The advancements in nanotechnology have created multifunctional nanomaterials aimed at enhancing diagnostic accuracy and treatment efficacy for cancer. However, the ability to target deep-seated tumors remains one of the most critical challenges for certain nanomedicine applications. To this end, X-ray-excited theranostic techniques provide a means of overcoming the limits of light penetration and tissue attenuation. Herein, a comprehensive overview of the recent advances in nanotechnology-enhanced X-ray-excited imaging and therapeutic methodologies is presented, with an emphasis on the design of multifunctional nanomaterials for contrast-enhanced computed tomography (CT) imaging, X-ray-excited optical luminescence (XEOL) imaging, and X-ray-excited multimodal synchronous/synergistic therapy. The latter is based on the concurrent use of radiotherapy with chemotherapy, gas therapy, photodynamic therapy, or immunotherapy. Moreover, the featured biomedical applications of X-ray-excited deep theranostics are discussed to highlight the advantages of X-ray in high-sensitivity detection and efficient elimination of malignant tumors. Finally, key issues and technical challenges associated with this deep theranostic technology are identified, with the intention of advancing its translation into the clinic.

Breaking the Depth Dependence by Nanotechnology-Enhanced X-Ray-Excited Deep Cancer Theranostics.

Vessel segmentation as a component of medical image processing is the prerequisite for accurate diagnosis of vascular-related diseases. Manual delineation of blood vessels has been turned out to be time consuming and observer dependent. Therefore, much effort has been dedicated to the automatic or semi-automatic vessel segmentation methods. Previous literatures have reviewed the state of vessel segmentation methods from various perspectives. However, their reviews did not take the modern machine-learning methods especially deep neural networks into account. In this paper, we reviewed the state-of-the-art vessel segmentation methods by dividing them into two categories, rule-based, and machine-learning-based methods. The rule-based methods discriminate vessel structure from background relying on intuitively and exquisitely designed rule sets, while the machine-learning-based methods carry out the segmentation by self-learned rules from the previous experience. Instead of exhaustively listing all vessel segmentation methods, this paper focuses on the well-known blood vessel segmentation methods in recent years, to give readers a glimpse of the current state and future direction of segmentation technique for blood vessels.

Segmentation of blood vessels using rule-based and machine-learning-based methods: a review

X-ray luminescence and X-ray fluorescence computed tomography (CT) are two emerging technologies in X-ray imaging that provide functional and molecular imaging capability. Both emission-type tomographic imaging modalities use external X-rays to stimulate secondary emissions, either light or secondary X-rays, which are then acquired for tomographic reconstruction. These modalities surpass the limits of sensitivity in current X-ray imaging and have the potential of enabling X-ray imaging to extract molecular imaging information. These new modalities also promise to break through the spatial resolution limits of other in vivo molecular imaging modalities. This paper reviews the development of X-ray luminescence and X-ray fluorescence CT and their relative merits. The discussion includes current problems and future research directions and the role of these modalities in future molecular imaging applications.

X-Ray Luminescence and X-Ray Fluorescence Computed Tomography: New Molecular Imaging Modalities

X-ray luminescence computed tomography (XLCT) is a new molecular imaging modality. In this Letter, we first in vivo tomographically image the near-IR-emitting nanophosphor (Gd2O3:Eu3+) in nude mice (N=2). In practically, incorporating the compressive sensing technique, the XLCT reconstruction is performed only using single-view data. The experimental results indicate that the single-view reconstruction is feasible to image XLCT in vivo. The location error is less than 1.5 mm. Further, the imaging time can be greatly reduced compared with previous XLCT systems. Therefore, it is suited for imaging fast distribution of x-ray-excitable nanophosphors within a biological object.

In vivo x-ray luminescence tomographic imaging with single-view data

Octahedral metal cluster complexes have high potential for biomedical applications. In order to evaluate the benefits of these moieties for combined CT/X-ray luminescence computed tomography, this paper compares photoluminescence, radiodensity and X-ray induced luminescence properties of eight related octahedral molybdenum and tungsten cluster complexes [{M6I8}L6]n (where M is Mo or W and L is I−, NO3−, OTs− or OH−/H2O). This article demonstrates that despite the fact that molybdenum cluster complexes are better photoluminescence emitters, tungsten cluster complexes, in particular (Bu4N)2[{W6I8}I6], demonstrate significantly higher X-ray induced luminescence due to a combination of relatively good photoluminescence properties and high X-ray attenuation. Additionally, photo-degradation of [{M6I8}(NO3)6]2− was evaluated.

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A comparative study of optical properties and X-ray induced luminescence of octahedral molybdenum and tungsten cluster complexes

Purpose: The appearance of x-ray luminescence computed tomography (XLCT) opens new possibilities to perform molecular imaging by x ray. In the previous XLCT system, the sample was irradiated by a sequence of narrow x-ray beams and the x-ray luminescence was measured by a highly sensitive charge coupled device (CCD) camera. This resulted in a relatively long sampling time and relatively low utilization of the x-ray beam. In this paper, a novel cone beam x-ray luminescence computed tomography strategy is proposed, which can fully utilize the x-ray dose and shorten the scanning time. The imaging model and reconstruction method are described. The validity of the imaging strategy has been studied in this paper.

Cone beam x-ray luminescence computed tomography: A feasibility study

X-ray luminescence tomography (XLT) is an imaging technology based on X-ray-excitable materials. The main purpose of this paper is to obtain quantitative luminescence concentration using the structural information of the X-ray computed tomography (XCT) in the hybrid cone beam XLT/XCT system. A multi-wavelength luminescence cone beam XLT method with the structural a priori information is presented to relieve the severe ill-posedness problem in the cone beam XLT. The nanophosphors and phantom experiments were undertaken to access the linear relationship of the system response. Then, an in vivo mouse experiment was conducted. The in vivo experimental results show that the recovered concentration error as low as 6.67% with the location error of 0.85 mm can be achieved. The results demonstrate that the proposed method can accurately recover the nanophosphor inclusion and realize the quantitative imaging.

