WO2021253599A1 - 一种头部移动ct探测器的自校准方法及扫描系统 - Google Patents

一种头部移动ct探测器的自校准方法及扫描系统 Download PDF

Info

Publication number
WO2021253599A1
WO2021253599A1 PCT/CN2020/108903 CN2020108903W WO2021253599A1 WO 2021253599 A1 WO2021253599 A1 WO 2021253599A1 CN 2020108903 W CN2020108903 W CN 2020108903W WO 2021253599 A1 WO2021253599 A1 WO 2021253599A1
Authority
WO
WIPO (PCT)
Prior art keywords
air
detector
patient
data
scan data
Prior art date
Application number
PCT/CN2020/108903
Other languages
English (en)
French (fr)
Inventor
徐丹
Original Assignee
南京安科医疗科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 南京安科医疗科技有限公司 filed Critical 南京安科医疗科技有限公司
Publication of WO2021253599A1 publication Critical patent/WO2021253599A1/zh

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • A61B6/032—Transmission computed tomography [CT]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/42—Arrangements for detecting radiation specially adapted for radiation diagnosis
    • A61B6/4208—Arrangements for detecting radiation specially adapted for radiation diagnosis characterised by using a particular type of detector
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/58—Testing, adjusting or calibrating thereof
    • A61B6/582—Calibration
    • A61B6/585—Calibration of detector units

