Ultrasound method and system for perfusion monitoring

Abstract

An ultrasonic method for perfusion monitoring, comprising the steps of: acquiring an ultrasonic signal of a biological tissue; extracting a corresponding ultrasonic blood flow signal derived from the moving red blood cells from the ultrasonic signal; determining the intensity of the ultrasonic blood flow signal; determining intensity distribution of the ultrasonic blood flow signals; determining a current perfusion parameter value representative of a perfusion in the biological tissue based on the determined intensity profile of the ultrasonic blood flow signal; estimating a reference perfusion parameter value representative of perfusion in the biological tissue in a normal state based on at least one reference intensity profile of the ultrasonic blood flow signal; determining at least one critical perfusion parameter threshold based on the reference perfusion parameter value; and iteratively monitoring perfusion in the plurality of blood vessels by comparing the current perfusion parameter value to the at least one critical perfusion parameter threshold.

Description

TECHNICAL FIELD

[0001] The field of the invention relates to an ultrasound method and system for perfusion monitoring of biological tissue containing multiple blood vessels. Particular embodiments relate to an ultrasound method and system for non-invasive intraoperative perfusion monitoring of highly vascularized tissue. BACKGROUND

[0002] During neurosurgical procedures, brain blood vessels must be carefully dissected and manipulated. For vascular lesions such as aneurysms, arteriovenous malformations and arteriovenous fistulas, the goal is to remove, occlude or exclude the pathological (part of) vessel without affecting blood flow in the normal vessels. Likewise, in the case of brain tumors, resection should be limited to the pathological vessels only, and the surgeon should be careful to avoid sacrificing normal vessels. Failure to maintain adequate blood flow in the normal vessels will result in ischemia and stroke in the involved vascular territory. Neurosurgical procedures are associated with a relatively high risk of blood vessel damage or injury leading to ischemia and stroke. According to a recent multicenter international cohort study, the overall incidence of ischemic complications in neurosurgical aneurysm treatment was approximately 18%, and can even reach more than 50% in complex cases. For brain tumor surgery, recent publications also report a considerable incidence of surgery-related infarction confirmed by postoperative MRI.

[0003] It is important to note that the deleterious effects of impaired blood flow are reversible if ischemia is detected in time and blood flow is restored. Ideally, this requires a real-time tool that can non-invasively assess cerebral perfusion, has a broad cerebral coverage, and simultaneously samples both cortical and subcortical regions. However, there is currently no monitoring tool in the operating room that can adequately measure cerebral perfusion. As a result, the operator is mostly unaware of impaired cerebral blood flow during the procedure. Currently, the gold standard for assessing surgery-related ischemia is a neurological assessment of the patient after emergence from anesthesia. But by this time, ischemia is detected too late to take immediate surgical action.

[0004] The occurrence of intraoperative ischemia also applies to other types of surgery and surgical fields. For example, any type of surgery where vascular anastomosis needs to be performed. Examples include, but are not limited to, brain bypass surgery, breast reconstruction surgery with vascularized flaps, liver and kidney transplantation, and open and endovascular revascularization surgery.

[0005] There are existing methods for direct quantitative measurement of blood flow in large vessels, such as large cerebral vessels. On the one hand, invasive techniques using blood flow sensitive catheters requiring arterial puncture are known. On the other hand, non-invasive techniques using Transcranial Doppler Flowmetry (TCD) are known. Furthermore, large scale imaging techniques such as Positron Emission Tomography (PET), Single Photon Emission Computer Tomography (SPECT), Computed Tomography Perfusion (CTP) and Dynamic Susceptibility Contrast Magnetic Resonance Imaging (DSC-MRI) are also known to be able to measure blood flow, but they require injection of a tracer or dye in the blood flow. Finally, intraoperative electrophysiological methods, known as evoked potentials, can be used to assess the latency of long tracts in the central nervous system as an indirect way of monitoring brain injury. Of course, these methods do not measure perfusion, are not sensitive and specific enough to reliably detect low perfusion and ischemia, and are limited to sensory and motor cortices.

[0006] Available intraoperative tools can measure blood flow in large arteries either based on blood flow velocity (Charbel probe) or quantitatively in a qualitative way by Doppler signal intensity in large arteries (microdoppler probe), by imaging the filling of vessels after injection of a dye (indocyanine green angiography). SUMMARY

[0007] The above known modalities and techniques have major limitations for intraoperative application during surgery. They are either incompatible because of the use of radioactive tracers; or not practical because of the large size of the machine; not practical because of the lack of portability; at risk of vascular complications because of the catheter technique; limited in vascular accessibility in the case of TCD / Charbel probe / microdoppler probe; only provide static imaging instead of the required real-time imaging; cannot perform continuous recording or monitoring; are time consuming, etc.

[0008] It is an object of embodiments of the present invention to provide an ultrasound method and system allowing non-invasive intraoperative perfusion monitoring of biological tissue containing multiple blood vessels. More particularly, it is an object of embodiments of the present invention to provide an ultrasound method and system overcoming at least some and preferably all of the above limitations of known techniques.

[0009] According to a first aspect of the present application, there is provided an ultrasound method for perfusion monitoring of a biological tissue comprising a plurality of blood vessels. The method comprises the steps of: i) acquiring a set of ultrasound signals of the biological tissue; ii) extracting a corresponding set of ultrasound blood flow signals originating from red blood cells moving in the plurality of blood vessels from the set of ultrasound signals; iii) determining intensities of the ultrasound blood flow signals; iv) determining an intensity distribution of the ultrasound blood flow signals based on the determined intensities; and v) determining a current perfusion parameter value representative of perfusion in the blood vessels of the biological tissue based on the determined intensity distribution of the ultrasound blood flow signals. The method further comprises the steps of: vi) estimating a reference perfusion parameter value representative of blood vessel perfusion in the biological tissue under normal conditions based on at least one normal intensity distribution of the ultrasound blood flow signals; vii) determining at least one critical perfusion parameter threshold value based on the reference perfusion parameter value. The method further comprises iteratively repeating steps i) - v) to monitor the current perfusion parameter value and comparing the monitored current perfusion parameter value to the at least one critical perfusion parameter threshold value.

[0010] Embodiments of the present application are based, inter alia, on the recognition that within a highly vascularized tissue (e.g. the brain), the overall distribution of blood vessels (especially at the arteriolar level) is essentially homogeneous, and on the recognition that information based on such blood vessel distribution (rather than information about individual blood vessels) can be used to efficiently monitor perfusion in real-time. Furthermore, since the blood vessel density in such tissues is close to constant, it can be assumed that the distribution of ultrasound signal (e.g. micro-Doppler signal) intensities between voxels is related to the distribution of blood volume within the imaged volume of the respective tissue. More particularly, it was found that examining the intensity distribution of the ultrasound blood flow signals allows for absolute value monitoring of perfusion, rather than just relative value monitoring. Furthermore, the computational intensity required to iteratively determine and examine the intensity distribution of the existing blood vessels is lower and takes less time than individually visualizing, imaging or examining each of the existing blood vessels. Thus, the proposed ultrasound method is particularly suitable for real-time perfusion monitoring.

[0011] In this aspect of the present application, the intensities of the ultrasound blood flow signals are used as an indicator of the blood volume distribution within the tissue. However, it will be clear to the skilled person that, alternatively or additionally, other parameters can be determined from the ultrasound signals, such as mean velocity and corresponding distribution, to provide information about the blood volume distribution and / or movement within the tissue.

