CN103871025A - Medical image enhancing method and system - Google Patents

Abstract

The invention provides a medical image enhancing method. The method comprises the following steps: an original medical image sequence of an image object is collected; one image is picked from the sequence as a reference image and an interesting area of the image object is picked as an enhanced part; a weighted value of each pixel in the sequence is calculated according to an expression shown in the specification, wherein a variable shown in the specification represents the location of a pixel of the interesting area, i represents the ith image in the sequence, N represents the total number of images in the sequence, Si represents a pixel value of the interesting area in the ith image of the sequence, and Sref, i represents a pixel value of the reference image; and a final enhanced image is calculated according to an expression shown in the specification. The method is suitable for medical imaging devices, low in cost, and small in calculation. Obtained enhanced images are high in quality, with abundant information. The method can reveal suspicious pathological changes which are buried in noise or cannot be understood directly. The invention also provides a medical image enhancing system.

Description

Medical imaging enhancement method and system thereof

Technical field

The present invention relates to a kind of medical imaging enhancement method and system thereof, relate in particular to a kind of medical imaging enhancement method and system thereof of pixel weighting based on organizing similarity.

Background technology

Medical image is the important information source of early discovery of disease, early treatment and clinical diagnosis etc., and it generally obtains by medical imaging devices.The value of medical imaging devices depends on information content and the precision of existing target to be detected in its image obtaining.Current high-end medical imaging devices, as 3.0T or above MRI (Magnetic Resonance Imaging, magnetic resonance imaging) medical imaging devices, CBCT (Cone Beam Computed Tomography, Cone-Beam CT) medical imaging devices, US (Ultra-Sonography, ultrasonic) medical imaging devices, X-ray (X-rays, X-ray imaging) medical imaging devices etc., relatively high image resolution can be provided.But, introduce these expensive imaging devices, will increase the fund input of hospital, and then increase the health care expenditures of sufferer, and be unfavorable for the implementation of whole people's low cost health plan.

The medical image or the image sequence that obtain based on existing medical imaging devices, by medical image post-processing approach, strengthen resolution, data message amount and the precision of information of image, become gradually a kind of feasible reliable method for doctor's diagnosis provides decision data relatively reliably.

At present, SR (Super-Resolution, super-resolution) technology is the post-processing approach of main medical imaging enhancement, its image data based on gathering, improve the resolution of image by interpolation or machine learning, mainly comprise traditional simple method of interpolation and emerging complicated machine learning method.Wherein, method of interpolation comprises linear interpolation, B spline interpolation etc., mainly (if the pixel value of image is to meet certain function regularity of distribution under certain hypothesis, or judgement that may be smoother in certain region), introduce new pixel, it can improve image resolution, but local detail information that but can fuzzy image data, as border, profile etc., and in medical image, these detailed information may be local petechial hemorrhages, and pseudocapsule, the fine vascular etc. of kidney have important value in medical diagnosis and surgical navigational.Machine learning method mainly comprises as the machine learning method based on image blocks, machine learning method based on data set training etc., it also can improve the resolution of image, but, result depends on the specific aim of image blocks or data training set, be difficult to stable SNR (the Signal to Noise Ratio that strengthens image data, signal noise ratio) and high CNR (Contrast to Noise Ratio, contrast noise ratio), and its interpolation scale-up factor must be integral multiple, and computing time is also long.Therefore, these two class methods all cannot stably strengthen image data, do not possess the ability of practical clinical.

Summary of the invention

For the problems referred to above, the object of this invention is to provide a kind of medical imaging enhancement method, described medical imaging enhancement method comprises the following steps:

Gather the primitive medicine image sequence of imaging object;

From described primitive medicine image sequence, choose a conduct with reference to image, and the area-of-interest of choosing imaging object is as strengthening part;

According to expression formula calculate the weighted value of each pixel in described primitive medicine image sequence, wherein

the location of pixels that represents area-of-interest, i represents that in described primitive medicine image sequence, i opens image, N represents the sum of image in described primitive medicine image sequence, S _irepresent that i in described primitive medicine image sequence opens the pixel value of the area-of-interest in image, S _{ref, i}represent the described pixel value with reference to image; And

According to expression formula

calculate final enhancing image.

