Patent Description:
In general, the current radiation dose measurement of objects requires the use of special and expensive dose measurement equipment, such as ion chamber, radiation dosimeter and proportional counter. This expensive dose-measurement equipment is installed on X-ray equipment or computed tomography scanner to measure the radiation dose will increase costs.

In addition, if a thermoluminescent dosimeter (TLD) is used for measurement, the immediate dose may not be obtained due to its principle. In addition, there are other ways to estimate radiation dose, such as applying the Monte Carlo simulation. However, it is quite time-consuming to calculate the dose and thickness of an object by the Monte Carlo simulation requiring a high-end computer.

<CIT> discloses an X-ray imaging method, in particular a method for estimating a material composition of an imaged object using an imaging system. The imaging system includes a radiation source and a digital detector. The method also includes scanning a plurality of calibration phantoms with varying material composition to acquire a plurality of reference calibration images, estimating an attenuation coefficient thickness product for each pixel in the reference calibration images, and estimating a material composition of a region of interest using the estimated pixelwise coefficient thickness product. In this document, the radiation incident on a scintillator material and the pixel photosensors measure, by way of change in the charge across the diode, the amount of light generated by x-ray interaction with the scintillator. As a result, each pixel produces an electronic signal that represents the intensity, after attenuation by object <NUM> (see <FIG>), of an x-ray beam impinging on detector array <NUM>. In one embodiment, detector array <NUM> is approximately <NUM> by <NUM> and is configured to produce views for an entire object of interest. Further, the method comprises: estimating a material composition of a ROI using the estimated coefficient thickness product of an object <NUM> (shown in <FIG>), the method includes using each pixel's photon count and the compressed breast thickness to create a calibration curve and an analytic expression for an estimate of tissue composition. The document also discloses a method for estimating a composition of a ROI comprising: a model-based estimation using a theoretical model of Iphamtom(i, j) which represents the photon count for a plurality of pixels at location i and j.

The present disclosure provides an X-ray imaging method. The X-ray imaging method includes the following steps: (a) performing a first object imaging process to obtain a first object intensity signal by detecting a plurality of X-rays passing through a first object; (b) performing a baseline imaging process to obtain a baseline intensity signal by detecting the X-rays when the first object is not in a FOV; and (c) obtaining the first thickness of the first object based on the first object intensity signal, the baseline intensity signal, and a first attenuation coefficient of the first object.

The present disclosure provides an X-ray imaging method. The X-ray imaging method includes the following steps: (a) performing a first object imaging process to obtain a first object intensity signal by detecting a plurality of X-rays passing through a first object; (b) performing a baseline imaging process to obtain a baseline intensity signal by detecting the X-rays when the first object is not in a FOV; (c) performing a second object imaging process to obtain a second object intensity signal by detecting the X-rays passing through a second object; (d) obtaining a sample intensity signal based on the first object intensity signal and the second object intensity signal, wherein the first object is a carrier, and the second object comprises a sample and the carrier; and (e) obtaining a sample thickness based on the sample intensity signal, the baseline intensity signal and a sample attenuation coefficient.

The prior art documents <CIT> at least fails to disclose that baseline intensity signal is obtained by detecting the X-rays when the object is not in the FOV as reference calibration images; and that the thickness of the object is obtained based on the baseline intensity signal and the first attenuation coefficient. Instead, the prior art document discloses: (step <NUM>) scanning a plurality of calibration phantoms <NUM> (see flow chart in <FIG> and side view of a plurality of calibration phantoms in <FIG>) with different material compositions to acquire a plurality of reference calibration images; (step <NUM>) estimating the attenuation coefficient thickness product of each pixel in the reference calibration image, and (step <NUM>) estimating the material composition of the region of interest (ROI) using the estimated pixel-by-pixel coefficient thickness product.