[0012] Although the ultrasound method for perfusion monitoring was initially developed as an intraoperative tool, it will be clear to the skilled person that the method can also be applied in other scenarios, such as in a radiology department, intensive care unit, outpatient department, or even at a patient's home. In essence, the proposed ultrasound method for perfusion monitoring can be performed on any biological soft tissue having a large number of blood vessels.

[0013] Preferably, determining the current perfusion parameter value comprises calculating an Area Under Curve (AUC) value of at least a relevant portion of the intensity profile (more preferably only the relevant portion of the intensity profile). The benefit of only considering the relevant portion of the intensity profile when calculating the AUC value is that outliers in the intensity profile are filtered out, thereby improving the accuracy of the monitoring method. The relevant portion of the intensity profile is preferably in the range of the 10th percentile to the 90th percentile of the intensity profile, more preferably in the range of the 15th percentile to the 85th percentile of the intensity profile, and most preferably in the range of the 20th percentile to the 80th percentile of the intensity profile. It was found that these percentiles of the intensity profile accurately represent the blood volume in small arteries that are of particular interest when monitoring perfusion.

[0014] In a preferred embodiment, the at least one normal intensity profile of the ultrasound blood flow signal corresponds to an intensity profile of the ultrasound blood flow signal determined prior to operating the biological tissue. The at least one normal intensity profile allows for determining a baseline value of normal or healthy perfusion in the respective biological tissue. In case the patient is about to undergo surgery, such a normal intensity profile can be determined by determining the intensity profile of the ultrasound blood flow signal of this particular patient prior to starting the surgery. Such a baseline value can be determined by iteratively determining the current perfusion parameter value over a predefined period of time (e.g. 5 minutes) prior to the surgery and averaging the current perfusion parameters determined over said predefined period of time. In this way (which is also referred to as a continuous method), the baseline value related to the respective patient can be determined in an efficient manner. It is clear to the person skilled in the art that the duration of the predefined period of time can vary and can be set on a case-by-case basis depending on the specific circumstances of the current case. Likewise, it is clear to the person skilled in the art that any known averaging technique can be used.

[0015] In an alternative preferred embodiment, the at least one normal intensity profile of the ultrasound blood flow signal corresponds to a predetermined baseline intensity profile of the ultrasound blood flow signal. Such a predetermined baseline intensity profile can be statistically determined based on ultrasound measurements performed on similar tissues of other patients. Thus, in this way, a baseline value of normal or healthy perfusion in the respective biological tissue can also be determined, albeit based on ultrasound measurements of other patients.

[0016] Although this method of determining a baseline value of normal or healthy perfusion (also referred to as a non-continuous method) can be less accurate for a particular patient compared to the previously described embodiments, it is more time efficient as no additional ultrasound measurements need to be directly performed prior to operating the biological tissue.

[0017] According to one embodiment, the biological tissue is brain tissue. The brain is a highly vascularized organ, receiving approximately 20% of the cardiac output to meet metabolic demands. Brain tissue is highly vascularized to facilitate the delivery of oxygen and glucose. The adult human brain consumes approximately 5 mg of glucose per 100 grams of tissue per minute and approximately 3 mL of oxygen per 100 grams of tissue per minute. Failure of the vascular system to deliver sufficient blood in a timely manner will result in dysfunction, which in turn leads to cessation of brain function and ultimately loss of brain tissue. Depending on the degree of reduced blood flow (and thus reduced oxygen delivery) and the extent of tissue damage, these conditions are referred to as hypoxemia, ischemia, or stroke. Under normal circumstances, the human Cerebral Blood Flow (CBF) is approximately 50 mL per 100 grams of brain tissue per minute. Historical studies in non-human primates have shown that the threshold for cessation of electrical activity is approximately 25 mL / 100 g / min of CBF. Further reduction to below approximately 10-15 mL / 100 g / min results in cell death and cerebral infarction, i.e. stroke. CBF is the main clinical parameter for assessing cerebral perfusion and this illustrates the importance of the presently proposed embodiments of the ultrasound method for perfusion monitoring. Although the proposed ultrasound method is particularly suitable for use during human brain surgery, the low perfusion, ischemia and / or hyperperfusion that can occur intraoperatively is also applicable to other types of surgery and surgical fields. More particularly, the proposed ultrasound method can be used beneficially in connection with procedures or surgeries involving any vital organ, allogeneic or autologous transplants. A non-exhaustive list of examples includes brain bypass surgery, breast reconstruction surgery with vascularized flaps, liver and kidney transplants, and open and endovascular revascularization surgery.

[0018] Preferably, the at least one critical parameter threshold comprises a first critical parameter threshold indicative of occurrence of low perfusion in the biological tissue. In one embodiment, the first critical parameter threshold is defined as half of the reference perfusion parameter value.

[0019] Preferably, the at least one critical parameter threshold comprises a second critical parameter threshold indicative of occurrence of ischemia in the biological tissue. In one embodiment, the second critical parameter threshold is defined as one fourth of the reference perfusion parameter value.

[0020] Preferably, the at least one critical parameter threshold comprises a third critical parameter threshold indicative of occurrence of hyperperfusion in the biological tissue. In one embodiment, the third critical parameter threshold is defined as an increase of 20% of the reference perfusion parameter value.

[0021] Preferably, determining the current perfusion parameter value comprises calculating a current skewness value of the determined intensity distribution.

[0022] In a preferred alternative embodiment, the ultrasound method further comprises calculating a current skewness value of the determined intensity distribution.

[0023] Preferably, the current skewness value is compared to a reference skewness value of at least one reference intensity profile of the ultrasound blood flow signal.

[0024] Preferably, determining the current perfusion parameter value comprises calculating a current kurtosis value of the determined intensity profile.

[0025] In a preferred alternative embodiment, the ultrasound method further comprises calculating a current kurtosis value of the determined intensity profile.

[0026] Preferably, the current kurtosis value is compared to a reference kurtosis value of at least one reference intensity profile of the ultrasound blood flow signal.

[0027] Preferably, extracting comprises selectively filtering the set of ultrasound blood flow signals originating from moving red blood cells in the plurality of blood vessels by applying a filter to the set of ultrasound signals. In one embodiment, applying a filter to the set of ultrasound signals comprises applying a high-pass filter to the set of ultrasound signals. The preferred high-pass filter is configured to filter out signals originating from tissue motion (relatively low velocity) and to retain signals originating from red blood cell motion (relatively high velocity). In this way, the remaining signals originate from moving red blood cells (velocity range of about 3 mm / s - 40 mm / s) and constitute the ultrasound blood flow signal. In an alternative embodiment, the ultrasound blood flow signal can be extracted by other known techniques, such as singular value decomposition of the acquired ultrasound signals.

[0028] Preferably, the set of ultrasound signals of the biological tissue is acquired by ultrafast ultrasound imaging, more preferably by ultrafast Doppler imaging.

[0029] In this way, signals originating from moving red blood cells can be efficiently captured. Ultrafast (i.e. plane wave) Doppler imaging (UFD) is known to be useful as a preclinical imaging modality. There are scientific publications on the use of this technique in rodents and non-human primates. In humans, it has been used for neonatal brain imaging, functional testing in awake surgery, and imaging of vasculature in brain tumor cases. UFD provides large field of view, deep penetration, and high spatio-temporal resolution (about 100 pm at 10 Hz). The spatial resolution is typically on the order of penetrating arterioles, which means that it is suitable for imaging the vasculature up to the capillary level. The Doppler shifts caused by the movement of red blood cells in these tiny blood vessels can be extracted from the raw signal, resulting in a micro-Doppler signal that has been shown to be strongly correlated with Cerebral Blood Volume (CBV). Unlike measuring blood flow or velocity in a single (large) vessel in the subarachnoid space, micro-Doppler allows for the measurement of CBV in small blood vessels within the tissue over time. From this, the relative changes in CBV over time can be tracked in each individual voxel of the image, which in turn can be used as a parameter for tissue perfusion.

