Ultrasound method and system for perfusion monitoring

Abstract

An ultrasound method for perfusion monitoring comprising following steps: obtaining ultrasound signals of biological tissue; extracting from the ultrasound signals corresponding ultrasound blood signals originating from moving red blood cells; determining an intensity of the ultrasound blood signals; determining an intensity distribution of the ultrasound blood signals; determining a current perfusion parameter value representative for perfusion in the biological tissue based on the determined intensity distribution of the ultrasound blood signals; estimating a reference perfusion parameter value representative for perfusion in the biological tissue in a normal state based on at least one reference intensity distribution of the ultrasound blood signals; 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 with the at least one critical perfusion parameter threshold.

Description

[0001] Ultrasound method and system for perfusion monitoring

[0002] Field of Invention

[0003] The field of the invention relates to an ultrasound method and system for perfusion monitoring in biological tissue that comprises a plurality of blood vessels. Particular embodiments relate to an ultrasound method and system for non-invasive, intra-operative perfusion monitoring in highly vascularized tissues.

[0004] During neurosurgical procedures careful dissection and manipulation of the cerebral vessels is mandatory. For vascular lesions such as aneurysms, arteriovenous malformations and arteriovenous fistulas the goal is to remove, occlude or exclude pathological (parts of) vessels, without compromising the blood flow in normal vessels. Likewise in case of a brain tumor, the resection should be restricted to the pathological vessels and the surgeon should be prudent not to sacrifice normal vessels. Failure to preserve substantial blood flow in the normal vessels will lead to ischemia and stroke in the vascular territory involved. Neurosurgical operations are associated to a relatively high risk of vascular compromise or vascular damage leading to ischemia and stroke. According to a recent multicenter international cohort study the overall incidence of ischemic complications in neurosurgical aneurysm treatment is around 18% and can even reach >50% in complex cases. For brain tumor surgery recent publications also report comparable rates of surgery related infarctions as confirmed by post-operative MRI.

[0005] It is important to note that the detrimental effects of compromised blood flow are reversable, provided that the ischemia is detected promptly and blood flow is restored. Ideally, this requires a tool that can assess brain perfusion non-invasively, with substantial brain coverage, sample both cortical and subcortical regions, and in real-time. However, currently there is no monitoring tool available in the operating room that can measure brain perfusion adequately. Therefore, the operator is mostly unaware of a compromised cerebral blood flow during surgery. At present the gold standard to assess for surgery related ischemia is a neurological evaluation after the patient awakes from anaesthesia. At this time point ischemia is detected too late and no immediate surgical actions can be taken.

[0006] The occurrence of intra-operative ischemia also applies for other types of surgeries and surgical fields. For instance, any type of surgery which requires performing a vascular anastomosis. Examples are but not limited to, cerebral bypass surgery, breast reconstruction surgery using vascularized flaps, liver and kidney transplants and open and endovascular revascularization procedures.

[0007] There are existing methods for direct quantitative measurements of the blood flow in large blood vessels, e.g. large cerebral blood vessels. On the one hand it is known to use invasive techniques with flow sensitive catheters which require arterial access. On the other hand it is known to use the non-invasive technique of transcranial doppler flowmetry (TCD). 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, yet they require the injection of a tracer or a dye in the blood stream. Finally, methods for intra-operative electrophysiology known as evoked potentials can be used to assess the latencies over long tracts in the central nervous system as an indirect manner for monitoring brain damage. Of course, these methods do not measure perfusion and are not sensitive enough nor specific enough to reliably detect hypoperfusion and ischemia and are limited to the sensory and motor cortices.

[0008] The available intra-operative tools can quantitatively measure the blood flow in the large arteries based on the blood flow velocity (Charbel probes) or in a qualitative manner by the intensity of the Doppler signal in the large arteries (micro-doppler probes), by imaging the filling of the vessels after injection of a dye (indocyanine green angiography).

[0009] The above-described known modalities and techniques present major limitations for intra-operative use during surgical procedures. Either they are incompatible due to use of irradiating tracers, not practical due to large size of the machinery, not practical because not portable, due to an existing risk of vascular complications for catheter techniques, due to a limited access to the vessels in case of TCD / Charbel probes / micro-doppler probes, due to the mere provision of static imaging instead of required real-time imaging, due to the inability to perform continuous recordings or monitoring, due to being time consuming, etc.

[0010] The object of embodiments of the invention is to provide an ultrasound method and system which allows for non-invasive, intra-operative perfusion monitoring in biological tissue that comprises a plurality of blood vessels. More in particular, it is an object of embodiments of the invention to provide an ultrasound method and system which overcomes at least some and preferably all of the above mentioned limitations of known techniques. According to a first aspect of the invention there is provided an ultrasound method for perfusion monitoring in biological tissue that comprises a plurality of blood vessels. The method comprises the steps of: i) obtaining a set of ultrasound signals of the biological tissue, ii) extracting from the set of ultrasound signals a corresponding set of ultrasound blood signals originating from moving red blood cells in the plurality of blood vessels, iii) determining an intensity of the ultrasound blood signals, iv) determining an intensity distribution of the ultrasound blood signals based on the determined intensity, and v) determining a current perfusion parameter value representative for perfusion in the blood vessels of the biological tissue based on the determined intensity distribution of the ultrasound blood signals. The method further comprises the steps of: vi) estimating a reference perfusion parameter value representative for perfusion of the blood vessels in the biological tissue in a normal state based on at least one normal intensity distribution of the ultrasound blood signals, and vii) determining at least one critical perfusion parameter threshold 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 with the at least one critical perfusion parameter threshold.

[0011] Embodiments of the invention are based inter alia on the insight that within highly vascularized tissues, such as the brain, the overall distribution of blood vessels, especially at the arteriolar level, is substantially homogeneous, and on the insight that information on this distribution of blood vessels, rather than information on individual blood vessels, can be used to efficiently monitor perfusion in real-time. Moreover, because the vascular density is close to constant in such tissues, a distribution of the intensity of an ultrasound signal, such as a micro-doppler signal, across voxels can be assumed to correlate with the distribution of the blood volume within the imaged volume of the respective tissue. More in particular, it has been found that examining the intensity distribution of the ultrasound blood signals allows to perform absolute value monitoring of perfusion rather than merely relative value monitoring. In addition, iteratively determining and examining an intensity distribution of the present blood vessels requires less computational intensity and is less time-consuming as compared to visualizing, imaging or examining each one of the present blood vessels individually. The presented ultrasound method is therefore especially suitable for real-time perfusion monitoring.

[0012] In this aspect of the invention, the intensity of the ultrasound blood signals is used as an indicator for the distribution of the blood volume within the tissue. However, it is clear to the skilled person that alternatively, or in addition, other parameters, such as a mean velocity, and a corresponding distribution can be determined from the ultrasound signals to provide information on the distribution and / or movement of the blood volume within the tissue.

