CN106308839A - CT (computed tomography) compatible breathing training and autonomous adjusting device and using method thereof - Google Patents

Abstract

The invention discloses a CT-compatible breathing training and self-adjusting device and its use method, belonging to the field of medical equipment, including a breathing detection device (1), a head-mounted display (2), and a processing terminal (3). The breathing detection device (1) and the head-mounted display (2) are respectively electrically connected to the processing terminal (3). The present invention provides a simple, safe and practical CT-compatible breathing training to solve the problems of the subject's respiratory rhythm disorder, poor breath-holding, different breath-holding depths each time, and unnecessary radiation damage during the examination process. and self-regulating devices. The device realizes artificial intelligence display, and the subject wears the display device under comfortable conditions to perform breathing training and automatically adjusts to regular breathing before imaging, instead of CT equipment adapting to the real-time breathing of the subject.

Description

CT-compatible breathing training and self-adjusting device and method of use thereof

technical field

The invention relates to a medical device and its usage, in particular to a CT-compatible breathing training and self-adjusting device and its usage method.

Background technique

During enhanced CT scanning, the scanning range of each phase is positioned according to the positioning image and the patient needs to hold his breath for many times. Special parts need to be imaged with respiratory gating, especially in the heart and blood vessels, such as congenital heart disease angiography, coronary angiography, etc. Thoracic and abdominal enhanced scans and thoracoabdominal vascular imaging, such as enhanced chest CT scan, whole abdominal CT enhanced scan, pulmonary artery imaging, dissecting aneurysm imaging, and liver angiography, have become routine clinical examinations. This type of examination technique requires breathing training for the examinee before the examination to ensure the image quality. It is difficult for the examinee to ensure the consistency of breathing during the entire examination process. During the examination process, respiratory rhythm disorder, poor apnea, and different apnea depths often occur during the examination process, resulting in blurred images, misaligned layers, and insufficient or excessive scanning range. It affects the image quality, and also causes unnecessary radiation damage to the patient. According to literature reports, the CT flash scanning technology launched by Siemens can solve the problem of poor breath retention of the subjects, but the flash scanning technology only has single-phase systolic or diastolic imaging instead of dual systolic and diastolic imaging of respiratory gating technology. Long-term imaging, the image quality is poor.

In view of this, how to effectively control the breathing of the subject and ensure the comfort and safety of the subject and high imaging quality has become an urgent problem to be solved in this field.

Contents of the invention

The present invention provides a simple, safe and practical CT-compatible breathing training to solve the problems of the subject's respiratory rhythm disorder, poor breath-holding, different breath-holding depths each time, and unnecessary radiation damage during the examination process. and self-regulating devices. The device realizes artificial intelligence display, and the subject wears the display device under comfortable conditions to perform breathing training and self-adapts to regular breathing for imaging, instead of CT equipment adapting to the real-time breathing of the subject.

In order to achieve the above object, the technical scheme adopted in the present invention is as follows:

The CT-compatible breathing training and self-adjusting device includes a breathing detection device, a head-mounted display, and a processing terminal, and the breathing detection device and the head-mounted display are respectively electrically connected to the processing terminal.

Preferably, the head-mounted display is a glasses display.

Preferably, the head-mounted display is a helmet-mounted display.

Further, the head-mounted display is wirelessly connected to the processing terminal.

Further, the breath detection device is wirelessly connected to the processing terminal.

Preferably, the processing terminal is connected to the CT detection instrument in a peripheral gating manner, and the processing terminal is electrically connected to the heart rate and respiration detector of the CT detection instrument, and the heart rate and respiration detector is provided with electrode sheets.

A detection method for a CT-compatible breathing training and self-adjusting device, comprising the following steps:

In the first step, the breath detection device is used to collect the breath waveforms of N cycles of the subject. The breath waveforms are waveforms of breathing intensity changing with breathing time. The breathing intensity is the difference between the pressure inside and outside the chest cavity, and the N ≥5, the respiratory waveform diagram is:

y=f(t) (I)

Where t is the breathing time in a breathing cycle, and y is the breathing intensity;

The second step is to input the data collected in the first step into the processing terminal for processing to obtain the background respiratory waveform:

y _b = x g(t) (II)

Where y _b is the breathing intensity in the background breathing waveform, x is a correction constant, 0.9≤x≤1.1;

In the third step, the background respiration waveform obtained in the second step is input into the head-mounted display to guide the subject to breathe, and at the same time, the background respiration waveform is used for the detection data collection of the CT detection pulse sequence;

In the third step, the background respiratory waveform is displayed on the head-mounted display as a standard waveform, and the real-time respiratory waveform detected by the heart rate and respiration monitor on the CT detection instrument is displayed on the head-mounted display to guide the subject The heart rate and breathing monitor is connected with electrode sheets, and the electrode sheets are attached to the chest of the subject during detection.