[0030] However, a problem with known UFD methods is that they do not provide absolute values of perfusion, and that these methods cannot distinguish between venous and arteriolar blood content. This means that even if arteriolar perfusion is reduced, venous congestion can induce an increase in CBV. This problem is solved by embodiments of the proposed ultrasound method, wherein a set of ultrasound signals of a biological tissue is acquired by ultrafast ultrasound imaging, more preferably by ultrafast Doppler imaging.

[0031] Furthermore, the exact velocity of blood in a single vessel can be determined using known ultrafast Doppler imaging techniques, as long as the angle of the vessel with respect to the ultrasound beam is known. This has been demonstrated in straight vessels of the rodent gyrenceless brain. In the gyrenceous human brain, the course of these typically tortuous vessels changes with the windings of the cerebral cortex, making reliable velocity analysis less feasible. Most importantly, such determinations are very cumbersome and require extensive calculations and multi-step analysis, and are therefore not suitable for clinical applications. This problem is solved by embodiments of the proposed ultrasound method, wherein the perfusion values are determined based on the intensity distribution of multiple vessels (rather than picking out a single vessel individually).

[0032] It will be appreciated by the skilled person that the technical considerations, functions and advantages described above for embodiments of the proposed ultrasound method apply mutatis mutandis to the corresponding ultrasound system embodiments described below.

[0033] According to a second aspect of the present invention, an ultrasound system for perfusion monitoring of a biological tissue comprising a plurality of blood vessels is provided. The ultrasound system comprises: - an ultrasound signal acquisition unit configured to acquire a set of ultrasound signals of a biological tissue; - an extraction unit configured to extract, from the set of ultrasound signals, a corresponding set of ultrasound blood flow signals originating from moving red blood cells; - an intensity determination unit configured to determine an intensity of the ultrasound blood flow signals; - an intensity distribution determination unit configured to determine an intensity distribution of the ultrasound blood flow signals based on the determined intensity; - a perfusion determination unit configured to: - determine, based on the determined intensity distribution of the ultrasound blood flow signals, a current perfusion parameter value representative of perfusion in blood vessels of the biological tissue; - estimate, based on at least one normal intensity distribution of the ultrasound blood flow signals, a reference perfusion parameter value representative of blood vessel perfusion in the biological tissue under normal conditions; and - determine, based on the reference perfusion parameter value, at least one critical perfusion parameter threshold value; and - a monitoring unit configured to monitor perfusion in the plurality of blood vessels by comparing the current perfusion parameter value with the at least one critical perfusion parameter threshold value.

[0034] In this aspect of the invention, the intensity of the ultrasound blood flow signals is used as an indicator of the blood volume distribution within the tissue. However, it will be clear to the skilled person that, alternatively or additionally, other parameters (e.g. mean velocity) and corresponding distributions can be determined from the ultrasound signals to provide information on the blood volume distribution and / or motion within the tissue.

[0035] Although the ultrasound system for perfusion monitoring is initially developed as an intraoperative tool, it will be clear to the skilled person that the system can also be applied in other scenarios, such as in a medical imaging department, in an intensive care unit, in an outpatient clinic, or even at a patient’s home. In essence, the proposed ultrasound system can be used to perform perfusion monitoring on any biological soft tissue having a large number of blood vessels.

[0036] Preferably, the perfusion determination unit is configured to determine the current perfusion parameter value by calculating an area under the curve (AUC) value of at least a relevant portion of the intensity distribution. Therein, the relevant portion of the intensity distribution is preferably in the range of the 10th percentile to the 90th percentile of the intensity distribution, more preferably in the range of the 15th percentile to the 85th percentile of the intensity distribution, and most preferably in the range of the 20th percentile to the 80th percentile of the intensity distribution.

[0037] In a preferred embodiment, the at least one normal intensity distribution of the ultrasound blood flow signals corresponds to an intensity distribution of the ultrasound blood flow signals determined prior to operating on the biological tissue.

[0038] In an alternative preferred embodiment, the at least one normal intensity profile of the ultrasound blood flow signal corresponds to a predetermined baseline intensity profile of the ultrasound blood flow signal.

[0039] According to one embodiment, the biological tissue is brain tissue.

[0040] Preferably, the at least one critical parameter threshold comprises a first critical parameter threshold indicative of occurrence of hypoperfusion in the biological tissue. In one embodiment, the first critical parameter threshold is defined as half of the reference perfusion parameter value.

[0041] Preferably, the at least one critical parameter threshold comprises a second critical parameter threshold indicative of occurrence of ischemia in the biological tissue. In one embodiment, the second critical parameter threshold is defined as one fourth of the reference perfusion parameter value.

[0042] Preferably, the at least one critical parameter threshold comprises a third critical parameter threshold indicative of occurrence of hyperperfusion in the biological tissue. In one embodiment, the third critical parameter threshold is defined as an increase of 20% of the reference perfusion parameter value.

[0043] Preferably, the current perfusion parameter value comprises a current skewness value of the determined intensity profile.

[0044] In a preferred alternative, the perfusion determination unit is configured to calculate a current skewness value of the determined intensity profile.

[0045] Preferably, the monitoring unit is configured to compare the current skewness value to a reference skewness value of the at least one reference intensity profile of the ultrasound blood flow signal.

[0046] Preferably, the current perfusion parameter value comprises a current kurtosis value of the determined intensity profile.

[0047] In a preferred alternative, the perfusion determination unit is configured to calculate a current kurtosis value of the determined intensity profile.

[0048] Preferably, the monitoring unit is configured to compare the current kurtosis value to a reference kurtosis value of the at least one reference intensity profile of the ultrasound blood flow signal.

[0049] Preferably, the extraction unit is configured to selectively filter the set of ultrasound blood flow signals originating from red blood cells moving in a plurality of blood vessels from the set of ultrasound signals. In one embodiment, the extraction unit is configured to apply a high-pass filter to the set of ultrasound signals.

[0050] Preferably, the ultrasound signal acquisition unit is configured to acquire the set of ultrasound signals of the biological tissue by applying ultrafast ultrasound imaging, more preferably by applying ultrafast plane wave Doppler imaging.

[0051] The skilled person will understand that the technical considerations, functions, preferred features and advantages described above in relation to the proposed ultrasound method embodiments according to the first aspect, mutatis mutandis, also apply to the ultrasound method embodiments according to the third aspect described below.

[0052] According to a third aspect of the application, there is provided an ultrasound method for perfusion monitoring of a biological tissue comprising a plurality of blood vessels. The method comprises the steps of: i) acquiring a set of ultrasound signals of the biological tissue; ii) extracting a corresponding set of ultrasound blood flow signals originating from moving red blood cells in the plurality of blood vessels from the set of ultrasound signals; iii) determining a velocity of the ultrasound blood flow signals, preferably determining a mean velocity pixel-wise or voxel-wise; iv) determining a velocity distribution of the ultrasound blood flow signals based on the determined velocities; and v) determining a current perfusion parameter value representative of perfusion in the blood vessels of the biological tissue based on the determined velocity distribution of the ultrasound blood flow signals. The method further comprises the steps of: vi) estimating a reference perfusion parameter value representative of blood vessel perfusion in the biological tissue in a normal state based on at least one normal velocity distribution of the ultrasound blood flow signals; vii) determining at least one critical perfusion parameter threshold value based on the reference perfusion parameter value. The method further comprises iteratively repeating steps i) - v) to monitor the current perfusion parameter value and comparing the monitored current perfusion parameter value to the at least one critical perfusion parameter threshold value.

