CN113545796A - Method for detecting pork quality character by using computer tomography scanning living body - Google Patents
Method for detecting pork quality character by using computer tomography scanning living body Download PDFInfo
- Publication number
- CN113545796A CN113545796A CN202110837951.4A CN202110837951A CN113545796A CN 113545796 A CN113545796 A CN 113545796A CN 202110837951 A CN202110837951 A CN 202110837951A CN 113545796 A CN113545796 A CN 113545796A
- Authority
- CN
- China
- Prior art keywords
- scanning
- image
- pig
- computed tomography
- pork quality
- Prior art date
- Legal status (The legal status 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 status listed.)
- Pending
Links
- 238000002591 computed tomography Methods 0.000 title claims abstract description 64
- 238000000034 method Methods 0.000 title claims abstract description 40
- 235000015277 pork Nutrition 0.000 title claims abstract description 23
- 238000007918 intramuscular administration Methods 0.000 claims abstract description 47
- 206010002091 Anaesthesia Diseases 0.000 claims description 13
- 230000037005 anaesthesia Effects 0.000 claims description 13
- 230000003444 anaesthetic effect Effects 0.000 claims description 8
- 238000002347 injection Methods 0.000 claims description 6
- 239000007924 injection Substances 0.000 claims description 6
- 238000002695 general anesthesia Methods 0.000 claims description 5
- 235000003642 hunger Nutrition 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 4
- OLBCVFGFOZPWHH-UHFFFAOYSA-N propofol Chemical compound CC(C)C1=CC=CC(C(C)C)=C1O OLBCVFGFOZPWHH-UHFFFAOYSA-N 0.000 claims description 4
- 229960004134 propofol Drugs 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 4
- 235000013305 food Nutrition 0.000 claims description 3
- 210000001364 upper extremity Anatomy 0.000 claims description 3
- 210000003462 vein Anatomy 0.000 claims description 2
- 230000002708 enhancing effect Effects 0.000 claims 1
- 238000001514 detection method Methods 0.000 abstract description 13
- 241000282887 Suidae Species 0.000 abstract description 12
- 238000009395 breeding Methods 0.000 abstract description 3
- 230000001488 breeding effect Effects 0.000 abstract description 3
- 235000019625 fat content Nutrition 0.000 description 40
- 210000003205 muscle Anatomy 0.000 description 19
- 238000003307 slaughter Methods 0.000 description 17
- 238000005259 measurement Methods 0.000 description 16
- 230000000875 corresponding effect Effects 0.000 description 12
- 238000011156 evaluation Methods 0.000 description 12
- 235000013372 meat Nutrition 0.000 description 9
- 238000012623 in vivo measurement Methods 0.000 description 7
- RTZKZFJDLAIYFH-UHFFFAOYSA-N Diethyl ether Chemical compound CCOCC RTZKZFJDLAIYFH-UHFFFAOYSA-N 0.000 description 6
- 238000012360 testing method Methods 0.000 description 6
- 210000001519 tissue Anatomy 0.000 description 6
- 238000000605 extraction Methods 0.000 description 5
- 230000036544 posture Effects 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 238000003556 assay Methods 0.000 description 4
- 238000005462 in vivo assay Methods 0.000 description 4
- 230000029058 respiratory gaseous exchange Effects 0.000 description 4
- 208000010496 Heart Arrest Diseases 0.000 description 3
- 238000000944 Soxhlet extraction Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 238000005303 weighing Methods 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 208000004756 Respiratory Insufficiency Diseases 0.000 description 2
- 206010038678 Respiratory depression Diseases 0.000 description 2
- 238000011869 Shapiro-Wilk test Methods 0.000 description 2
- 238000001949 anaesthesia Methods 0.000 description 2
- 238000001574 biopsy Methods 0.000 description 2
- 238000010219 correlation analysis Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000003193 general anesthetic agent Substances 0.000 description 2
- 238000010832 independent-sample T-test Methods 0.000 description 2
- 238000012417 linear regression Methods 0.000 description 2
- 239000004081 narcotic agent Substances 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 235000009161 Espostoa lanata Nutrition 0.000 description 1
- 240000001624 Espostoa lanata Species 0.000 description 1
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 238000004497 NIR spectroscopy Methods 0.000 description 1
- 238000010220 Pearson correlation analysis Methods 0.000 description 1
- ATJFFYVFTNAWJD-UHFFFAOYSA-N Tin Chemical compound [Sn] ATJFFYVFTNAWJD-UHFFFAOYSA-N 0.000 description 1
- 239000002390 adhesive tape Substances 0.000 description 1
- 229940035674 anesthetics Drugs 0.000 description 1
- 239000010868 animal carcass Substances 0.000 description 1
- 210000001217 buttock Anatomy 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- 239000000796 flavoring agent Substances 0.000 description 1
- 235000019634 flavors Nutrition 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000001727 in vivo Methods 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
- 238000010253 intravenous injection Methods 0.000 description 1
- 238000002386 leaching Methods 0.000 description 1
- 235000020997 lean meat Nutrition 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 235000013622 meat product Nutrition 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 238000009400 out breeding Methods 0.000 description 1