Preferably, in the second step,

First, the average breathing time of a single breathing cycle is calculated by the total time of N breathing cycles:

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; I</span>}\mathrm{<span\; class="notranslate">\; I</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; )</span>$

Among them, t _p is the breathing time of an average breathing cycle, and t _i is the breathing time of the i-th breathing cycle;

Then use the average time to normalize the respiratory waveform of each respiratory cycle:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}\frac{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; I</span>}\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; )</span>$

Among them, y _i is the respiratory intensity after normalization of the i-th respiratory cycle time;

Then average the N respiration waveforms after time normalization processing to obtain the background respiration waveform:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&CenterDot;</span>\frac{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&CenterDot;</span>\frac{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}\frac{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; )</span>$

The y _b is y _b in (II).

Further, step a can also be added between the second step and the third step:

Train the breathing pattern of the subject so that the breathing intensity error between the real-time breathing waveform diagram and the background breathing waveform diagram of the subject not less than three consecutive breathing cycles is ≤ 10% of the maximum breathing intensity, and the maximum breathing intensity is background breathing The maximum breath intensity of the waveform.

The present invention has the following beneficial effects:

1. The present invention is easy to operate, safe and practical.

2. Using the present invention, the subject can self-adjust to obtain regular respiration before performing magnetic resonance imaging, instead of using the non-magnetic resonance imaging to adapt to the subject's unadjusted real-time respiration for imaging.

3. The present invention can ensure the comfort of the subject to be tested without any pressure device on the body surface.

4. The present invention can improve the application success rate of magnetic resonance breath-hold imaging or respiratory gating imaging technology, rather than the success rate of a single part or a certain technology, covering a wide range.

5. The present invention can improve image quality and guide clinical symptomatic and precise treatment.

Description of drawings

Fig. 1 is the schematic diagram of the first step of detection method of the present invention;

Fig. 2 is the schematic diagram of the first step of detection method of the present invention;

Fig. 3 is the schematic diagram of the third step of the detection method of the present invention;

Figure 4 is a schematic diagram of the detection process;

In the figure: 1-respiration detection device, 2-head-mounted display, 3-processing terminal, 4-CT detection instrument, 41-heart rate and respiration monitor, 42-electrode sheet.

detailed description

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings.

As shown in Figure 1, CT is compatible with breathing training and self-adjusting devices, including a breathing detection device 1, a head-mounted display 2, and a processing terminal 3. The breathing detection device 1 and the head-mounted display 2 are respectively electrically connected to the processing terminal 3, and the head The wearable display 2 is a glasses display, the head-mounted display 2 is wirelessly connected to the processing terminal 3, the breathing detection device 1 is wirelessly connected to the processing terminal 3, the processing terminal 3 is connected to the CT detection instrument 4 in the manner of peripheral gate control, and the processing terminal and the processing terminal 3 are connected wirelessly. The heart rate and respiration detector of the CT detection instrument is electrically connected, and the heart rate and respiration detector is provided with electrode sheets.

Of course, the glasses display can also be replaced by a helmet display, VR glasses, Google glasses, etc.

As shown in Figure 2, the detection method of CT compatible breathing training and self-adjusting device includes the following steps:

The first step is to use the respiratory detection device 1 to collect the respiratory waveform of the subject for N periods. The respiratory waveform is the waveform of the respiratory intensity changing with the breathing time. The respiratory intensity is the pressure difference between the inside and outside of the thoracic cavity. Pictured:

y=f(t) (I)

Where t is the breathing time in a breathing cycle, and y is the breathing intensity;

In the second step, input the data collected in the first step into the processing terminal 3 for processing to obtain the background respiratory waveform:

y _b = x g(t) (II)

Where y _b is the breathing intensity in the background breathing waveform, x is a correction constant, 0.9≤x≤1.1,

First, the average breathing time of a single breathing cycle is calculated by the total time of N breathing cycles:

${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; I</span>}\mathrm{<span\; class="notranslate">\; I</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; )</span>$

Among them, t _p is the breathing time of an average breathing cycle, and t _i is the breathing time of the i-th breathing cycle;

Then use the average time to normalize the respiratory waveform of each respiratory cycle:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}\frac{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; I</span>}\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; )</span>$

Among them, y _i is the respiratory intensity after normalization of the i-th respiratory cycle time;

Then average the N respiration waveforms after time normalization processing to obtain the background respiration waveform:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&CenterDot;</span>\frac{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&CenterDot;</span>\frac{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}\frac{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; )</span>$

y _b is y _b in (II);