[0053] The skilled person will understand that the technical considerations, functions, preferred features and advantages described above in relation to the proposed ultrasound system embodiments according to the second aspect, mutatis mutandis, also apply to the ultrasound system embodiments according to the fourth aspect described below.

[0054] According to a fourth aspect of the application, there is provided an ultrasound system for perfusion monitoring of a biological tissue comprising a plurality of blood vessels. The ultrasound system comprises: - an ultrasound signal acquisition unit configured to acquire a set of ultrasound signals of the biological tissue; - an extraction unit configured to extract a corresponding set of ultrasound blood flow signals originating from moving red blood cells from the set of ultrasound signals; - a velocity determination unit configured to determine a velocity of the ultrasound blood flow signals, preferably determining a mean velocity pixel-wise or voxel-wise; - a velocity distribution determination unit configured to determine a velocity distribution of the ultrasound blood flow signals based on the determined velocities; - a perfusion determination unit configured to: - determine a current perfusion parameter value representative of perfusion in the blood vessels of the biological tissue based on the determined velocity distribution of the ultrasound blood flow signals; - estimating a reference perfusion parameter value representative of the perfusion of blood vessels in biological tissue under normal conditions based on at least one normal velocity profile of an ultrasound blood flow signal; and - determining at least one critical perfusion parameter threshold value based on the reference perfusion parameter value; and - a monitoring unit configured to monitor perfusion in the plurality of blood vessels by comparing the current perfusion parameter value to the at least one critical perfusion parameter threshold value.

[0055] BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are used to illustrate the presently preferred non-limiting exemplary embodiments of methods and systems in accordance with aspects of the present application. The above and other advantages of the present application will become more apparent when viewed in conjunction with the accompanying drawings, in which: Figure 1 a flow chart illustrating an exemplary embodiment of an ultrasound method for perfusion monitoring in accordance with the present application is shown; Figure 2 an exemplary embodiment of an ultrasound system for perfusion monitoring in accordance with the present application is schematically shown; Figure 3A an intensity profile of an ultrasound blood flow signal in case of normal or healthy perfusion is shown; Figure 3B an intensity profile of an ultrasound blood flow signal in case of abnormal perfusion is shown; Figure 4 a more detailed example of a deviation of the determined intensity profile from the reference intensity profile due to changes that can occur in perfusion during surgery is shown; Fig. 5 shows experimental results of performing the method according to the first aspect using the system according to the second aspect to monitor perfusion in a patient's forearm. More particularly, Figure 5A an ultrasound image with a region of interest (ROI) indicated is shown, Figure 5B the evolution of the intensity in the ROI over time is shown, and Figure 5C the corresponding intensity profile over time is shown; and Fig. 6 shows experimental results of performing the method according to the first aspect using the system according to the second aspect to monitor perfusion in a patient's brain. More particularly, Figure 6A an ultrasound image with a region of interest (ROI) indicated is shown, Figure 6B the evolution of the intensity in the ROI over time is shown, and Figure 6C the corresponding intensity profile over time is shown. DETAILED DESCRIPTION

[0057] Figure 1is a flowchart of a preferred embodiment of an ultrasound method 100 for perfusion monitoring of a biological tissue comprising a plurality of blood vessels.

[0058] The ultrasound method comprises a step 110 of acquiring a set of ultrasound signals of the biological tissue. Typically, the ultrasound signals are acquired or collected by an ultrasound signal acquisition unit, which preferably comprises an ultrasound transducer connected to a beamformer. The acquired set of ultrasound signals can be defined as a (r t j), where: ri, i = 1,..., N denote a plurality of target spatial points, which correspond to the coordinates of the imaged voxels; and t j, j = 1,..., n denote the acquisition times.

[0059] After step 110, the ultrasound method 100 comprises a step 120 of extracting a corresponding set of ultrasound blood flow signals originating from the moving red blood cells in the plurality of blood vessels from the set of ultrasound signals. This step 120 is performed to remove the static and / or relatively low dynamic parts of the imaged tissue from the acquired ultrasound signals and to preserve the relatively high dynamic parts of the imaged tissue in the acquired ultrasound signals. These relatively high dynamic parts or relatively fast moving parts of the imaged tissue, i.e. with a velocity range of about 3 mm / s - 40 mm / s, originate from the moving red blood cells in the plurality of blood vessels and are thus related to ultrasound blood flow signals. A preferred way of extracting the set of ultrasound blood flow signals is by applying a high-pass filter to the acquired set of ultrasound signals, selectively filtering out ultrasound signals originating from static or low dynamic tissue and preserving ultrasound signals originating from moving red blood cells. The ultrasound blood flow signals b can be defined as b (r i , t j ) = filter ( a (r i , t j )). A preferred high-pass filter aims at preserving signals corresponding to or originating from red blood cells with a velocity between 3 mm / s - 40 mm / s. As an alternative to applying a filter to the ultrasound signals, a spatio-temporal analysis of the ultrasound signals can be performed by singular value decomposition to distinguish signals originating from tissue, e.g. the brain, and signals originating from blood.

[0060] When the ultrasound blood flow signals have been extracted in step 120, a step 130 is performed of determining the intensity of the ultrasound blood flow signals. The intensity I of the ultrasound blood flow signals, i.e. the ultrasound blood flow image, is determined on a voxel-by-voxel basis and can be calculated as: .

[0061] After step 130, and based on the determined intensity I of each voxel of the ultrasound blood flow image, an intensity distribution of the ultrasound blood flow signal across the voxels is determined in step 140. p I

[0062] After step 140, a current perfusion parameter value is determined in step 150 based on the determined intensity distribution of the ultrasound blood flow signal. This current perfusion parameter value is representative of the perfusion in the blood vessels of the biological tissue occurring at that moment in time. As indicated by arrow 160, steps 110-150 are iteratively repeated, so that an evolution of the current perfusion parameter value over time can be determined, or in other words, so that the current perfusion parameter value can be monitored over time. The current perfusion parameter value can be selected or determined from the intensity distribution p I Several perfusion parameters can be selected or determined from the intensity distribution of the ultrasound blood flow signal. However, it was found that the area under the curve (AUC) is a particularly beneficial parameter that can be associated with perfusion in the biological tissue. Therefore, the AUC of the determined intensity distribution of the ultrasound blood flow signal is the preferred perfusion parameter, as will be further explained below in connection with Fig. 3.

[0063] In addition to repeatedly performing steps 110-150, the ultrasound method comprises a step 145 of estimating a reference perfusion parameter value representative of perfusion in the biological tissue under normal conditions, based on at least one reference intensity distribution of the ultrasound blood flow signal, and a step 155 of determining at least one critical perfusion parameter threshold value, based on the reference perfusion parameter value. Step 145 provides a reference or baseline value of perfusion, against which the monitored current perfusion parameter value can be compared, in order to be able to better quantify changes that can occur over time in the monitored current perfusion parameter value. The preferred reference or baseline value is a value representative of perfusion in the biological tissue under normal, i.e. healthy, conditions. In this way, any deviation from the reference or baseline value can be indicative of an undesired change in perfusion. In Figure 1 In a preferred embodiment indicated by the dashed arrow a in Fig. 1, the at least one reference intensity distribution of the ultrasound blood flow signal corresponds to an intensity distribution of the ultrasound blood flow signal determined prior to the procedure on the biological tissue. In other words, a reference or baseline measurement is performed on the patient prior to starting the procedure on the tissue, and the monitoring of the current perfusion parameter value is performed during the procedure on the tissue. Since there is a continuity in terms of imaging region, volume and / or patient between the baseline or reference measurement on the one hand and the monitoring measurement on the other hand, the method will be referred to as a continuous method. For the sake of completeness, it is noted that Figure 1 and Figure 2 The steps, units or elements indicated in dashed lines in Figs. 1-3 are considered as preferred or optional features of the illustrated embodiments, and therefore do not represent essential features of the respective embodiments.

