Significance: Deep-tissue penetration by x-rays to induce optical responses of specific molecular reporters is a new way to sense and image features of tissue function in vivo. Advances in this field are emerging, as biocompatible probes are invented along with innovations in how to optimally utilize x-ray sources.
Aim: A comprehensive review is provided of the many tools and techniques developed for x-ray-induced optical molecular sensing, covering topics ranging from foundations of x-ray fluorescence imaging and x-ray tomography to the adaptation of these methods for sensing and imaging in vivo.
Approach: The ways in which x-rays can interact with molecules and lead to their optical luminescence are reviewed, including temporal methods based on gated acquisition and multipoint scanning for improved lateral or axial resolution.
Results: While some known probes can generate light upon x-ray scintillation, there has been an emergent recognition that excitation of molecular probes by x-ray-induced Cherenkov light is also possible. Emission of Cherenkov radiation requires a threshold energy of x-rays in the high kV or MV range, but has the advantage of being able to excite a broad range of optical molecular probes. In comparison, most scintillating agents are more readily activated by lower keV x-ray energies but are composed of crystalline inorganic constituents, although some organic biocompatible agents have been designed as well. Methods to create high-resolution structured x-ray-optical images are now available, based upon unique scanning approaches and/or a priori knowledge of the scanned x-ray beam geometry. Further improvements in spatial resolution can be achieved by careful system design and algorithm optimization. Current applications of these hybrid x-ray-optical approaches include imaging of tissue oxygenation and pH as well as of certain fluorescent proteins.
Conclusions: Discovery of x-ray-excited reporters combined with optimized x-ray scan sequences can improve imaging resolution and sensitivity.
In a standard computed tomography (CT) image, pixels having the same Hounsfield Units (HU) can correspond to different materials and it is therefore challenging to differentiate and quantify materials. Dual-energy CT (DECT) is desirable to differentiate multiple materials, but DECT scanners are not widely available as singleenergy CT (SECT) scanners. Here we develop a deep learning approach to perform DECT imaging by using standard SECT data. The end point of the deep learning approach is a model capable of providing the high-energy CT image for a given input low-energy CT image. We retrospectively studied 22 patients who received contrast-enhanced abdomen DECT scan. The difference between the predicted and original high-energy CT images are 3.47 HU, 2.95 HU, 2.38 HU, and 2.40 HU for spine, aorta, liver and stomach, respectively. The difference between virtual non-contrast (VNC) images obtained from original DECT and deep learning DECT are 4.10 HU, 3.75 HU, 2.33 HU and 2.92 HU for spine, aorta, liver and stomach, respectively. The aorta iodine quantification difference between iodine maps obtained from original DECT and deep learning DECT images is 0.9%. This study demonstrates that highly accurate DECT imaging with single low-energy data is achievable by using a deep learning approach. The proposed method can significantly simplify the DECT system design, reducing the scanning dose and imaging cost.
Luminescence molecular tomography with Cherenkov excitation offers the ability to non-invasively image and quantify temporal changes in fluorescence throughout the body, and then further realize tumor localization. This can be done in radiotherapy to determine the response to treatment in fractionated therapy. To obtain high signal-to-background or signal-to-noise ratio measurement, it is critical to know the best post time point of in-vivo agent-based molecular imaging, which could account on a high signal ratio of target to skin (TSR). For this purpose, ex-vivo murine experiments were performed to quantify the biokinetics and biodistribution of the major organs, plasma, tumor, and skin.