The present disclosure provides an X-ray imaging system. The X-ray imaging system includes an X-ray source, a detector and a processor. The X-ray source is configured to perform a first object imaging process so that a plurality of X-rays pass through a first object placed in a field of view (FOV) and perform a baseline imaging process as the first object is not in the FOV. The detector is configured to obtain a baseline intensity signal in the baseline imaging process and obtain a first object intensity signal in the first object imaging process. And, the processor is coupled to the detector. The processor is configured to operate instructions, comprising: calculating a first thickness of the first object based on the first object intensity signal, the baseline intensity signal, and a first attenuation coefficient of the first object.

The scope of the invention is best determined by baseline to the appended claims.

The present invention will be described with respect to particular embodiments and with baseline to certain drawings, but the invention is not limited thereto and is only limited by the claims. It will be further understood that the terms "comprises," "comprising," "comprises" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

Use of ordinal terms such as "first", "second", "third", etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having the same name (but for use of the ordinal term) to distinguish the claim elements.

Please refer to <FIG>, <FIG>, <FIG>, <FIG> is a flowchart of an X-ray imaging method <NUM> in accordance with one embodiment of the present disclosure. <FIG> is a flowchart of X-ray imaging method <NUM> in accordance with one embodiment of the present disclosure. <FIG> are schematic diagrams of an X-ray imaging system in accordance with one embodiment of the present disclosure.

Please refer to <FIG>. In <FIG>, the X-ray imaging system includes at least an X-ray source SR, a detector DT, and a processor PC.

In one embodiment, the X-ray source SR is used to generate a plurality of X-rays.

In an embodiment, the detector DT is correspondingly arranged in the direction of the X-rays emitted by the X-ray source SR. In addition, the detector DT is used to detect X-rays passing through medium (such as gas, solid, or liquid).

In an embodiment, the processor PC is used to operate instructions. The processor PC can also be implemented by a microcontroller, a microprocessor, a digital signal processor, an application specific integrated circuit (ASIC) or a logic circuit, but is not limited thereto.

The flow of the X-ray imaging method <NUM> will be described below with baseline to <FIG>.

In step <NUM>, an X-ray source SR performs an object imaging process, and a detector DT obtains an object intensity signal by detecting the X-rays passing through an object in a field of view (FOV).

In one embodiment, when the X-ray source SR performs the object imaging process so that the detector DT obtains the object intensity signal, and transmits the object intensity signal to the processor PC. The object intensity signal can be an X-ray image, and the X-ray image can be presented by a two-dimensional projection image IMG.

In one embodiment, the processor PC is coupled to the detector DT. In addition, the processor PC is configured to receive the object intensity signal generated by the detector DT.

In step <NUM>, the X-ray source SR performs a baseline imaging process, and the detector DT obtains a baseline intensity signal by detecting the X-rays when the object is not in the FOV.

In step <NUM>, a processor PC estimates a thickness of an object based on the object intensity signal and the baseline intensity signal.

In one embodiment, the object described in <FIG> can be a carrier. In another embodiment, the object described in <FIG> can be a combination of a carrier and a sample (in other words, a combination of sample(s) and the carrier is regarded as the object described in <FIG>). In one embodiment, the object described in step <NUM> is not in the FOV, which means that the carrier and sample are not placed in the FOV, and the X-rays are directly shot to the detector DT, so as to capture a baseline image.

The following describes the three types of imaging cases in detail, such as blank image (the object is not in the FOV), the object is the carrier TB (hereinafter referred to as the first object), and the object is the combination of the carrier TB and the sample OBJ (hereinafter referred to as the second object). However, the definition of the first object and the second object in the present invention is not limited thereto.

In step <NUM>, the X-ray source SR performs the baseline imaging process so that the detector DT obtains the baseline intensity signal by detecting the X-rays when a first object is not in the FOV.

In one embodiment, as shown in <FIG>, no object is placed in the FOV. The detector DT detects the X-rays when the object is not in the FOV, such as the sample and the carrier TB are not in the FOV and the baseline imaging process is performed by the X-ray imaging system to obtain a baseline intensity signal. The baseline intensity signal is also called a blank image. The distribution of the number of X-ray photons in the energy range of the X-ray source is called X-ray energy spectrum (see <FIG>). The X-ray energy spectrum can be obtained by a known lookup table, measurement or calculation. The baseline intensity signal obtained by the detector DT is an intensity value which is the sum of the X-rays received by the detector DT under the X-ray energy spectrum.