[0013] Although the ultrasound method for perfusion monitoring was initially developed as an intraoperative tool, it is clear to the skilled person that this method can also be applied in other settings such as a medical imaging department, intensive care unit, policlinic, or even at the patient’s home. In essence the presented ultrasound method for perfusion monitoring can be performed on any biological soft tissue having substantial blood vessels.

[0014] Preferably, determining the current perfusion parameter value comprises calculating an area under curve (AUC) value of at least a relevant part of the intensity distribution, more preferably only of a relevant part of the intensity distribution. Taking into account only a relevant part of the intensity distribution while calculating the AUC value has the benefit that outliers in the intensity distribution are filtered out, thereby increasing accuracy of the monitoring method. The relevant part of the intensity distribution preferably ranges from the 10th to the 90th percentile of the intensity distribution, more preferably from the 15th to the 85th percentile of the intensity distribution, and most preferably from the 20th to the 80th percentile of the intensity distribution. These percentiles of the intensity distribution have been found to accurately represent blood volume in the arterioles which are of particular interest when monitoring perfusion.

[0015] In a preferred embodiment the at least one normal intensity distribution of the ultrasound blood signals corresponds with the determined intensity distribution of the ultrasound blood signals prior to manipulation of the biological tissue. The at least one normal intensity distribution allows to determine a baseline value for normal or healthy perfusion in the respective biological tissue. In case of an upcoming surgery of a patient, such normal intensity distribution can be determined by determining the intensity distribution of the ultrasound blood signals of that particular patient before starting the surgery. Such baseline value can be determined by iteratively determining a current perfusion parameter value during a predefined time frame, for example 5 minutes, before surgery, and performing averaging of the determined current perfusion parameters during said predefined time frame. In this manner, which is also referred to as continuous approach, a baseline value which is relevant for the respective patient can be determined in an efficient manner. It is clear to the skilled person that the duration of the predefined time frame can vary and may be set on a case-by-case basis depending on the specifics of the case at hand. Similarly, it is clear to the skilled person that any known averaging technique can be used.

[0016] In an alternative preferred embodiment the at least one normal intensity distribution of the ultrasound blood signals corresponds with a predetermined baseline intensity distribution of the ultrasound blood signals. Such predetermined baseline intensity distribution can be determined statistically based on executed ultrasound measurements on similar tissue of other patients. Thus, in this manner it is also possible to determine a baseline value for normal or healthy perfusion in the respective biological tissue albeit based on ultrasound measurements on other patients. Although this way of determining the baseline value for normal or healthy perfusion, also referred to as discontinuous approach, might be less accurate for a particular patient as compared with the previously described embodiment, it is more time efficient since no additional ultrasound measurements need to take place directly prior to manipulation of the biological tissue.

[0017] According to an embodiment the biological tissue is brain tissue. The brain is a highly vascularized organ and receives by approximation 20% of the cardiac output in terms of blood flow to meet the metabolic demand. Brain tissue is highly vascularized to facilitate the delivery of oxygen and glucose. The adult human brain consumes about 5mg of glucose per 100 grams of tissue per minute and about 3 mL of oxygen per 100 grams of tissue per minute. Failure of the vascular system to deliver enough blood promptly results in dysfunction followed by cessation of brain function and eventually to loss of brain tissue. These conditions are called hypoxemia, ischemia or stroke depending on the level of reduction in blood flow, hence decreased delivery of oxygen, and the extent of tissue damage. In normal conditions the cerebral blood flow (CBF) in humans is about 50 mL per 100 gram brain tissue per minute. Historical studies in non-human primates have demonstrated a threshold for the occurrence of cessation of electrical activity to be around a CBF of about 25mL / 100g / min. A further decrease below about 10-15mL / 100g / min results in cell death and brain infarction, i.e. stroke. CBF is the main clinical parameter to evaluate the perfusion of the brain and this illustrates the importance of embodiments of the presently presented ultrasound method for perfusion monitoring. Although the presented ultrasound method is especially suitable for use during operation on the human brain, the possible occurrence of intra-operative hypoperfusion, ischemia and / or hyperperfusion also applies for other types of surgeries and surgical fields. More in particular, the presented ultrasound method can be beneficially used in combination with operations or surgery involving any vital organ, allotransplants or autotransplants. A non-exhaustive list of examples comprises cerebral bypass surgery, breast reconstruction surgery using vascularized flaps, liver and kidney transplants and open and endovascular revascularization procedures.

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

[0019] Preferably the at least one critical parameter threshold comprises a third critical parameter threshold which is indicative of the occurrence of hyperperfusion in the biological tissue. In an embodiment the third critical parameter threshold is defined as the reference perfusion parameter value increased by 20%.

[0020] Preferably, determining the current perfusion parameter value comprises calculating a current skewness value of the determined intensity distribution.

[0021] In a preferred alternative embodiment, the ultrasound method further comprises calculating a current skewness value of the determined intensity distribution.

[0022] Preferably, the current skewness value is compared with a reference skewness value of the at least one reference intensity distribution of the ultrasound blood signals.

[0023] Preferably, determining the current perfusion parameter value comprises calculating a current kurtosis value of the determined intensity distribution.

[0024] In a preferred alternative embodiment, the ultrasound method further comprises calculating a current kurtosis value of the determined intensity distribution.

[0025] Preferably the current kurtosis value is compared with a reference kurtosis value of the at least one reference intensity distribution of the ultrasound blood signals.

[0026] Preferably the extracting comprises selectively filtering the set of ultrasound blood signals originating from moving red blood cells in the plurality of blood vessels by applying a filter to the set of ultrasound signals. In an embodiment, applying a filter to the set of ultrasound signals comprises applying a high pass filter to the set of ultrasound signals. A preferred high pass filter is configured to filter out signals which derive from tissue movement (relatively low velocity) and to maintain signals which derive from movement of red blood cells (relatively high velocity). In this manner, the remaining signals originate from moving red blood cells (velocity range of about 3-40 mm / s) and constitute the ultrasound blood signals. In an alternative embodiment ultrasound blood signals can be extracted by other known techniques such as singular value decomposition of the obtained ultrasound signals.

[0027] Preferably, obtaining a set of ultrasound signals of the biological tissue is done by ultrafast ultrasound imaging, more preferably by ultrafast doppler imaging.

[0028] In this manner signals originating from moving red blood cell can efficiently be captured. It is known that ultrafast, i.e. planewave, doppler imaging (UFD) is available as a pre-clinical imaging modality. There are scientific publications on the use of this technology in rodents and non-human primates. In humans it has been used to image the brain of neonates, perform functional tests in awake surgery and to image the vasculature in brain tumor cases. UFD offers a large field of view, in depth, and at high spatiotemporal resolution (about 100 pm at 10 Hz). The spatial resolution is typically in the order of the penetrating arterioles, which implies it is suitable to image the vascular system up to the level of the capillaries. The Doppler-shift caused by the movement of red blood cells in these tiny vessels can be extracted from the raw signal, resulting in a micro-doppler signal which has been demonstrated to strongly correlate with the cerebral blood volume (CBV). As opposed to measuring blood flow or velocity in individual (large) vessels of the subarachnoid space, micro-doppler allows to measure CBV over time in the small vessels within the tissue. From this the relative changes in CBV over time can be tracked in each individual voxel of the image, which in turn can be a parameter for tissue perfusion.

