AIMC Topic: Phantoms, Imaging

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Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy.

Radiation oncology (London, England) Oct 14, 2021
OBJECTIVE: To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy.
Radiotherapy Dosage Radiotherapy, Intensity-Modulated Phantoms, Imaging Organs at Risk Radiotherapy Planning, Computer-Assisted Cone-Beam Computed Tomography Image Processing, Computer-Assisted Lung Neoplasms Bone Neoplasms Soft Tissue Neoplasms Prognosis Tomography, X-Ray Computed Deep Learning Humans Neural Networks, Computer
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Deep learning-augmented radioluminescence imaging for radiotherapy dose verification.

Medical physics Oct 5, 2021
PURPOSE: We developed a novel dose verification method using a camera-based radioluminescence imaging system (CRIS) combined with a deep learning-based signal processing technique.
Radiotherapy, Intensity-Modulated Phantoms, Imaging Radiometry Deep Learning Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage
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Comparison of visibility of in-stent restenosis between conventional- and ultra-high spatial resolution computed tomography: coronary arterial phantom study.

Japanese journal of radiology Sep 29, 2021
PURPOSE: The purposes of this experimental study were to compare the quantitative and qualitative visibility of in-stent restenosis between conventional-resolution CT (CRCT) and ultra-high-resolution CT (U-HRCT) and to investigate the effects of the ...
Humans Algorithms Tomography, X-Ray Computed Stents Deep Learning Radiation Dosage Coronary Angiography Phantoms, Imaging Coronary Restenosis
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Novel Computer-Aided Diagnosis Software for the Prevention of Retained Surgical Items.

Journal of the American College of Surgeons Sep 27, 2021
BACKGROUND: Retained surgical items are a serious human error. Surgical sponges account for 70% of retained surgical items. To prevent retained surgical sponges, it is important to establish a system that can identify errors and avoid the occurrence ...
Foreign Bodies Radiography Surgical Sponges Sensitivity and Specificity Cadaver Humans Diagnosis, Computer-Assisted Postoperative Period Software Deep Learning Phantoms, Imaging Torso
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Deep learning-enabled EPID-based 3D dosimetry for dose verification of step-and-shoot radiotherapy.

Medical physics Sep 24, 2021
PURPOSE: The study aims at a novel dosimetry methodology to reconstruct a 3D dose distribution as imparted to a virtual cylindrical phantom using an electronic portal imaging device (EPID).
Humans Algorithms Deep Learning Radiotherapy, Intensity-Modulated Phantoms, Imaging Radiometry Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage
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An MR-Safe Endovascular Robotic Platform: Design, Control, and Ex-Vivo Evaluation.

IEEE transactions on bio-medical engineering Sep 20, 2021
OBJECTIVE: Cardiovascular diseases are the most common cause of global death. Endovascular interventions, in combination with advanced imaging technologies, are promising approaches for minimally invasive diagnosis and therapy. More recently, teleope...
Equipment Design Robotic Surgical Procedures Endovascular Procedures Magnetic Resonance Imaging Robotics Phantoms, Imaging Humans
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Rupture Detection During Needle Insertion Using Complex OCT Data and CNNs.

IEEE transactions on bio-medical engineering Sep 20, 2021
OBJECTIVE: Soft tissue deformation and ruptures complicate needle placement. However, ruptures at tissue interfaces also contain information which helps physicians to navigate through different layers. This navigation task can be challenging, wheneve...
Needles Phantoms, Imaging Mechanical Phenomena Robotics Tomography, Optical Coherence
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Accurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS).

Medical physics Sep 13, 2021
BACKGROUND: Sparse-view CT image reconstruction problems encountered in dynamic CT acquisitions are technically challenging. Recently, many deep learning strategies have been proposed to reconstruct CT images from sparse-view angle acquisitions showi...
Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Phantoms, Imaging Humans Neural Networks, Computer Algorithms
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Using synthetic data generation to train a cardiac motion tag tracking neural network.

Medical image analysis Sep 10, 2021
A CNN based method for cardiac MRI tag tracking was developed and validated. A synthetic data simulator was created to generate large amounts of training data using natural images, a Bloch equation simulation, a broad range of tissue properties, and ...
Motion Phantoms, Imaging Computer Simulation Magnetic Resonance Imaging Heart Humans Neural Networks, Computer
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Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data.

Diagnostic and interventional imaging Sep 5, 2021
PURPOSE: The purpose of this study was to compare the effect of two deep learning image reconstruction (DLR) algorithms in chest computed tomography (CT) with different clinical indications.
Radiation Dosage Phantoms, Imaging Humans Algorithms Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted
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