In step <NUM>, the first object (such as the carrier TB) is placed in the FOV. As shown in <FIG>, the X-ray source SR performs a first object imaging process, and the detector DT obtains a first object intensity signal by detecting the X-rays passing through the first object.

More specifically, the X-ray source SR provides the X-rays and shoot the first object. Then the detector DT obtains the first object intensity signal, and transmits the first object intensity signal to the processor PC.

In step <NUM>, as shown in <FIG>, a second object (such as a combination of the carrier TB and the sample OBJ) is placed in the FOV. The X-ray source SR performs a second object imaging process. The detector DT obtains a second object intensity signal by detecting the X-rays passing through the second object.

More specifically, the sample OBJ is set on the carrier TB, the X-ray source SR radiograph the carrier TB and the sample OBJ. Then, the detector DT obtains the second object intensity signal, and transmits the second object intensity signal to the processor PC. The first object intensity signal and the second object intensity signal are X-ray photon signals. As shown in the manner of the two-dimensional projection image IMG in <FIG>, the gray-scale block GRY in the two-dimensional projection image IMG represents an X-ray block passing through the second object.

In one embodiment, for example, if a pixel k of the detector DT receives the lowest amount of the X-rays when the second object imaging process is performed, the thickest portion of the sample OBJ is positioned corresponding to the pixel k.

The above steps <NUM> to <NUM> are not limited in sequence. In one embodiment, the area where the detector DT receives the X-rays is referred to the FOV.

In step <NUM>, a processor PC obtains a sample intensity signal based on the first object intensity signal and the second object intensity signal, and obtains a sample thickness x corresponding to the sample intensity signal according to a sample thickness characteristic curve. In one embodiment, the processor PC subtracts the first object intensity signal from the second object intensity signal to obtain the sample intensity signal.

In one embodiment, the processor PC obtains the sample thickness characteristic curve according to the baseline intensity signal, the X-ray energy spectrum, and a sample attenuation coefficient.

In one embodiment, the thickness characteristic curve, for examples, a sample thickness characteristic curve, a first thickness characteristic curve, and a second thickness characteristic curve, which can be calculated by the following function (<NUM>) Beer-Lambert Law.

In one embodiment, the thickness characteristic curve is obtained based on the calculation of the baseline intensity signal, the X-ray energy spectrum and the attenuation coefficient. The operation includes the results of discrete multiplication, and then sum the results of discrete multiplication.

In one embodiment, using a certain imaging parameters set (including the voltage and the current of the X-ray source SR or different types of filters, etc.), a two-dimensional projection blank image is captured as no object in the FOV, that is, the detector DT obtains the baseline intensity signal. Then, an object of any known material, such as water, laboratory animals, acrylic, etc., is placed in the FOV. Using the same imaging parameters set, a two-dimensional projection image of an object (the following object can refer to the first object or the second object) is captured, that is, the detector DT obtains the object intensity signal. Through the following function (<NUM>) calculation, a thickness of the object can be estimated from the signals obtained by the detector DT. <MAT> The symbol N<NUM>i represents the intensity value of the baseline intensity signal for a pixel, and is the number of photons at a specific energy i of the X-ray energy spectrum provided by the X-ray source. The symbol µ represents the attenuation coefficient of an object of known material. According to different values of X-ray energy of the spectrum, the attenuation coefficient will be a different value, and the attenuation coefficient can be a linear attenuation coefficient. The symbol N<NUM> represents the intensity value obtained by the detector DT after the object is placed in the FOV. The symbol x represents the thickness of the object positioned corresponding to a pixel of the detector DT. Therefore, by using the intensity signal obtained from the image detector DT, the processor PC can acquire the object thickness by the thickness characteristic curve.