[0029] The problem with known UFD methods however is that they do not provide an absolute value for perfusion, and that these methods are non-discriminative of venous versus arteriolar blood content. This implies that venous congestion can also induce a rise in CBV, albeit a decreased arteriolar perfusion. This problem is solved by embodiments of the presented ultrasound method wherein obtaining a set of ultrasound signals of the biological tissue is done by ultrafast ultrasound imaging, more preferably by ultrafast doppler imaging.

[0030] Moreover, the exact velocity of the blood in single vessels can be determined using known ultrafast Doppler imaging techniques, provided that the vessel’s angle relative to the ultrasound beams is known. This has been demonstrated in the straight vessels of lissencephaly brains of rodents. In the human gyrencephalic brain, the orientation of these often-tortuous vessels changes together with the windings of the cortex, making a reliable velocity analysis far less feasible. Above all such determinations are cumbersome and require exhaustive computations and multi-step analysis, making it unsuitable for clinical applications. This problem is solved by embodiments of the presented ultrasound method wherein perfusion values are determined based on intensity distributions of a plurality of blood vessels as opposed to singling out individual blood vessels.

[0031] The skilled person will understand that the hereinabove described technical considerations, functionalities and advantages for the presented ultrasound method embodiments also apply to the below described corresponding ultrasound system embodiments, mutatis mutandis.

[0032] According to a second aspect of the invention there is provided an ultrasound system for perfusion monitoring in biological tissue that comprises a plurality of blood vessels. The ultrasound system comprises:

[0033] - an ultrasound signal obtaining unit configured to obtain a set of ultrasound signals of the biological tissue;

[0034] - an extraction unit configured to extract from the set of ultrasound signals a corresponding set of ultrasound blood signals originating from moving red blood cells;

[0035] - an intensity determining unit configured to determine an intensity of the ultrasound blood signals;

[0036] - an intensity distribution determining unit configured to determine an intensity distribution of the ultrasound blood signals based on the determined intensity;

[0037] - a perfusion determining unit configured to:

[0038] - determine a current perfusion parameter value representative for perfusion in the blood vessels of the biological tissue based on the determined intensity distribution of the ultrasound blood signals;

[0039] - estimate a reference perfusion parameter value representative for perfusion of the blood vessels in the biological tissue in a normal state based on at least one normal intensity distribution of the ultrasound blood signals; and

[0040] - determine at least one critical perfusion parameter threshold based on the reference perfusion parameter value; and

[0041] - a monitoring unit configured to monitor perfusion in the plurality blood vessels by comparing the current perfusion parameter value with the at least one critical perfusion parameter threshold.

[0042] In this aspect of the invention, the intensity of the ultrasound blood signals is used as an indicator for the distribution of the blood volume within the tissue. However, it is clear to the skilled person that alternatively, or in addition, other parameters, such as a mean velocity, and a corresponding distribution can be determined from the ultrasound signals to provide information on the distribution and / or movement of the blood volume within the tissue. Although the ultrasound system for perfusion monitoring was initially developed as an intraoperative tool, it is clear to the skilled person that this system can also be applied in other settings such as a medical imaging department, intensive care unit, policlinic, or even at the patient’s home. In essence the presented ultrasound system can be used to perform perfusion monitoring in any biological soft tissue having substantial blood vessels.

[0043] Preferably, the perfusion determining unit is configured to determine the current perfusion parameter value by calculating an area under curve (AUC) value of at least a relevant part of the intensity distribution. Wherein the relevant part of the intensity distribution preferably ranges from the 10th to the 90th percentile of the intensity distribution, more preferably from the 15th to the 85th percentile of the intensity distribution, and most preferably from the 20th to the 80th percentile of the intensity distribution.

[0044] In a preferred embodiment the at least one normal intensity distribution of the ultrasound blood signals corresponds with the determined intensity distribution of the ultrasound blood signals prior to manipulation of the biological tissue.

[0045] In an alternative preferred embodiment the at least one normal intensity distribution of the ultrasound blood signals corresponds with a predetermined baseline intensity distribution of the ultrasound blood signals.

[0046] According to an embodiment the biological tissue is brain tissue.

[0047] Preferably, the at least one critical parameter threshold comprises a first critical parameter threshold which is indicative of the occurrence of hypoperfusion in the biological tissue. In an embodiment the first critical parameter threshold is defined as half of the reference perfusion parameter value.

[0048] Preferably, the at least one critical parameter threshold comprises a second critical parameter threshold which is indicative of the occurrence of ischemia in the biological tissue. In an embodiment the second critical parameter threshold is defined as a quarter of the reference perfusion parameter value.

[0049] Preferably, the at least one critical parameter threshold comprises a third critical parameter threshold which is indicative of the occurrence of hyperperfusion in the biological tissue. In an embodiment the third critical parameter threshold is defined as the reference perfusion parameter value increased by 20%.

[0050] Preferably the current perfusion parameter value comprises a current skewness value of the determined intensity distribution.

[0051] In a preferred alternative embodiment the perfusion determining unit is configured to calculate a current skewness value of the determined intensity distribution.

[0052] Preferably the monitoring unit is configured to compare the current skewness value with a reference skewness value of the at least one reference intensity distribution of the ultrasound blood signals.

[0053] Preferably the current perfusion parameter value comprises a current kurtosis value of the determined intensity distribution.

[0054] In a preferred alternative embodiment the perfusion determining unit is configured to calculate a current kurtosis value of the determined intensity distribution.

[0055] Preferably the monitoring unit is configured to compare the current kurtosis value with a reference kurtosis value of the at least one reference intensity distribution of the ultrasound blood signals.

[0056] Preferably the extraction unit is configured to selectively filter the set of ultrasound blood signals originating from moving red blood cells in the plurality of blood vessels from the set of ultrasound signals. In an embodiment the extraction unit is configured to apply a high pass filter to the set of ultrasound signals.

[0057] Preferably the ultrasound signal obtaining unit is configured to obtain a set of ultrasound signals of the biological tissue by applying ultrafast ultrasound imaging, more preferably by applying ultrafast planewave doppler imaging.

[0058] The skilled person will understand that the hereinabove described technical considerations, functionalities, preferred features, and advantages for the presented ultrasound method embodiments according to the first aspect also apply to the below described ultrasound method embodiments according to a third aspect, mutatis mutandis. According to a third aspect of the invention there is provided an ultrasound method for perfusion monitoring in biological tissue that comprises a plurality of blood vessels. The method comprises the steps of: i) obtaining a set of ultrasound signals of the biological tissue, ii) extracting from the set of ultrasound signals a corresponding set of ultrasound blood signals originating from moving red blood cells in the plurality of blood vessels, iii) determining a velocity of the ultrasound blood signals, preferably a mean velocity on a pixel-by-pixel or voxel-by-voxel basis, iv) determining a velocity distribution of the ultrasound blood signals based on the determined velocity, and v) determining a current perfusion parameter value representative for perfusion in the blood vessels of the biological tissue based on the determined velocity distribution of the ultrasound blood signals. The method further comprises the steps of: vi) estimating a reference perfusion parameter value representative for perfusion of the blood vessels in the biological tissue in a normal state based on at least one normal velocity distribution of the ultrasound blood signals, and vii) determining at least one critical perfusion parameter threshold 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 with the at least one critical perfusion parameter threshold.