- 238000000643 oven drying Methods 0.000 description 1
- 239000002243 precursor Substances 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 230000037351 starvation Effects 0.000 description 1
- 238000010025 steaming Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000003325 tomography Methods 0.000 description 1
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
- A61B6/46—Arrangements for interfacing with the operator or the patient
- A61B6/467—Arrangements for interfacing with the operator or the patient characterised by special input means
- A61B6/469—Arrangements for interfacing with the operator or the patient characterised by special input means for selecting a region of interest [ROI]
-
- 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
- A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
- A61B6/508—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for non-human patients
-
- 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
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
-
- 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
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5258—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2503/00—Evaluating a particular growth phase or type of persons or animals
- A61B2503/40—Animals
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Biomedical Technology (AREA)
- Surgery (AREA)
- High Energy & Nuclear Physics (AREA)
- Physics & Mathematics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Optics & Photonics (AREA)
- Pathology (AREA)
- Radiology & Medical Imaging (AREA)
- Veterinary Medicine (AREA)
- Heart & Thoracic Surgery (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Human Computer Interaction (AREA)
- Dentistry (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Pulmonology (AREA)
- Theoretical Computer Science (AREA)
- Apparatus For Radiation Diagnosis (AREA)
Abstract
The invention discloses a method for detecting pork quality traits by using a computed tomography living body. The method comprises the steps of carrying out three-Section scanning on a general anesthetized pig, carrying out image thin layer reconstruction and denoising on an image sequence obtained in a scanning range to improve image quality and extract image characteristic information, and adjusting the CT scanning threshold range in an X-Section window to generate the proportion of the fat area of the region of interest to the area of the region of interest, namely the intramuscular fat content. The invention realizes the detection of the intramuscular fat content of the living body by using the computed tomography for the first time, can improve the accuracy of the detection result compared with the B-ultrasonic detection method, and provides technical support for evaluating the pork quality character and breeding the breeding pigs.
Description
Technical Field
The invention relates to pork quality character evaluation, in particular to establishment of a method for rapidly and accurately detecting intramuscular fat content of pigs by utilizing a computer tomography living body.
Background
The meat quality character plays an important role in the breeding process of the boar, wherein the intramuscular fat content is a key index. Intramuscular fat (IMF) is an important precursor of pork flavor, and its content affects other meat quality traits, including tenderness, juiciness, flesh color, water retention capacity and the like. Therefore, the intramuscular fat content can be used as an index for measuring the pork quality, so as to identify genetic individuals with excellent quality in the population. Consumers generally prefer lean meat in meat products, and the suitable range of intramuscular fat content meeting pork consumption requirements is 2.5% -3%.
For the measurement of intramuscular fat content, two main categories of methods are distinguished: the first type comprises traditional detection technologies such as sensory evaluation and chemical analysis, and is specifically determined by marbling scoring or by a soxhlet extraction method, but the measurement efficiency is low and the method is not suitable for meat quality character evaluation of living bodies; the second category includes nondestructive detection technologies such as computer vision, near infrared spectroscopy, living body ultrasonic detection and the like, wherein only B ultrasonic detection methods can measure the intramuscular fat content of living bodies, and other methods can only detect slaughtered pigs.
According to the principle of the B-ultrasonic detection method, after a living body is scanned by B-ultrasonic, different interface reflections are generated due to the fact that the acoustic resistance values of the fat tissue and the muscle tissue of a pig are greatly different, and finally, the corresponding image characteristics in the generated image are different, so that the method can be used for measuring the intramuscular fat content of the living body. Although the operation of measuring the intramuscular fat content by the B ultrasonic detection method is convenient, the method has the problem of low accuracy of the measurement result, namely, the method has larger deviation with the slaughter actual measurement result.