[0064] In​​​Figure 1 In an alternative embodiment, not shown in the figures, at least one reference intensity profile of the ultrasound blood flow signal corresponds to a predetermined baseline intensity profile of the ultrasound blood flow signal. Such a predetermined baseline intensity profile can be statistically determined based on a plurality of previous measurements of the same patient or of a plurality of different patients. For example, for a particular highly vascularized organ to be examined or operated on, one hundred intensity profiles of said organ can be determined from one hundred different patients, and an average baseline intensity profile can be determined therefrom. This method will be referred to as a non-continuous method, since there is a discontinuity between the baseline measurements on the one hand and the monitoring measurements on the other hand, as these two measurements are performed on different patients and obviously at different moments in time. This non-continuous method is less time consuming compared to the continuous method, and can therefore be the preferred choice in emergency situations where there is no time to perform any reference or baseline measurements. The continuous method is more accurate compared to the non-continuous method, since the baseline perfusion measurements and the current perfusion measurements are performed on the same patient, and can therefore be the preferred choice in non-urgent or less urgent situations. It is clear to the skilled person that the preference for either the continuous method or the non-continuous method depends on the details and circumstances of the particular case.

[0065] In addition to having a reference or baseline value in order to be able to determine a deviation from such a reference or baseline value, as done in step 155, it is important to define one or more threshold values in order to efficiently monitor the perfusion. Such one or more threshold values represent a boundary between, for example, a relatively small deviation in perfusion which is allowed (which does not pose an immediate threat to the health of the patient) and a relatively large deviation in perfusion which is problematic (which poses a threat to the health of the patient and requires immediate attention).

[0066] For example, such one or more threshold values can comprise a first critical parameter threshold value indicative of a hypoperfusion occurring in the biological tissue, wherein the first critical parameter threshold value is defined as half of the reference perfusion parameter value (e.g. the AUC value from the reference intensity profile).

[0067] Alternatively or additionally, such one or more threshold values can comprise a second critical parameter threshold value indicative of an ischemia occurring in the biological tissue, wherein the second critical parameter threshold value is defined as a quarter of the reference perfusion parameter value (e.g. the AUC value from the reference intensity profile).

[0068] Alternatively or additionally, such one or more threshold values can comprise a third critical parameter threshold value indicative of a hyperperfusion occurring in the biological tissue, wherein the third critical parameter threshold value is defined as a 20% increase of the reference perfusion parameter value (e.g. the AUC value from the reference intensity profile).

[0069] It is clear to the skilled person that additional or other critical parameter thresholds can be defined, e.g. based on a respective standard deviation of the involved measurements and / or calculations, depending on the specific case of the patient, e.g. based on the monitored organ or highly vascularized tissue.

[0070] Finally, Figure 1 A step 170 is shown, i.e. comparing the monitored current perfusion parameter value with the at least one critical perfusion parameter threshold. This step is performed based on the results of step 150 (i.e. the current perfusion parameter value) and step 155 (i.e. the critical perfusion parameter threshold). In case the current perfusion parameter value reaches or exceeds the critical perfusion parameter, the method preferably further comprises indicating the reaching or exceeding of the critical perfusion parameter threshold, respectively. Preferably, said indication comprises a visual indication of the reaching or exceeding of the critical perfusion parameter to the respective medical staff, e.g. on a screen in the operating room. It is clear to the skilled person that in addition to the above-mentioned preferred critical perfusion parameter thresholds, further critical perfusion parameter thresholds can be defined depending on the specific case of the patient, e.g. the type of surgery and / or the type of organ or tissue to be monitored.

[0071] In Figure 1 In embodiments of the application, the intensity of the ultrasound blood flow signal is used as an indicator for the distribution of blood volume within the tissue. However, it is clear to the skilled person that alternatively or additionally, other parameters (e.g. mean velocity) can be determined from the ultrasound signal and a respective distribution can be determined to provide information about the distribution of blood volume and / or motion within the tissue. More particularly, the mean velocity of each voxel or pixel in the ultrasound blood flow image can be determined and a respective mean velocity distribution can be determined.

[0072] Figure 2 An exemplary embodiment of an ultrasound system for perfusion monitoring is schematically shown. More particularly, Figure 2An ultrasound system 200 is shown for perfusion monitoring of biological tissue (typically highly vascularized tissue) containing multiple blood vessels. The ultrasound system 200 includes: an ultrasound signal acquisition unit 210, preferably connected to an ultrasound probe of a beam generator, configured to acquire a set of ultrasound signals from the biological tissue; an extraction unit 220 configured to extract a corresponding set of ultrasound blood flow signals originating from moving erythrocytes from the set of ultrasound signals; an intensity determination unit 230 configured to determine the intensity of the ultrasound blood flow signals; and an intensity distribution determination unit 240 configured to determine the intensity distribution of the ultrasound blood flow signals based on the determined intensity. The ultrasound system 200 also includes a perfusion determination unit 250 configured to: determine current perfusion parameter values ​​representing perfusion in blood vessels of the biological tissue based on the determined intensity distribution of the ultrasound blood flow signals; estimate reference perfusion parameter values ​​representing vascular perfusion in the biological tissue under normal conditions based on at least one normal intensity distribution of the ultrasound blood flow signals; and determine at least one critical perfusion parameter threshold based on the reference perfusion parameter values. The ultrasound system 200 also includes a monitoring unit 270 configured to monitor perfusion in multiple vessels by comparing current perfusion parameter values ​​with at least one critical perfusion parameter threshold. Figure 2 The dashed outlines in the diagram group units 220 and 230 on one hand, and units 240, 250, and 270 on the other, indicating exemplary configurations of the corresponding units in possible hardware setups. This exemplary configuration is suggested because units 220 and 230 typically involve preprocessing of the ultrasound signal before obtaining an ultrasound blood flow image (i.e., the determined intensity of the ultrasound blood flow signal), while units 240, 250, and 270 typically involve post-processing of the ultrasound blood flow image. It will be apparent to those skilled in the art that other configurations are possible.

[0073] Those skilled in the art will understand that the above description of the target Figure 1 The technical considerations, functions, and advantages of the proposed ultrasonic method embodiments are also applicable, with appropriate modifications. Figure 2 The corresponding ultrasound system embodiment is described below. Therefore, to avoid repetition, a detailed discussion of the illustrated units 210 to 270 of the ultrasound system 200 will be omitted.

[0074] exist Figure 2 In the diagram, optional element b, indicated by a dashed line, provides input to the perfusion determination unit 250. Element b represents an external database containing predetermined baseline intensity distributions of ultrasound blood flow signals from different organs or highly vascularized tissues, and / or statistical information used to determine said predetermined baseline intensity distributions. In other words, database b enables the ultrasound system to apply methods such as... Figure 1 The discontinuous method, which has been described in detail in [the text], serves as such Figure 1 The alternatives or supplements to the optional continuous methods indicated by arrow a.