In the present invention's one preferred embodiments, described primitive medicine image sequence is original MRI image sequence, original X ray image sequence, original CT image sequence or original ultrasonic image sequence.

In the present invention's one preferred embodiments, while gathering the original MRI image sequence of imaging object, according to expression formula I _mri=N _h* (1-e ^-TR/T1) * (e ^-TE/T2) gather, wherein, N _hfor H proton density, the T of imaging object ₁for longitudinal relaxation time, T ₂for T2, TR is that repetition time, TE are the echo time, only changes in TR and TE in gatherer process.

In the present invention's one preferred embodiments, described original MRI image sequence comprises many echoes T2* weighting magnetic resonance image sequence, many echoes magnetic susceptibility weighting image sequence or many b value Diffusion-Weighted MR Imaging sequences.

In the present invention's one preferred embodiments, described is any in described primitive medicine image sequence with reference to image.

The present invention provides a kind of medical imaging enhancement system in addition, and it comprises:

Image collection unit, it gathers the primitive medicine image sequence of imaging object;

With reference to Extraction of Image unit, it extracts a conduct with reference to image from described primitive medicine image sequence;

Strengthen extracting section unit, its area-of-interest that extracts imaging object is as strengthening part; And

Processing unit, its with described image collection unit, be describedly connected with reference to Extraction of Image unit and described enhancing extracting section unit, described processing unit is processed described primitive medicine image sequence, comprises the following steps:

Determine the tissue of interest of each image in described primitive medicine image sequence and with reference to the square error between imaged tissue

wherein,

the location of pixels that represents area-of-interest, i represents that in described primitive medicine image sequence, i opens image, N represents the sum of image in described primitive medicine image sequence, S _irepresent that i in described primitive medicine image sequence opens the pixel value of the area-of-interest in image, S _{ref, i}represent the described pixel value with reference to image;

Introduce ratio of similitude parameter lambda and make S _ipixel and S _{ref, i}be close, obtain ${\mathrm{MSE}}_{\mathrm{\λ}}\left(\stackrel{\→}{r}\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{(S_i\left(\stackrel{\→}{r}\right)-\mathrm{\λ}\left(\stackrel{\→}{r}\right)\·S_{\mathrm{ref},i}\left(\stackrel{\→}{r}\right))}^2;$

Calculate the weighted value of final each pixel of image $\mathrm{\λ}\left(\stackrel{\→}{r}\right)=\frac{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{S}_i\left(\stackrel{\→}{r}\right)\·S_{\mathrm{ref},i}\left(\stackrel{\→}{r}\right)}{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{S}_{\mathrm{ref},i}^2\left(\stackrel{\→}{r}\right)};$ And

Calculate final enhancing image $S_{\mathrm{SPWI}}=\mathrm{\λ}\left(\stackrel{\→}{r}\right)\·S_{\mathrm{ref},i}\left(\stackrel{\→}{r}\right).$

In the present invention's one preferred embodiments, image collection unit carries out MRI imaging, CT imaging, x-ray imaging or ultrasonic imaging to imaging object.

In the present invention's one preferred embodiments, be describedly connected with described image collection unit with reference to Extraction of Image unit.

Compared to prior art, medical imaging enhancement method provided by the invention is applicable to medical imaging devices, and especially, on low side medical imaging devices, cost is low, calculated amount is little; The enhancing quality of image of utilizing described medical imaging enhancement method to obtain is high, quantity of information is abundant, can excavate the suspicious lesions of being buried or cannot intuitively understand by noise in medical image sequence, as pseudocapsule of blood vessel petechial hemorrhage, kidney etc., for doctor's inspection, discovery, diagnosis and surgical navigational etc. provide sufficient image foundation.Described medical imaging enhancement method can change the image sequence gathering by the one-parameter of processing area-of-interest (corresponding linked groups), can reliablely and stablely obtain the enhancing image of high SNR and high CNR.