CT scanning shows that the fat tissue and muscle tissue of live pigs have different attenuation values of X-rays due to density difference, and the obtained cross-sectional images can be used for evaluating the carcass composition and the intramuscular fat content, for example, Chinese patent CN110179488A can read the proportion of the longest dorsal-lumbar intramuscular fat tissue by adjusting the tissue density parameters of selected CT tomography pictures. Although a great deal of research begins to evaluate the accuracy of computed tomography in measuring animal carcass composition and intramuscular fat content, the following problems still exist to be solved by using computed tomography to detect pork quality traits:
1) the pig is easily killed due to respiratory depression, cardiac arrest, and the like, depending on the selection of the anesthetic and the dose to be administered and the fixation mode of the living body after anesthesia.
2) The general anesthesia time and the CT scanning time of the pig are difficult to be kept consistent completely, and the problem that the image picture generated in the computer tomography scanning process has serious artifacts due to slight changes of the prone posture and the internal tissue structure of the pig exists.
3) There is a lack of more convenient and widely applicable related scan parameter settings and processing flow standards for computer tomography image sequences used for measuring meat quality traits of living bodies, such as intramuscular fat content of pigs.
Disclosure of Invention
The invention aims to provide a method for detecting pork quality traits by using a computer tomography living body.
In order to achieve the purpose, the invention adopts the following technical scheme:
1) performing general anesthesia on the pig;
2) after the pig enters an anesthesia state, the pig is placed on a CT scanning bed and is fixed, so that the spine median line of the pig is superposed with a CT scale; after determining a scanning range by scanning the scout image, performing computed tomography image sequence scanning and image preprocessing;
3) intramuscular fat content determination:
after the step 2), positioning an image plane of intramuscular fat content to be measured in a cross section window by combining a sagittal plane window and a coronal plane window; then selecting a region of interest on the image level; and then, by adjusting the CT scanning threshold range corresponding to the fat, obtaining the proportion of the fat area of the region of interest to the area of the region of interest, namely the intramuscular fat content of the region of interest.
Preferably, the step 1) specifically comprises the following steps: the pig is weighed and numbered before anesthesia and is subjected to hunger treatment through food deprivation and continuous water supply, and injection anesthesia is performed after 12-24 hours of hunger treatment, so that the anesthetized pig can lie down and breathe stably without struggling.
Preferably, the injection anesthesia specifically comprises the following steps: the anesthetic is injected through the ear edge vein, the injection dose of the anesthetic is 0.6-0.7 mL/kg, and the anesthetic consists of propofol and sultam in the volume ratio of 2-4: 1.
Preferably, in the step 2), before the pig is fixed on the CT scanning bed, the tongue of the pig is pulled out, and two front legs of the pig are straightened; the scout images are obtained by scanning different body segments of the pig respectively.
Preferably, the method for scanning the sequence of computed tomography images is flat scanning, and the scanning parameters are set as follows: the tube voltage is 120kV, the tube current is 180mA, the layer thickness is 3-5 mm, and the layer spacing is 3-5 mm.
Preferably, the image preprocessing specifically includes the following steps: and sequentially carrying out image thin layer reconstruction and denoising on the original thin layer image obtained by the scanning of the computed tomography image sequence.
Preferably, the image thin layer reconstruction specifically includes the following steps: and (3) reconstructing a thin layer image generated by flat scanning of the CT scanner according to a set layer thickness of 0.625-1.25 mm.
Preferably, the thin layer image reconstruction mode is stnd, and the IQ Enhance is used in the reconstruction mode to Enhance the definition of the thin layer image.
Preferably, the denoising specifically includes the following steps: and removing noise of the reconstructed thin-layer image by using an ASiR algorithm and setting the weight to be 40% -60% to obtain a corresponding image layer.
Preferably, the adjusting the threshold range of the CT scan corresponding to the fat specifically includes the following steps: in the X-Section display mode, the maximum value and the minimum value of the CT scanning threshold are set according to the fat density, so that the CT scanning threshold range is-140 IU-10 IU, wherein the interested area is selected from the transverse Section of the longissimus dorsi, gluteus medius, biceps femoris or semimembranosus.
The invention has the beneficial effects that:
on the basis of acquiring a live body computer tomography image sequence of a pig by utilizing CT scanning, the method can directly acquire the measurement value of the intramuscular fat content according to the proportion of the fat area of the region of interest to the area of the region of interest automatically calculated by an image workstation by selecting the corresponding bedding surface area of the muscle part of the pig with any intramuscular fat content to be measured as the region of interest and adjusting the CT scanning threshold range corresponding to the fat. The invention realizes the detection of the intramuscular fat content of the living body by using the computed tomography for the first time, can improve the accuracy of the detection result compared with a B-ultrasonic detection method, and provides technical support for rapidly and comprehensively evaluating the meat quality characters of different muscle parts of the pigs and carrying out breeding of the pigs.