[0075] Those skilled in the art will readily recognize that the steps of the various methods described above can be performed by a programmed computer. In this document, some embodiments are also intended to cover program storage devices, such as digital data storage media, which are machine- or computer-readable and encode machine-executable or computer-executable programs of instructions that perform some or all of the steps of the methods described above. Program storage devices can be, for example, digital memories, magnetic storage media (e.g., disks and magnetic tapes), hard disk drives, or optically readable digital data storage media. These embodiments are also intended to cover computers programmed to perform the steps of the methods described above.

[0076] The functionality of the various elements shown in the accompanying figures (including any functional blocks labeled “unit,” “processor,” or “module”) can be provided using dedicated hardware and hardware capable of executing software in association with appropriate software. When provided by a processor, the functionality can be provided by a single dedicated processor, a single shared processor, or multiple independent processors (some of which may be shared). Furthermore, the explicit use of the terms “processor” or “controller” should not be construed as referring only to hardware capable of executing software, but may implicitly include, but is not limited to, Digital Signal Processor (DSP) hardware, network processors, Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), Read Only Memory (ROM) for storing software, Random Access Memory (RAM), and non-volatile memory. Other conventional and / or custom hardware may also be included. Similarly, any switches shown in the accompanying figures are conceptual only. Their functions can be executed through program logic operations, through dedicated logic, through program control and interaction with dedicated logic, or even manually. The specific technology can be chosen by the implementer, as can be understood more specifically from the context.

[0077] Those skilled in the art will understand that any block diagram herein represents a conceptual view of an illustrative circuit embodying the principles of the invention. Similarly, it should be understood that any flowchart, schematic diagram, state transition diagram, pseudocode, etc., represents various processes that can be represented substantially in a computer-readable medium and therefore executed by a computer or processor, whether or not such computer or processor is explicitly shown.

[0078] Figure 3A The intensity distribution of ultrasound blood flow signals under normal or healthy perfusion conditions is shown, while Figure 3BTwo intensity distributions of ultrasound blood flow signals under abnormal perfusion conditions are shown. In other words, Figure 3A This shows the reference intensity distribution of the ultrasound blood flow signal, which generally corresponds to a normal distribution; while Figure 3B Two examples of the determined intensity distributions 356 and 357 are shown, which may indicate low perfusion 356 and ischemia 357, respectively. In both figures, the x-axis represents signal intensity I, and the y-axis represents the voxel quantity from which the corresponding signal intensity was measured. It should be emphasized that the intensity distributions shown are based on voxel quantity, and the intensity measured from a particular voxel may originate from one or more blood vessels present in that voxel. Preferably, signal intensity I corresponds to the ultrafast Doppler signal intensity. Figure 3A and Figure 3B In this context, the relevant central portion of the intensity distribution is indicated by range 346. In this case, range 346 covers 80% of the entire intensity distribution and extends from the 10th percentile to the 90th percentile of the measured intensity I. Within this range 346, Figure 3A The AUC in the diagram is indicated by a slash. Figure 3A The AUC indicated in the figure represents a reference perfusion parameter value for vascular perfusion in biological tissues under normal conditions, and is referred to as AUC0. Within the range of 346, Figure 3B The AUC of the intensity distribution 356 determined in the figure is indicated by a vertical line. Figure 3B The AUC indicated by the middle can indicate the occurrence of low perfusion. Therefore, [the following is missing from the original text: "will be used"] Figure 3B The AUC indicated in the middle is compared with the critical perfusion parameter threshold (which can be defined as AUC0 / 2). If Figure 3B If the AUC drops to or exceeds this threshold, the relevant operator or other medical personnel can be notified in real time that a critical perfusion parameter threshold has been reached or exceeded, allowing for action to address any complications. Within the range of 346, Figure 3B The AUC of the intensity distribution 357 determined in the figure is indicated by a horizontal line. Figure 3B The AUC indicated by the value can indicate the occurrence of ischemia. Therefore, [the following is used:] Figure 3B The AUC indicated in the middle is compared with the critical perfusion parameter threshold (which can be defined as AUC0 / 4). If Figure 3A If the AUC drops to or exceeds a certain threshold, the relevant operator or other medical personnel can be notified in real time that a critical perfusion parameter threshold has been reached or exceeded, allowing for action to address any complications. As can be clearly seen from the annotated intensity distribution shown, AUC is a powerful tool for intraoperative perfusion monitoring. On the one hand, AUC can be calculated directly and quickly, allowing for real-time perfusion monitoring; on the other hand, AUC has been found to be closely related to perfusion within biological tissues. Preferably, only the AUC of the relevant portion of the intensity distribution is considered. An exemplary relevant portion is the central portion or central range of the intensity distribution. Figure 3B andFigure 3B In the example, the relevant central portion of the intensity distribution is selected from the 10th to the 90th percentile. By examining this central portion of the intensity distribution (which typically corresponds to a substantially normal distribution in highly vascularized human tissue), outliers in the intensity spectrum are filtered out, improving data quality.

[0079] Preferably, the relevant portion of the intensity distribution is within the range of the 10th to 90th percentile of the intensity distribution, more preferably within the range of the 15th to 85th percentile of the intensity distribution, and even more preferably within the range of the 20th to 80th percentile of the intensity distribution. Although these ranges are preferred as the relevant portion of the intensity distribution, it will be apparent to those skilled in the art that these ranges may vary depending on the specific circumstances of the current case.

[0080] Figure 4 Two preferred critical perfusion parameter thresholds are shown, which can be used to check for hypoperfusion and / or ischemia while monitoring current perfusion parameter values. In this case, the first critical parameter threshold indicates hypoperfusion in biological tissue and is defined as half of the reference perfusion parameter value.

[0081] In addition, the second critical parameter threshold indicates ischemia in biological tissue and is defined as one-quarter of the reference perfusion parameter value.

[0082] Figure 3A A more detailed example is shown of possible deviations from a reference intensity distribution due to complications that may occur during surgery; as combined with Figure 3B and Figure 3A As shown, AUC based on the corresponding blood flow signal intensity distribution of imaging voxels is a powerful tool for efficiently monitoring perfusion. In addition to the valuable information obtained by monitoring numerical AUC values, additional information can be extracted from the blood flow signal intensity distribution and / or its deviations. Although combined with... Figure 3B , Figure 4 and Figure 4 It is assumed that a basically normal intensity distribution represents healthy, highly vascularized tissue, but it is clear that the teachings of this invention also apply to any other type of distribution that serves as a reference distribution, such as log-normal, skewed, logistic, etc.

[0083] exist Figure 4In the diagram, the x-axis represents signal intensity I, and the y-axis represents the voxel quantity of the corresponding measured signal intensity. It is important to emphasize that the intensity distributions shown are based on voxel quantity, and the intensity measured from a particular voxel may originate from one or more blood vessels present within that voxel. Preferably, signal intensity I corresponds to ultrafast Doppler signal intensity. For illustrative purposes, multiple ultrasound blood flow signal intensity distributions 455, 456a, 456b, 456c, and 456d are shown in the same graph. Intensity distribution 455 represents a normal distribution, which can be used as a reference intensity distribution and represents the expected intensity distribution of healthy biological tissue under normal conditions (i.e., unmanipulated). In other words, intensity distribution 455 represents a normal perfusion state. Intensity distributions 456a, 456b, 456c, and 456d represent cases where perfusion has changed and deviated from the normal state. Figure 4 As shown, and referring to intensity distributions 456a and 456b, the current skewness value (SV) of the determined intensity distribution can be calculated to obtain more information about the shape of the intensity distribution. More specifically, the current skewness value SV can provide valuable insights into whether the peak of the intensity distribution is shifted to the left or right, and thus can indicate the occurrence of low or high perfusion (especially locally). Referring to intensity distribution 456a, a negative SV (SV < 0) indicates that the peak is shifted to the left of the normal intensity distribution 455, and indicates the occurrence of low perfusion. On the other hand, referring to intensity distribution 456b, a positive SV (SV > 0) indicates that the peak is shifted to the right of the normal intensity distribution 455, and indicates the occurrence of high perfusion.