Above-mentioned explanation is only the general introduction of technical solution of the present invention, in order to better understand technological means of the present invention, and can be implemented according to the content of instructions, and for above and other objects of the present invention, feature and advantage can be become apparent, below especially exemplified by embodiment, and coordinate accompanying drawing, be described in detail as follows.

Accompanying drawing explanation

The schematic diagram of the medical imaging enhancement system that Fig. 1 provides for first embodiment of the invention.

The process flow diagram of the medical imaging enhancement method that Fig. 2 provides for second embodiment of the invention.

Fig. 3 is the background that adopts the emulation experiment one that shown in Fig. 2, medical imaging enhancement method is carried out.

SNR and the CNR value of SPWI image and raw video in emulation experiment one shown in Fig. 3 when Fig. 4 is Noise 5%.

SNR and the CNR value of SPWI image and raw video in emulation experiment one shown in Fig. 3 when Fig. 5 is Noise 10%.

SNR and the CNR value of SPWI image and raw video in emulation experiment one shown in Fig. 3 when Fig. 6 is Noise 20%.

Fig. 7 is background and the simulation experiment result that adopts the emulation experiment two that shown in Fig. 2, medical imaging enhancement method is carried out.

Fig. 8 is the lifting scale map that shown in Fig. 7, in emulation experiment two, SNR, the CNR of SPWI image under the different noise contents of primitive medicine image is worth most.

Fig. 9 adopts the clinical trial result that shown in Fig. 2, medical imaging enhancement method strengthens the SWI image of brain.

Figure 10 adopts the clinical trial result that shown in Fig. 2, medical imaging enhancement method strengthens the T2* weighting MR image of spleen.

Figure 11 and Figure 12 adopt the clinical trial result that shown in Fig. 2, medical imaging enhancement method strengthens respectively two groups of kidney neoplasms patients' T2* weighting MR image.

Figure 13 and Figure 14 are that SNR and the CNR of two patient's images shown in Figure 11 and Figure 12 strengthens scale map.

Embodiment

Below in conjunction with drawings and the specific embodiments, the present invention is further detailed explanation.

Refer to Fig. 1, first embodiment of the invention provides a kind of medical imaging enhancement system 100, it comprises image collection unit 10, with reference to Extraction of Image unit 20, strengthen extracting section unit 30 and processing unit 40, described processing unit 40 and described image collection unit 10, be describedly connected with reference to Extraction of Image unit 20 and described enhancing extracting section unit 30.

Described image collection unit 10 gathers the primitive medicine image sequence of imaging object, is appreciated that described primitive medicine image sequence comprises that N opens image.

Described image collection unit 10 can be medical imaging devices, as MRI image documentation equipment, X ray image equipment, CT image documentation equipment, ultrasonic image equipment or other low side medical imaging devices, the primitive medicine image sequence of the imaging object of its collection can be original MRI image sequence, original X ray image sequence, original CT image sequence, original ultrasonic image sequence or other image sequences.

Be understandable that, the primitive medicine image sequence of the imaging object that described image collection unit 10 gathers can be stored in the storage unit of described image collection unit 10, also can be stored in described processing module 40.

Describedly from described primitive medicine image sequence, extract a conduct with reference to Extraction of Image unit 20 with reference to image, in the present embodiment, described is any in described primitive medicine image sequence with reference to image.

Be understandable that, describedly can be connected with described image collection unit 10 with reference to Extraction of Image unit 20, thus, describedly directly extract in the storage unit from described image collection unit 10 described with reference to image with reference to Extraction of Image unit 20.Certainly, describedly also can from described processing unit 40, extract described with reference to image with reference to Extraction of Image unit 20.

