Evaluation and comparison of a CdTe based photon counting detector with an energy integrating detector for X-ray phase sensitive imaging of breast cancer

PURPOSE: To compare imaging performance of a cadmium telluride (CdTe) based photon counting detector (PCD) with a CMOS based energy integrating detector (EID) for potential phase sensitive imaging of breast cancer. METHODS: A high energy inline phase sensitive imaging prototype consisting of a microfocus X-ray source with geometric magnification of 2 was employed. The pixel pitch of the PCD was 55μm, while 50μm for EID. The spatial resolution was quantitatively and qualitatively assessed through modulation transfer function (MTF) and bar pattern images. The edge enhancement visibility was assessed by measuring edge enhancement index (EEI) using the acrylic edge acquired images. A contrast detail (CD) phantom was utilized to compare detectability of simulated tumors, while an American College of Radiology (ACR) accredited phantom for mammography was used to compare detection of simulated calcification clusters. A custom-built phantom was employed to compare detection of fibrous structures. The PCD images were acquired at equal, and 30% less mean glandular dose (MGD) levels as of EID images. Observer studies along with contrast to noise ratio (CNR) and signal to noise ratio (SNR) analyses were performed for comparison of two detection systems. RESULTS: MTF curves and bar pattern images revealed an improvement of about 40% in the cutoff resolution with the PCD. The excellent spatial resolution offered by PCD system complemented superior detection of the diffraction fringes at boundaries of the acrylic edge and resulted in an EEI value of 3.64 as compared to 1.44 produced with EID image. At MGD levels (standard dose), observer studies along with CNR and SNR analyses revealed a substantial improvement of PCD acquired images in detection of simulated tumors, calcification clusters, and fibrous structures. At 30% less MGD, PCD images preserved image quality to yield equivalent (slightly better) detection as compared to the standard dose EID images. CONCLUSION: CdTe-based PCDs are technically feasible to image breast abnormalities (low/high contrast structures) at low radiation dose levels using the high energy inline phase sensitive imaging technique.

Review on fractional vortex beam

As an indispensable complement to an integer vortex beam, the fractional vortex beam has unique physical properties such as radially notched intensity distribution, complex phase structure consisting of alternating charge vortex chains, and more sophisticated orbital angular momentum modulation dimension. In recent years, we have noticed that the fractional vortex beam was widely used for complex micro-particle manipulation in optical tweezers, improving communication capacity, controllable edge enhancement of image and quantum entanglement. Moreover, this has stimulated extensive research interest, including the deep digging of the phenomenon and physics based on different advanced beam sources and has led to a new research boom in micro/nano-optical devices. Here, we review the recent advances leading to theoretical models, propagation, generation, measurement, and applications of fractional vortex beams and consider the possible directions and challenges in the future.

Improving the Quantification of the Lateral Geniculate Nucleus in Magnetic Resonance Imaging Using a Novel 3D-Edge Enhancement Technique

The lateral geniculate nucleus (LGN) is a small, inhomogeneous structure that relays major sensory inputs from the retina to the visual cortex. LGN morphology has been intensively studied due to various retinal diseases, as well as in the context of normal brain development. However, many of the methods used for LGN structural evaluations have not adequately addressed the challenges presented by the suboptimal routine MRI imaging of this structure. Here, we propose a novel method of edge enhancement that allows for high reliability and accuracy with regard to LGN morphometry, using routine 3D-MRI imaging protocols. This new algorithm is based on modeling a small brain structure as a polyhedron with its faces, edges, and vertices fitted with one plane, the intersection of two planes, and the intersection of three planes, respectively. This algorithm dramatically increases the contrast-to-noise ratio between the LGN and its surrounding structures as well as doubling the original spatial resolution. To show the algorithm efficacy, two raters (MA and ML) measured LGN volumes bilaterally in 19 subjects using the edge-enhanced LGN extracted areas from the 3D-T1 weighted images. The averages of the left and right LGN volumes from the two raters were 175 ± 8 and 174 ± 9 mm3, respectively. The intra-class correlations between raters were 0.74 for the left and 0.81 for the right LGN volumes. The high contrast edge-enhanced LGN images presented here, from a 7-min routine 3T-MRI acquisition, is qualitatively comparable to previously reported LGN images that were acquired using a proton density sequence with 30–40 averages and 1.5-h of acquisition time. The proposed edge-enhancement algorithm is not limited only to the LGN, but can significantly improve the contrast-to-noise ratio of any small deep-seated gray matter brain structure that is prone to high-levels of noise and partial volume effects, and can also increase their morphometric accuracy and reliability. An immensely useful feature of the proposed algorithm is that it can be used retrospectively on noisy and low contrast 3D brain images previously acquired as part of any routine clinical MRI visit.

An Image-Guided Network for Depth Edge Enhancement

With the rapid development of 3D coding and display technologies, numerous applications are emerging to target human immersive entertainments. To achieve a prime 3D visual experience, high accuracy depth maps play a crucial role. However, depth maps retrieved from most devices still suffer inaccuracies at object boundaries. Therefore, a depth enhancement system is usually needed to correct the error. Recent developments by applying deep learning to deep enhancement have shown their promising improvement. In this paper, we propose a deep depth enhancement network system that effectively corrects the inaccurate depth using color images as a guide. The proposed network contains both depth and image branches, where we combine a new set of features from the image branch with those from the depth branch. Experimental results show that the proposed system achieves a better depth correction performance than state of the art advanced networks. The ablation study reveals that the proposed loss functions in use of image information can enhance depth map accuracy effectively.