Step a: Train the breathing pattern of the subject so that the breathing intensity error between the real-time breathing waveform diagram and the background breathing waveform diagram of the subject not less than three consecutive breathing cycles is ≤ 10% of the maximum breathing intensity, and the maximum breathing intensity is the background The maximum respiratory intensity of the respiration waveform. If there is always a certain deviation between the real-time respiration waveform and the background respiration waveform, the background respiration waveform can be adjusted by modifying the constant x;

In the third step, input the background respiration waveform obtained in the second step into the head-mounted display 2 to instruct the subject to breathe, and at the same time use the background respiration waveform for the detection data collection of the CT detection pulse sequence. In the third step, the background respiration The waveform diagram is displayed on the head-mounted display 2 as a standard waveform, and the real-time respiratory waveform detected by the heart rate and respiration monitor 41 on the CT detection instrument 4 is displayed on the head-mounted display 2 at the same time, so as to instruct the subject to breathe, heart rate and respiration The monitor 41 is connected with an electrode sheet 42, and the electrode sheet 42 is attached to the chest of the subject during detection.

Certainly, the present invention also can have other multiple embodiments, without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and deformations according to the present invention, but these corresponding changes All changes and modifications should belong to the scope of protection of the appended claims of the present invention.

Claims (9)

1. CT compatible breathing training and self-adjusting device, characterized in that: comprising a breathing detection device (1), a head-mounted display (2), a processing terminal (3), the breathing detection device (1) and a head-mounted display (2) are respectively electrically connected to the processing terminals (3). 2. The CT-compatible breathing training and self-adjusting device according to claim 1, characterized in that: the head-mounted display (2) is a glasses display. 3. The CT-compatible breathing training and self-adjusting device according to claim 1, characterized in that: the head-mounted display (2) is a helmet-mounted display. 4. The CT-compatible breathing training and self-adjusting device according to any one of claims 1-3, characterized in that: the head-mounted display (2) is wirelessly connected to the processing terminal (3). 5. The CT-compatible breathing training and self-adjusting device according to claim 4, characterized in that: the breathing detection device (1) is wirelessly connected to the processing terminal (3). 6. The CT-compatible breathing training and self-adjusting device according to claim 1, characterized in that: the processing terminal (3) is connected to the CT detection instrument (4) in a peripheral gating manner. 7. A CT detection method applying the device according to any one of claims 1-6, characterized in that it comprises the following steps: In the first step, the breath detection device (1) is used to collect the breath waveforms of N cycles of the subject, and the breath waveforms are waveforms of breathing intensity changing with breathing time, and the breathing intensity is the pressure difference between the inside and outside of the chest cavity Said N≥5, said respiratory waveform diagram is: y=f(t) (I) Where t is the breathing time in a breathing cycle, and y is the breathing intensity; In the second step, input the data collected in the first step into the processing terminal (3) to process the background respiratory waveform: y _b = x g(t) (II) Where y _b is the breathing intensity in the background breathing waveform, x is a correction constant, 0.9≤x≤1.1; In the third step, the background respiration waveform obtained in the second step is input into the head-mounted display (2) to instruct the subject to breathe, and at the same time, the background respiration waveform is used for the detection data collection of the CT detection pulse sequence; In the 3rd step, the background respiration waveform is displayed on the head-mounted display (2) as a standard waveform, and the real-time respiration waveform detected by the heart rate respiration monitor (41) on the CT detection instrument (4) is displayed on the head simultaneously. on the wearable display (2) to instruct the subject to breathe. 8. The CT detection method according to claim 7, characterized in that: in the second step, First, the average breathing time of a single breathing cycle is calculated by the total time of N breathing cycles: ${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; I</span>}\mathrm{<span\; class="notranslate">\; I</span>}\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; )</span>$ Among them, t _p is the breathing time of an average breathing cycle, and t _i is the breathing time of the i-th breathing cycle; Then use the average time to normalize the respiratory waveform of each respiratory cycle: ${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}\frac{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; I</span>}\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; )</span>$ Among them, y _i is the respiratory intensity after normalization of the i-th respiratory cycle time; Then average the N respiration waveforms after time normalization processing to obtain the background respiration waveform: ${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&Center\; Dot;</span>\frac{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&CenterDot;</span>\frac{{<span\; class="notranslate">\; \&Sigma;</span>}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; N</span>}}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}\frac{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}}}{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; )</span>$ The y _b is y _b in (II). 9. The CT detection method according to claim 7, characterized in that: step a can also be added between the second step and the third step: Train the breathing pattern of the subject so that the breathing intensity error between the real-time breathing waveform diagram and the background breathing waveform diagram of the subject not less than three continuous breathing cycles is ≤ 10% of the maximum breathing intensity, and the maximum breathing intensity is background breathing The maximum breath intensity of the waveform.