[0059] The skilled person will understand that the hereinabove described technical considerations, functionalities, preferred features, and advantages for the presented ultrasound system embodiments according to the second aspect also apply to the below described ultrasound system embodiments according to a fourth, mutatis mutandis.

[0060] According to a fourth aspect of the invention there is provided an ultrasound system for perfusion monitoring in biological tissue that comprises a plurality of blood vessels. The ultrasound system comprises:

[0061] - an ultrasound signal obtaining unit configured to obtain a set of ultrasound signals of the biological tissue;

[0062] - an extraction unit configured to extract from the set of ultrasound signals a corresponding set of ultrasound blood signals originating from moving red blood cells;

[0063] - a velocity determining unit configured to determine a velocity of the ultrasound blood signals, preferably a mean velocity on a pixel-by-pixel or voxel-by-voxel basis;

[0064] - a velocity distribution determining unit configured to determine a velocity distribution of the ultrasound blood signals based on the determined velocity;

[0065] - a perfusion determining unit configured to: - determine a current perfusion parameter value representative for perfusion in the blood vessels of the biological tissue based on the determined velocity distribution of the ultrasound blood signals;

[0066] - estimate a reference perfusion parameter value representative for perfusion of the blood vessels in the biological tissue in a normal state based on at least one normal velocity distribution of the ultrasound blood signals; and

[0067] - determine at least one critical perfusion parameter threshold based on the reference perfusion parameter value; and

[0068] - a monitoring unit configured to monitor perfusion in the plurality blood vessels by comparing the current perfusion parameter value with the at least one critical perfusion parameter threshold.

[0069] Brief description of the figures

[0070] The accompanying drawings are used to illustrate presently preferred non-limiting exemplary embodiments of methods and systems in accordance with aspects of the present invention. The above and other advantages of the features and objects of the invention will become more apparent and the invention will be better understood from the following detailed description when read in conjunction with the accompanying drawings, in which:

[0071] Figure 1 illustrates a flowchart of an exemplary embodiment of an ultrasound method for perfusion monitoring according to the invention;

[0072] Figure 2 illustrates schematically an exemplary embodiment of an ultrasound system for perfusion monitoring according to the invention;

[0073] Figure 3A illustrates an intensity distribution of the ultrasound blood signals in case of a normal or healthy perfusion;

[0074] Figure 3B illustrates an intensity distribution of the ultrasound blood signals in case of abnormal perfusion;

[0075] Figure 4 illustrates more detailed examples of deviations in the determined intensity distribution in view of a reference intensity distribution due to possibly occurring alterations in perfusion during surgery;

[0076] Figure 5 illustrates 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 in particular, figure 5A illustrates an ultrasound image with an indicated region of interest (ROI), figure 5B illustrates the evolution of the intensity in the ROI over time, and figure 5C illustrates the corresponding intensity distribution over time; and

[0077] Figure 6 illustrates 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 in particular, figure 6A illustrates an ultrasound image with an indicated region of interest (ROI), figure 6B illustrates the evolution of the intensity in the ROI over time, and figure 6C illustrates the corresponding intensity distribution over time.

[0078] Description of embodiments

[0079] Figure 1 is a flowchart of a preferred embodiment of an ultrasound method 100 for perfusion monitoring in biological tissue that comprises a plurality of blood vessels.

[0080] The ultrasound method comprises a step 110 of obtaining a set of ultrasound signals of the biological tissue. Typically, ultrasound signals are obtained or acquired by means of an ultrasound signal obtaining unit which preferably comprises an ultrasound transducer connected to a beam generator. The obtained set of ultrasound signals can be defined as a(ri, tj), wherein: ri ,i = 1,..., N represents a plurality of targeted spatial points, corresponding with coordinates of imaged voxels; and tj, j = 1,..., n represents an acquisition time.

[0081] After step 110, the ultrasound method 100 comprises a step 120 of extracting from the set of ultrasound signals a corresponding set of ultrasound blood signals originating from moving red blood cells in the plurality of blood vessels. This step 120 is performed to remove static parts and / or relatively low dynamic parts of the imaged tissue from the obtained ultrasound signals and to maintain relatively high dynamic parts of the imaged tissue in the obtained ultrasound signals. These relatively high dynamic parts or relatively fast moving parts, i.e. within a velocity range of about 3-40 mm / s, of the imaged tissue originate from moving red blood cells in the plurality of blood vessels, and therefore pertain to ultrasound blood signals. A preferred manner of extracting the set of ultrasound blood signals is selectively filtering out ultrasound signals originating from static or low dynamic tissue and maintaining the ultrasound signals origination from moving red blood cells by applying a high pass filter to the obtained set of ultrasound signals. The ultrasound blood signals b can be defined as b(ri , tj) = filter (a(r; , tj)). A preferred high pass filter is aimed at maintaining signals corresponding to or originating from red blood cells having a velocity of between 3-40 mm / s. Alternatively to applying a filter to the ultrasound signals, spatio-temporal analysis of the ultrasound signals can be performed by means of singular value decomposition to distinguish between signals originating from tissue, such as the brain, and signals origination from blood.

[0082] When the ultrasound blood signals have been extracted in step 120, a step 130 of determining an intensity of the ultrasound blood signals is performed. The intensity I of the ultrasound blood signals, i.e. the ultrasound blood image, is determined on a voxel-per-voxel basis and can be calculated

[0083] After step 130, and based on the determined intensity I for each voxel of the ultrasound blood image, an intensity distribution p( / ) of the ultrasound bloods signals across voxels is determined in step 140.

[0084] After step 140, a current perfusion parameter value is determined based on the determined intensity distribution of the ultrasound blood signals in step 150. This current perfusion parameter value is representative for perfusion in the blood vessels of the biological tissue which is occurring at that moment. Steps 110 - 150 are iteratively repeated as illustrated by arrow 160, such that an evolution in time of the current perfusion parameter value can be determined or, in other words, such that the current perfusion parameter value can be monitored over time. Several perfusion parameters could be selected or determined from the intensity distribution p( / ). However, it has been found that the area under curve (AUC) is a particularly beneficial parameter which can be linked to the perfusion in the biological tissue. Therefore the AUC of the determined intensity distribution of ultrasound blood signals is a preferred perfusion parameter as will be further elaborated below in connection with figure 3.