Definitions

  • the invention relates to the technical field of medical imaging, in particular to a self-calibration method and scanning system of a CT detector with a head moving.
  • CT Computer Tomography
  • the mobile head CT system is a special CT scanning system developed on the basis of traditional CT.
  • the traditional CT machine is fixed in a dedicated shielded room of the radiology department of a hospital, and the patient must be scanned in the shielded room when scanning.
  • a large part of patients who need head scanning cannot move autonomously. Moving the patient from the ordinary bed to the CT scan bed itself will bring a great risk of deterioration of the condition.
  • emergency patients such as stroke or cerebral infarction
  • the time for diagnosis is very precious.
  • the mobile head CT system can solve these problems well.
  • the mobile head CT system can scan without moving the patient, or it can be equipped on an emergency vehicle to scan at the first time. Therefore, although the mobile head CT system can only perform part of the diagnostic functions of traditional CT, its portability and mobility are unmatched by traditional CT.
  • the use time is uncertain, and once needed, the time is also very urgent.
  • the head-moving CT can be turned on. Scan, there can be no calibration process. This has caused a lot of changes for the actual use of the hospital, and greatly improved the stability of the head-moving CT detector, which has brought cost and price increases.
  • the present invention discloses a self-calibration method and scanning system for a head-moving CT detector. Air scan is added in the process, and the patient scan data or patient image data is self-calibrated through the air scan to realize the scan immediately after starting up, reduce the preparation time of the head moving CT detector, reduce the demand for CT stability, and reduce the system cost.
  • a self-calibration method for head-moving CT detectors includes the following steps:
  • the head moves the CT detector from near the patient's shoulder to away from the patient to perform CT scan.
  • the scanned part includes the patient's head and part of the air above the head to obtain the patient's scan data And air scan data;
  • Data reconstruction Perform data reconstruction on the preprocessed patient scan data to obtain patient image data; perform data reconstruction on the preprocessed air scan data to obtain air image data;
  • Self-calibration using air scan data includes self-calibration of patient scan data in the projection domain or self-calibration of patient image data in the image domain;
  • the process of self-calibration of the patient scan data in the projection domain is: obtain the air scan data in S2, calculate the detector gain from the air scan data, execute S3, perform detector background calibration and negative logarithmic processing on the patient scan data Detector gain calibration is added in between, and the patient scan data after the detector background calibration is performed according to the calculated detector gain; execute S4 to obtain the patient image data, which is the self-calibrated patient image data Final image
  • the process of self-calibrating patient image data in the image domain is: acquiring air scan data in S2, executing S3 and S4 on the air scan data, acquiring air image data, and executing S6 on the air image data;
  • Image domain data processing Perform image subtraction processing on patient image data and air image data in the image domain to obtain the final image after self-calibration.
  • the detector gain based on the air scan data in S5 it further includes determining whether there is object data from the air scan data and removing the influence of the object data on the detector gain.
  • the specific process is as follows:
  • the air scan data exceeds the scan data threshold, it is determined that there is no obscured object during the air scan, and the air scan data does not include the object data, that is, the air scan data is directly used to calculate the detector gain;
  • the air scan data does not exceed the scan data threshold, it is judged that there is an obstructing object during the air scan.
  • the air scan data includes object data.
  • the weight of the part of the air scan data that does not exceed the scan data threshold is set, and then the weight is set according to the part of the air. Scan data and remaining air scan data to calculate the detector gain.
  • the method before performing the image subtraction processing on the patient image data and the air image data in S6, the method further includes judging whether there is an object image and removing the object image on the air image data.
  • the specific process is:
  • the air image data does not exceed the image data threshold, it is determined that there is no obscured object during the air scan, and the air image data does not include the object image, that is, the air image data and the patient image data are directly subjected to image subtraction processing;
  • the air image data includes object images.
  • the air image data is divided into air image and non-air image.
  • the image is divided into the detector gain image, the detector gain image and the air part image are combined into the air calibration image, the air calibration image is used as the new air image data, and the image subtraction is performed with the patient image data.
  • the calculation formula of the calculation model for calculating the gain of the detector from the air scan data in S5 is:
  • G(i,k) g i (i) ⁇ g ⁇ (i, ⁇ k )
  • i is the channel of the detector
  • g i (i) is the detector gain that varies from channel to channel
  • k is the sampling in the rotation direction ⁇ k
  • g ⁇ (i, ⁇ k ) is the same channel in the rotation direction The gain of the detector on the change.
  • A(i,k) is the air scan data
  • N k is the total number of samples.
  • the detector gain calibration is added between the detector background calibration and the negative logarithmic processing of the patient scan data
  • S(i,k) is the patient scan data after the background calibration of the detector
  • G(i,k) is the calculation model of the detector gain.
  • the head moving CT detector in step S2 performs CT scanning from a position close to the shoulder of the patient to a direction away from the patient.
  • the scanning part includes the patient's head and part of the air above the head.
  • X is lowered.
  • the scanning current mA of the ray reduces the radiation dose received by the patient.
  • a self-calibration scanning system for head-moving CT detectors is used to implement any one of the above-mentioned self-calibration methods for head-moving CT detectors, which includes a head-moving CT detector rack, a rack rail, The headrest, the hospital bed and the patient; the head-moving CT detector frame is installed on the frame guide rail of the movable base, and the head moves the CT detector frame to scan the patient.
  • the patient lies on the bed and the patient's head The part is placed on the head rest.
  • the head moving the CT detector frame to scan the patient includes performing axial scan or spiral scan on the patient's head.
  • the present invention adds air scanning during patient scanning, and self-calibrates patient scan data through air scanning data, or self-calibrates patient image data through air image data, so as to realize scanning at startup and reduce head movement Preparation time of CT detector;
  • the head-moving CT detector does not require a heating belt and a controller to maintain a constant temperature, which reduces the need for CT stability and reduces system costs;
  • Figure 1 is a schematic diagram of the system of the present invention
  • 1 is the head moving CT detector rack
  • 2 is the rack rail
  • 3 is the head rest
  • 4 is the hospital bed
  • 5 is the patient
  • Figure 2 is a schematic diagram of the scanning range of the present invention.
  • Fig. 3a is a flow from power-on to scan of a traditional CT
  • Fig. 3b is a flow from power-on to scan of CT in the present invention
  • Figure 4 is a flow chart of the method in the first embodiment of the present invention.
  • Fig. 5 is a flowchart of processing air scan data in Fig. 4;
  • Figure 6 is a flow chart of the method in the second embodiment of the present invention.
  • Fig. 7 is a flowchart of processing air image data in Fig. 6.
  • the head-moving CT detector frame 1 is mounted on a movable base.
  • the head-moving CT detector The machine frame 1 moves back and forth on the frame guide rail 2 of the base, or it can move back and forth together with the entire base; at the same time, the head moves the CT detector frame 1 is also rotating, thus completing the axial scanning of the head, or spiral scanning.
  • the starting position of the scan is close to the shoulder of the patient 5, and the direction of the head movement of the CT detector frame 1 is away from the patient 5.
  • the head of the patient 5 is generally placed on a headrest 3.
  • the CT detector frame 1 can move a little longer by moving the head, and continue scanning for a certain distance after the scanning field of the detector leaves the patient's head. In this scan, the patient is not in the X-ray exposure area, so the additional radiation dose received by the patient increases very little.
  • the scanning current mA can also be reduced to further reduce the radiation dose received by the patient.
  • the head moving CT detector frame 1 is located between the normal CT scan range and the air, there is still a top area of the head. The exposure data of this part is not helpful to the image, so there is no need for exposure (because the doctor does not need to see it) , You can turn down or even turn off the X-ray to reduce the radiation dose to the patient.
  • the air scan area included in the air scan data is the area required for the head to move the CT detector frame 1 to complete a rotation; if the head moves the CT detector frame 1 and the detector are stable enough, it can be reduced appropriately ;
  • the size of this part of the area is related to the coverage of the detector and the moving speed of the gantry, usually within 5cm.
  • the air scan data can effectively calibrate the detector within a certain time range. In this time range, the air scan does not need to be included when scanning the patient.
  • the time range here is the time required to complete a patient scan, usually within a few minutes Inside.