Furthermore, the invention can ensure that the pigs which are subjected to general anesthesia can not die due to respiratory depression or cardiac arrest in the computed tomography scanning process by optimizing the anesthetic drugs, the composition ratio (the propofol ratio is greater than that of the patient) and the fixed postures of the pigs in the pig anaesthesia process, and simultaneously ensure that the external postures and the internal structures of the pigs are stable (for example, stable respiration is kept) in the scanning process, thereby avoiding the generation of artifacts in an image sequence obtained by scanning to a greater extent and generating high-quality original thin-layer images.
Furthermore, the invention preprocesses the original thin layer image through thin layer reconstruction and denoising of the image, can eliminate irrelevant information, improves the image quality and is beneficial to identifying and analyzing the fat in the region of interest through a computer.
Further, the invention reduces the thickness of an image layer (for example, from 3.75mm to 0.625mm, 1.25mm and the like) in image reconstruction, and adopts a certain reconstruction mode (for example, stnd), thereby improving the extraction of image characteristic information (fat) and reducing image thin layer artifacts caused by pig respiration.
Furthermore, the invention firstly scans the positioning image to determine the scanning range and then scans the image sequence, thereby being beneficial to the later data analysis.
Drawings
Fig. 1 is a schematic view of a pig CT scanning process: (A) the pig is fixed in a horizontal position, and the central line of the pig spine coincides with the red line (infrared positioning line) of the CT scale; (B) CT scanning a positioning image; (C) a CT scan sequence.
FIG. 2 is a thin layer reconstruction and denoising of a cross-sectional image acquired by computed tomography: (A) one of the 3.75mm layer thickness cross-sectional images obtained after the chest and waist are scanned by the computer tomography and the corresponding 1.25mm layer thickness cross-sectional image generated after the image thin layer reconstruction and the denoising; (B) one of the 3.75mm layer thickness cross-sectional images obtained by the computed tomography buttocks and a corresponding 0.625mm layer thickness cross-sectional image generated after image thin layer reconstruction and denoising.
FIG. 3 is a reconstructed and denoised lamellar image of the intramuscular fat content to be measured: (A) the longissimus dorsi; (B) the gluteus medius muscle; (C) the biceps femoris muscle; (D) the semimembranous muscle.
FIG. 4 is a schematic diagram of intramuscular fat content measurement in vivo by CT: (A) selecting the longissimus dorsi part as an interested area in an image layer; (B) the CT scan threshold range (maximum, minimum) is adjusted and the corresponding proportion of fat area to the area of the region of interest (i.e. intramuscular fat content).
FIG. 5 shows the results of a one-dimensional linear regression analysis of intramuscular fat content in different muscle regions of pigs obtained from CT biopsy and slaughter measurements: (A) longissimus dorsi, (B) gluteus medius, (C) biceps femoris, and (D) semimembranosus.
Detailed Description
The present invention will be described in further detail with reference to the following drawings and examples, which are provided for illustration only and are not intended to limit the scope of the present invention.
Detecting pork quality character by using computer tomography scanning living body
1. Experimental Equipment preparation
Aiming at the experiment of detecting the pork quality characters by CT scanning living bodies, experimental clothes, gloves, foot covers, a mask, an experimental record book, a marking pen, an alcohol cotton ball, a disposable needle tube, a pig stretcher, a garbage bag, clean water, narcotics, a refrigerator at 4 ℃ and the like are required to be prepared.
2. Pig anesthesia treatment
The day before the test, starvation (no food and no water) for 24 hours, weighing and numbering.
According to the weight of each pig, general anesthesia (0.6-0.7 mL/kg) is carried out on the pig by adopting ear marginal intravenous injection according to the volume ratio of propofol to Shutai (both anesthetics are commercial reagents) of 2: 1.
The dosage and the proportion of the anesthetic selected above can ensure that the pigs lie down by themselves after anesthesia, breathe stably and do not struggle. Therefore, the pig anaesthesia time and the CT scanning time are basically consistent, the death caused by pig respiration inhibition, cardiac arrest and other reasons under the condition of overuse of narcotics and the artifact (such as spiral artifact in an image picture) caused by slight change of the appearance posture and the internal structure of the pig under the condition of insufficient dose are avoided.
The three people stably lift the anesthetized pig on a stretcher for transportation, the pig body is ensured not to be twisted in the transportation process, then the pig is lifted on a CT scanning bed and placed according to the prone posture of the head, the tail and the back, the pig is prevented from breathing blockage during placement (the tongue of the pig needs to be pulled out, two front legs of the pig need to be straightened, and the whole body is fixed), and the pig is kept in a horizontal state.