Cherenkov-excited luminescence scanned imaging (CELSI) is achieved with External Beam Radiotherapy, to map out molecular luminescence intensity or lifetime in tissue. In order to realize a deeper imaging depth with a reasonable spatial resolution, we optimized the original scanning gesture to do in a similar way to computed tomography (CT) and the image reconstruction was instead used a customized Maximum-likelihood expectation maximization (ML-EM) for CELSI. In tomographic CELSI (TCELSI), tomographic images are generated by irradiating the subject using a sequence of programmed X-ray beams at a fixed projection angle, while sensitive measurement is to take a sum for all image pixels from an intensified charge-coupled device. By restricting the X-ray excitation to a single, narrow beam of radiation, the origin of the optical photons can be inferred regardless of where these photons were detected, and how many times they scattered in tissue. Measurement geometry was designed for clinical expectation: CT scanning was achieved by a clinical linear accelerator (LINAC), where X-ray beam sequence and multiple projections were realized with multi leaf collimator (MLC) and gantry movement, respectively. Furthermore, in most modern External Beam Radiotherapy, MLC movement is synchronized with gantry angle to release a uniform radiation, and some of treatment plans, e.g., Intensity Modulated Radiation Therapy (IMRT), have a potential to match the scanning way mentioned. By including Cherenkov imaging results, medium surface profile can be additionally acquired, which can be used as boundary reference to do depth correction and co-register with molecular images. Resolution phantom studies showed that a 0.3 mm diameter capillary tube containing 0.01 nM luminescent nanospheres could be recognized at a depth of 21 mm into tissue-like media. Small animal imaging with a 1 mm diameter cylindrical target demonstrated that fast 3D data acquisition was achieved by a multi-pinhole collimator to image local luminescence 20mm deep.
Solid tumors often exhibit abnormal morphology which can be characterized by increased permeability and low perfusion. The resulting tumor hypoxia has been correlated with poor prognosis, which may be due to ineffective therapy or survival of more aggressive phenotypes. External beam radiation therapy (EBRT) is often used to treat such tumors, where radiation dose is delivered on a daily fractionated basis over the course of weeks. A non-contact optical method for measuring in vivo oxygen levels during EBRT treatments has been developed to provide early indications of hypoxic tumor environments. This method uses a time-gated intensified imaging device to measure both Cherenkov emissions, which are generated in tissue by high energy electrons traveling faster than the phase-velocity of the medium, and Cherenkov-excited luminescence generated by the oxygen-sensitive phosphorescent compound, PtG4. Murine models have shown the ability to discriminate phosphorescence lifetime changes before and after animal sacrifice. Pixel-maps of the estimated pO2 can be generated from this data to show high spatial variability within a region of interest. By further camera optimization, this method can be expanded to show pO2 distributions for other physiological conditions in near real-time. Our imaging method has the unique ability to be integrated within existing clinical applications while providing a wide-field mapping of oxygen saturation, which is currently unavailable with existing point probes.
KEYWORDS: Short wave infrared radiation, Luminescence, Cameras, In vivo imaging, Infrared imaging, Infrared radiation, Electron beams, Tissues, Radiotherapy, Absorption
Cherenkov emission induced by external beam radiation therapy from a clinical linear accelerator (LINAC) can be used to excite phosphors deep in biological tissues. As with all luminescence imaging, there is a desire to minimize the spectral overlap between the excitation light and emission wavelengths, here between the Cherenkov and the phosphor. Cherenkov excited short-wavelength infrared (SWIR, 1000 to 1700 nm) fluorescence imaging has been demonstrated for the first time, using long Stokes-shift fluorophore PdSe quantum dots (QD) with nanosecond lifetime and an optimized SWIR detection. The 1 / λ2 intensity spectrum characteristic of Cherenkov emission leads to low overlap of this into the fluorescence spectrum of PdSe QDs in the SWIR range. Additionally, using a SWIR camera itself inherently ignores the stronger Cherenkov emission wavelengths dominant across the visible spectrum. The SWIR luminescence was shown to extend the depth sensitivity of Cherenkov imaging, which could be used for applications in radiotherapy sensing and imaging in human tissue with targeted molecular probes.
KEYWORDS: Luminescence, Tissue optics, Signal to noise ratio, Tissues, 3D image processing, Computed tomography, Phosphorescence, Lymphatic system, Skin, Signal detection
Radiation therapy produces Cherenkov optical emission in tissue, and this light can be utilized to activate molecular probes. The feasibility of sensing luminescence from a tissue molecular oxygen sensor from within a human body phantom was examined using the geometry of the axillary lymph node region. Detection of regions down to 30-mm deep was feasible with submillimeter spatial resolution with the total quantity of the phosphorescent sensor PtG4 near 1 nanomole. Radiation sheet scanning in an epi-illumination geometry provided optimal coverage, and maximum intensity projection images provided illustration of the concept. This work provides the preliminary information needed to attempt this type of imaging in vivo.