  • this section of exposure data can be used to calibrate the gain of the detector. Because the exposure data of this section of air is very close to the patient’s scan in time, even when the temperature changes drastically when the detector is just powered on, the change in the detector gain can be ignored. It can be considered that this section of air exposure data is calculated
  • the obtained detector gain is the detector gain during actual exposure.
  • other objects may also appear in the scanning field of view, such as infusion tubes. At this time, it is necessary to add judgments in both self-calibration methods. If objects are found, they can be removed from the air scan data or air image data. Data blocked by objects, or reduce the proportion of these data in the calculation, to avoid these objects from affecting the final image.
  • the guide rail of the detector will be equipped with a heating belt and a temperature sensor, that is, the detector is preheated to control the temperature stability of the detector module.
  • the detector is used in the present invention. It does not require heating belts and controllers to maintain a constant temperature, which can reduce the cost of the detector.
  • Detector background calibration refers to: In X-ray detectors, due to the influence of dark current in electronic devices, there will be a fixed background offset in the signal; this offset can be obtained by dark field calibration of the detector. And after acquiring the detector data, it is subtracted. This process is the background calibration of the detector. In the following, unless otherwise specified, the measurement data refers to the data after background calibration.
  • Ray-hardening calibration means X-rays used for shooting are composed of photons of different energies. Since low-energy photons are attenuated much more when they penetrate an object than high-energy photons, the energy spectrum of the rays passing through the object occurs. Change, this is the ray hardening effect, which affects the quality of image reconstruction and brings about “cup-shaped” artifacts.
  • the essence of ray hardening calibration is to correct multi-energy spectrum rays into single-energy spectrum rays as much as possible.
  • the commonly used method is the calibration method of polynomial fitting.
  • the specific process of the self-calibration method of a head-moving CT detector adopted in the first embodiment is as follows:
  • Step 1 Power on the mobile CT
  • Step 2 CT scan: the detector frame moves forward and backward through the base, or on the guide rail of the base, to scan the patient.
  • the starting position of the scan is close to the shoulder of the patient, and the moving direction of the frame is away from the patient.
  • the direction of CT scan is helical scan or multi-step axial scan.
  • the scan data is acquired during the scanning process, and the scan data includes patient scan data and air scan data.
  • Step 3 Data preprocessing: Perform detector background calibration on the patient scan data in the projection domain, calculate the detector gain for the air scan data, and perform the detector on the patient scan data after the detector background calibration based on the detector gain Gain calibration, and then perform negative logarithmization and ray hardening calibration on the data, and output preprocessed patient scan data, where the preprocessed patient scan data is calibrated by the detector gain.
  • i is the channel of the detector
  • B(i) is the background of the detector
  • B(i) is only related to the channel, not to the sampling point.
  • the detector gain consists of two parts, one is the change between channels, and the other is the change in the rotation direction of the same channel.
  • the calculation model of the detector gain is shown in formula (2):
  • i is the channel of the detector
  • g i (i) is the detector gain that varies from channel to channel
  • k is the sampling in the rotation direction ⁇ k
  • g ⁇ (i, ⁇ k ) is the same channel in the rotation direction Detector gain that varies on top;
  • T i is a parameter related to the calibration of detector channels, reflecting the detector gain in the rotational direction of the variation width.
  • the air scan data obtained by rotating the detector frame for one revolution is A(i,k), because the average value of the detector gain g ⁇ ( ⁇ k ) in the rotating direction of the same channel changing in the rotating direction is 1, so A(i,k) is averaged in the rotation direction, and the detector gain g i (i) that varies from channel to channel can be obtained:
  • A(i,k) is the air scan data
  • G(i,k) is the calculation model of the detector gain
  • Nk is the total number of samples.
  • the detector gain g ⁇ ( ⁇ k ) that changes in the rotation direction of the same channel can be calculated by the following formula:
  • S(i,k) is the patient scan data after the detector background is calibrated.
  • Step 4 Perform data reconstruction on the pre-processed patient scan data after the gain calibration of the detector, transform the scan data into image data, and obtain the patient image data, which is the final image.
  • the data reconstruction algorithm includes patient data rearrangement, filtering and back projection.
  • iterative reconstruction algorithms and artificial intelligence reconstruction algorithms can also be used to transform projection data into image data.
  • the specific size of the scan data threshold can be set to be slightly less than 1.
  • the normalized air scan data is judged by the scan data threshold, and then it is effectively judged that the detector channel i is in the rotation direction ⁇ k Whether it is occluded by an object, adjust the weight w of the air scan data in this part according to the occlusion situation.
  • the weight w is the ratio w given in Figure 5, even if this part of the data is removed, the formula (3) and formula (4) ) Calculate the detector gain to achieve the purpose of reducing or even eliminating the influence of the occluded data on the final calibration result.
  • the specific process of the self-calibration method of a head-moving CT detector adopted in the second embodiment is as follows:
  • Step 1 Power on the mobile CT
  • Step 2 CT scan: the detector frame moves forward and backward through the base, or on the guide rail of the base, to scan the patient.
  • the starting position of the scan is near the shoulder of the patient, and the moving direction of the frame is away from the patient.
  • the direction of CT scan is helical scan or multi-step axial scan.
  • the scan data is acquired during the scanning process, and the scan data includes patient scan data and air scan data.
  • Step 3 Data preprocessing: perform detector background calibration, negative logarithmization and ray hardening calibration on the patient scan data and air scan data in the projection domain respectively, and output the preprocessed patient scan data and air scan data.
  • Step 4 Perform data reconstruction on patient scan data and air scan data, obtain patient image data and air image data, perform image subtraction on the patient image data and air image data in the image domain, and obtain the final image.
  • Image subtraction is the subtraction of gray values or color components of pixels at the same position in two images.
  • the data reconstruction algorithm includes patient data rearrangement, filtering and back projection.
  • iterative reconstruction algorithms and artificial intelligence reconstruction algorithms can also be used to transform projection data into image data.
  • the air image data needs to be judged whether there is an object, the air calibration image is obtained, and the air calibration image is subtracted from the patient image data.
  • the air calibration image is obtained, and the air calibration image is subtracted from the patient image data.
  • other occlusion objects may appear on the top of the patient's head. Because the weight of the occlusion data cannot be reduced during the image reconstruction process, the air image data will be affected by the occlusion objects.
  • the object in the CT scanning process has a spatial frequency relative to the artifact caused by the detector gain. It is different, that is, the speed of image signal change is different.
  • Objects in CT scanning are generally low-frequency, while small ring artifacts caused by detector gain are generally high-frequency, which can be filtered after polar coordinate transformation to obtain detector gain The high-frequency image brought by the influence can realize the separation of the detector gain image and the object image.
  • judge the image data threshold of the air image data set an image data threshold, the specific size of the image data threshold can be set to zero, divide the air image data into the air part image and the non-air part image, and then the non-air part image
  • the high-frequency image and the low-frequency image are obtained by filtering after the polar coordinate transformation in space.
  • the high-frequency image corresponds to the artifact image caused by the detector gain, and the low-frequency image is the image of the obstruction during the scanning process.
  • the air part image is combined into an air calibration image, and the air calibration image is used for image subtraction with the patient image data.
  • the present invention adds air scanning during patient scanning, and self-calibrates patient scan data through air scanning data, or self-calibrates patient image data through air image data, realizes scanning at startup and reduces CT detection of head movement Preparation time of the device.
  • self-calibration Before performing self-calibration through air scan data or air image, add the judgment of whether there is an object in the air scan process, and perform weight processing on the air scan data when there is an object, or remove the object image from the air image data, reduce or even remove the object.
  • the impact on the self-calibration process greatly improves the accuracy of the self-calibration.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Medical Informatics (AREA)
  • Radiology & Medical Imaging (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Optics & Photonics (AREA)
  • Pathology (AREA)
  • Physics & Mathematics (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Pulmonology (AREA)
  • Theoretical Computer Science (AREA)
  • Apparatus For Radiation Diagnosis (AREA)