3. Computed tomography scanning
The horizontal position of the pig is adjusted slightly to ensure that the central line of the pig spine coincides with the red line of the CT scale (see figure 1A).
Setting the process and parameters of the CT scanning device:
the living body computer tomography of the pig adopts a Brivo CT 385Series 16-row spiral CT scanner of the GE company of America, and the positioning image adopts three-section scanning, namely the positioning image is scanned by the head, the chest, the waist and the hip separately; the sequential scanning method is flat scanning, wherein the scanning parameters are set as follows: tube voltage 120kV, tube current 180mA, layer thickness (initial thin layer thickness) 3.75mm, layer spacing 3.75 mm.
Firstly, scanning the scout view, and then, carrying out sequence scanning (figure 1B and figure 1C) after determining the scanning range, wherein the body position of the pig in the scanning process is fixed as the prone position. And respectively establishing a detection report sheet of the original image sequence of the computed tomography according to the scanning range determined by each positioning image.
4. Thin layer reconstruction and denoising of images
Selecting a thin layer image to be reconstructed from an original thin layer image (an image layer with the layer thickness of 3.75 mm) in a DICOM format generated by a CT scanner in a GE image workstation, clicking to create a new sequence, setting the layer thickness to be 1.25mm or 0.625mm, and adopting a reconstruction mode of stnd. Then, ASiR algorithm is used and the weight is set to 50% to reduce the noise of the reconstructed thin-layer image, i.e., to reduce the image noise (fig. 2).
5. Evaluation of pork quality traits
Taking the thin-layer image processed by the GE imaging workstation to measure the intramuscular fat content of the pig (CT living body measurement for short) as an example, the specific process is as follows:
firstly, continuously selecting a reconstructed and denoised image sequence in a GE image workstation, and clicking Reform. After entering the main interface, the display mode of the upper left window is adjusted to be X-Section (namely the upper left window is the X-Section window), the image plane is adjusted in the cross Section window, and the sagittal plane window and the coronal plane window are combined to be positioned to a certain image plane to be measured. Then, the region of interest is selected by delineation on this level (delineation of four different muscle regions results see fig. 3).
After selecting an interested area on a certain image layer, adjusting the range of a CT scanning threshold in an X-Section window (as shown in figure 4B, the abscissa represents a threshold interval, two dotted lines represent the maximum threshold and the minimum threshold corresponding to fat respectively, the area between a curve and the abscissa is the area of the interested area), namely setting the threshold range corresponding to fat tissue in the interested area according to fat density, automatically calculating the proportion of the fat area of the interested area to the area of the interested area by a workstation, namely obtaining the intramuscular fat content of a muscle part corresponding to the interested area, and recording data.
In particular, the selected region of interest in fig. 4A is the portion of the longissimus dorsi displayed in one image plane, as can be seen from the parameters displayed above the abscissa (upper right corner of the curve) in fig. 4B: 1) the adjusted maximum threshold value is-3.0 IU, and the adjusted minimum threshold value is-97.0 IU (namely, the area between the curve segment between the two dotted lines on the abscissa in the graph 4B and the abscissa is determined); 2) automatically calculating the fat area of the region of interest to be 24mm according to the threshold range of-97.0 IU to-3.0 IU2And a ratio of the fat area of the region of interest to the area of the region of interest of 1.1% was calculated (region of interest parameters are shown in fig. 4B)Below the abscissa of the table). From this, the intramuscular fat content of the longissimus dorsi was determined to be 1.1%.
(II) measuring the content of intramuscular fat by a Soxhlet extraction method (for short, slaughter measurement) after slaughter
Preparing experimental equipment: aiming at the experiment for detecting the meat quality after slaughter, a scalpel, tinfoil paper, a vernier caliper, a liquid nitrogen tank, a tape measure, a pair of surgical scissors, a label, a marking pen, a weight scale, a tray, a goods shelf, a containing bag, an adhesive tape, a rubber band and the like are prepared.
The specific experimental procedures are as follows:
1) sampling part: after slaughter, the animals were transported to the laboratory and the longissimus dorsi, gluteus medius, biceps femoris and semimembranosus at the 3 rd and 4 th ribs were taken.
2) Pretreatment: cutting, and making into air-dried meat sample at 65 deg.C.