[0084] Similarly, and referring to intensity distributions 456c and 456d, the current kurtosis value (KV) of the determined intensity distribution can be calculated to obtain more information about the shape of the intensity distribution. More specifically, the current kurtosis value KV can provide valuable insights into whether the peaks of the intensity distribution are steep or flat, and thus can indicate the occurrence of global hypoperfusion or hyperperfusion, respectively. It will be apparent to those skilled in the art that the use of skewness value SV and kurtosis value KV can be performed independently of each other. However, when the calculations of skewness value SV and kurtosis value KV are combined, it is believed that any actual blood flow signal intensity distribution can be accurately characterized. Furthermore, those skilled in the art are aware of different methods and formulas for calculating skewness value SV and kurtosis value KV. Possible formulas include, but are not limited to, the following coefficients.

[0085] Figure 4 The Fisher skewness coefficient used is defined as follows:

[0086] as well as Figure 5A The kurtosis factor used is defined as:

[0087] Where x = mean, m = mode, n = number of observations, and S = standard deviation.

[0088] Figure 5 illustrates a first example of using the system according to the second aspect to perform the method according to the first aspect to monitor perfusion in a patient's forearm. More specifically, Figure 5B An ultrasound image with an indicated region of interest (ROI) is shown. Figure 5C The evolution of intensity in the ROI over time is shown, and Figure 5A The intensity distribution over time is shown.

[0089] Figure 5B These are ultrasound images based on ultrasound signals, and more specifically, power Doppler ultrasound images of skeletal muscles (especially the flexor muscles of the forearm). Rectangle R indicates the ROI, which is selected to determine the relevant ultrasound blood flow image for monitoring perfusion.

[0090] Figure 5C The hemodynamic time series of the mean signal intensity within the selected ROI is shown, i.e., its variation over time. The bold bars on the horizontal axis indicate the inflation phase (>300 mm Hg) of the compression cuff located in the patient's upper arm. This inflation results in a significant reduction in perfusion in the patient's forearm, which can also be seen from the corresponding sharp drop and trough in the relative signal intensity of the ROI.

[0091] Figure 6A The corresponding intensity distributions of the ROI signal after 5s, 15s, 25s, 35s, and 45s are shown. The intensity distribution at 5s before the compression cuff is inflated is determined and used as a reference intensity distribution, from which reference perfusion parameters, such as AUC, can be estimated. The intensity distributions at 15s and 25s are determined when the compression cuff is inflated, showing a decrease in AUC and a leftward shift of the peak value, which can indicate a reduction in perfusion. The intensity distribution at 35s is determined shortly after the compression cuff is released from the upper arm, showing an increase in AUC and a rightward shift of the peak value compared to the reference distribution, which can indicate a temporary increase in perfusion, i.e., high perfusion.

[0092] Figure 6 illustrates a second example of using the system according to the second aspect to perform the method according to the first aspect to monitor perfusion in a patient's brain. More specifically, Figure 6B An ultrasound image with an indicated region of interest (ROI) is shown. Figure 6C The evolution of intensity in the ROI over time is shown, and Figure 6A The intensity distribution over time is shown.

[0093] Figure 6AThese are ultrasound images based on ultrasound signals, and more specifically, power Doppler ultrasound images of brain regions. More specifically, Figure 6B The ultrasound images in the image are associated with an intraoperative recording during the resection of the cerebral arteriovenous malformation. This recording includes a temporary clamping of the feeding artery for approximately 60 seconds, resulting in reduced perfusion of the adjacent brain parenchyma, denoted by a rectangle R as the selected region of interest (ROI), and corresponding to relevant ultrasound flow images used to monitor perfusion. The power Doppler images shown were taken prior to the clamping (i.e., at baseline).

[0094] Figure 6C The hemodynamic time series of the mean signal intensity within the selected ROI is shown, i.e., its variation over time. The bold bars on the horizontal axis indicate clamping of the feeding arteries, which leads to a significant reduction in perfusion of the adjacent brain parenchyma, as can be seen from the corresponding sharp drops and troughs in the relative signal intensity of the ROI.

[0095] ​ The intensity distributions of the ROI signal at 5s, 20s, 40s, 60s, 80s, 100s, and 120s are shown. The intensity distribution at 5s, prior to clamping the feeding artery, was determined and used as a reference intensity distribution, from which reference perfusion parameters, such as AUC, can be estimated. Clamping was initiated immediately before the 40-second time point. The intensity distributions at 40s, 60s, and 80s during feeding artery clamping show a decrease in AUC and a leftward shift of the peak value, which may indicate reduced perfusion. The intensity distribution at 100s, determined very shortly after the release of the temporary vascular clamp, shows an increase in AUC and a rightward shift of the peak value compared to the distributions at 60s and 80s, which appears to indicate the beginning of normalization of perfusion. The intensity distribution at 120s, determined after the release of the temporary vascular clamp, shows an increase in AUC and a rightward shift of the peak value compared to the reference distribution, which may indicate a temporary increase in perfusion, i.e., hyperperfusion.

[0096] Although the principles of the invention have been explained above with reference to specific embodiments, it should be understood that this description is by way of example only and is not intended to limit the scope of protection defined by the appended claims.

Claims

1. An ultrasound method for perfusion monitoring of a biological tissue comprising a plurality of blood vessels, the method comprising the steps of: i) acquiring a set of ultrasound signals of the biological tissue; ii) extracting a corresponding set of ultrasound flow signals originating from red blood cells moving in a plurality of blood vessels from the set of ultrasound signals; iii) determining intensities of the ultrasound flow signals; iv) determining an intensity distribution of the ultrasound flow signals based on the determined intensities; v) determining a current perfusion parameter value representative of perfusion in blood vessels of the biological tissue based on the determined intensity distribution of the ultrasound flow signals; vi) estimating a reference perfusion parameter value representative of blood vessel perfusion in the biological tissue under normal conditions based on at least one reference intensity distribution of the ultrasound flow signals; vii) determining at least one critical perfusion parameter threshold value based on the reference perfusion parameter value; and viii) iteratively repeating steps i) - v) to monitor the current perfusion parameter value and comparing the monitored current perfusion parameter value with the at least one critical perfusion parameter threshold value. Determining the current perfusion parameter value comprises calculating an area under the curve, AUC, value of at least a relevant portion of the intensity distribution.

2. The ultrasonic method of claim 1, wherein, The relevant portion of the intensity distribution is in the range of the 10th percentile to the 90th percentile of the intensity distribution, preferably in the range of the 15th percentile to the 85th percentile of the intensity distribution, and more preferably in the range of the 20th percentile to the 80th percentile of the intensity distribution.

3. The ultrasonic method of claim 2, wherein, The at least one reference intensity distribution of the ultrasound flow signals corresponds to an intensity distribution of ultrasound flow signals determined prior to operating on the biological tissue.