Described enhancing extracting section unit 30 extracts the area-of-interest of imaging object as strengthening part, as interested in the spinal cord region in the image of patient's waist transversal section, extracts vertebra region as strengthening part.

Described processing unit 40 is processed described primitive medicine image sequence, comprising:

Define the tissue of interest of each image in described primitive medicine image sequence and with reference to the square error between imaged tissue:

$\mathrm{MSE}\left(\stackrel{\→}{r}\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{(S_i\left(\stackrel{\→}{r}\right)-S_{\mathrm{ref},i}\left(\stackrel{\→}{r}\right))}^2---\left(1\right)$

Wherein,

the location of pixels that represents area-of-interest, i represents the numbering of each image in described primitive medicine image sequence, N represents the sum of image in described primitive medicine image sequence, S _irepresent that i in described primitive medicine image sequence opens the pixel value of the area-of-interest in image, S _{ref, i}represent the described pixel value with reference to image.

For making S _ipixel and S _{ref, i}more approaching, in above-mentioned expression formula (1), introduce ratio of similitude parameter lambda, obtain:

${\mathrm{MSE}}_{\mathrm{\λ}}\left(\stackrel{\→}{r}\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{(S_i\left(\stackrel{\→}{r}\right)-\mathrm{\λ}\left(\stackrel{\→}{r}\right)\·S_{\mathrm{ref},i}\left(\stackrel{\→}{r}\right))}^2---\left(2\right)$

Expression formula (2) is asked

first order derivative and second derivative, obtain respectively:

${\mathrm{MSE}}_{\mathrm{\λ}}^{\′}\left(\stackrel{\→}{r}\right)=2\·(\mathrm{\λ}\left(\stackrel{\→}{r}\right)\·\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{S}_{\mathrm{ref},i}^2\left(\stackrel{\→}{r}\right)-\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{S}_i\left(\stackrel{\→}{r}\right)\·S_{\mathrm{ref},i}\left(\stackrel{\→}{r}\right))---\left(3\right)$

${\mathrm{MSE}}_{\mathrm{\λ}}^{\′\′}\left(\stackrel{\→}{r}\right)=2\·\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{S}_{\mathrm{ref},i}^2\left(\stackrel{\→}{r}\right)---\left(4\right)$

Can know from expression formula (4),

there is minimum value.

Make expression formula (3) equal 0, made when minimum value

value:

$\mathrm{\λ}\left(\stackrel{\→}{r}\right)=\frac{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{S}_i\left(\stackrel{\→}{r}\right)\·S_{\mathrm{ref},i}\left(\stackrel{\→}{r}\right)}{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{S}_{\mathrm{ref},i}^2\left(\stackrel{\→}{r}\right)}---\left(5\right)$

Expression formula (5) has been determined the weighted value of final each pixel of image, also can find out from expression formula (5), λ is area-of-interest and the weighting ratio of the signal intensity with reference to image, it has reflected area-of-interest and with reference to the similarity between corresponding two tissues of image, has possessed the information of all described primitive medicine image sequences simultaneously.

Determine final enhancing image, its expression formula is:

$S_{\mathrm{SPWI}}=\mathrm{\λ}\left(\stackrel{\→}{r}\right)\·S_{\mathrm{ref},i}\left(\stackrel{\→}{r}\right)---\left(6\right)$

Due to there is each image and the proportional similarity information with reference to image in described primitive medicine image sequence, therefore can find out S by expression formula (6) _sPWIcombine the key message of described primitive medicine image sequence, i.e. S _sPWIcharacterized described primitive medicine image sequence, and it strengthens to described primitive medicine image sequence.

Refer to Fig. 2, second embodiment of the invention provides a kind of medical imaging enhancement method, and it comprises the following steps:

The primitive medicine image sequence of S1, collection imaging object.

Be understandable that, can utilize MRI image documentation equipment, CT image documentation equipment, X ray image equipment or ultrasonic image equipment imaging object to be carried out to the collection of primitive medicine image sequence.

