Image reconstruction is an important task in X-ray computed tomography. In this paper we discuss the results of morphological analysis of test objects in high-noise conditions and compare image reconstruction using two widely employed methods in computed tomography, namely Filtered Back Projection and Algebraic Reconstruction Technique. In our test tomography experiments, we used a laboratory X...

The instrument development and design of a prototype frequency-domain optical imaging device for breast cancer detection is described in detail. This device employs radio-frequency intensity modulated near-infrared light to image quantitatively both the scattering and absorption coefficients of tissue. The functioning components of the system include a laser diode and a photomultiplier tube, wh...

Recently, compressed sensing (CS) computed tomography (CT) using sparse projection views has been extensively investigated to reduce the potential risk of radiation to patient. However, due to the insufficient number of projection views, an analytic reconstruction approach results in severe streaking artifacts and CS-based iterative approach is computationally very expensive. To address this is...

Abstract In practical applications of tomographic imaging, there are often challenges for image reconstruction due to under-sampling and insufficient data. In computed tomography (CT), for example, image reconstruction from few views would enable rapid scanning with a reduced x-ray dose delivered to the patient. Limited-angle problems are also of practical significance in CT. In this work, we d...

With the availability of more powerful computers, iterative reconstruction algorithms are the subject of an ongoing work in the design of more efficient reconstruction algorithms for X-ray computed tomography. In this work, we show how two analytical reconstruction algorithms can be improved by correcting the corresponding reconstructions using a randomized iterative reconstruction algorithm. T...

Synchrotron radiation (SR) X-ray micro-computed tomography (CT) is an effective imaging modality for high-resolution investigation of small objects, with several applications in medicine, biology and industry. However, the limited size of the detector field of view (FOV) restricts the sample dimensions to only a few millimeters. When the sample size is larger than the FOV, images reconstructed ...

OBJECTIVE This work aims to explore the effects of adaptive statistical iterative reconstruction (ASiR) and model-based iterative reconstruction (MBIR) algorithms in reducing computed tomography (CT) radiation dosages in abdominal imaging. METHODS CT scans on a standard male phantom were performed at different tube currents. Images at the different tube currents were reconstructed with the fi...

This article explores convolutional generative networks as an alternative to iterative reconstruction algorithms in medical image reconstruction. The task of involves mapping projection domain data collected from the detector domain. is done typically through which are time consuming and computationally expensive. Trained deep learning provide faster outputs proven various tasks across computer...

ObjectivesThe objective of this study was to quantitatively assess the image quality Advanced Modeled Iterative Reconstruction (ADMIRE) and PixelShine (PS) deep learning algorithm for optimization low-dose computed tomography protocols in midfacial trauma.Study DesignSix fresh frozen human cadaver head specimens were scanned by using both standard scan protocols. Three iterative reconstruction ...