3) Fat extraction: 3-5 g of the air-dried meat sample is weighed by an analytical balance, wrapped by filter paper and numbered. And wrapping the filter paper in an oven at 103-105 ℃ for drying for 2 h. Transferring into Soxhlet fat extractor such as semi-automatic Soxhlet extractor (2055SOXTEC Manual Extraction Unit) leaching tube, adding anhydrous diethyl ether, steaming for 2 hr, recovering diethyl ether, oven drying at 103-105 deg.C for 10min, cooling in drier for 30min, and weighing (weighing result is W)2)。
Fat content F (%) ═ (W)2－W1)/W×100
Wherein W is the weight (g) of the air-dried meat, W1The weight (g) of the fat collection bottle before extraction is W2The bottle weight (g) was collected for the fat after extraction.
The fat content F is the intramuscular fat content determined by soxhlet extraction.
(III) correlation analysis of evaluation results of intramuscular fat content of each muscle part obtained by CT in vivo measurement and actual value of intramuscular fat content of each muscle part obtained by slaughter measurement
The experimental data were initially collated with Microsoft Excel 365 and then analyzed using SPSS 26.0 software. The data analysis is carried out by checking the normality of the data (Shapiro-Wilk test) and the homogeneity of the variance (Leven test), performing arcsine transformation on the data which does not meet the test requirement so as to meet the parameter hypothesis, and then carrying out independent sample t test. Correlation analysis was Pearson correlation analysis.
Referring to table 1, the correlation coefficients (r) of the evaluation results of the CT in vivo measurements and the actual values of the slaughter measurements were 0.837, 0.815, 0.764, and 0.786 for the intramuscular fat contents of the four different muscle sites of the longissimus dorsi, gluteus medius, biceps femoris, and semimembranosus, respectively, which indicates that the correlation between the CT in vivo measurements and the intramuscular fat contents of the slaughter measurements was strong and proved to be very significant (P < 0.01). Meanwhile, the intramuscular fat contents of three different muscle parts, namely longissimus dorsi, gluteus medius and semimembranosus, have insignificant difference between the evaluation result of CT in vivo measurement and the actual value of slaughter measurement; whereas for the biceps femoris muscle the difference between the evaluation results of the in vivo assay and the actual values of the slaughter assay was significant (P < 0.05). The analysis results show that the intramuscular fat content can be accurately measured by CT in vivo measurement based on computed tomography.
TABLE 1 correlation of CT in vivo measurement evaluation results of intramuscular fat content with actual values of slaughter measurements
Note: indicates that the evaluation result of the CT in-vivo assay is strongly correlated with the actual value of the slaughter assay, and indicates that the evaluation result of the CT in-vivo assay is significantly different from the actual value of the slaughter assay
The experimental result also shows that the IMF content measurement results of the same muscle part in a plurality of different image layers have no obvious difference.
(IV) regression analysis of the results of the evaluation of the intramuscular fat content in each muscle site obtained by CT in vivo assay and the actual value of the intramuscular fat content in each muscle site obtained by slaughter assay
The experimental data were initially collated with Microsoft Excel 365 and then analyzed using SPSS 26.0 software. The data analysis is carried out by checking the normality of the data (Shapiro-Wilk test) and the homogeneity of the variance (Leven test), performing arcsine transformation on the data which does not meet the test requirement so as to meet the parameter hypothesis, and then carrying out independent sample t test.
Finally, taking the intramuscular fat content measured by the CT living body as an independent variable and the intramuscular fat content measured by slaughter as a dependent variable, and carrying out unary linear regression analysis. Referring to fig. 5, the determination coefficients (R) of the linear model are established for the intramuscular fat (IMF) content of four different muscle sites of longissimus dorsi, gluteus medius, biceps femoris and semimembranosus2) Respectively 0.700, 0.664, 0.583 and 0.618, and has extremely obvious linear positive correlation (P) between the intramuscular fat content obtained by the CT biopsy and the slaughter test<0.01), indicating that CT in vivo measurement based on computed tomography can obtain the measurement result consistent with the real intramuscular fat content.
Claims (10)
1. A method for detecting pork quality traits by using computed tomography is characterized by comprising the following steps: the method comprises the following steps:
1) performing general anesthesia on the pig;
2) after the pig enters an anesthesia state, the pig is placed on a CT scanning bed and is fixed, so that the spine median line of the pig is superposed with a CT scale; after determining a scanning range by scanning the scout image, performing computed tomography image sequence scanning and image preprocessing;
3) intramuscular fat content determination:
after the step 2), positioning an image plane of intramuscular fat content to be measured in a cross section window by combining a sagittal plane window and a coronal plane window; then selecting a region of interest on the image level; and then, by adjusting the CT scanning threshold range corresponding to the fat, obtaining the proportion of the fat area of the region of interest to the area of the region of interest, namely the intramuscular fat content of the region of interest.
