Imaging phantom

About: Imaging phantom is a research topic. Over the lifetime, 28170 publications have been published within this topic receiving 510003 citations. The topic is also known as: phantom.

Simultaneous Acquisition of Multislice PET and MR Images: Initial Results with a MR-Compatible PET Scanner

Abstract: PET and MRI are powerful imaging techniques that are largely complementary in the information they provide. We have designed and built a MR-compatible PET scanner based on avalanche photodiode technology that allows simultaneous acquisition of PET and MR images in small animals. Methods: ThePETscannerinsertusesmagneticfield‐insensitive,positionsensitive avalanche photodiode (PSAPD) detectors coupled, via short lengths of optical fibers, to arrays of lutetium oxyorthosilicate (LSO) scintillator crystals. The optical fibers are used to minimize electromagnetic interference between the radiofrequency and gradient coils and the PET detector system. The PET detector module components and the complete PET insert assembly are described. PET data were acquired with and without MR sequences running, and detector flood histograms were compared with the ones generated from the data acquired outside the magnet. A uniform MR phantom was also imaged to assess the effect of the PET detector on the MR data acquisition. Simultaneous PET and MRI studies of a mouse were performed ex vivo. Results: PSAPDs can be successfully used to read out large numbers of scintillator crystals coupled through optical fibers with acceptable performance in terms of energy and timing resolution and crystal identification. The PSAPD-LSO detector performs well in the 7-T magnet, and no visible artifacts are

Confocal Microwave Imaging for Breast Cancer Detection: Delay-Multiply-and-Sum Image Reconstruction Algorithm

Abstract: A new image reconstruction algorithm, termed as delay-multiply-and-sum (DMAS), for breast cancer detection using an ultra-wideband confocal microwave imaging technique is proposed. In DMAS algorithm, the backscattered signals received from numerical breast phantoms simulated using the finite-difference time-domain method are time shifted, multiplied in pair, and the products are summed to form a synthetic focal point. The effectiveness of the DMAS algorithm is shown by applying it to backscattered signals received from a variety of numerical breast phantoms. The reconstructed images illustrate improvement in identification of embedded malignant tumors over the delay-and-sum algorithm. Successful detection and localization of tumors as small as 2 mm in diameter are also demonstrated.

Short-lag spatial coherence of backscattered echoes: imaging characteristics

Abstract: Conventional ultrasound images are formed by delay-and-sum beamforming of the backscattered echoes received by individual elements of the transducer aperture. Although the delay-and-sum beamformer is well suited for ultrasound image formation, it is corrupted by speckle noise and challenged by acoustic clutter and phase aberration. We propose an alternative method of imaging utilizing the short-lag spatial coherence (SLSC) of the backscattered echoes. Compared with matched B-mode images, SLSC images demonstrate superior SNR and contrast-to-noise ratio in simulated and experimental speckle-generating phantom targets, but are shown to be challenged by limited point target conspicuity. Matched B-mode and SLSC images of a human thyroid are presented. The challenges and opportunities of real-time implementation of SLSC imaging are discussed.

A comparison of reconstruction algorithms for breast tomosynthesis.

Abstract: Three algorithms for breast tomosynthesis reconstruction were compared in this paper, including (1) a back-projection (BP) algorithm (equivalent to the shift-and-add algorithm), (2) a Feldkamp filtered back-projection (FBP) algorithm, and (3) an iterative Maximum Likelihood (ML) algorithm. Our breast tomosynthesis system acquires 11 low-dose projections over a 50 degree angular range using an a-Si (CsI:Tl) flat-panel detector. The detector was stationary during the acquisition. Quality metrics such as signal difference to noise ratio (SDNR) and artifact spread function (ASF) were used for quantitative evaluation of tomosynthesis reconstructions. The results of the quantitative evaluation were in good agreement with the results of the qualitative assessment. In patient imaging, the superimposed breast tissues observed in two-dimensional (2D) mammograms were separated in tomosynthesis reconstructions by all three algorithms. It was shown in both phantom imaging and patient imaging that the BP algorithm provided the best SDNR for low-contrast masses but the conspicuity of the feature details was limited by interplane artifacts; the FBP algorithm provided the highest edge sharpness for microcalcifications but the quality of masses was poor; the information of both the masses and the microcalcifications were well restored with balanced quality by the ML algorithm, superior to the results from the other two algorithms.

Adaptive streak artifact reduction in computed tomography resulting from excessive x-ray photon noise

Abstract: The quality of a computed tomography (CT) image is often degraded by streaking artifacts resulting from excessive x-ray quantum noise. Often, a patient has to be rescanned at a higher technique or at a larger slice thickness in order to obtain an acceptable image for diagnosis. This results in a higher dose to the patient, a degraded cross plane resolution, or a reduced patient throughput. In this paper, we propose an adaptive filtering approach in Radon space based on the local statistical properties of the CT projections. We first model the noise characteristics of a projection sample undergoing important preprocessing steps. A filter is then designed such that its parameters are dynamically adjusted to adapt to the local noise characteristics. Because of the adaptive nature of the filter, a proper balance between streak artifact suppression and spatial resolution preservation is achieved. Phantom and clinical studies have been conducted to evaluate the robustness of our approach. Results demonstrate that the adaptive filtering approach is effective in reducing or eliminating quantum noise induced artifacts in CT. At the same time, the impact on the spatial resolution is kept at a low level.