Cherenkov-excited luminescence scanned imaging (CELSI) has been proposed for radiation-dose determination in medical physics due to its high spatial-resolution over centimeters of tissue. However, dense line-scanning illumination in typical CELSI is time-cost owing to the mechanical movement of the leaves in multi leaf collimator (MLC), resulting into increased radiation exposure. As a result, a scanningless Cherenkov luminescence imaging modality is herein proposed through structuring epi-illumination with MLC-based Hadamard-patterns, which utilizes a reduced radiation does by limiting illumination patterns, extremely shortening the sampling process. In order to effectively reconstruct unknowns from the resultant underdetermined linear system with sparse samplings, a compressed sensing-based reconstruction methodology with l1-norm regularization is adopted. Numerical and phantom experiments show that the proposed methodology achieves the same image quality as the traditional CELSI does.
Coupling between transport theory and its diffusion approximation in subdomain-based hybrid models for enhanced description of near-field photon-migration can be computationally complex, or even physically inaccurate. We report on a physically consistent coupling method that links the transport and diffusion physics of the photons according to transient photon kinetics, where distribution of the fully diffusive photons at a transition time is provided by a computation-saving auxiliary time-domain diffusion solution. This serves as a complementary or complete isotropic source of the temporally integrated transport equation over the early stage and the diffusion equation over the late stage, respectively, from which the early and late photodensities can be acquired independently and summed up to achieve steady-state modeling of the whole transport process. The proposed scheme is validated with numerical simulations for a cubic geometry.
In a typical laminar optical tomography (LOT) system, the dip-angle between the incident light (or the emitting light) and the normal of the detection plane randomly changes during raster-scanning. The inconstant dip-angle causes consistency between the measurement and the light transportation model where a fixed dip-angle of the incident light is generally required. To eliminate the effect from this dip angle, methods such as keeping the angle unchangeable by moving the phantom instead of scanning the light were investigated. In this paper, a LOT system with small dip-angle over the whole detection range is developed. Simulation and experimental evaluation show that the dip-angle of the modified system is much smaller than that of the traditional system. For example, the relative angle between the two incident light at (x=0mm, y=0mm) and (x=0mm, y=2.5mm) on the image plane is about 0.7° for the traditional system while that is only about 0.02° for the modified system. The main parameters of the system are also evaluated and an image reconstruction algorithm is developed based on Monte Carlo simulation. The reconstructed images show that the spatial resolution and quantitative ratio is improved by the modified system without loss of the scanning speed.
Most analytical methods for describing light propagation in turbid medium exhibit low effectiveness in the near-field of a collimated source. Motivated by the Charge Simulation Method in electromagnetic theory as well as the established discrete source based modeling, we have reported on an improved explicit model, referred to as "Virtual Source" (VS) diffuse approximation (DA), to inherit the mathematical simplicity of the DA while considerably extend its validity in modeling the near-field photon migration in low-albedo medium. In this model, the collimated light in the standard DA is analogously approximated as multiple isotropic point sources (VS) distributed along the incident direction. For performance enhancement, a fitting procedure between the calculated and realistic reflectances is adopted in the nearfield to optimize the VS parameters (intensities and locations). To be practically applicable, an explicit 2VS-DA model is established based on close-form derivations of the VS parameters for the typical ranges of the optical parameters. The proposed VS-DA model is validated by comparing with the Monte Carlo simulations, and further introduced in the image reconstruction of the Laminar Optical Tomography system.
Two-layered slab is a rational simplified sample to the near-infrared functional brain imaging using diffuse optical
tomography (DOT).The quality of reconstructed images is substantially affected by the accuracy of the background optical
properties. In this paper, region step wise reconstruction method is proposed for reconstructing the background optical
properties of the two-layered slab sample with the known geometric information based on continuous wave (CW) DOT. The optical properties of the top and bottom layers are respectively reconstructed utilizing the different
source-detector-separation groups according to the depth of maximum brain sensitivity of the source-detector-separation. We demonstrate the feasibility of the proposed method and investigate the application range of the
source-detector-separation groups by the numerical simulations. The numerical simulation results indicate the proposed
method can effectively reconstruct the background optical properties of two-layered slab sample. The relative
reconstruction errors are less than 10% when the thickness of the top layer is approximate 10mm. The reconstruction of target caused by brain activation is investigated with the reconstructed optical properties as well. The quantitativeness ratio
of the ROI is about 80% which is higher than that of the conventional method. The spatial resolution of the reconstructions (R) with two targets is investigated, and it demonstrates R with the proposed method is better than that with the
conventional method as well.