2. The method for detecting pork quality traits by using computed tomography as claimed in claim 1, wherein: the step 1) specifically comprises the following steps: the pig is weighed and numbered before anesthesia and is subjected to hunger treatment through food deprivation and continuous water supply, and injection anesthesia is performed after 12-24 hours of hunger treatment, so that the anesthetized pig can lie down and breathe stably without struggling.
3. The method for detecting pork quality traits by using computed tomography as claimed in claim 2, wherein: the injection anesthesia specifically comprises the following steps: the anesthetic is injected through the ear edge vein, the injection dose of the anesthetic is 0.6-0.7 mL/kg, and the anesthetic consists of propofol and sultam in the volume ratio of 2-4: 1.
4. The method for detecting pork quality traits by using computed tomography as claimed in claim 1, wherein: in the step 2), before the pig is fixed on the CT scanning bed, pulling out the tongue of the pig and straightening the front leg of the pig; the scout images are obtained by scanning different body segments of the pig respectively.
5. The method for detecting pork quality traits by using computed tomography as claimed in claim 1, wherein: the method for scanning the computed tomography image sequence is flat scanning, and scanning parameters are set as follows: the tube voltage is 120kV, the tube current is 180mA, the layer thickness is 3-5 mm, and the layer spacing is 3-5 mm.
6. The method for detecting pork quality traits by using computed tomography as claimed in claim 1, wherein: the image preprocessing specifically comprises the following steps: and sequentially carrying out image thin layer reconstruction and denoising on the original thin layer image obtained by the scanning of the computed tomography image sequence.
7. The method for detecting pork quality traits by using computed tomography as claimed in claim 6, wherein: the image thin layer reconstruction specifically comprises the following steps: and (3) reconstructing a thin layer image generated by flat scanning of the CT scanner according to a set layer thickness of 0.625-1.25 mm.
8. The method for detecting pork quality traits by using computed tomography as claimed in claim 7, wherein: the thin layer image reconstruction mode is stnd, and meanwhile IQ Enhance is used for enhancing the definition of the thin layer image in the reconstruction mode.
9. The method for detecting pork quality traits by using computed tomography as claimed in claim 7, wherein: the denoising specifically comprises the following steps: and removing noise of the reconstructed thin-layer image by using an ASiR algorithm and setting the weight to be 40% -60% to obtain a corresponding image layer.
10. The method for detecting pork quality traits by using computed tomography as claimed in claim 1, wherein: the method for adjusting the CT scanning threshold range corresponding to the fat specifically comprises the following steps: in the X-Section display mode, the maximum value and the minimum value of the CT scanning threshold are set according to the fat density, so that the CT scanning threshold range is-140 IU-10 IU, wherein the interested area is selected from the transverse Section of the longissimus dorsi, gluteus medius, biceps femoris or semimembranosus.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110837951.4A CN113545796A (en) | 2021-07-23 | 2021-07-23 | Method for detecting pork quality character by using computer tomography scanning living body |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110837951.4A CN113545796A (en) | 2021-07-23 | 2021-07-23 | Method for detecting pork quality character by using computer tomography scanning living body |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113545796A true CN113545796A (en) | 2021-10-26 |
Family
ID=78104271
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110837951.4A Pending CN113545796A (en) | 2021-07-23 | 2021-07-23 | Method for detecting pork quality character by using computer tomography scanning living body |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113545796A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004057275A (en) * | 2002-07-25 | 2004-02-26 | Hitachi Medical Corp | Image diagnostic device |
| CN101120882A (en) * | 2006-08-08 | 2008-02-13 | 西门子（中国）有限公司 | Control device for orientation scanning length and method thereof |
| CN105078496A (en) * | 2014-04-30 | 2015-11-25 | 上海西门子医疗器械有限公司 | Method for scanning double positioning images and computed tomography device |
| CN105433971A (en) * | 2014-07-29 | 2016-03-30 | 上海西门子医疗器械有限公司 | Computed tomography method and device |