S3, from described primitive medicine image sequence, choose a conduct with reference to image, and the area-of-interest of choosing imaging object is as strengthening part.

In the present embodiment, utilize and describedly from described primitive medicine image sequence, choose any conduct with reference to Extraction of Image unit 20 with reference to image; Utilize area-of-interest that described enhancing extracting section unit 30 extracts imaging object as strengthening part.

S5, calculate the weighted value of each pixel in described primitive medicine image sequence.

In the present embodiment, described processing unit 40 calculates weighted value according to above-mentioned expression formula (5), has determined described enhancing part (area-of-interest) and described with reference to the similarity between corresponding two tissues of image.

S7, calculate final enhancing image.

In the present embodiment, calculate final enhancing image according to above-mentioned expression formula (6), obtain the image after described primitive medicine image sequence is strengthened.

For MRI image, while gathering the original MRI image sequence of imaging object, because MRI image is to utilize the magnetic resonance physical phenomenon of H proton to realize, its image-forming principle is:

I _mri＝N _H*(1-e ^-TR/T1)*(e ^-TE/T2) (7)

From expression formula (7), MRI image depends primarily on 5 parameters, and these 5 parameters can be divided into 2 classes: 1) immutable parameter, also can be described as biological nature parameter, and comprise the H proton density N at tissue/organ/position to be imaged _h, longitudinal relaxation time T ₁with T2 T ₂.2) variable element, also can be described as equipment physical parameter, comprises repetition time TR (Time of Repetition), echo time TE (Time of Echo).

Be understandable that, in the time that MRI imaging is carried out in a certain particular organization, organ or position, TR and TE are 2 very important parameters.By the setting of these 2 parameters, can be divided into T1 weighting image, T2 weighting image, proton weighting image.The variation of TR and two parameters of TE, has reflected different biological tissue's information.

In the present embodiment, imaging object is carried out in process that original MRI image sequence gathers, in process, only changing in two parameters of TR and TE.As for a certain tissue to be imaged, fixing TR is fixed value, and changes TE, has and in expression formula (7), has constant:

C＝N _H*(1-e ^-TR/T1) (8)

Thus, the expression formula of MRI video imaging principle (7) can be rewritten as:

I _mri＝C*(e ^-TE/T2) (9)

Obviously, for this tissue to be imaged, TE is set as to a certain zone of reasonableness, can obtains N and open original MRI image sequence.Be this N to open original MRI image sequence be for same object to be imaged, in TE zone of reasonableness, imaging is obtained for N time.

Similarly, when fixing TE, also can obtain the original MRI image sequence changing based on TR.

Be understandable that, described original MRI image sequence can comprise many echoes T2* weighting magnetic resonance image sequence, many echoes magnetic susceptibility weighting image sequence or many b value Diffusion-Weighted MR Imaging sequences.

Be understandable that, be different from CT imaging, x-ray imaging or the ultrasonic imaging etc. of MRI video imaging principle for image-forming principle, one in its parameter be can only change equally, original CT image sequence, original X ray image sequence or original ultrasonic image sequence opened to obtain N.

Refer to Fig. 3, for adopting the background of the analog simulation experiment one that described medical imaging enhancement method carries out.Wherein, primitive medicine image sequence is the MRI image data of T2* weighting.The step-length of TE is 2ms, initial TE=2ms, last TE=24ms eventually, the raw 12 width images of common property.Organize 1:T2*=20ms, from left to right, live width and line interval are respectively 1-5 pixel; Organize 2:T2*=40ms, from left to right, live width and line interval are respectively 1-5 pixel; Organize 3:T2*=80ms, from left to right, live width and line interval are respectively 1-5 pixel; Organize 4:T2*=50ms, background resolution is 256*256; Full figure true resolution is 384*384.

In view of being weighted summation to the pixel in primitive medicine image sequence, the similarity of described medical imaging enhancement method provided by the invention based on tissue process, be defined as SPWM (Similarity-based Pixel-Weighted Method herein, based on the pixel weighting technique of organizing similarity), the enhancing image finally obtaining is referred to as SPWI (Similarity-based Pixel-Weighted Image, based on the image of pixel weighting technique of organizing similarity).