Abstract

一种头部移动CT探测器的自校准方法及扫描系统，应用于医学成像技术领域，自校准方法中包括：扫描前对头部移动CT探测器上电（S1），扫描中获取病人扫描数据和空气扫描数据（S2），扫描后在投影域对病人数据进行数据预处理和数据重建（S3）（S4），利用空气扫描数据进行自校准（S5），包括在投影域对病人扫描数据进行自校准或在图像域对病人图像数据进行自校准；获取自校准后的最终图像。本方法通过在病人扫描的过程中增加空气扫描，并通过空气扫描数据对病人扫描数据进行自校准，或通过空气图像数据对病人图像数据进行自校准，实现开机即扫，减少头部移动CT探测器的准备时间，降低对CT稳定性的需求，并降低系统成本。

Description

一种头部移动CT探测器的自校准方法及扫描系统 技术领域
本发明涉及医学成像技术领域，尤其涉及一种头部移动CT探测器的自校准方法及扫描系统。
背景技术
CT(Computed Tomography)是一种重要的临床影像学诊断手段。移动头部CT系统是在传统CT的基础上发展出来的一种专用CT扫描系统。传统的CT机是固定在一个医院放射科的专用屏蔽室内，病人进行扫描时必须在屏蔽室内进行。但是需要进行头部扫描的病人中，很大一部分是无法自主移动的。将病人从普通病床移动到CT扫描病床上这个动作本身就会带来很大的病情恶化风险。另外对于脑中风或者脑梗塞这种急诊病人，进行确诊的时间非常宝贵。移动头部CT系统能很好地解决上述的这些难题。移动头部CT系统能在不移动病人的前提下进行扫描，也可以装备在急救车上在第一时间进行扫描。所以虽然移动头部CT系统只能完成传统CT的一部分诊断功能，但是其轻便性和可移动性是传统CT无法比拟的。
一般传统的CT系统中，系统刚上电是不能进行扫描的，因为探测器稳定需要一段时间。操作上需要进行一些基本的预热和校准才能保证图像中没有伪影。这对于传统CT来说并不是一个太大的问题，因为其使用方式是在固定的地点集中扫描一批病人，因此每天早上在固定的时间做预热和校准即可。移动头部CT中，因为其扫描方式的需求要比传统CT少很多，因此对校准的需求也少很多；但是移动头部CT由于其使用方式的特殊性，无法保证探测器时刻保持上电状态并随时可以工作。在移动头部CT的应用场景中，常见的是直到要使用该系统时，才刚刚给系统上电；然而探测器校准在目前的移动头部CT中，还是一项在上电以后、扫描病人之前必须进行的操作，因此这给移动头部CT的应用造成了很多困扰和不便。
在头部移动CT的特殊应用场景中(比如急救或者是术中)，使用时间是不确定的，而且一旦需要使用时，时间也是非常紧迫的，基本上要求头部移动CT能做到开机即扫，不能有校准的流程。这为医院的实际使用造成了很多不变，而大幅提高头部移动CT的探测器的稳定性，这又带来了成本和价格的上升。
发明内容
技术目的：针对现有技术中头部移动CT在使用前需要校准，不能达到开机即扫的缺陷，本发明公开了一种头部移动CT探测器的自校准方法及扫描系统，通过在病人扫描的过程中增加空气扫描，并通过空气扫描对病人扫描数据或病人图像数据进行自校准，实现开机即扫，减少头部移动CT探测器的准备时间，降低对CT稳定性的需求，并降低系统成本。
技术方案：为实现上述技术目的，本发明采用以下技术方案。
一种头部移动CT探测器的自校准方法，包括以下步骤：
S1、扫描前对头部移动CT探测器上电；
S2、扫描中获取病人扫描数据和空气扫描数据：头部移动CT探测器从靠近病人肩膀处向远离病人方向进行CT扫描，扫描部分包括病人头部及头部上方的部分空气，获取病人扫描数据和空气扫描数据；
S3、扫描后在投影域进行数据预处理：将病人扫描数据依次进行探测器本底校准、负对数化处理和射线硬化校准，输出预处理后的病人扫描数据；将空气扫描数据依次进行探测器本底校准、负对数化处理和射线硬化校准，输出预处理后的空气扫描数据；
S4、数据重建：对预处理后的病人扫描数据进行数据重建，获取病人图像数据；对预处理后的空气扫描数据进行数据重建，获取空气图像数据；
S5、利用空气扫描数据进行自校准：利用空气扫描数据进行自校准包括在投影域对病人扫描数据进行自校准或在图像域对病人图像数据进行自校准；
在投影域对病人扫描数据进行自校准的过程为：获取S2中的空气扫描数据，通过空气扫描数据计算探测器增益，执行S3，在病人扫描数据进行探测器本底校准和负对数化处理之间增加探测器增益校准，根据计算得到的探测器增益对探测器本底校准后的病人扫描数据进行探测器增益校准；执行S4，获取病人图像数据，该病人图像数据即为自校准后的最终图像；
在图像域对病人图像数据进行自校准的过程为：获取S2中的空气扫描数据，将空气扫描数据执行S3和S4，获取空气图像数据，并将空气图像数据执行S6；
S6、图像域数据处理：在图像域对病人图像数据和空气图像数据进行图像减法处理，获取自校准后的最终图像。
优选地，所述S5中通过空气扫描数据计算探测器增益之前，还包括对空气扫描数据判断是否存在物体数据以及去除物体数据对探测器增益的影响，其具体过程为：
对空气扫描数据进行归一化处理；
设置扫描数据阈值，对归一化处理后的空气扫描数据进行扫描数据阈值判断：
若空气扫描数据超出扫描数据阈值，则判断空气扫描时没有遮挡物体，空气扫描数据不包括物体数据，即直接将空气扫描数据计算探测器增益；
若空气扫描数据未超出扫描数据阈值，则判断空气扫描时存在遮挡物体，空气扫描数据包括物体数据，对未超出扫描数据阈值的该部分空气扫描数据设置权重，再根据设置权重后的该部分空气扫描数据及剩余空气扫描数据计算探测器增益。
优选地，所述S6中对病人图像数据和空气图像数据进行图像减法处理之前，还包括对空气图像数据判断是否存在物体图像以及去除物体图像，其具体过程为：
设置图像数据阈值，对空气图像数据进行图像数据阈值判断：
若空气图像数据未超出图像数据阈值，则判断空气扫描时没有遮挡物体，空气图像数据不包括物体图像，即直接将空气图像数据与病人图像数据进行图像减法处理；
若空气图像数据超出图像数据阈值，则判断空气扫描时存在遮挡物体，空气图像数据包括物体图像，根据图像数据阈值判断结果将空气图像数据分割为空气部分图像和非空气部分图像，在非空气部分图像中分割成探测器增益图像，将探测器增益图像和空气部分图像合并成空气校准图像，将空气校准图像作为新的空气图像数据，并与病人图像数据进行图像减法运算。
优选地，所述
S5中通过空气扫描数据计算探测器增益的计算模型计算公式为：
G(i，k)＝g i(i)·g θ(i，θ k)
其中，i为探测器的通道，g i(i)为通道与通道之间变化的探测器增益，k为旋转方向θ k上的采样，g θ(i，θ k)为同一通道在旋转方向上变化的探测器增益。