[0085] In addition to the repeated execution of steps 110 - 150, the ultrasound method comprises step 145 of estimating a reference perfusion parameter value representative for perfusion in the biological tissue in a normal state based on at least one reference intensity distribution of the ultrasound blood signals and step 155 of determining at least one critical perfusion parameter threshold based on the reference perfusion parameter value. Step 145 provides a reference value or baseline value for perfusion with which the monitored current perfusion parameter value can be compared in order to be able to better quantify the changes to the monitored current perfusion parameter value that might occur over time. A preferred reference or baseline value is a value that is representative for perfusion in the biological tissue in a normal, i.e. healthy state. In this manner, any deviation from the reference or baseline value can be indicative for the occurrence of unwanted changes in perfusion. In a preferred embodiment, which is indicated by the dashed arrow a in figure 1 , the at least one reference intensity distribution of the ultrasound blood signals corresponds with the determined intensity distribution of the ultrasound blood signals prior to manipulation of the biological tissue. In other words, reference measurements or baseline measurements are performed on a patient before starting manipulations to the tissue, and monitoring of the current perfusion parameter value is performed during manipulation of the tissue. This approach will be referred to as the continuous approach since there is a continuity between the baseline or reference measurements at the one hand, and the monitoring measurements on the other hand in terms of the region, volume and / or patient that is being imaged. For the sake of completeness it is noted that steps, units or elements in figures 1 and 2 which are indicated in dashed lines are considered to be preferred or optional features for the illustrated embodiment and therefore do not represent essential features of the respective embodiment.

[0086] In an alternative embodiment, which is not shown in figure 1 , the at least one reference intensity distribution of the ultrasound blood signals corresponds with a predetermined baseline intensity distribution of the ultrasound blood signals. Such predetermined baseline intensity distribution can be determined statistically based on a plurality of previous measurements of either the same patient or of a plurality of different patients. For example, for a particular highly vascularized organ to be examined or operated on, a hundred intensity distributions of said organ may be determined from a hundred different patients, and an average baseline intensity distribution can be determined therefrom. This approach will be referred to as the discontinuous approach since there is a discontinuity between the baseline measurements at the one hand, and the monitoring measurements on the other hand in since both measurements occur on different patients and evidently at different moments in time. This discontinuous approach is less time consuming as compared to the continuous approach and might therefore be the preferred option in urgent cases where there is no time to do any reference or baseline measurements. The continuous approach is more accurate as compared to the discontinuous approach since baseline perfusion measurements and current perfusion measurements are carried out on the same patient and might therefore be the preferred option in cases where there is no or less urgency involved. It is clear to the skilled person that the preference for any one of the continuous and discontinuous approach depends on the details and circumstances of a particular case.

[0087] In addition to having a reference value or baseline value in order to be able to determine deviations from such reference value or baseline value, it is important to define one or more thresholds to efficiently monitor perfusion as is done in step 155. Such one or more threshold(s) represent a boundary between for example allowable, relatively small, deviations in perfusion which do not present an immediate threat to the patients’ health, and problematic, relatively large deviations in perfusion, which are a threat to the patients’ health and require immediate attention.

[0088] For example, such one or more thresholds may comprise a first critical parameter threshold which is indicative of the occurrence of hypoperfusion in the biological tissue, wherein the first critical parameter threshold is defined as half of the reference perfusion parameter value, such as an AUC value from a reference intensity distribution.

[0089] Alternatively or in addition, such one or more thresholds may comprise a second critical parameter threshold which is indicative of the occurrence of ischemia in the biological tissue, wherein the second critical parameter threshold is defined as a quarter of the reference perfusion parameter value, such as an AUC value from a reference intensity distribution.

[0090] Alternatively or in addition, such one or more thresholds may comprise a third critical parameter threshold which is indicative of the occurrence of hyperperfusion in the biological tissue, wherein the third critical parameter threshold is defined as the reference perfusion parameter value, such as a AUC value from a reference intensity distribution increased by 20%.

[0091] It is clear to the skilled person that additional or other critical parameters thresholds can be defined, for example based on respective standard deviations of the involved measurements and / or calculations, depending on the specifics of the case, for example based on which organ or highly vascularized tissue is being monitored.

[0092] Finally, figure 1 shows the step 170 of 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 both steps 150, i.e. the current perfusion parameter value, and 155, i.e. the critical perfusion parameter threshold. When the current perfusion parameter value reaches or passes the critical perfusion parameter, the method preferably further comprises indicating that the critical perfusion parameter threshold is reached or passed, respectively. Preferably said indicating comprises visually indicating to the respective medical personnel, e.g. on a screen in the operating room, that the critical perfusion parameter is reached or passed. It is clear to the skilled person that, in addition to the above mentioned preferred critical perfusion parameter thresholds, other critical perfusion parameter thresholds can be defined depending on the specifics of the case such as the type of operation and / or type of organ or tissue to be monitored.

[0093] In the embodiment of figure 1 the intensity of the ultrasound blood signals is used as an indicator for the distribution of the blood volume within the tissue. However, it is clear to the skilled person that alternatively, or in addition, other parameters such as a mean velocity can be determined from the ultrasound signals and a corresponding distribution can be determined to provide information on the distribution and / or movement of the blood volume within the tissue. More in particular a mean velocity for each voxel or pixel in the ultrasound blood image can be determined along with a corresponding mean velocity distribution.

[0094] Figure 2 illustrates schematically an exemplary embodiment of an ultrasound system for perfusion monitoring. More in particular, figure 2 illustrates an ultrasound system 200 for perfusion monitoring in biological tissue, typically a highly vascularized tissue, that comprises a plurality of blood vessels. The ultrasound system 200 comprises an ultrasound signal obtaining unit 210, preferably an ultrasound probe connected to a beam generator, configured to obtain a set of ultrasound signals of the biological tissue; an extraction unit 220 configured to extract from the set of ultrasound signals a corresponding set of ultrasound blood signals originating from moving red blood cells; an intensity determining unit 230 configured to determine an intensity of the ultrasound blood signals; an intensity distribution determining unit 240 configured to determine an intensity distribution of the ultrasound blood signals based on the determined intensity. The ultrasound system 200 further comprises a perfusion determining unit 250 configured to: determine a current perfusion parameter value representative for perfusion in the blood vessels of the biological tissue based on the determined intensity distribution of the ultrasound blood signals; estimate a reference perfusion parameter value representative for perfusion of the blood vessels in the biological tissue in a normal state based on at least one normal intensity distribution of the ultrasound blood signals; and determine at least one critical perfusion parameter threshold based on the reference perfusion parameter value. The ultrasound system 200 further comprises a monitoring unit 270 configured to monitor perfusion in the plurality blood vessels by comparing the current perfusion parameter value with the at least one critical perfusion parameter threshold. The dashed outlines in figure 2 which group units 220 and 230 on the one hand, and units 240, 250 and 270 on the other hand, represent an exemplary configuration of the respective units in a possible hardware set-up. This exemplary configuration is suggested since units 220 and 230 are typically involved in pre-processing of the ultrasound signals prior to arriving at an ultrasound blood image, i.e. determined intensity of the ultrasound blood signal, whereas units 240, 250 and 270 are typically involved in postprocessing of the ultrasound blood image. It is clear to the skilled person that other configurations are possible.