For verifying the reliability of described medical imaging enhancement method, in emulation experiment, adding average is 0, and deviation is respectively the peaked normal state Gaussian noise of pixel of 5%, 10%, 20% TE=2ms image.

For described medical imaging enhancement method image sequence after treatment is analyzed, list TE=2ms, 8ms, 14ms, 20ms, and the SNR separately of SPWI image after treatment and CNR value.Noise 20% in Noise 10%, Fig. 6 in Noise 5%, Fig. 5 in Fig. 4.

Can visually see from Fig. 3 to 6, along with the increase (5% of noise, 10%, 20%), image effect through described medical imaging enhancement method SPWI after treatment is relatively best, comprise that tissue of interest 1,2,3 has all obtained certain manifesting, and is more better than the effect of raw video.

Detailed SNR and CNR improve ratio please respectively referring to table 1 and table 2, and wherein, tissue 4 as a reference.

The SNR of table 1 primitive medicine image _maxwith the SNR adopting after described medical imaging enhancement method strengthens _sPWIcontrast

The CNR of table 2 primitive medicine image _maxwith the CNR after enhancing _sPWI

Can quantitatively be seen by table 1, SNR is minimum 185.86% raising (reference tissue 4, noise 5%).Can quantitatively be seen by table 2, to organize 4 as with reference to tissue, CNR is minimum 78.74% raising (tissue of interest 3-reference tissue 4, noise 20%).Thus, prove that described medical imaging enhancement method provided by the invention, in MRI image strengthens, can improve SNR and CNR effectively.

Refer to Fig. 7, for adopting background and the simulation experiment result of the analog simulation experiment two that described medical imaging enhancement method carries out.Wherein, raw video sequence is the imaging data of T2* weighting.The step-length of TE is 6ms, initial TE=2ms, and last TE=20ms eventually, symbiosis becomes 4 width images.White arrow indication is in image, to be used for the fine structure tissue 1,2,3 that compares.For verifying the reliability of described medical imaging enhancement method, adding average is 0, and deviation is respectively the peaked normal state Gaussian noise of pixel of 4%, 8%, 16% TE=2ms image.

Can be seen intuitively by Fig. 7, along with the increase (4%, 8% of noise, 16%), the quality of image through described medical imaging enhancement method SPWI after treatment is best, comprises that tissue of interest 1,2,3 has all obtained certain manifesting, and is more better than the effect of raw video.

Refer to Fig. 8, the lifting scale map being worth most for SNR, the CNR of SPWI image under the different noise contents of primitive medicine image.

Can quantitatively see from Fig. 8, SNR has great enhancing, for different tissues, SNR strengthens that ratio is minimum respectively a 294% (tissue 1, noise 4%), 272% (tissue 2, noise 4%), 219% (tissue 1, noise 4%); Meanwhile, to organize 1 as with reference to tissue, CNR is minimum 201% raising (tissue of interest 2-reference tissue 1, noise 16%).Thus, show that described medical imaging enhancement method provided by the invention has improved SNR and the CNR of image effectively, has increased quantity of information and the precision of information of image.

Referring to Fig. 9, is the clinical trial result that adopts SWI (susceptibility weighted imaging, the magnetosensitive sensitivity weighted imaging) image of described medical imaging enhancement method to brain to strengthen.Wherein, primitive medicine image sequence is the SWI image of many echoes.TE is respectively 6.76ms, 14.11ms, 21.46ms and 28.81ms, totally 4 width images, and SPWI is the image after strengthening.In figure, 1) lenticular nucleus: entity white arrow indication; 2) cerebrospinal fluid: dotted line white arrow indication; 3) blood capillary: entity red arrow indication.