优选地，
所述通道与通道之间变化的探测器增益g i(i)计算公式为：
Figure PCTCN2020108903-appb-000001
其中，A(i，k)为空气扫描数据，N k为总的采样数。
优选地，所述步骤S5中在病人扫描数据进行探测器本底校准和负对数化处理之间增加探测器增益校准，
探测器增益校准后的病人扫描数据计算公式为：
S′(i，k)＝S(i，k)/G(i，k)
其中，S(i，k)为探测器本底校准后的病人扫描数据，G(i，k)为探测器增益的计算模型。
优选地，所述步骤S2的头部移动CT探测器从靠近病人肩膀处向远离病人方向进行CT扫描，扫描部分包括病人头部及头部上方的部分空气中，在进行空气扫描时，降低X射线的扫描电流mA来减少病人接收的辐射剂量。
一种头部移动CT探测器的自校准扫描系统，用于实现以上任一所述的一种头部移动CT探测器的自校准方法，包括头部移动CT探测器机架、机架导轨、头托、病床和病人；所述头部移动CT探测器机架安装于可移动底座的机架导轨上，头部移动CT探测器机架对病人进行扫描，病人躺在病床上，病人的头部放置于头托上。
优选地，所述头部移动CT探测器机架对病人进行扫描包括对病人的头部进行轴扫或螺旋扫描。
有益效果：
1、本发明通过在病人扫描的过程中增加空气扫描，并通过空气扫描数据对病人扫描数据进行自校准，或通过空气图像数据对病人图像数据进行自校准，实现开机即扫，减少头部移动CT探测器的准备时间；
2、本发明中通过在扫描过程后进行自校准，头部移动CT探测器不需要加热带和控制器来保持恒温，降低对CT稳定性的需求，并降低系统成本；
3、在通过空气扫描数据或空气图像进行自校准之前，加入空气扫描过程中是否存在物体的判断，并对存在物体时对空气扫描数据进行权重处理，或对空气图像数据去除物体图像，降低甚至去除物体对自校准过程的影响，大大提高自校准的精度。
附图说明
图1为本发明的系统示意图；
其中1为头部移动CT探测器机架，2为机架导轨，3为头托，4为病床，5为病人；
图2为本发明的扫描范围示意图；
图3a为传统CT上电到扫描的流程，图3b为本发明中CT上电到扫描的流程；
图4为本发明实施例一中的方法流程图；
图5为图4中空气扫描数据的处理流程图；
图6为本发明实施例二中的方法流程图；
图7为图6中空气图像数据的处理流程图。
具体实施方式
以下结合附图和实施例对本方案的一种头部移动CT探测器的自校准方法及扫描系统做进一步的说明和解释。
如附图1所示，一种头部移动CT探测器的自校准扫描系统中，头部移动CT探测器机架1装配在可移动的底座上，在扫描的过程中，头部移动CT探测器机架1在底座的机架导轨2上前后移动，也可以是和整个底座一起前后移动；同时头部移动CT探测器机架1也在旋转，从而完成头部的轴扫，或者是螺旋扫描。一般情况下，为了病床4上病人5的安全，扫描的起始位置在靠近病人5的肩膀处，头部移动CT探测器机架1的运动方向是远离病人5的方向。病人5的头部一般放置在一个头托3上。
如附图2所示，在扫描的过程中，病人的头顶处一般没有其他物体。因此，扫描的过程中，头部移动CT探测器机架1可以多移动一些距离，在探测器的扫描视野离开病人的头部后，还继续扫描一段距离。这段扫描中病人并不在X射线的曝光区域中，因此病人收到的额外的辐射剂量的增加非常少。在空气扫描时，也可以降低扫描的电流mA来进一步减少病人收到的辐射剂量。在头部移动CT探测器机架1位于普通CT扫描范围和空气之间时，还存在一个头顶区域，这部分的曝光数据对图像并没有帮助，因此也不需要曝光(因为医生不需要看)，可以调低甚至关闭X射线来降低对病人的辐射剂量。
通常情况下，空气扫描数据包括的空气扫描区域是需要头部移动CT探测器机架1完成一圈转动需要的区域；如果头部移动CT探测器机架1和探测器足够稳定的话可以适当降低；这部分区域的大小与探测器的覆盖范围和机架移动速度相关，通常在5cm之内。空气扫描数据可以在一定时间范围内对探测器进行有效的校正，在该时间范围内，扫描病人时可以不用包括空气扫描，这里的时间范围是完成一次病人扫描所需要的时间，通常在几分钟内。
这段曝光区域中，扫描视野里面一般没有任何物体的存在，因此可以使用这一段的曝光数据来对探测器的增益进行校准。由于这段空气的曝光数据在时间上距离病人的扫描非常近，因此即使在探测器刚刚上电时温度剧烈变化的过程中，探测器增益的变化也可以忽略，可以认为这段空气曝光数据计算得出的探测器增益就是实际曝光时的探测器增益。但是，扫描视野中也可能出现其他物体，如输液管等，此时需要对两种自校准方法中都可以加入判断，如果发现物体的存在，可以在空气扫描数据或者空气图像数据中，去除这些被物体挡住的数据，或者降低这些数据在计算中的比例，以避免这些物体对最终图像造成影响。
如图3a和图3b所示，传统的CT探测器设计中，探测器的导轨上会配有加热带和温度传感器，即通过探测器预热，以控制探测器模块温度的稳定性。在本发明中，由于探测器的增益是在扫描过程中获取的，因此探测器增益的准确性并不受探测器模块实际温度的影响，即实现上电即扫；因此，探测器在本发明中并不需要加热带和控制器来保持恒温，可以降低探测器的成本。
以下通过实施例一和实施例二分别阐述两种自校准方法。下文中涉及到探测器本底校准和射线硬化校准，以下给出这两种校准方式的介绍：
探测器本底校准是指：X射线探测器中，由于电子器件中暗电流的影响，信号中会存在一个固定的本底偏移；这个偏移量可以通过对探测器进行暗场校准获取，并在获取探测器数据后将其减去，此过程即为探测器本底校准。在下文中，如无特殊说明，测量数据都是指本底校准后的数据。
射线硬化校准是指：用于拍摄的X射线是由不同能量的光子组成的，由于能量低的光子比能量高的光子在穿透物体时衰减得多，使得透过物体的射线能谱发生了变化，这就是射线的硬化效应，影响图像的重建质量，带来“杯状”伪迹，射线硬化校准的实质就是尽可能地将多能谱射线校正为单能谱射线，目前最简单也是最常用的方法是多项式拟合的校准方法。
实施例一
如附图4所示，实施例一中采用的一种头部移动CT探测器的自校准方法的具体过程如下：
步骤1、移动CT上电；
步骤2、CT扫描：探测器机架通过底座前后移动，也可以在底座的导轨上前后移动， 对病人进行扫描，扫描的起始位置在靠近病人的肩膀处，机架的运动方向是远离病人的方向，CT扫描方式为螺旋扫描或多步轴向扫描。在扫描过程中获取扫描数据，扫描数据包括病人扫描数据和空气扫描数据。