[0095] The skilled person will understand that the hereinabove described technical considerations, functionalities and advantages for the presented ultrasound method embodiment of figure 1 also apply to the corresponding ultrasound system embodiment of figure 2, mutatis mutandis. Therefore, in order to avoid repetition, a detailed discussion on the illustrated units 210 - 270 of the ultrasound system 200 will be omitted.

[0096] In figure 2 an optional element b is indicated in dashed lines which delivers input to the perfusion determining unit 250. Element b represents an external database containing predetermined baseline intensity distributions of ultrasound blood signals for different organs or highly vascularized tissue, and / or statistical information to determine said predetermined baseline intensity distribution(s). In other words, the database b enables the ultrasound system to apply the discontinuous approach as already elaborated in view of figure 1 , alternative to or in addition to the optional continuous approach as indicated by arrow a in figure 1. A person of skill in the art would readily recognize that steps of various above-described methods can be performed by programmed computers. Herein, some embodiments are also intended to cover program storage devices, e.g., digital data storage media, which are machine or computer readable and encode machine-executable or computer-executable programs of instructions, wherein said instructions perform some or all of the steps of said above-described methods. The program storage devices may be, e.g., digital memories, magnetic storage media such as a magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media. The embodiments are also intended to cover computers programmed to perform said steps of the above-described methods.

[0097] The functions of the various elements shown in the Figures, including any functional blocks labelled as “units”, “processors” or “modules”, may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), and non volatile storage. Other hardware, conventional and / or custom, may also be included. Similarly, any switches shown in the Figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the implementer as more specifically understood from the context.

[0098] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the invention. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.

[0099] Figure 3A illustrates an intensity distribution of the ultrasound blood signals in case of a normal or healthy perfusion, whereas Figure 3B illustrates two intensity distributions of the ultrasound blood signals in case of abnormal perfusion. In other words, figure 3A presents a reference intensity distribution of the ultrasound blood signals which typically substantially corresponds with a normal distribution, whereas figure 3B presents two examples of determined intensity distributions 356, 357 which might be indicative of hypoperfusion 356 and ischemia 357, respectively. In both figures the signal intensity I is indicated on the x-axis, and the amount of voxels, for which the respective signal intensities are measured, are indicated on the y-axis. It is emphasized that the illustrated intensity distribution is in view of the amount of voxels and that the intensity measured from a particular voxel can originate from one or multiple blood vessels present in said voxel. Preferably the signal intensity I corresponds with ultrafast Doppler signal intensity. Both in figures 3 A and 3B, a relevant, central part of the intensity distribution is indicated by the range 346. In this case the range 346 pertains to 80% of the entire intensity distribution and extends from the 10thpercentile to the 90thpercentile of the measured intensity I. Within this range 346 the AUC in figure 3A is indicated with diagonal lines. The indicated AUC in figure 3A represents the reference perfusion parameter value representative for perfusion of the blood vessels in the biological tissue in a normal state and is referred to as AUCo- Within the range 346 the AUC of determined intensity distribution 356 in figure 3B is indicated with vertical lines. This indicated AUC in figure 3B can be indicative of the occurrence of hypoperfusion. To this effect the indicated AUC in figure 3B is compared with a critical perfusion parameter threshold which can be defined as AUCo / 2. If the AUC in figure 3B drops to or passes this threshold, it can be notified to the respective operator or other medical personnel the critical perfusion parameter threshold is reached or passed in real-time such that action can be taken to resolve the occurring complication. Within the range 346 the AUC of determined intensity distribution 357 in figure 3B is indicated with horizontal lines. This indicated AUC in figure 3B can be indicative of the occurrence of ischemia. To this effect the indicated AUC in figure 3B is compared with a critical perfusion parameter threshold which can be defined as AUCo / 4. If the AUC in figure 3B drops to or passes this threshold, it can be notified to the respective operator or other medical personnel the critical perfusion parameter threshold is reached or passed in real-time such that action can be taken to resolve the occurring complication. From the shown annotated intensity distributions it is clear that the AUC is a powerful tool to monitor perfusion intra-operatively. On the one hand, the AUC can be calculated in a straightforward and quick manner which allows for real-time monitoring of perfusion, and on the other hand the AUC has been found to correlate well with perfusion within the biological tissue. Preferably only the AUC of a relevant part of the intensity distribution. An exemplary relevant part is a central part or central range of the intensity distribution. In the example of figures 3A and 3B the relevant, central part of the intensity distribution is chosen from the 10thto the 90thpercentile of the intensity distribution. By looking at this central part of the intensity distribution, which typically corresponds with a substantially normal distribution for healthy highly vascularized tissues in humans, outliers in the intensity spectrum are filtered out and data quality is improved. Preferably the relevant part of the intensity distribution ranges from the 10thto the 90thpercentile of the intensity distribution, more preferably from the 15thto the 85thpercentile of the intensity distribution, and even more preferably from the 20thto the 80thpercentile of the intensity distribution. Although these ranges are preferred to act as relevant part of the intensity distribution, it is clear to the skilled person that deviations of these ranges may occur depending on the details of the case at hand.

[0100] Figure 3B illustrates two preferred critical perfusion parameter thresholds which can be applied to check whether hypoperfusion and / or ischemia occurs while monitoring the current perfusion parameter value. In this case the first critical parameter threshold is indicative of the occurrence of hypoperfusion in the biological tissue, and is defined as half of the reference perfusion parameter value.

[0101] In addition, the second critical parameter threshold is indicative of the occurrence of ischemia in the biological tissue, and is defined as a quarter of the reference perfusion parameter value.

[0102] Figure 4 illustrates more detailed examples of possible deviations in the determined intensity distribution in view of a reference intensity distribution due to possibly occurring complications during surgery. As demonstrated in connection with figures 3 A and 3B, the AUC of respective blood signal intensity distributions in view of imaged voxels is a powerful tool to monitor perfusion in an efficient manner. In addition to the valuable information obtained by monitoring a numerical AUC value, additional information can be extracted from a blood signal intensity distribution and / or deviations thereof. Although in connection with figures 3A, 3B and 4 it is assumed that a substantially normal intensity distribution is representative for healthy highly vascularized tissue, it is clear that the teachings of the invention also apply to any other type of distributions as reference distribution such as lognormal distributions, skewed distributions, logistic distributions, and the like.