More specifically, the above function (<NUM>) can be expanded into function (<NUM>). <FIG> is a graph of the X-ray energy-photon number in accordance with one embodiment of the present disclosure. The processor PC imports the X-ray energy spectrum into Beer's Law. As shown in <FIG>, the horizontal axis represents the X-ray energy and the vertical axis represents the number of photons. The X-ray energy spectrum will have different photon flux distributions according to the filter material, filter thickness and the maximum voltage of X-ray source. As shown in <FIG>, the solid line represents the energy spectrum of the X-ray source with the maximum voltage value of 50keV and without a filter. The dotted line represents the energy spectrum of the X-ray source with the maximum voltage value of 50keV with <NUM> aluminum filter. The processor PC estimates the spectral distribution of the pixels by simulating, calculating or measuring the X-ray energy spectrum with the selected imaging parameters including the energy of the X-ray source, the current of the X-ray source, or types of filters. More specifically, a curve of the object thickness and the object intensity signal, that is the thickness characteristic curve, can be obtained by the following function (<NUM>) as shown in <FIG> is a graph of a thickness characteristic curve in accordance with one embodiment of the present disclosure. <MAT> The symbol µ represents the attenuation coefficient of the object, which has different values according to the X-ray energy of the spectrum. The symbol i represents the specific energy of the X-ray energy spectrum (from 1keV to the maximum voltage value set by the X-ray source). The symbol x represents the thickness of the object positioned corresponding to a pixel.

<FIG> is a graph of a thickness characteristic curve in accordance with one embodiment of the present disclosure. The solid line in <FIG> is the thickness characteristic curve of the function (<NUM>), the horizontal axis of <FIG> represents the object intensity signal, and the vertical axis represents the thickness (the unit is, for example, cm). Therefore, the processor PC can obtain the object thickness x according to the detected object intensity signal in the thickness characteristic curve of the function (<NUM>) in the step <NUM>. In one embodiment in the step <NUM>, N<NUM> of the function (<NUM>) represents an intensity value of the sample intensity signal for the pixel.

Please refer to <FIG> is a flowchart of a method <NUM> for calculating the thickness of an object using X-rays in accordance with one embodiment of the present disclosure. Steps <NUM>, <NUM>, and <NUM> in <FIG> are the same as steps <NUM>, <NUM>, and <NUM> in <FIG>, respectively, and therefore will not be described again.

In one embodiment, the processor PC executes estimating a first thickness of the first object based on the first object intensity signal and the baseline intensity signal. More specifically, in step <NUM>, the processor PC obtains the first thickness characteristic curve according to the X-ray energy spectrum of the baseline intensity signal and the first attenuation coefficient of the first object. Besides, N<NUM> of the function (<NUM>) represents an intensity value of the first object intensity signal for the pixel. In addition, the processor PC estimates the first thickness of the first object according to the first thickness characteristic curve and the first object intensity signal.

In one embodiment, the processor PC executes the estimation of a second thickness of the second object based on the second object intensity signal and the baseline intensity signal. More specifically, in step <NUM>, the processor PC obtains a second thickness characteristic curve according to the X-ray energy spectrum of the baseline intensity signal and the second attenuation coefficient of the second object. Besides, N<NUM> of the function (<NUM>) represents an intensity value of the second object intensity signal for the pixel. In addition, the processor PC estimates the second thickness of the second object according to the second thickness characteristic curve and the second object intensity signal.

In step <NUM>, the processor PC subtracts the first thickness from the second thickness to obtain a sample thickness. The first object includes a carrier TB, and the second object includes a sample OBJ and the carrier TB.

In one embodiment, if the object material (such as the sample OBJ) is known, the density od of the object is also known. In addition, the body thickness x of the sample OBJ can be obtained from the above function. Further, the area of the sample (pixel size) corresponding to the pixel k can be calculated by the area of the pixel k. The following formula (<NUM>) can be used to calculate the weight per pixel (wk) of the sample corresponding to the pixel k. <MAT> <MAT> The symbol wk represents the weight of the sample having the thickness x corresponding to the pixel k, and the unit is kilogram or gram. The symbol psL represents the length (in cm or m) of the pixel size. The symbol psW represents the width (in cm or m) of the pixel size. The symbol od represents the density of the sample, and the unit is kg/m<NUM> or g/cm<NUM>. Then, according to the function (<NUM>), the weight wk of the sample corresponding to all of the pixels are added to obtain the total weight of sample in the FOV.

Based on the above steps, the processor PC estimates the total weight of sample in the FOV based on the thickness x, the density, and the pixel size of the sample.

For example, after the processor PC receives the baseline intensity signal (blank image), the above function (<NUM>) is used to import the X-ray energy spectrum, the sample (linear) attenuation coefficient, and the baseline intensity signal to obtain a sample thickness characteristic curve. The attenuation coefficient is shown in <FIG> is a graph of the attenuation coefficient in accordance with one embodiment of the present disclosure. The horizontal axis represents X-ray energy (keV), and the vertical axis represents the linear attenuation coefficient (µ). The unit of the linear attenuation coefficient is cm-<NUM>. The X-ray imaging system obtains the sample thickness characteristic curve through the step <NUM> above, and obtains the sample thickness x corresponding to the sample intensity signal according to the sample thickness characteristic curve, and imports the sample thickness x , the sample density and the pixel size into the function (<NUM>). Then the sample weight (sample pixel size * sample thickness * sample density = sample weight) corresponding to the pixel k can be obtained. To sum of each sample weight per pixel is the total weight of sample in the FOV.

In one embodiment, the X-ray imaging system can further calculate the absorbed dose of the object after the total weight of sample in the FOV is obtained. When the values imported in the following functions (<NUM>) and (<NUM>) are parameters of the carrier TB and/or the sample OBJ, the absorbed dose of the carrier TB and/or the sample OBJ will be calculated correspondingly. The following takes the calculation of the sample absorbed dose as an example.

In an embodiment, the number of photons absorbed by the sample can be calculated by the following functions (<NUM>)-(<NUM>). <MAT> <MAT><MAT> is the number of residual photons which are not absorbed by the sample after the X-rays of specific energy i of the X-ray energy spectrum pass through the sample along the sample thickness x , and the unit is count. <MAT> is the baseline intensity signal for one pixel and the unit is count. This pixel is, for example, the pixel k in <FIG>, symbol i is the specific energy of the X-ray energy spectrum, and the maximum value is E (the unit is keV). Nabsi is the number of absorbed photons in the sample as the X-rays pass through the sample along the thickness x at a specific energy i. µeni is the absorption coefficient of the sample. µen has different values according to the X-ray energy i. Please refer to <FIG>, a graph of the sample absorption coefficient in accordance with one embodiment of the present disclosure. The horizontal axis of <FIG> represents X-ray energy (keV), and the vertical axis represents the absorption coefficient (µen) of the sample. According to this, the processor PC can calculate the number of residual photons which are not absorbed by the sample according to the baseline intensity signal, the sample absorption coefficient, and the sample thickness. The processor PC subtracts the number of residual photons from the baseline intensity signal, thereby knowing the number of photons absorbed by the sample.

In one embodiment, the X-ray imaging system calculates the number of absorbed photons in the sample at the specific energy i of the X-ray energy spectrum for the pixel k (as shown in <FIG>). The number of absorbed photons per pixel of the sample at the specific energy i of the X-ray energy spectrum can be converted into the absorbed energy per pixel by the function (<NUM>), and the average absorbed dose of the sample can be calculated by the function (<NUM>) (unit: Gy, J / kG): <MAT> <MAT> The symbol E is the maximum voltage of the X-ray source. The symbol i is the specific energy of the X-ray energy spectrum, and the maximum value is E (unit: keV). The symbol Nabsi is the number of photons absorbed by the sample as the X-rays pass through the sample along the thickness x at a specific energy i of the X-ray energy spectrum. The symbol Object weight is the total weight of sample in the FOV detected and calculated by the X-rays imaging system, and its unit is kilogram. The string "detector pixel number" is the number of pixels in the two-dimensional projection image IMG in <FIG>. Therefore, the processor PC can obtain the absorbed energy of the sample along the thickness x corresponding to the pixel k according to the number of absorbed photons. The processor PC then calculates the sum of the absorbed energy of all pixels for the sample in the FOV.

In one embodiment, please refer to <FIG> is a flowchart of a method <NUM> for calculating a sample absorbed dose in accordance with one embodiment of the present disclosure.

In step <NUM>, the processor PC receives a baseline intensity signal and a sample thickness.

In step <NUM>, the processor PC calculates the number of residual photons which are not absorbed by the sample as the X-rays pass through the sample along the thickness x, the baseline intensity signal and the sample absorption coefficient. In one embodiment, the processor PC applies the function (<NUM>) to calculate the number of residual photons per pixel as the X-rays at a specific energy i of the X-ray energy spectrum pass through the sample along the thickness x.

In step <NUM>, the processor PC calculates the number of absorbed photons in the sample as the X-rays pass through the sample along the thickness x. In one embodiment, the processor PC applies the function (<NUM>) to calculate the number of absorbed photons per pixel as the X-rays pass through the sample along the thickness x at a specific energy i of the X-ray energy spectrum.

In step <NUM>, the processor PC converts the number of absorbed photons into the absorbed energy of the sample. In one embodiment, the processor PC applies function (<NUM>) to calculate the absorbed energy per pixel.

In step <NUM>, the processor PC calculates the sum of the absorbed energy for all pixels and the average absorbed dose of the sample in the FOV. In one embodiment, the processor PC applies the function (<NUM>) to calculate the average absorbed dose of the sample in the FOV.

In one embodiment, the X-ray imaging system can calculate the X-ray dose rate emitted by the X-ray source SR by the method <NUM>, such as simulating a sample as a ion chamber (not shown). The free cavity of the ion chamber is filled with air, for example, the known free cavity thickness x and the sample absorption coefficient (air absorption coefficient) are imported into the functions (<NUM>) to (<NUM>). Therefore the average absorbed dose of the ion chamber is obtained. Then the X-ray radiation dose rate emitted by the X-ray source SR can be obtained based on the average absorbed dose.

The X-ray imaging method and system shown in the present invention calculate the object thickness, the average absorbed dose and the radiation dose according to the intensity signals on the detector. Through the calculations, the thickness and weight of the object and the radiation dose absorbed in the object can be known directly from the X-ray image. This technology can be applied to instantly provide the dose rate, cumulative dose delivered by the current X-ray source, and the average dose absorbed by the object. In practical applications, the operator can know the thickness of the object through X-ray imaging and can know the current X-ray dose rate and the average dose absorbed by the object without additional expensive dose measurement equipment.

The method of the present invention, or a specific form or part thereof, may exist in the form of a code. The code can be included in physical media, such as floppy disks, CD-ROMs, hard disks, or any other machine-readable (such as computer-readable) storage media, or is not limited to external forms of computer program products. When the code is loaded and executed by a machine, such as a computer, the machine becomes a device for participating in the present invention. The code can also be transmitted through some transmission media, such as wire or cable, optical fiber, or any transmission type. When the code is received, loaded, and executed by a machine, such as a computer, the machine becomes a device for participating in the present invention. When implemented in a general-purpose processing unit, the code in combination with the processing unit provides a unique device that operates similar to an application-specific logic circuit.

Claim 1:
An X-ray imaging method (<NUM>), comprising:
(a) performing (<NUM>) a first object imaging process to obtain a first object intensity signal by detecting a plurality of X-rays of a X-ray source passing through a first object in an field of view, FOV; characterized by:
(b) performing (<NUM>) a baseline imaging process to obtain a baseline intensity signal by detecting the X-rays when the first object is not in the FOV; and;
(c) obtaining (<NUM>) a first thickness of the first object based on the first object intensity signal, the baseline intensity signal, and a first attenuation coefficient of the first object.