As seen from Figure 9, adopt medical imaging enhancement method of the present invention to carry out after figure image intensifying, in primitive medicine image, can be observed more clearly by the tiny blood vessels of noise takeover, be that SPWI image effect is best, even be hidden in the blood capillary in noise, carry out, after figure image intensifying, also can revealing more clearly through described medical imaging enhancement method.

Detailed SNR and CNR improve ratio please respectively referring to table 3 and table 4, and wherein table 3 is organized as reference using white matter and lenticular nucleus respectively, and table 4 is to organize as reference each other.

Table 3 strengthens image and the raising of raw video in SNR parameter

As shown in Table 3, compared to the maximum S/N R of primitive medicine image, carry out after figure image intensifying through described medical imaging enhancement method, the SNR of SPWI image can obtain minimum 52.91% and 24.87% lifting respectively.

Table 4 strengthens image and the raising of raw video in CNR parameter

The low energy of CNR of as shown in Table 4, carrying out the SPWI image after figure image intensifying through described medical imaging enhancement method obtains 73.41% lifting.

Referring to Figure 10, is the clinical trial result that adopts described medical imaging enhancement method to strengthen the T2* weighting MR image of spleen.Wherein, primitive medicine image data sequence is the T2* weighting image of many echoes.TE is respectively 2.35ms, 8.29ms, 14.23ms and 20.17ms, totally 4 width images, and SPWI is the image after strengthening.In figure, 1) spleen: entity white arrow indication; 2) blood capillary: dotted line white arrow indication.

As seen from Figure 10, adopt medical imaging enhancement method of the present invention to carry out, after figure image intensifying, in primitive medicine image, can being observed more clearly by the tiny blood vessels of noise takeover.

Detailed SNR and CNR improve ratio please respectively referring to table 5 and table 6, and wherein, table 5 is organized as reference using spleen and blood capillary respectively, and Fig. 6 is to organize as reference each other.

Table 5 strengthens image and the raising of raw video in SNR parameter

As shown in Table 5, compared to the maximum S/N R of primitive medicine image, carry out after figure image intensifying through described medical imaging enhancement method, the SNR of SPWI image can obtain minimum 14.50% and 100.50% lifting respectively.

Table 6 strengthens image and the raising of raw video in CNR parameter

The low energy of CNR of as shown in Table 6, carrying out the SPWI image after figure image intensifying through described medical imaging enhancement method obtains 82.95% lifting.

Referring to Figure 11 and Figure 12, is the clinical trial result that adopts described medical imaging enhancement method to strengthen two groups of kidney neoplasms patients' (#1 and #2) T2* weighting MR image.Wherein, primitive medicine image sequence is many echoes T2* weighting image of two patients' (#1 and #2) kidney.TE is respectively 2.35ms, 8.29ms, 14.23ms and 20.17ms, totally 4 width images (A-D), and E is that the SPWI take kidney essence as reference tissue strengthens image.White arrow in Figure 10 refers to several petechial hemorrhages position of kidney; White arrow in Figure 11 refers to the high signal area of the pseudocapsule in kidney neoplasms.

All can be found out by Figure 11 and Figure 12, adopt medical imaging enhancement method of the present invention to carry out after figure image intensifying, in primitive medicine image, all can be observed more clearly by noise takeover or fuzzy several petechial hemorrhages position.

As shown in figure 13, the SNR of two patient's images and CNR strengthen ratio as shown in figure 14 to the enhancing effect of described two patients' SNR and CNR.

All can be found out by Figure 13 and Figure 14, the SNR and the CNR that carry out the SPWI image after figure image intensifying through described medical imaging enhancement method all can obtain preferably and promote.

The above, only embodiments of the invention, not the present invention is done to any pro forma restriction, although the present invention discloses as above with embodiment, but not in order to limit the present invention, any those skilled in the art, do not departing within the scope of technical solution of the present invention, when can utilizing the technology contents of above-mentioned announcement to make a little change or being modified to the equivalent embodiment of equivalent variations, in every case be not depart from technical solution of the present invention content, any simple modification of above embodiment being done according to technical spirit of the present invention, equivalent variations and modification, all still belong in the scope of technical solution of the present invention.