[0103] In figure 4 the signal intensity I is indicated on the x-axis, and the amount of voxels, for which the respective signal intensities are measured, are indicated on the y-axis. It is emphasized that the illustrated intensity distribution is in view of the amount of voxels and that the intensity measured from a particular voxel can originate from one or multiple blood vessels present in said voxel. Preferably the signal intensity I corresponds with ultrafast Doppler signal intensity. For illustrative purposes, multiple ultrasound blood signal intensity distributions 455, 456a, 456b, 456c and 456d are shown in the same graph. Intensity distribution 455 represents a normal distribution, which could serve as a reference intensity distribution, and is representative for an expected intensity distribution for healthy biological tissue in a normal state, i.e. without manipulation. In other words, intensity distribution 455 represents a state of normoperfusion. Intensity distributions 456a, 456b, 456c and 456d represent situations wherein the perfusion has changed and deviates from the normal state. As shown in figure 4, and with reference to intensity distributions 456a and 456b, a current skewness value SV of the determined intensity distribution can be calculated in order to gain more information on the shape of the intensity distribution. More in particular, a current skewness value SV may provide valuable insights on whether the peak of the intensity distribution has shifted to the left or to the right, and hence may rather be indicative for the occurrence of hypoperfusion or hyperperfusion, respectively, especially locally. Referring to intensity distribution 456a, a negative SV (SV < 0) indicates a peak shift to the left of the normal intensity distribution 455 and is indicative for the occurrence of hypoperfusion. On the other hand, referring to intensity distribution 456b, a positive SV (SV > 0) indicates a peak shift to the right of the normal intensity distribution 455 and is indicative for the occurrence of hyperperfusion.

[0104] Similarly, and with reference to intensity distributions 456c and 456d, a current kurtosis value KV of the determined intensity distribution can be calculated in order to gain more information on the shape of the intensity distribution. More in particular, a current kurtosis value KV may provide valuable insights on whether the peak of the intensity distribution is sharp or flat, and hence may rather be indicative for the occurrence of global hypoperfusion or hyperperfusion, respectively. It is clear to the skilled person that the use of a skewness value SV and a kurtosis value KV can be implemented independently from each other. However, when calculation of a skewness value SV and a kurtosis value KV are combined, it is believed that any realistically occurring blood signal intensity distribution can be accurately characterized. In addition, the skilled person is aware of different ways and formulas to calculate a skewness value SV and kurtosis value KV. Possible formulas include, but are not limited to the following coefficients.

[0105] The Fisher’s skewness coefficient as employed in figure 4 and defined as: and the Kurtosis coefficient as employed in figure 4 and defined as: wherein x = mean, m = mode, n = number of observations and S = standard deviation.

[0106] Figure 5 illustrates a first example 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 in particular, figure 5A illustrates an ultrasound image with an indicated region of interest (ROI), figure 5B illustrates the evolution of the intensity in the ROI over time, and figure 5C illustrates determined intensity distribution over time. Figure 5A is an ultrasound image based on ultrasound signals, more in particular a power Doppler ultrasound image of a skeletal muscle, more in particular the flexor digitorum muscle in the forearm. Rectangle R indicates an ROI which was selected to determine a relevant ultrasound blood image to monitor perfusion.

[0107] Figure 5B illustrates a hemodynamic time series, i.e. the changes over time, of the average signal intensity within the selected ROI. The bold bar on the horizontal axis indicates the inflation (> 300 mm Hg) of a pressure cuff which is positioned on the upper arm of the patient. This inflation results in a significant reduction of the perfusion in the patient’s forearm, as is also visible by the corresponding drop and dip in relative intensity of the ROI signal.

[0108] Figure 5C illustrates corresponding intensity distributions of the ROI signal after 5s, 15s, 25s, 35s and 45s. The intensity distribution at 5s is determined before the pressure cuff is inflated and serves as reference intensity distribution from which a reference perfusion parameter can be estimated, such as an AUC. The intensity distributions at 15s and 25s are determined when the pressure cuff is inflated and demonstrate a reduction of the AUC and also a peak shift to the left, which may be indicative for a reduced perfusion. The intensity distribution at 35s is determined shortly after releasing the pressure cuff from the upper arm and displays an increase of the AUC and a peak shift to the right as compared to the reference distribution, which may be indicative for a temporary increase in perfusion, i.e. hyperperfusion.

[0109] Figure 6 illustrates a second example 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 in particular, figure 6A illustrates an ultrasound image with an indicated region of interest (ROI), figure 6B illustrates the evolution of the intensity in the ROI over time, and figure 6C illustrates determined intensity distribution over time.

[0110] Figure 6 A is an ultrasound image based on ultrasound signals, more in particular a power Doppler ultrasound image of a brain region. More in particular, the ultrasound image in figure 6A relates to an intra-operative recording during resection of a brain arteriovenous malformation. The recording includes a temporary clipping for a period of about 60 seconds of a feeding artery, which results in a decreased perfusion in the adjacent brain parenchyma which is indicated by rectangle R as the selected ROI and which corresponds with a relevant ultrasound blood image to monitor perfusion. The illustrated power Doppler image was taken before clipping, i.e. at baseline.

[0111] Figure 6B illustrates a hemodynamic time series, i.e. the changes over time, of the average signal intensity within the selected ROI. The bold bar on the horizontal axis indicates the clipping of the feeding artery, which clipping results in a significant reduction of the perfusion in the adjacent brain parenchyma, as is also visible by the corresponding drop and dip in relative intensity of the ROI signal.

[0112] Figure 6C illustrates corresponding intensity distributions of the ROI signal after 5s, 20s, 40s, 60s, 80s, 100s and 120s. The intensity distribution at 5s is determined well before clipping of the feeding artery and serves as reference intensity distribution from which a reference perfusion parameter can be estimated, such as an AUC. The clipping was initiated briefly before the 40s time point. The intensity distributions at 40s, 60s and 80s are determined during the clipping of the feeding artery and demonstrate a reduction of the AUC and also a peak shift to the left, which may be indicative for a reduced perfusion. The intensity distribution at 100s is determined very shortly after releasing the temporary clip and displays an increase of the AUC and a peak shift to the right as compared to the distributions at 60s and 80s, which seems to point towards the start of a normalisation of the perfusion. The intensity distribution at 120s is determined after releasing the temporary clip and displays an increase of the AUC and a peak shift to the right as compared to the reference distribution, which may be indicative for a temporary increase in perfusion, i.e. hyperperfusion.

[0113] Whilst the principles of the invention have been set out above in connection with specific embodiments, it is to be understood that this description is merely made by way of example and not as a limitation of the scope of protection which is determined by the appended claims.

Claims

Claims1. An ultrasound method for perfusion monitoring in biological tissue that comprises a plurality of blood vessels, the method comprising following steps: i) obtaining a set of ultrasound signals of the biological tissue; ii) extracting from the set of ultrasound signals a corresponding set of ultrasound blood signals originating from moving red blood cells in the plurality of blood vessels; iii) determining an intensity of the ultrasound blood signals; iv) determining an intensity distribution of the ultrasound blood signals based on the determined intensity; v) determining a current perfusion parameter value representative for perfusion in the blood vessels of the biological tissue based on the determined intensity distribution of the ultrasound blood signals; vi) estimating a reference perfusion parameter value representative for perfusion of the blood vessels in the biological tissue in a normal state based on at least one reference intensity distribution of the ultrasound blood signals; vii) determining at least one critical perfusion parameter threshold 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.

