Non-Metallic MR-Guided Concentric Tube Robot for Intracerebral Hemorrhage Evacuation.

IEEE transactions on bio-medical engineering
OBJECTIVE: We aim to develop and evaluate an MR-conditional concentric tube robot for intracerebral hemorrhage (ICH) evacuation.

Adaptive machine learning method for photoacoustic computed tomography based on sparse array sensor data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Photoacoustic computed tomography (PACT) is a non-invasive biomedical imaging technology that has developed rapidly in recent decades, especially has shown potential for small animal studies and early diagnosis of human dise...

Three-dimensional CT imaging in extensor tendons using deep learning reconstruction: optimal reconstruction parameters and the influence of dose.

Physical and engineering sciences in medicine
The purpose of this study was to assess the optimal reconstruction parameters and the influence of tube current in extensor tendons three-dimensional computed tomography (3D CT) using deep learning reconstruction, using iterative reconstruction as a ...

Validation of deep learning-based CT image reconstruction for treatment planning.

Scientific reports
Deep learning-based CT image reconstruction (DLR) is a state-of-the-art method for obtaining CT images. This study aimed to evaluate the usefulness of DLR in radiotherapy. Data were acquired using a large-bore CT system and an electron density phanto...

Real-time liver motion estimation via deep learning-based angle-agnostic X-ray imaging.

Medical physics
BACKGROUND: Real-time liver imaging is challenged by the short imaging time (within hundreds of milliseconds) to meet the temporal constraint posted by rapid patient breathing, resulting in extreme under-sampling for desired 3D imaging. Deep learning...

Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: The axial field of view (AFOV) of a positron emission tomography (PET) scanner greatly affects the quality of PET images. Although a total-body PET scanner (uEXPLORER) with a large AFOV is more sensitive, it is more expensive and difficult t...

Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting.

NMR in biomedicine
We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T and T maps acquired with the magnetic resonance fingerprinting (MRF) methodology. We applied two different MRF sequence routines to acquire images of ex...

Image quality improvement in bowtie-filter-equipped cone-beam CT using a dual-domain neural network.

Medical physics
BACKGROUND: The bowtie-filter in cone-beam CT (CBCT) causes spatially nonuniform x-ray beam often leading to eclipse artifacts in the reconstructed image. The artifacts are further confounded by the patient scatter, which is therefore patient-depende...

AI for PET image reconstruction.

The British journal of radiology
Image reconstruction for positron emission tomography (PET) has been developed over many decades, with advances coming from improved modelling of the data statistics and improved modelling of the imaging physics. However, high noise and limited spati...

Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI.

European radiology
OBJECTIVE: This study aimed to evaluate the image quality and lesion conspicuity of the deep learning image reconstruction (DLIR) algorithm compared with standard image reconstruction algorithms on abdominal enhanced computed tomography (CT) scanning...