Claims (8)

1. a medical imaging enhancement method, is characterized in that, described medical imaging enhancement method comprises the following steps:

Gather the primitive medicine image sequence of imaging object;

From described primitive medicine image sequence, choose a conduct with reference to image, and the area-of-interest of choosing imaging object is as strengthening part;

According to expression formula calculate the weighted value of each pixel in described primitive medicine image sequence, wherein,

the location of pixels that represents area-of-interest, i represents that in described primitive medicine image sequence, i opens image, N represents the sum of image in described primitive medicine image sequence, S _irepresent that i in described primitive medicine image sequence opens the pixel value of the area-of-interest in image, S _{ref, i}represent the described pixel value with reference to image; And

According to expression formula

calculate final enhancing image.

2. medical imaging enhancement method as claimed in claim 1, is characterized in that, described primitive medicine image sequence is original MRI image sequence, original X ray image sequence, original CT image sequence or original ultrasonic image sequence.

3. medical imaging enhancement method as claimed in claim 2, is characterized in that, while gathering the original MRI image sequence of imaging object, according to expression formula I _mri=N _h* (1-e ^-TR/T1) * (e ^-TE/T2) gather, wherein, N _hfor H proton density, the T of imaging object ₁for longitudinal relaxation time, T ₂for T2, TR is that repetition time, TE are the echo time, only changes in TR and TE in gatherer process.

4. medical imaging enhancement method as claimed in claim 2, is characterized in that, described original MRI image sequence comprises many echoes T2* weighting magnetic resonance image sequence, many echoes magnetic susceptibility weighting image sequence or many b value Diffusion-Weighted MR Imaging sequences.

5. medical imaging enhancement method as claimed in claim 1, is characterized in that, described is any in described primitive medicine image sequence with reference to image.

6. a medical imaging enhancement system, is characterized in that, described medical imaging enhancement system comprises:

Image collection unit, it gathers the primitive medicine image sequence of imaging object;

With reference to Extraction of Image unit, it extracts a conduct with reference to image from described primitive medicine image sequence;

Strengthen extracting section unit, its area-of-interest that extracts imaging object is as strengthening part; And

Processing unit, its with described image collection unit, be describedly connected with reference to Extraction of Image unit and described enhancing extracting section unit, described processing unit is processed described primitive medicine image sequence, comprises the following steps:

Determine the tissue of interest of each image in described primitive medicine image sequence and with reference to the square error between imaged tissue

wherein,

the location of pixels that represents area-of-interest, i represents that in described primitive medicine image sequence, i opens image, N represents the sum of image in described primitive medicine image sequence, S _irepresent that i in described primitive medicine image sequence opens the pixel value of the area-of-interest in image, S _{ref, i}represent the described pixel value with reference to image;

Introduce ratio of similitude parameter lambda and make S _ipixel and S _{ref, i}be close, obtain ${\mathrm{MSE}}_{\mathrm{\λ}}\left(\stackrel{\→}{r}\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{(S_i\left(\stackrel{\→}{r}\right)-\mathrm{\λ}\left(\stackrel{\→}{r}\right)\·S_{\mathrm{ref},i}\left(\stackrel{\→}{r}\right))}^2;$

Calculate the weighted value of final each pixel of image

and

Calculate final enhancing image $S_{\mathrm{SPWI}}=\mathrm{\λ}\left(\stackrel{\→}{r}\right)\·S_{\mathrm{ref},i}\left(\stackrel{\→}{r}\right).$

7. medical imaging enhancement system as claimed in claim 6, is characterized in that, image collection unit carries out MRI imaging, CT imaging, x-ray imaging or ultrasonic imaging to imaging object.

8. medical imaging enhancement system as claimed in claim 6, is characterized in that, is describedly connected with described image collection unit with reference to Extraction of Image unit.

Images