2. Ultrasound method according to claim 1 , wherein determining the current perfusion parameter value comprises calculating an area under curve, AUC, value of at least a relevant part of the intensity distribution.

3. Ultrasound method according to claim 2, wherein the relevant part of the intensity distribution ranges from the 10thto the 90thpercentile of the intensity distribution, preferably from the 15thto the 85thpercentile of the intensity distribution, and more preferably from the 20thto the 80thpercentile of the intensity distribution.

4. Ultrasound method according to any one of the preceding claims, wherein the at least one reference intensity distribution of the ultrasound blood signals corresponds with the determined intensity distribution of the ultrasound blood signals prior to manipulation of the biological tissue.

5. Ultrasound method according to any one of the preceding claims 1-3, wherein the at least one reference intensity distribution of the ultrasound blood signals corresponds with a predetermined baseline intensity distribution of the ultrasound blood signals.

6. Ultrasound method according to any one of the preceding claims, wherein the biological tissue is brain tissue.

7. Ultrasound method according to any one of the preceding claims, wherein the at least one critical parameter threshold comprises a first critical parameter threshold which is indicative of the occurrence of hypoperfusion in the biological tissue.

8. Ultrasound method according to the preceding claim wherein the first critical parameter threshold is defined as half of the reference perfusion parameter value.

9. Ultrasound method according to any one of the preceding claims, wherein the at least one critical parameter threshold comprises a second critical parameter threshold which is indicative of the occurrence of ischemia in the biological tissue.

10. Ultrasound method according to the preceding claim wherein the second critical parameter threshold is defined as a quarter of the reference perfusion parameter value.

11. Ultrasound method according to any one of the preceding claims, wherein the at least one critical parameter threshold comprises a third critical parameter threshold which is indicative of the occurrence of hyperperfusion in the biological tissue.

12. Ultrasound method according to the preceding claim wherein the third critical parameter threshold is defined as the reference perfusion parameter value increased by 20%.

13. Ultrasound method according to any one of the preceding claims, wherein determining the current perfusion parameter value comprises calculating a current skewness value of the determined intensity distribution.

14. Ultrasound method according to any one of the preceding claims 1 - 12, further comprising calculating a current skewness value of the determined intensity distribution.

15. Ultrasound method according to claims 13 or 14, wherein the current skewness value is compared with a reference skewness value of the at least one reference intensity distribution of the ultrasound blood signals.

16. Ultrasound method according to any one of the preceding claims, wherein determining the current perfusion parameter value comprises calculating a current kurtosis value of the determined intensity distribution.

17. Ultrasound method according to any one of the preceding claims 1 - 15, further comprising calculating a current kurtosis value of the determined intensity distribution.

18. Ultrasound method according to claims 16 or 17, wherein the current kurtosis value is compared with a reference kurtosis value of the at least one reference intensity distribution of the ultrasound blood signals.

19. Ultrasound method according to any one of the preceding claims wherein extracting comprises selectively filtering the set of ultrasound blood signals originating from moving red blood cells in the plurality of blood vessels by applying a filter to the set of ultrasound signals.

20. Ultrasound method according to 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. Ultrasound method according to any one of the preceding claims, wherein obtaining a set of ultrasound signals of the biological tissue is done by ultrafast ultrasound imaging, preferably by ultrafast doppler imaging.

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

23. Ultrasound system according to claim 22, wherein the perfusion determining unit is configured to determine the current perfusion parameter value by calculating an area under curve, AUC, value of at least a relevant part of the intensity distribution24. Ultrasound system according to claim 23, wherein the relevant part of the intensity distribution ranges from the 10thto the 90thpercentile of the intensity distribution, preferably from the 15thto the 85thpercentile of the intensity distribution, and more preferably from the 20thto the 80thpercentile of the intensity distribution.

25. Ultrasound system according to any one of the previous claims 22 - 24, wherein the at least one reference intensity distribution of the ultrasound blood signals corresponds with the determined intensity distribution of the ultrasound blood signals prior to manipulation of the biological tissue.

26. Ultrasound system according to any one of the previous claims 22 - 24, wherein the at least one reference intensity distribution of the ultrasound blood signals corresponds with a predetermined baseline intensity distribution of the ultrasound blood signals.

27. Ultrasound system according to any one of the previous claims 22 - 26, wherein the biological tissue is brain tissue28. Ultrasound system according to any one of the previous claims 22 - 27, wherein the at least one critical parameter threshold comprises a first critical parameter threshold which is indicative of the occurrence of hypoperfusion in the biological tissue.

29. Ultrasound system according to the preceding claim wherein the first critical parameter threshold is defined as half of the reference perfusion parameter value.

30. Ultrasound system according to any one of the previous claims 22 - 29, wherein the at least one critical parameter threshold comprises a second critical parameter threshold which is indicative of the occurrence of ischemia in the biological tissue.

30. Ultrasound system according to the preceding claim wherein the second critical parameter threshold is defined as a quarter of the reference perfusion parameter value.

31. Ultrasound system according to any one of the preceding claims 22 - 30, wherein the at least one critical parameter threshold comprises a third critical parameter threshold which is indicative of the occurrence of hyperperfusion in the biological tissue.

32. Ultrasound system according to the preceding claim wherein the third critical parameter threshold is defined as the reference perfusion parameter value increased by 20%.

33. Ultrasound system according to any one of the preceding claims 22 - 32, wherein the current perfusion parameter value comprises a current skewness value of the determined intensity distribution.

34. Ultrasound system according to any one of the preceding claims 22 - 32, wherein the perfusion determining unit is configured to calculate a current skewness value of the determined intensity distribution.

35. Ultrasound system according to claims 33 or 34, wherein the monitoring unit is configured to compare the current skewness value with a reference skewness value of the at least one reference intensity distribution of the ultrasound blood signals.

36. Ultrasound system according to any one of the preceding claims 22 - 35, wherein the current perfusion parameter value comprises a current kurtosis value of the determined intensity distribution.

37. Ultrasound system according to any one of the preceding claims 22 - 36, wherein the perfusion determining unit is configured to calculate a current kurtosis value of the determined intensity distribution.

38. Ultrasound method according to claims 36 or 37, wherein the monitoring unit is configured to compare the current kurtosis value with a reference kurtosis value of the at least one reference intensity distribution of the ultrasound blood signals.

39. Ultrasound system according to any one of the preceding claims 22 - 38, wherein the extraction unit is configured to selectively filter the set of ultrasound blood signals originating from moving red blood cells in the plurality of blood vessels by applying a filter to the set of ultrasound signals.

40. Ultrasound system according to the preceding claim, wherein the extraction unit is configured to apply a high pass filter to the set of ultrasound signals.

41. Ultrasound system according to any one of the preceding claims 22 - 40, wherein the ultrasound signal obtaining unit is configured to obtain a set of ultrasound signals of the biological tissue by ultrafast ultrasound imaging, preferably by ultrafast doppler imaging.

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