Quantitative cone beam X-ray luminescence tomography/X-ray computed tomography imaging

X-ray luminescence computed tomography (XLCT) has become a promising imaging technology for biological application based on phosphor nanoparticles. There are mainly three kinds of XLCT imaging systems: pencil beam XLCT, narrow beam XLCT and cone beam XLCT. Narrow beam XLCT can be regarded as a balance between the pencil beam mode and the cone-beam mode in terms of imaging efficiency and image quality. The collimated X-ray beams are assumed to be parallel ones in the traditional narrow beam XLCT. However, we observe that the cone beam X-rays are collimated into X-ray beams with fan-shaped broadening instead of parallel ones in our prototype narrow beam XLCT. Hence we incorporate the distribution of the X-ray beams in the physical model and collected the optical data from only two perpendicular directions to further speed up the scanning time. Meanwhile we propose a depth related adaptive regularized split Bregman (DARSB) method in reconstruction. The simulation experiments show that the proposed physical model and method can achieve better results in the location error, dice coefficient, mean square error and the intensity error than the traditional split Bregman method and validate the feasibility of method. The phantom experiment can obtain the location error less than 1.1 mm and validate that the incorporation of fan-shaped X-ray beams in our model can achieve better results than the parallel X-rays.

X-ray luminescence computed tomography imaging based on X-ray distribution model and adaptively split Bregman method

Combining two or more imaging modalities to provide complementary information has become commonplace in clinical practice and in preclinical and basic biomedical research. By incorporating the structural information provided by computed tomography (CT) or magnetic resonance imaging (MRI), the ill poseness nature of bioluminescence tomography (BLT) can be reduced significantly, thus improve the accuracies of reconstruction and in vivo quantification. In this paper, we present a small animal imaging system combining multi-view and multi-spectral BLT with MRI. The independent MRI-compatible optical device is placed at the end of the clinical MRI scanner. The small animal is transferred between the light tight chamber of the optical device and the animal coil of MRI via a guide rail during the experiment. After the optical imaging and MRI scanning procedures are finished, the optical images are mapped onto the MRI surface by interactive registration between boundary of optical images and silhouette of MRI. Then, incorporating the MRI structural information, a heterogeneous reconstruction algorithm based on finite element method (FEM) with L-1 normalization is used to reconstruct the position, power and region of the light source. In order to validate the feasibility of the system, we conducted experiments of nude mice model implanted with artificial light source and quantitative analysis of tumor inoculation model with MDA-231-GFP-luc. Preliminary results suggest the feasibility and effectiveness of the prototype system. (C) 2014 Optical Society of America

Incorporating MRI structural information into bioluminescence tomography: system, heterogeneous reconstruction and in vivo quantification

Purpose: The goal of this paper is to address three problems existing in vessel extraction of murine hindlimb. First, the bone can hardly be separated from blood vessels because the intensity of contrast enhanced blood vessels is similar to that of bones. Second, as an automatic blood vessel segmentation method, the vesselness method is sensitive to sharp boundaries, resulting in false positive effect in nonvascular regions. Finally, thin blood vessels are always broken after segmentation because of the low signal-to-noise ratio. Methods: The proposed automatic segmentation method for bone and blood vessel in this paper includes three important modules. (1) To eliminate the interference of bones on the segmentation of blood vessels, the authors employ split Bregman method to segment bones in the first place. (2) The authors propose an edge extension strategy to cope with the false positive effect of the vesselness method on the sharp boundaries of hindlimb after the removal of bones. Then, the authors segment the blood vessels using the vesselness method combined with multiscale bi-Gaussian filtering. (3) The authors reconnect the broken blood vessels after segmentation based on centerline and morphological dilation. Results: The bones’ segmentation from the murine hindlimbs was conducted using the split Bregman, manual, and thresholding methods, respectively. Compared with the thresholding method, the split Bregman method could finely segment the bones from blood vessels, and the results were comparable to that of manual segmentation. After removing bones, the vesselness method combined with the bi-Gaussian filtering with and without edge extension was performed. The vesselness results with the edge extension strategy could effectively eliminate the false positive effect on sharp boundaries in nonvascular regions. Some of the blood vessels segmented by thresholding from the vesselness results were disconnected. Thus, the authors employed the vascular connection method based on centerline and morphological dilation to connect the broken blood vessels. Compared with the vascular connection utilizing the spatial-variant and -invariant morphological closing methods, the proposed vascular connection method reconnected the broken blood vessels and meanwhile maintained the nonbroken ones unchanged. Conclusions: Our proposed method is suitable for the segmentation of bones and blood vessels in murine hindlimbs. For the segmentation of bones, the split Bregman method improves the distinguishability between bones and blood vessels, since both the intensity information and the geometrical size are exploited. For the segmentation of blood vessels, vesselness method with the edge extension strategy eliminates the false positive effect on the nonvascular sharp boundaries. After segmentation, the proposed vascular connection method based on centerline and morphological dilation can reconnect the broken blood vessels without affecting the nonbroken ones.

Dongmei Chen

Papers

Cone beam x-ray luminescence computed tomography: A feasibility study

Quantitative cone beam X-ray luminescence tomography/X-ray computed tomography imaging

X-ray luminescence computed tomography imaging based on X-ray distribution model and adaptively split Bregman method

Incorporating MRI structural information into bioluminescence tomography: system, heterogeneous reconstruction and in vivo quantification

Automatic segmentation method for bone and blood vessel in murine hindlimb