| CN110179488A (en) * | 2019-06-11 | 2019-08-30 | 西北农林科技大学 | A method of it is formed using CT scan data detection pig living body trunk |
| CN110327071A (en) * | 2019-08-06 | 2019-10-15 | 上海联影医疗科技有限公司 | CT scan method, apparatus, equipment and storage medium |
| CN111199566A (en) * | 2019-12-31 | 2020-05-26 | 上海联影智能医疗科技有限公司 | Medical image processing method, medical image processing device, storage medium and computer equipment |
-
2021
- 2021-07-23 CN CN202110837951.4A patent/CN113545796A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004057275A (en) * | 2002-07-25 | 2004-02-26 | Hitachi Medical Corp | Image diagnostic device |
| CN101120882A (en) * | 2006-08-08 | 2008-02-13 | 西门子（中国）有限公司 | Control device for orientation scanning length and method thereof |
| CN105078496A (en) * | 2014-04-30 | 2015-11-25 | 上海西门子医疗器械有限公司 | Method for scanning double positioning images and computed tomography device |
| CN105433971A (en) * | 2014-07-29 | 2016-03-30 | 上海西门子医疗器械有限公司 | Computed tomography method and device |
| CN110179488A (en) * | 2019-06-11 | 2019-08-30 | 西北农林科技大学 | A method of it is formed using CT scan data detection pig living body trunk |
| CN110327071A (en) * | 2019-08-06 | 2019-10-15 | 上海联影医疗科技有限公司 | CT scan method, apparatus, equipment and storage medium |
| CN111199566A (en) * | 2019-12-31 | 2020-05-26 | 上海联影智能医疗科技有限公司 | Medical image processing method, medical image processing device, storage medium and computer equipment |
Non-Patent Citations (2)
| Title |
|---|
| 刘婉如: "应用低剂量扫描技术与低浓度对比剂行宝石CT增强扫描的实验研究", 中国优秀硕士学位论文全文数据库, no. 9, 15 September 2013 (2013-09-15), pages 24 * |
| 陈甫，张传美，高利副: "《兽医学》", 30 June 2013, 中国海洋大学出版社, pages: 500 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Fowler et al. | Validation of the in vivo measurement of adipose tissue by magnetic resonance imaging of lean and obese pigs | |
| CN111539944B (en) | Method, device, electronic equipment and storage medium for acquiring statistical attribute of lung focus | |
| Harrington et al. | Fast and reproducible method for the direct quantitation of adipose tissue in newborn infants | |
| Gray et al. | Magnetic resonance imaging with k-means clustering objectively measures whole muscle volume compartments in sarcopenia/cancer cachexia | |
| Copley et al. | Effect of aging on lung structure in vivo: assessment with densitometric and fractal analysis of high-resolution computed tomography data | |
| Hamada | Objective Data Assessment (ODA) methods as nutritional assessment tools | |
| Tang et al. | High-resolution magnetic resonance imaging tracks changes in organ and tissue mass in obese and aging rats | |
| Schliewert et al. | Comparison of radiographic and computed tomographic images of the lungs in healthy neonatal foals | |
| Kindler et al. | Load-specific physical activity scores are related to tibia bone architecture | |
| CN113545796A (en) | Method for detecting pork quality character by using computer tomography scanning living body | |
| CN110179488B (en) | Method for detecting pig living body carcass composition by using CT scanning data | |
| Young et al. | Computerised tomography for carcass analysis | |
| Font-i-Furnols et al. | Non-destructive evaluation of carcass and ham traits and meat quality assessment applied to early and late immunocastrated Iberian pigs | |
| Morandi et al. | Correlation of helical and incremental high-resolution thin-section computed tomographic imaging with histomorphometric quantitative evaluation of lungs in dogs | |
| Köver et al. | In vivo measurement of body parts and fat deposition in rabbits by MRI. | |
| Ceballos-Francisco et al. | Radiological characterization of gilthead seabream (Sparus aurata) fat by X-ray micro-computed tomography | |
| Mitchell et al. | Techniques for measuring body composition of swine | |
| CN106885816A (en) | A kind of method of Non-Destructive Testing injecting glue aquatic products | |
| Romvári et al. | Noninvasive characterization of the turkey heart performance and its relationship to skeletal muscle volume | |
| CN111990331B (en) | Living body breeding method for pork streaky pork | |
| Kongsro | Genetic gain on body composition in pigs by computed tomography (CT): return on investment | |
| Del Chicca et al. | MRI-based quantification of adipose tissue distribution in healthy adult cats during body weight gain | |
| Clelland | Use of computed tomography based predictors of meat quality in sheep breeding programmes | |
| Berthold et al. | Magnetic resonance imaging and spectroscopy (MRI, MRS) of seasonal patterns of body composition: a methodological pilot study in White Storks (Ciconia ciconia) | |
| Kleczek et al. | Relationships between breast muscle thickness measured by ultrasonography and meatiness and fatness in broiler chickens |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |