Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy

Unlike the high imaging radiation dose of computed tomography (CT), cone-beam CT (CBCT) has smaller radiation dose and presents less harm to patients. Therefore, CBCT is often used for target delineation, dose planning, and postoperative evaluation in the image-guided radiotherapy (IGRT) of various cancers. In the process of IGRT, CBCT images usually need to be collected multiple times in a radiotherapy stage for postoperative evaluation. The effectiveness of radiotherapy is measured by comparing and analyzing the registered CBCT and the source CT image obtained before radiotherapy. Hence, the registration of CBCT and CT is the most important step in IGRT. CBCT images usually have poor visual effects due to the small imaging dose used, which adversely affects the registration performance. In this paper, we propose a novel adaptive visual saliency feature enhancement method for CBCT in IGRT. Firstly, we denoised CBCT images using a structural similarity based low-rank approximation model (SSLRA) and then enhanced the denoised results with a visual saliency feature enhancement (VSFE)-based method. Experimental results show that the enhancement performance of the proposed method is superior to the comparison enhancement algorithms in visual objective comparison. In addition, the extended experiments prove that the proposed enhancement method can improve the registration accuracy of CBCT and CT images, demonstrating their application prospects in IGRT-based cancer treatment

Multi-Intensity Optimization-Based CT and Cone Beam CT Image Registration

Cancer is a highly lethal disease that is mainly treated by image-guided radiotherapy. Because the low dose of cone beam CT is less harmful to patients, cone beam CT images are often used for target delineation in image-guided radiotherapy of various cancers, especially in breast and lung cancer. However, breathing and heartbeat can cause position errors in images taken during different periods, and the low dose of cone beam CT also results in insufficient imaging clarity, rendering existing registration methods unable to meet the CT and cone beam CT registration tasks. In this paper, we propose a novel multi-intensity optimization-based CT and cone beam CT registration method. First, we use a multi-weighted mean curvature filtering algorithm to preserve the multi-intensity details of the input image pairs. Then, the strong edge retention results are registered using and intensity-based method to obtain the multi-intensity registration results. Next, a novel evaluation method called intersection mutual information is proposed to evaluate the registration accuracy of the different multi-intensity registration results. Finally, we determine the optimal registration transformation by intersection mutual information and apply it to the input image pairs to obtain the final registration results. The experimental results demonstrate the excellent performance of the proposed method, meeting the requirements of image-guided radiotherapy

Multi-Intensity Optimization-Based CT and Cone Beam CT Image Registration

Cancer is a highly lethal disease that is mainly treated by image-guided radiotherapy. Because the low dose of cone beam CT is less harmful to patients, cone beam CT images are often used for target delineation in image-guided radiotherapy of various cancers, especially in breast and lung cancer. However, breathing and heartbeat can cause position errors in images taken during different periods, and the low dose of cone beam CT also results in insufficient imaging clarity, rendering existing registration methods unable to meet the CT and cone beam CT registration tasks. In this paper, we propose a novel multi-intensity optimization-based CT and cone beam CT registration method. First, we use a multi-weighted mean curvature filtering algorithm to preserve the multi-intensity details of the input image pairs. Then, the strong edge retention results are registered using and intensity-based method to obtain the multi-intensity registration results. Next, a novel evaluation method called intersection mutual information is proposed to evaluate the registration accuracy of the different multi-intensity registration results. Finally, we determine the optimal registration transformation by intersection mutual information and apply it to the input image pairs to obtain the final registration results. The experimental results demonstrate the excellent performance of the proposed method, meeting the requirements of image-guided radiotherapy

A Fast Image Guide Registration Supported by Single Direction Projected CBCT

Image registration is an important research topic in medical image-guided therapy, which is dedicated to registering the high-dose imaging sequences with low-dose/faster means. Registering computer tomography (CT) scanning sequences with cone beam computer tomography (CBCT) scanning sequences is a typical application and has been widely used in CBCT-guided radiotherapy. The main problem is the difference in image clarity of these two image sequences. To solve this problem, for the single projection image sequence matching tasks encountered in medical practice, a novel local quality based curved section encoding strategy is proposed in this paper, which is called the high-quality curved section (HQCS). As an optimized cross-section regularly encoded along the sequence of image, this curved section could be used in order to solve the matching problem. Referencing the independent ground truth provided by medical image physicians, with an experiment combined with the four most widely used indicators used on image registration, matching performance of HQCS on CT/CBCT datasets was tested with varying clarity. Experimental results show that the proposed HQCS can register the CT/CBCT effectively and outperforms the commonly used methods. Specifically, the proposed HQCS has low time complexity and higher scalability, which indicates that the application enhanced the task of diagnosis

A Fast Image Guide Registration Supported by Single Direction Projected CBCT

Image registration is an important research topic in medical image-guided therapy, which is dedicated to registering the high-dose imaging sequences with low-dose/faster means. Registering computer tomography (CT) scanning sequences with cone beam computer tomography (CBCT) scanning sequences is a typical application and has been widely used in CBCT-guided radiotherapy. The main problem is the difference in image clarity of these two image sequences. To solve this problem, for the single projection image sequence matching tasks encountered in medical practice, a novel local quality based curved section encoding strategy is proposed in this paper, which is called the high-quality curved section (HQCS). As an optimized cross-section regularly encoded along the sequence of image, this curved section could be used in order to solve the matching problem. Referencing the independent ground truth provided by medical image physicians, with an experiment combined with the four most widely used indicators used on image registration, matching performance of HQCS on CT/CBCT datasets was tested with varying clarity. Experimental results show that the proposed HQCS can register the CT/CBCT effectively and outperforms the commonly used methods. Specifically, the proposed HQCS has low time complexity and higher scalability, which indicates that the application enhanced the task of diagnosis

Portable Sensor for the Detection of Choline and Its Derivatives Based on Silica Isoporous Membrane and Gellified Nanointerfaces

A portable amperometric ion sensor was fabricated by integrating silica isoporous membrane (SIM) and organogel composed of polyvinyl chloride and 1,2-dichloroethane (PVC-DCE) on a 3D-printed polymer chip. The detection of ionic species in aqueous samples could be accomplished by adding a microliter of sample droplet to the sensor and by identifying the ion-transfer potential and current magnitude at the water/organogel interface array templated by SIM. Thanks to the ultrasmall channel size (2–3 nm in diameter), high channel density (4 × 10⁸ μm^–2), and ultrathin thickness (80 nm) of SIM, the ensemble of nanoscopic water/organogel (nano-W/Gel) interface array behaved like a microinterface with two back-to-back hemispherical mass diffusion zones. So, the heterogeneous ion-transfer across the nano-W/Gel interface array generated a steady-state sigmoidal current wave. The detection of choline (Ch) and its derivatives, including acetylcholine (ACh), benzoylcholine (BCh), and atropine (AP), in aqueous samples was examined with this portable sensor. Using differential pulse stripping voltammetry (DPSV), the quantification of these analytes was achieved with a limit of detection (LOD) down to 1 μM. Moreover, the portable ion sensor was insensitive to various potential interferents that might coexist in vivo, owing to size-/charge-based selectivity and antifouling capacity of SIM. With this priority, the portable ion sensor was able to quantitatively determine Ch and its derivatives in diluted urine and blood samples. The LODs for Ch, ACh, AP, and BCh in urine were 1.12, 1.30, 1.08, and 0.99 μM, and those for blood samples were 3.61, 3.38, 2.32, and 1.81 μM, respectively

Low‐light image enhancement for infrared and visible image fusion

Abstract Infrared and visible image fusion (IVIF) is an essential branch of image fusion, and enhancing the visible image of IVIF can significantly improve the fusion performance. However, many existing low‐light enhancement methods are unsuitable for the visible image enhancement of IVIF. In order to solve this problem, this paper proposes a new visible image enhancement method for IVIF. Firstly, the colour balance and contrast enhancement‐based self‐calibrated illumination estimation (CCSCE) is proposed to improve the input image's brightness, contrast, and colour information. Then, the method based on Mutually Guided Image Filtering (muGIF) is adopted to design a strategy to extract details adaptively from the original visible image, which can keep details without introducing additional noise effectively. Finally, the proposed visible image enhancement technique is used for IVIF tasks. In addition, the proposed method can be used for the visible image enhancement of IVIF and other low‐light images. Experiment results on different public datasets and IVIF demonstrate the authors’ method's superiority from both qualitative and quantitative comparisons. The authors’ code will be publicly available at https://github.com/yiqiao666/low‐light‐enhancement‐for‐IVIF/tree/master

Permselective Ion Transport Across the Nanoscopic Liquid/Liquid Interface Array

Free-standing silica nanochannel membrane (SNM) with perforated channels was utilized to create arrays of nanoscale interfaces between two immiscible electrolyte solutions (nano-ITIES), at which permselective ion transfer and detection were achieved. The SNM consisted of a high density of straight nanochannels with a diameter of 2–3 nm and a length of 70 nm. The silicon wafer coated by 150 nm-thick porous silicon nitride film (p-SiNF) with pores of 5 μm-in-diameter was used to support the SNM in a form of nanochannel-on-micropore. Considering the material surface lipophilicity, the nano-ITIES array was formed at the boundary between SNM and p-SiNF, with a diffusion geometry equivalent to two back-to-back inlaid microdisc interfaces. Thus, the transfer of tetraethylammonium (TEA⁺) across the nano-ITIES array yielded symmetric sigmoidal current responses. In addition, because of the ultrasmall size and negatively charged surface of silica nanochannels, the nano-ITIES displayed obvious size and charge permselectivities. Transfer of ions with a size comparable with or larger than the nanochannel was sterically blocked. Also that of anions with a size smaller than the nanochannels encountered the strong electrostatic repulsion from channel walls, showing obvious dependence on the ionic strength of aqueous solution. The present approach is facile and inexpensive for building a nano-ITIES array with potential applications in ion detection and separation