步骤3、数据预处理：在投影域中对病人扫描数据进行探测器本底校准，并对空气扫描数据计算探测器增益，根据探测器增益对探测器本底校准后的病人扫描数据进行探测器增益校准，之后对数据进行负对数化和射线硬化校准，输出预处理后的病人扫描数据，这里的预处理后的病人扫描数据是经过探测器增益校准的。
获取X射线探测器的病人扫描数据M(i，k)以后，通过公式(1)进行探测器本底校准，得到探测器本底校准后的病人扫描数据S(i，k)：
S(i，k)＝M(i，k)-B(i)    (1)
其中，i为探测器的通道，B(i)为探测器的本底，B(i)只与通道相关，而与采样点无关。
探测器增益包括两个部分，一个是通道与通道之间的变化，另一个是同一通道在旋转方向上的变化，探测器增益的计算模型如公式(2)所示：
G(i，k)＝g i(i)·g θ(i，θ k)    (2)
其中，i为探测器的通道，g i(i)为通道与通道之间变化的探测器增益，k为旋转方向θ k上的采样，g θ(i，θ k)为同一通道在旋转方向上变化的探测器增益；
这里将同一通道在旋转方向上变化的探测器增益计算公式为：
Figure PCTCN2020108903-appb-000002
其中J为与角度相关的固定系数矩阵，T i为与探测器通道相关的校准参数，反映了探 测器增益在旋转方向上的变化幅度。
探测器机架旋转一圈得到的空气扫描数据为A(i，k)，因为同一通道在旋转方向上变化的探测器增益g θ(θ k)在旋转方向上的平均值为1，因此将A(i，k)在旋转方向上求平均，可以得到通道与通道之间变化的探测器增益g i(i)：
Figure PCTCN2020108903-appb-000003
其中，A(i，k)为空气扫描数据，G(i，k)为探测器增益的计算模型，N k为总的采样数。
对于每一个探测器通道i，可以通过如下公式计算同一通道在旋转方向上变化的探测器增益g θ(θ k)：
g θ(i，θ k)＝A(i，k)/g i(i)    (5)
通过拟合，即用空气扫描数据A(i，k)计算得到的通道与通道之间变化的探测器增益g i(i)和同一通道在旋转方向上变化的探测器增益g θ(θ k)来计算探测器增益的计算模型G(i，k)。
探测器增益校准后的病人扫描数据S′(i，k)计算公式为：
S′(i，k)＝S(i，k)/G(i，k)    (6)
其中，S(i，k)为探测器本底校准后的病人扫描数据。
步骤4、对经过探测器增益校准的预处理后的病人扫描数据进行数据重建，将扫描数据变换为图像数据，获取病人图像数据，该病人图像数据即最终图像。
数据重建算法中包括病人数据重排、滤波和反投影。数据重建算法中除了采用的滤 波反投影算法以外，还可以采用迭代重建算法和人工智能重建算法等，都可以将投影数据变换为图像数据。
如附图5所示，空气扫描数据在计算探测器增益前，还需要对空气扫描数据判断是否存在物体。在扫描的过程中，病人的头顶处可能出现其他遮挡物，此时就需要对空气扫描数据进行阈值判断。对于空气扫描数据，先在旋转方向上进行归一化处理，若是扫描视野中没有遮挡物，归一化处理后的数据应接近于1，并伴随有轻微的角度方向上的变化；若是扫描视野中存在遮挡物，由于X射线的衰减是指数衰减，因此归一化处理后的数据在遮挡物处会快速下降。设置一个扫描数据阈值，该扫描数据阈值的具体大小可以设置为略小于1，对归一化处理后的空气扫描数据进行扫描数据阈值判断，进而有效判断在探测器通道i在旋转方向θ k上有没有被物体遮挡，根据遮挡情况调整空气扫描数据在该部分的权重w，该权重w即附图5中给出的比例w，甚至去除该部分数据，进而通过公式(3)和公式(4)计算探测器增益，达到减小甚至消除被遮挡的数据对最终校准结果影响的目的。
实施例二
如附图6所示，实施例二中采用的一种头部移动CT探测器的自校准方法的具体过程如下：
步骤1、移动CT上电；
步骤2、CT扫描：探测器机架通过底座前后移动，也可以在底座的导轨上前后移动，对病人进行扫描，扫描的起始位置在靠近病人的肩膀处，机架的运动方向是远离病人的方向，CT扫描方式为螺旋扫描或多步轴向扫描。在扫描过程中获取扫描数据，扫描数据包括病人扫描数据和空气扫描数据。
步骤3、数据预处理：在投影域中对病人扫描数据和空气扫描数据分别依次进行探测器本底校准、负对数化和射线硬化校准，输出预处理后的病人扫描数据和空气扫描数据。
步骤4、对病人扫描数据和空气扫描数据进行数据重建，获取病人图像数据和空气图像数据，在图像域中对病人图像数据和空气图像数据进行图像减法运算，获取最终图像。图像减法运算就是两幅图像同一位置像素的灰度值或彩色分量进行相减。
数据重建算法中包括病人数据重排、滤波和反投影。数据重建算法中除了采用的滤 波反投影算法以外，还可以采用迭代重建算法和人工智能重建算法等，都可以将投影数据变换为图像数据。
如附图7所示，空气图像数据在与病人图像数据运算获取最终图像前，还需要对空气图像数据判断是否存在物体，获取空气校准图像，空气校准图像再与病人图像数据进行图像减法运算。在扫描的过程中，病人的头顶处可能出现其他遮挡物，因为不能在图像重建的过程中降低遮挡数据的权重，所以空气图像数据会受到遮挡物的影响。
由于在空气图像数据中，空气部分的CT值的均值为零，而物体的CT值大于零，同时CT扫描过程中的物体，相对于探测器增益造成的伪影来说，在空间上的频率是不同的，即图像信号变化的速度不同，CT扫描中的物体一般属于低频，而探测器增益造成的细小的环形伪影一般属于高频，可通过极坐标变换后进行滤波，获取探测器增益影响带来的高频图像，实现探测器增益图像与物体图像分离。
首先对空气图像数据进行图像数据阈值判断，设置一个图像数据阈值，该图像数据阈值的具体大小可以设置为零，将空气图像数据分割为空气部分图像和非空气部分图像，再对非空气部分图像在空间上通过极坐标变换后进行滤波，获取高频图像和低频图像，其中高频图像对应的是探测器增益带来的伪影图像，低频图像是扫描过程中遮挡物图像，将高频图像和空气部分图像合并成空气校准图像，该空气校准图像用于与病人图像数据进行图像减法运算。
本发明通过在病人扫描的过程中增加空气扫描，并通过空气扫描数据对病人扫描数据进行自校准，或通过空气图像数据对病人图像数据进行自校准，实现开机即扫，减少头部移动CT探测器的准备时间。在通过空气扫描数据或空气图像进行自校准之前，加入空气扫描过程中是否存在物体的判断，并对存在物体时对空气扫描数据进行权重处理，或对空气图像数据去除物体图像，降低甚至去除物体对自校准过程的影响，大大提高自校准的精度。
以上所述仅是本发明的优选实施方式，应当指出：对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。

Claims (9)

  1. 一种头部移动CT探测器的自校准方法，其特征在于，包括以下步骤：
    S1、扫描前对头部移动CT探测器上电；
    S2、扫描中获取病人扫描数据和空气扫描数据：头部移动CT探测器从靠近病人肩膀处向远离病人方向进行CT扫描，扫描部分包括病人头部及头部上方的部分空气，获取病人扫描数据和空气扫描数据；
    S3、扫描后在投影域进行数据预处理：将病人扫描数据依次进行探测器本底校准、负对数化处理和射线硬化校准，输出预处理后的病人扫描数据；将空气扫描数据依次进行探测器本底校准、负对数化处理和射线硬化校准，输出预处理后的空气扫描数据；
    S4、数据重建：对预处理后的病人扫描数据进行数据重建，获取病人图像数据；对预处理后的空气扫描数据进行数据重建，获取空气图像数据；
    S5、利用空气扫描数据进行自校准：利用空气扫描数据进行自校准，包括在投影域对病人扫描数据进行自校准或在图像域对病人图像数据进行自校准；
    在投影域对病人扫描数据进行自校准的过程为：获取S2中的空气扫描数据，通过空气扫描数据计算探测器增益，执行S3，在病人扫描数据进行探测器本底校准和负对数化处理之间增加探测器增益校准，根据计算得到的探测器增益对探测器本底校准后的病人扫描数据进行探测器增益校准；执行S4，获取病人图像数据，该病人图像数据即为自校准后的最终图像；
    在图像域对病人图像数据进行自校准的过程为：获取S2中的空气扫描数据，将空气扫描数据执行S3和S4，获取空气图像数据，并将空气图像数据执行S6；
    S6、图像域数据处理：在图像域对病人图像数据和空气图像数据进行图像减法处理，获取自校准后的最终图像。
  2. 根据权利要求1所述的一种头部移动CT探测器的自校准方法，其特征在于：所述S5中通过空气扫描数据计算探测器增益之前，还包括对空气扫描数据判断是否存在物体数据以及去除物体数据对探测器增益的影响，其具体过程为：
    对空气扫描数据进行归一化处理；
    设置扫描数据阈值，对归一化处理后的空气扫描数据进行扫描数据阈值判断：
    若空气扫描数据超出扫描数据阈值，则判断空气扫描时没有遮挡物体，空气扫描数据不包括物体数据，即直接将空气扫描数据计算探测器增益；
    若空气扫描数据未超出扫描数据阈值，则判断空气扫描时存在遮挡物体，空气扫描 数据包括物体数据，对未超出扫描数据阈值的该部分空气扫描数据设置权重，再根据设置权重后的该部分空气扫描数据及剩余空气扫描数据计算探测器增益。
  3. 根据权利要求1所述的一种头部移动CT探测器的自校准方法，其特征在于：所述S6中对病人图像数据和空气图像数据进行图像减法处理之前，还包括对空气图像数据判断是否存在物体图像以及去除物体图像，其具体过程为：
    设置图像数据阈值，对空气图像数据进行图像数据阈值判断：
    若空气图像数据未超出图像数据阈值，则判断空气扫描时没有遮挡物体，空气图像数据不包括物体图像，即直接将空气图像数据与病人图像数据进行图像减法处理；
    若空气图像数据超出图像数据阈值，则判断空气扫描时存在遮挡物体，空气图像数据包括物体图像，根据图像数据阈值判断结果将空气图像数据分割为空气部分图像和非空气部分图像，在非空气部分图像中分割成探测器增益图像，将探测器增益图像和空气部分图像合并成空气校准图像，将空气校准图像作为新的空气图像数据，并与病人图像数据进行图像减法运算。
  4. 根据权利要求1所述的一种头部移动CT探测器的自校准方法，其特征在于：所述S5中通过空气扫描数据计算探测器增益的计算模型计算公式为：
    G(i，k)＝g i(i)·g θ(i，θ k)
    其中，i为探测器的通道，g i(i)为通道与通道之间变化的探测器增益，k为旋转方向θ k上的采样，g θ(i，θ k)为同一通道在旋转方向上变化的探测器增益。
  5. 根据权利要求4所述的一种头部移动CT探测器的自校准方法，其特征在于：所述通道与通道之间变化的探测器增益g i(i)计算公式为：
    Figure PCTCN2020108903-appb-100001
    其中，A(i，k)为空气扫描数据，N k为总的采样数。
  6. 根据权利要求1所述的一种头部移动CT探测器的自校准方法，其特征在于，所述步骤S5中在病人扫描数据进行探测器本底校准和负对数化处理之间增加探测器增益校准，探测器增益校准后的病人扫描数据计算公式为：
    S′(i，k)＝S(i，k)/G(i，k)
    其中，S(i，k)为探测器本底校准后的病人扫描数据，G(i，k)为探测器增益的计算模型。
  7. 根据权利要求1所述的一种头部移动CT探测器的自校准方法，其特征在于，所述步骤S2的头部移动CT探测器从靠近病人肩膀处向远离病人方向进行CT扫描，扫描部分包括病人头部及头部上方的部分空气中，在进行空气扫描时，降低X射线的扫描电流mA来减少病人接收的辐射剂量。
  8. 一种头部移动CT探测器的自校准扫描系统，用于实现如权利要求1-7任一所述的一种头部移动CT探测器的自校准方法，其特征在于：包括头部移动CT探测器机架(1)、机架导轨(2)、头托(3)、病床(4)和病人(5)；所述头部移动CT探测器机架(1)安装于可移动底座的机架导轨(2)上，头部移动CT探测器机架(1)对病人(5)进行扫描，病人(5)躺在病床(4)上，病人(5)的头部放置于头托(3)上。
  9. 根据权利要求8所述的一种头部移动CT探测器的自校准扫描系统，其特征在于：所述头部移动CT探测器机架(1)对病人(5)进行扫描包括对病人(5)的头部进行轴扫或螺旋扫描。
PCT/CN2020/108903 2020-06-17 2020-08-13 一种头部移动ct探测器的自校准方法及扫描系统 WO2021253599A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010551079.2 2020-06-17
CN202010551079.2A CN111436963B (zh) 2020-06-17 2020-06-17 一种头部移动ct探测器的自校准方法及扫描系统

Publications (1)

Publication Number Publication Date
WO2021253599A1 true WO2021253599A1 (zh) 2021-12-23

Family

ID=71655378

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/108903 WO2021253599A1 (zh) 2020-06-17 2020-08-13 一种头部移动ct探测器的自校准方法及扫描系统

Country Status (2)

Country Link
CN (1) CN111436963B (zh)
WO (1) WO2021253599A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115454189A (zh) * 2022-09-26 2022-12-09 明峰医疗系统股份有限公司 基于探测器数据的管电流实时控制方法、系统及计算机可读存储介质

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111436963B (zh) * 2020-06-17 2020-09-15 南京安科医疗科技有限公司 一种头部移动ct探测器的自校准方法及扫描系统
CN112237437A (zh) * 2020-11-13 2021-01-19 南京安科医疗科技有限公司 步进变螺旋的移动ct扫描方法、系统及存储介质
CN116807502B (zh) * 2023-06-28 2024-03-15 赛诺威盛医疗科技(扬州)有限公司 用于校正计算机断层扫描设备的扫描数据的方法及装置
CN116842321B (zh) * 2023-07-11 2024-03-15 赛诺威盛医疗科技(扬州)有限公司 计算机断层扫描设备的扫描数据的校正方法及校正装置
CN117243624B (zh) * 2023-11-17 2024-02-09 苏州波影医疗技术有限公司 一种ct参考探测器的遮挡补偿方法及ct系统