The cervical cancer screening at a pre-cancer stage is beneficial to reduce the mortality of women. An opto-electronic
joint detection system based on DSP aiming at early cervical cancer screening is introduced in this paper. In this system,
three electrodes alternately discharge to the cervical tissue and three light emitting diodes in different wavelengths
alternately irradiate the cervical tissue. Then the relative optical reflectance and electrical voltage attenuation curve are
obtained by optical and electrical detection, respectively. The system is based on DSP to attain the portable and cheap
instrument. By adopting the relative reflectance and the voltage attenuation constant, the classification algorithm based
on Support Vector Machine (SVM) discriminates abnormal cervical tissue from normal. We use particle swarm
optimization to optimize the two key parameters of SVM, i.e. nuclear factor and cost factor. The clinical data were
collected on 313 patients to build a clinical database of tissue responses under optical and electrical stimulations with the
histopathologic examination as the gold standard. The classification result shows that the opto-electronic joint detection
has higher total coincidence rate than separate optical detection or separate electrical detection. The sensitivity,
specificity, and total coincidence rate increase with the increasing of sample numbers in the training set. The average
total coincidence rate of the system can reach 85.1% compared with the histopathologic examination.
Laminar optical tomography (LOT) is a new mesoscopic functional optical imaging technique. Currently, the forward problem of LOT image reconstruction is generally solved on the basis of Monte-Carlo (MC) methods. However, considering the nonlinear nature of the image reconstruction in LOT, with the increasing number of source positions, methods based on MC takes too much computation time. Based on the scheme of trajectory translation and target voxel regression (TT&TVR) proposed by our group, this paper develops a fast 3D image reconstruction algorithm. The algorithm is applied to the absorption reconstruction of the layered inhomogeneous media. Results demonstrate that the reconstructing time is less than 15min with the X-Y-Z section of the sample subdivided into 50 × 50 × 10 voxels, and the target size and quantitativeness ratio can be obtained in a satisfying accuracy.
Laminar optical tomography (LOT) is a new mesoscopic functional optical imaging technique, which is an extension of a confocal microscope and diffuse optical tomography to acquire both the coaxial and off-axis scattered light at the same time. In this paper, a LOT system with a larger detection area aiming at the in vivo detection of early cervical cancer is developed. The field of view of our system is 10 mm x 10 mm. In order to improve the image quality of the system, two methods were performed: the correction of image distortion and the restriction of returning light. The performance of the system with aperture stop was assessed by liquid phantom experiments. Comparing with the Monte Carlo simulation, the measurement results show that the average relative errors of eight different source-detector distances corresponding to 4 source points are lower than the errors of the system taking the frame of objective lens as the aperture stop by 5.7%, 4.8%,6.1%,6.1% respectively. Moreover, the experiment based on the phantom with specified structure and optical parameters to simulate the cervix demonstrates that the system perform well for the cervix measurement.
As a new non-invasive medical imaging technology, diffuse optical tomography (DOT) has received considerable attention that can provide vast quantities of functional information of tissues. The reconstruction problem of DOT is highly ill-posed, meaning that a small error in the measurement data can bring about drastic errors of the reconstruction optical properties. In this paper, the shape-based image reconstruction algorithm of DOT is proposed for reducing the ill-poseness under the assumption that the optical properties of target region distribute uniformly. Since some human organs and tumors can be simplified as an ellipsoid, in this paper, the shape of the inhomogeneity is described as an ellipsoid. In the forward problem, the boundary element method (BEM) is implemented to solve the continuous wave diffusion equation (DE). By the use of the ellipsoid parametric method, the description of the shape, location and optical properties of the inhomogeneity, and the value of the background could be realized with only a small number of parameters. In the inverse calculation, a Levenberg-Marquardt algorithm with line searching is implemented to solve the underlying nonlinear least-squares problem. Simulation results show that the algorithm developed in this paper is effective in reducing the ill-poseness and robust to the noise.
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