4. The ultrasound method of any of the preceding claims, wherein, The at least one reference intensity distribution of the ultrasound flow signals corresponds to a predetermined baseline intensity distribution of the ultrasound flow signals.

5. The ultrasound method of any of the preceding claims 1 to 3, wherein, The biological tissue is a brain tissue.

6. The ultrasound method of any of the preceding claims, wherein, The at least one critical parameter threshold value comprises a first critical parameter threshold value indicative of low perfusion occurring in the biological tissue.

7. The ultrasonic method of any of the preceding claims, wherein, The first critical parameter threshold value is defined as half of the reference perfusion parameter value.

8. The ultrasonic method of the preceding claim, wherein, The at least one critical parameter threshold value comprises a second critical parameter threshold value indicative of ischemia occurring in the biological tissue.

9. The ultrasound method of any of the preceding claims, wherein, The second critical parameter threshold value is defined as one fourth of the reference perfusion parameter value.

10. The ultrasonic method of the preceding claim, wherein, The at least one critical parameter threshold value comprises a third critical parameter threshold value indicative of high perfusion occurring in the biological tissue.

11. The ultrasonic method of any of the preceding claims, wherein, The third critical parameter threshold value is defined as an increase of 20% of the reference perfusion parameter value.

12. The ultrasonic method of the preceding claim, wherein, Determining the current perfusion parameter value comprises calculating a current skewness value of the determined intensity distribution.

13. The ultrasonic method of any of the preceding claims, wherein, 14. The ultrasound method according to any of the preceding claims 1 to 12, further comprising calculating a current skewness value of the determined intensity distribution. Comparing the current skewness value with a reference skewness value of at least one reference intensity distribution of the ultrasound flow signals.

15. The ultrasonic method of claim 13 or 14, wherein, Determining the current perfusion parameter value comprises calculating a current kurtosis value of the determined intensity distribution.

16. The ultrasonic method of any of the preceding claims, wherein, ​ 17. The ultrasound method according to any one of the preceding claims 1 to 15, further comprising computing a current kurtosis value of the determined intensity distribution.

18. The ultrasonic method of claim 16 or 17, wherein, comparing the current kurtosis value with a reference kurtosis value of at least one reference intensity distribution of the ultrasound blood flow signal.

19. The ultrasonic method of any of the preceding claims, wherein, extracting comprises selectively filtering out a set of ultrasound blood flow signals originating from moving red blood cells in the plurality of blood vessels by applying a filter to the set of ultrasound signals.

20. The ultrasonic method of the preceding claim, wherein, applying a filter to the set of ultrasound signals comprises applying a high-pass filter to the set of ultrasound signals.

21. The ultrasonic method of any of the preceding claims, wherein, acquiring the set of ultrasound signals of the biological tissue is by ultrafast ultrasound imaging, preferably by ultrafast Doppler imaging.

22. An ultrasound system for perfusion monitoring of a biological tissue comprising a plurality of blood vessels, the system comprising: - an ultrasound signal acquisition unit configured to acquire a set of ultrasound signals of a biological tissue; - an extraction unit configured to extract a corresponding set of ultrasound blood flow signals originating from moving red blood cells from the set of ultrasound signals; - an intensity determination unit configured to determine an intensity of the ultrasound blood flow signals; - an intensity distribution determination unit configured to determine an intensity distribution of the ultrasound blood flow signals based on the determined intensity; - a perfusion determination unit configured to: - determine a current perfusion parameter value representative of perfusion in blood vessels of the biological tissue based on the determined intensity distribution of the ultrasound blood flow signals; - estimate a reference perfusion parameter value representative of blood vessels perfusion in the biological tissue in a normal state based on at least one reference intensity distribution of the ultrasound blood flow signals; and - determine at least one critical perfusion parameter threshold value based on the reference perfusion parameter value; and - a monitoring unit configured to monitor perfusion in the plurality of blood vessels by comparing the current perfusion parameter value with the at least one critical perfusion parameter threshold value.

23. The ultrasound system of claim 22, wherein, the perfusion determination unit is configured to determine the current perfusion parameter by computing an area under the curve, AUC, value of at least a relevant portion of the intensity distribution.

24. The ultrasound system of claim 23, wherein, the relevant portion of the intensity distribution is in the range of the 10th percentile to the 90th percentile of the intensity distribution, preferably in the range of the 15th percentile to the 85th percentile of the intensity distribution, and more preferably in the range of the 20th percentile to the 80th percentile of the intensity distribution.

25. The ultrasound system of any of the preceding claims 22 to 24, wherein, the at least one reference intensity distribution of the ultrasound blood flow signals corresponds to an intensity distribution of ultrasound blood flow signals determined prior to operating on the biological tissue.

26. The ultrasound system of any of the preceding claims 22 to 24, wherein, the at least one reference intensity distribution of the ultrasound blood flow signals corresponds to a predetermined baseline intensity distribution of the ultrasound blood flow signals.

27. The ultrasound system of any of the preceding claims 22 to 26, wherein, the biological tissue is a brain tissue.

28. The ultrasound system of any of the preceding claims 22 to 27, wherein, the at least one critical parameter threshold value comprises a first critical parameter threshold value indicative of occurrence of hypoperfusion in the biological tissue.

29. The ultrasound system of the preceding claim, wherein, the first critical parameter threshold value is defined as half of the reference perfusion parameter value.

30. The ultrasound system of any of the preceding claims 22 to 29, wherein, the at least one critical parameter threshold value comprises a second critical parameter threshold value indicative of occurrence of ischemia in the biological tissue.

31. The ultrasound system of the preceding claim, wherein, The second critical parameter threshold is defined as a quarter of the reference perfusion parameter value.

32. The ultrasound system of any of the preceding claims 22 to 31, wherein, The at least one critical parameter threshold comprises a third critical parameter threshold indicating occurrence of hyperperfusion in the biological tissue.

33. The ultrasound system of the preceding claim, wherein, The third critical parameter threshold is defined as a 20% increase of the reference perfusion parameter value.

34. The ultrasound system of any of the preceding claims 22 to 33, wherein, The current perfusion parameter value comprises a current kurtosis value of the determined intensity profile.

35. The ultrasound system of any of the preceding claims 22 to 33, wherein, The perfusion determination unit is configured to calculate a current kurtosis value of the determined intensity profile.

36. The ultrasound system of claim 34 or 35, wherein, The monitoring unit is configured to compare the current kurtosis value to a reference kurtosis value of at least one reference intensity profile of the ultrasound blood flow signal.

37. The ultrasound system of any of the preceding claims 22 to 36, wherein, The current perfusion parameter value comprises a current kurtosis value of the determined intensity profile.

38. The ultrasound system of any of the preceding claims 22 to 37, wherein, The perfusion determination unit is configured to calculate a current kurtosis value of the determined intensity profile.

39. The ultrasonic method of claim 37 or 38, wherein, The monitoring unit is configured to compare the current kurtosis value to a reference kurtosis value of at least one reference intensity profile of the ultrasound blood flow signal.

40. The ultrasound system of any of the preceding claims 22 to 39, wherein, The extraction unit is configured to selectively filter out the set of ultrasound blood flow signals originating from moving red blood cells in the plurality of blood vessels by applying a filter to the set of ultrasound signals.

41. The ultrasound system of the preceding claim, wherein, The extraction unit is configured to apply a high-pass filter to the set of ultrasound signals.

42. The ultrasound system of any of the preceding claims 22 to 41, wherein, The ultrasound signal acquisition unit is configured to acquire a set of ultrasound signals of the biological tissue by ultrafast ultrasound imaging, preferably by ultrafast Doppler imaging.

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