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5047861A (en) * 1990-07-31 1991-09-10 Eastman Kodak Company Method and apparatus for pixel non-uniformity correction
EP0855678A2 (en) * 1997-01-27 1998-07-29 Canon Kabushiki Kaisha Method and apparatus for sensing an image
US6265720B1 (en) * 1997-01-14 2001-07-24 Canon Kabushiki Kaisha Radiographic apparatus
CN1552288A (zh) * 2003-12-18 2004-12-08 沈阳东软数字医疗系统股份有限公司 校正ct机球管焦点偏移的方法
JP2010178914A (ja) * 2009-02-05 2010-08-19 Hitachi Medical Corp 移動型ｘ線撮影装置
CN102652674A (zh) * 2011-03-04 2012-09-05 首都师范大学 一种消除ct图像中的几何伪影的方法和系统
CN102800073A (zh) * 2012-06-28 2012-11-28 西北工业大学 一种锥束ct环形伪影的自动判别与校正方法
CN103596502A (zh) * 2011-04-05 2014-02-19 皇家飞利浦有限公司 针对断层摄影成像系统的自适应校准
CN106373164A (zh) * 2016-08-30 2017-02-01 首都师范大学 一种消除显微ct图像几何伪影的方法和应用
CN109730712A (zh) * 2018-12-28 2019-05-10 深圳安科高技术股份有限公司 一种ct球管焦点跟踪方法及其系统
JP2019205796A (ja) * 2018-05-30 2019-12-05 大日本印刷株式会社 医用画像処理装置、医用画像処理方法、プログラム、及びｍｐｒ像生成方法
CN111436963A (zh) * 2020-06-17 2020-07-24 南京安科医疗科技有限公司 一种头部移动ct探测器的自校准方法及扫描系统

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3989031B2 (ja) * 1995-07-10 2007-10-10 株式会社東芝 Ｘ線ｃｔスキャナ
US7298825B2 (en) * 2005-11-22 2007-11-20 General Electric Co. Portable digital detector system
US8057098B2 (en) * 2006-01-12 2011-11-15 Kabushiki Kaisha Toshiba X-ray CT apparatus and method of creating correction data for X-ray CT
CN101672805B (zh) * 2008-09-11 2012-03-28 同方威视技术股份有限公司 更新校正表的方法、校正探测数据的方法及安全检查系统
JP5216680B2 (ja) * 2009-04-23 2013-06-19 株式会社日立メディコ Ｘ線ｃｔ装置およびこれを用いたデータ取得方法
JP2012139257A (ja) * 2010-12-28 2012-07-26 Fujifilm Corp 撮影制御装置及び放射線画像撮影システム
US20130156163A1 (en) * 2011-12-19 2013-06-20 General Electric Company Method and apparatus for reconstructing an image of an object
CN102894990B (zh) * 2012-10-12 2015-02-04 深圳安科高技术股份有限公司 一种移动式ct扫描装置
WO2019010648A1 (en) * 2017-07-12 2019-01-17 Shenzhen United Imaging Healthcare Co., Ltd. SYSTEM AND METHOD FOR CORRECTION TO AIR
CN108056785B (zh) * 2017-12-04 2021-05-07 上海奕瑞光电子科技股份有限公司 一种平板探测器增益校正模板的更新方法

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5047861A (en) * 1990-07-31 1991-09-10 Eastman Kodak Company Method and apparatus for pixel non-uniformity correction
US6265720B1 (en) * 1997-01-14 2001-07-24 Canon Kabushiki Kaisha Radiographic apparatus
EP0855678A2 (en) * 1997-01-27 1998-07-29 Canon Kabushiki Kaisha Method and apparatus for sensing an image
CN1552288A (zh) * 2003-12-18 2004-12-08 沈阳东软数字医疗系统股份有限公司 校正ct机球管焦点偏移的方法
JP2010178914A (ja) * 2009-02-05 2010-08-19 Hitachi Medical Corp 移動型ｘ線撮影装置
CN102652674A (zh) * 2011-03-04 2012-09-05 首都师范大学 一种消除ct图像中的几何伪影的方法和系统
CN103596502A (zh) * 2011-04-05 2014-02-19 皇家飞利浦有限公司 针对断层摄影成像系统的自适应校准
CN102800073A (zh) * 2012-06-28 2012-11-28 西北工业大学 一种锥束ct环形伪影的自动判别与校正方法
CN106373164A (zh) * 2016-08-30 2017-02-01 首都师范大学 一种消除显微ct图像几何伪影的方法和应用
JP2019205796A (ja) * 2018-05-30 2019-12-05 大日本印刷株式会社 医用画像処理装置、医用画像処理方法、プログラム、及びｍｐｒ像生成方法
CN109730712A (zh) * 2018-12-28 2019-05-10 深圳安科高技术股份有限公司 一种ct球管焦点跟踪方法及其系统
CN111436963A (zh) * 2020-06-17 2020-07-24 南京安科医疗科技有限公司 一种头部移动ct探测器的自校准方法及扫描系统

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115454189A (zh) * 2022-09-26 2022-12-09 明峰医疗系统股份有限公司 基于探测器数据的管电流实时控制方法、系统及计算机可读存储介质
CN115454189B (zh) * 2022-09-26 2023-07-18 明峰医疗系统股份有限公司 基于探测器数据的管电流实时控制方法、系统及计算机可读存储介质

Also Published As

Publication number Publication date
CN111436963B (zh) 2020-09-15
CN111436963A (zh) 2020-07-24

Similar Documents

Publication Publication Date Title
WO2021253599A1 (zh) 一种头部移动ct探测器的自校准方法及扫描系统
CN100374080C (zh) 用于截断补偿的方法和装置
EP2454925B1 (en) System and method for automatic tube potential selection for radiation dose reduction in ct
Zijp et al. Extraction of the respiratory signal from sequential thorax cone-beam X-ray images
JP6747885B2 (ja) 放射線診断装置及び放射線診断装置作動方法
US6848827B2 (en) Method and apparatus for calibrating detector spectral response
US7983457B2 (en) Method and system for automatically determining regions in a scanned object
US11497459B2 (en) Methods and system for optimizing an imaging scan based on a prior scan
US9687201B2 (en) X-ray CT system
KR101094180B1 (ko) 파노라마 영상 획득 방법 및 장치
US20110116594A1 (en) X-ray ct apparatus
JP2006142016A (ja) Ｃｔデータ補正の方法及びシステム
CN104077758B (zh) Ct图像重建方法
JP2008104761A (ja) Ｘ線断層撮影装置およびアーチファクトの低減方法
WO2009096361A1 (ja) Ｘ線ct装置
US20030058994A1 (en) Computed tomography method and apparatus for registering data with reduced radiation stress to the patient
Inoue Radiation dose modulation of computed tomography component in positron emission tomography/computed tomography
US9265471B2 (en) Determination of a multi-energy image
US20070098140A1 (en) Radiographic imaging method and apparatus
JP2023501675A (ja) 放射線医学的撮像方法
US20170004636A1 (en) Methods and systems for computed tomography motion compensation
JP2003299643A (ja) 断層撮影装置
WO2015020981A2 (en) Systems and methods for determining radiation dose in computed tomography scans
JP6510180B2 (ja) Ｘ線コンピュータ断層撮影装置及びスキャン計画設定支援装置
JP2002153454A (ja) Ｘ線ｃｔ装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20941155

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20941155

Country of ref document: EP

Kind code of ref document: A1