Monitoring of a hemodynamic parameter

Abstract

The invention relates to methods of processing of Doppler signals or echoes obtained from insonation of a tissue, in particular a cerebral tissue. The methods in particular enable determination of a hemodynamic parameter independent of the insonated tissue region and enable comparison between different individuals in an operator-independent manner.

Description

[0001] MONITORING OF A HEMODYNAMIC PARAMETER

[0002] FIELD OF THE INVENTION

[0003] The invention relates to methods of processing of Doppler signals or echoes obtained from insonation of a tissue, in particular a cerebral tissue. The methods in particular enable determination of a hemodynamic parameter independent of the insonated tissue region and enable comparison between different individuals in an operator-independent manner.

[0004] BACKGROUND TO THE INVENTION

[0005] The idea of inserting a miniaturized ultrasound transducer into a drill or burr hole in the skull for monitoring the entire brain and cerebral blood flow (in the major intracranial cerebral vessels) was disclosed in KR20070077837A / KR10-0784117B, which referred to a transducer of circular structure with a diameter of 10 mm or less, emitting fan-shaped ultrasonic beams, and capable of pulsed and color Doppler. This idea was proposed as a solution of the problem of reduced reliability of transcranial Doppler caused by the attenuation of the ultrasound by the skull bones.

[0006] An improvement to this technology allowing whole-brain microvasculature dynamics in response to brain activation with high spatiotemporal resolution was described by Mace et al. 2011 (Nat Methods 8:662-664) and Mace et al. 2013 (IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 60:492-506), and relied on plane-wave ultrasonic beams as opposed to focused beams. Ultrasonic images are acquired at high frequency (>800 Hz; to increase signal to noise ratio) to allow for visualization of small capillaries, thus leading to ultrafast continuous imaging and monitoring. These improvements were termed functional ultrasound (fUS) based on micro-Doppler ultrasound imaging. Further modifications of fUS include improved discrimination between tissue movement and red blood cell movement (Demene et al. 2015, IEEE Trans Medical Imag 34:2271-2285), and generation of high resolution images of the vasculature in ultrasound Doppler images (WO2020165412A1).

[0007] Quantification of blood perfusion, and in particular quantification of cerebral blood perfusion is not trivial. Ideally the methodology used for quantifying cerebral blood perfusion is: a) non-invasive or minimally invasive and no use of contrast agents; b) capable of monitoring a patient continuously (e.g. 24 / 7), thus allowing monitoring at the bedside (e.g. in an intensive care unit or setting); c) capable of measuring blood flow deep enough in the brain and with a sufficiently large field of view (superficial measurement is not sufficient); d) capable of measuring blood flow in a large distribution of vessels with diameters ranging from some pm to some mm, and with blood flow velocities ranging from ~100 mm / s to as low as ~lmm / s; e) capable of quantifying a blood or hemodynamic parameter independent of the brain region and comparable between different individuals, and independent of the operator.

[0008] Large medical devices such as MRI or X-ray scanners fail in point b). All methods based on optics fail in point c). Low-frequency electromagnetic methods have shallow resolution and fail in d). Standard transcranial ultrasound solves a), b) and c) but fails in d). Three significant limitations of transcranial Doppler ultrasound (TCD) are that 1) it is highly operator dependent, with the handheld technique requiring detailed three-dimensional knowledge of cerebrovascular anatomy and its variations; 2) the use of TCD is hampered by the 10 to 15% rate of inadequate acoustic windows related to thickness and porosity of the bone and attenuation of the ultrasound energy transmission; and 3) TCD measurements are limited to the large basal arteries and can only provide an index of global cerebral blood flow velocity (CBFv) rather than of local CBFv. An overview of TCD and its applications is provided by e.g. Purkayastha and Sorond 2012 (Semin Neurol 32:411-420).

[0009] The newly developed functional ultrasound imaging technique (fUS; ultrafast continuous microDoppler ultrasound imaging using a plane-wave ultrasonic beam emitter) can measure a large distribution of vessels and addresses the point d). However, quantification as specified in point e) remains unsolved. The need remains for a standardized method to measure CBFv in multiple types of vessels in a reliable and reproducible fashion, independent of the operator, independent of the tissue (e.g. brain) region, independent of the subject, and in real-time.

[0010] Perhaps the best explored metric to represent or monitor cerebrovascular regulation (CA) is the pressure reactivity index (PRx index), a moving correlation coefficient calculated from 30 consecutive 10-second averages of slow spontaneous waves in intracranial pressure (ICP) and arterial blood pressure (ABP) (Czosnyka et al. 2017, Acta Neurochir 159 :2063-2065; Czosnyka et al. 1997, Neurosurgery 41:11-17).

[0011] As standard of care, traumatic brain injury (TBI) patients are closely monitored. This includes monitoring and management of intracranial pressure and cerebral perfusion pressure (CPP). Often, a number of sensors is "bundled" and invasively introduced in the brain.

[0012] SUMMARY OF THE INVENTION

[0013] The current disclosure relates in one aspect to methods for determining a hemodynamic parameter, such methods comprising:

[0014] - measuring the Doppler signals obtained from an ultrasound wave emitted in a tissue, the intensity of the Doppler signals correlating with the diameter of blood vessels in the tissue;

[0015] - computing the distribution of intensities of the measured Doppler signals;

[0016] - normalizing the computed distribution to a reference Doppler signal intensity distribution; - select a range of Doppler signal intensities within the normalized distribution, therewith selecting a set of blood vessels with a range of diameters correlating with the selected range of Doppler signal intensities;

[0017] - determining the hemodynamic parameter in the selected set of blood vessels.

[0018] Herein, the hemodynamic parameter can be one or more from blood volume, blood flow, blood velocity, blood mean velocity, or blood mean velocity multiplied by the normalized intensity.

[0019] Herein the hemodynamic parameter can be continuously quantified based on continuously obtained Doppler signals.

[0020] More in particular to any of the above, the ultrasound waves can be ultrasound plane waves.

[0021] More in particular to any of the above, the Doppler signals are obtained from plane wave ultrasound imaging.

[0022] In a further embodiment to any of the above, such methods further include discrimination of tissue and blood cell movement.

[0023] In a further embodiment to any of the above, such methods further include generating a sharpened image for each component image of multiple component images.

[0024] More in particular to any of the above, such methods are real-time methods.

[0025] More in particular to any of the above, the tissue is brain tissue. In one further embodiment thereto, emission of the ultrasound wave and the measurement of the Doppler signals is minimally invasive, such as through a cranial window, a burr hole, a trepanation or a thinned skull bone. In one further embodiment thereto, the design of the ultrasound wave transducer / Doppler signal receiver is following the contours, shape, or delineation of a burr hole; or is more in particular following the contours, shape, delineation of a segment of a burr hole.

[0026] The current disclosure relates in a further aspect to methods of assessing or monitoring cerebral physiology or function, such methods comprising: measuring or monitoring a cerebral hemodynamic parameter with any method as described hereinabove measuring or monitoring the cerebral perfusion pressure (CPP) assessing or monitoring cerebral physiology or function based on the correlation between the measured or monitored cerebral hemodynamic parameter and the measured or monitored CPP.

[0027] The current disclosure relates in a further aspect to an ultrasound wave transducer / Doppler signal receiver having a design following the contours, shape, or delineation of a burr hole, or having a design following the contours, shape, or delineation of a segment of a burr hole. The current disclosure further relates to computer programs having instructions which when executed cause a computing or data processing system or device to carry out or perform any method as described hereinabove, or to carry out or perform a step of any method as described hereinabove.

[0028] The current disclosure further relates to computing or data processing systems or devices, or machine readable media comprising a means for carrying out or performing any method as described hereinabove, or for carrying out or performing a step of any method as described hereinabove.

[0029] BRIEF DESCRIPTION OF THE DRAWINGS

[0030] FIGURE 1. Combined microscopy data of seven experiments plotted into one graph. The Y-axis refers to the RBC flux (V * ir * r2) acquired by processing of the microscopy data, which is displayed in percentages change to baseline.

[0031] FIGURE 2. Imaging acquired in one pig, during an experiment of gradual hypotension. (A) Raw standard funtional ultrasound image, showing an the cortex of the brain with the blood vessels in white. The box shows the region of interest that is used for the further calculations. (B) Graph showing the velocity versus time during the experiment, acquired by postprocessing of the images. Flow towards the cortex is labelled as negative ("Flow -"), towards the center as positive ("Flow +"). The arrow indicates the decline in cerebral blood flow velocity ("velocity") starting at about 120 minutes, reflecting the decrease of cerebral perfusion pressure (CPP) as indicated in (C) during the gradual hypotension.

[0032] FIGURE 3. Postprocessing of the data shown in Figure 2, data of one cortical region of interest in one pig undergoing gradual hypotension. CBFv, CBV and UfD product (CBFv*CBV) are shown; (A) opposed to time in the x-axis, (B) opposed to CCP in the x-axis. A clear deflection point is seen for velocity- associated measures when loweringABP, this in both directions of flow (positive and negative: "Flow+",

[0033] FIGURE 4. (A) Six different regions of interest (ROIs) are indicated in the raw standard funtional ultrasound image. These were used to compare further calculations. (B) CBFv ("V") - CBV-fUS product ("V*CBV") are displayed with CPP on the x-axis. The measurements were done during the same experiment of gradual hypotension depicted in Figure 2.

[0034] FIGURE 5. Combined fUs data of seven experiments plotted into one graph. On the Y-axis, the calculated flow (fUS product = velocity*CBV) measured by fUS is indicated, normalized (with 1 being the calculated flow at the start of the experiment (meaning normotension)).

[0035] FIGURE 6. (A) Exemplary ultrasound images of the same brain but taken in different imaging planes. Arrows indicate large vessels. Signal intensity is proportional to the blood volume. (B) Ultrasound images indicating vessels of different physiology. In view of the different signal intensity levels, relying on the vessel anatomy alone is not possible.

[0036] FIGURE 7. (A) Distribution of branching blood vessels as a fractal and distribution of blood vessel diameter versus number of vessels (left panel). In an ultrasound image, blood vessel diameter is represented by signal intensity (number of voxels), and the distribution of blood vessel intensity versus number of vessels (right panel) is similar to the blood vessel diameter distribution (left panel). (B) Blood vessel signal intensity distribution in an image not comprising large blood vessels (left panel) or comprising large blood vessels (right panel). (C) The same set of branching blood vessels leads to different distributions of blood vessel signal intensity depending on whether the blood vessels are undilated or dilated. (D) Blood vessel signal intensity distribution of images of 4 different pig brains, and indicating the effect of applying a constant threshold (leaving out the larger blood vessels). (E) same ad (D) but based on images of 2 different brains and not indicating the constant threshold.

[0037] FIGURE 8. (A) The images of Figure 7 (E) are repeated on top. The blood vessel signal intensity distributions have subsequently been normalized towards an experimentally acquired normal blood vessel signal intensity distribution leading to a perfect overlap in the intensity distributions obtained from both independent brain ultrasound images. The voxels associated with the same selection within the intensity distributions were subsequently selected in the original ultrasound images, thus giving rise to the "selected voxel" images of both independent original brain ultrasound images. The "selected voxel" images clearly are much more similar to each other (effectively reflecting the same set of selected blood vessels) compared to the original ultrasound images. (B) Fitting of a blood vessel signal intensity distribution discarding the large vessels. Left panel: as in Figure 7B (right panel). Right panel: left panel including fitted distribution (dashed line). (C) Fitting as in (B) is independent of whether blood vessels are normal (top panel) or constricted (reduced blood flow; bottom panel, inset showing enlargement of the distribution tail representing the large vessels). (D) Selected voxel images created from the images of Figure 6 (B) as done in Figure 8 (A). Selected voxel images form the basis for quantifying a hemodynamic parameter.

[0038] FIGURE 9. Real-time tracking of the hemodynamic correlates of a spreading depolarization (SD).

[0039] FIGURE 10. (A) Cranial window in piglet indicating the stainless steel ring (101) with 3 injection ports (101a) and the ultrasound transducer / Doppler signal receiver (102) placed over the cranial window. The ultrasound transducer / Doppler signal receiver module of (102) can be re-designed to fit over (part of) a burr hole, and can e.g. be circular, half-circular, or quarter-circular in shape (if not protruding from the device comprising the ultrasound transducer / Doppler signal receiver module) or e.g. cylindrical (B), half-cylindrical (C) or quarter-cylindrical (D) in shape (if protruding from the device comprising the ultrasound transducer / Doppler signal receiver module). DETAILED DESCRIPTION

[0040] Blood perfusion is defined as the volume of blood flowing through a gram of tissue during one minute. Cerebral blood flow (CBF) is defined as the blood volume that flows per unit mass per unit time in brain tissue and is typically expressed in units of ml blood / (100g tissue. min). The typical average CBF in adult humans is about 50ml / (100g.min). Adequate brain perfusion is required to support normal brain function, achieve successful aging, and to navigate acute and chronic medical conditions. Blood perfusion in the brain is tightly controlled by a mechanism called cerebral autoregulation (CA or CAR). CA(R) is a process by which cerebral arteries maintain a constant cerebral blood flow (CBF) during variation of cerebral perfusion pressure (CPP), this by actively regulating the diameter of the blood vessels, i.e. by vasoconstriction or vasodilation. This process is especially well developed in brain ensuring stability of blood flow, and thus of nutrient and oxygen supply. This cerebrovascular reactivity (CVR) is contributed by the large extra- and intracranial arteries (~40% of CVR), but the majority is managed more distally by the pial arterioles (~20%) and by the penetrating arterioles, capillaries, and venules (~40%) . There has been no tool so far which can specifically depict the hemodynamic behavior of the penetrating arterioles. It has been assumed that downstream vessels carry the upstream hemodynamic information, but not vice versa. Hence, capturing hemodynamic behavior of the more distal arterioles seems crucial. However, assumptions on distal CVR cannot be made: with decreasing diameters also the numbers of smooth muscle cells (SMC) are declining, so the data of the superficial arterioles cannot be extrapolated to more distal vessels.

[0041] If CA(R) is impaired (e.g. in case of traumatic brain injury, TBI; or e.g. in case of a stroke, in case of subarachnoid haemorrhage or prematurity-related intracranial haemorrhage, vascular dementia or Alzheimer's disease), then too low CPP (resulting in lower CBF) can lead to cerebral ischemia, and too high CPP (resulting in higher CBF) to edema formation. Continuous monitoring of blood perfusion is thus highly recommended such that a treatment to steer blood pressure can be taken quickly when perfusion is starting to deviate from the physiologically acceptable range such as to avoid (irreversible) damage. Spreading depressions (SD) are often occurring in subjects suffering from aneurismal subarachnoid hemorrhage, delayed ischemic stroke after subarachnoid hemorrhage, malignant hemispheric stroke, spontaneous intracerebral hemorrhage or TBI. A spreading depression (SD) results in regional cerebral blood flow (rCBF) increases of more than 100%, also referred to as spreading hyperemia (SH). After end of the SH phase, the rCBF declines for up to two hours (also referred to as spreading oligemia). Targetting SD or the inverse hemodynamic response may potentially treat these neurological conditions (therapies that target spreading depolarization or the inverse hemodynamic response may potentially treat these neurological conditions) (Dreier et al. 2011, Nature Medicine 17:439-447).

[0042] However, there is no suitable method to measure blood perfusion continuously in the brain at a subject's or patient's bedside. A further complicating factor is that different physiological conditions result in different perfusion states (extent of contraction or dilation of the blood vessels) in different subjects or patients. The perfusion state of an individual subject or patient, healthy or non-healthy, is a priori unknown.

[0043] Any solution enabling quantitation of blood flow or perfusion thus must ensure i) that data sampling in the same tissue (e.g. brain) is equivalent, and ii) that data sampling in tissues (e.g. brains) of different origin is comparable.

[0044] Ultrasound can image the blood volume and blood velocity. From a mathematical point of view, a trivial solution should have been to multiply both measures to provide the perfusion. However, this measure is unstable and not constant inside a tissue (e.g. brain); and it is therefore also impossible to compare tissues (e.g. brains) of different origin.

[0045] To explain this problem, Figure 6 shows an example of 3 images of the same brain but taken in different planes; in these images the signal intensity is proportional to the blood volume. The signal intensity values inside the image range between 1 to 1000 on a linear scale. Quantifying the blood volume in these images faces problems, primarily due to the extensive distribution of the signal intensity values. Inside a given volume, different kinds of vessels can be differentiated: small vessels perfuse the blood locally, and larger vessels transport blood to other brain regions. To measure the (local) perfusion specifically, the big vessels need to be rejected. Indeed these big vessels have signal intensities 100 to 1000 times higher than the small ones and these dominating signals create a considerable error in the quantification of the blood volume in all vessels. A simple solution would be to remove / filter the contribution of all large vessels.

[0046] However, a simple threshold cannot be used. Indeed, a fixed and predefined threshold must be avoided because it depends on the physiological conditions that should be measured. If, for example, a brain has low perfusion, the threshold would need to be reduced. But as the perfusion state is a priori not known, it is impossible to apply a simple threshold. Likewise, a fixed threshold does not resolve this problem in view of subject to subject variation in brain physiology or anatomy (see e.g. Figures 7D and 7E). Furthermore, the spatial resolution of the ultrasound is not sufficient to measure the actual diameter of all vessels.

[0047] In work leading to the current disclosure, a method was developed to classify the vessels independent of the absolute value of the micro-Doppler ultrasound signal intensity, but rather based on the distribution of the signal intensities. In this method, the tissue (e.g. brain) vasculature was considered as a fractal, with some big vessels that split into smaller vessels multiple times until reaching the smallest capillary size (Figure 7 A). Such distribution of vessel diameters can plausibly be assumed to be the same in tissues (e.g. brains) of different subjects or individuals and even in different parts of the same tissue (e.g. brain). Critically, with the ultrasound image intensity being proportional to the blood volume, there is a direct link between the distribution of vessel diameters (via the blood volume) and the distribution of intensities. This link allows for blood vessel diameter distribution to be measured indirectly within the ultrasound image without having to measure the actual diameter of each of the vessels. Furthermore, in this method, changes in vessel diameters (by constriction or dilation) are not affecting the basic shape of the blood vessel signal intensity distribution, the curve as a whole shifting, however, to lower intensity values (in case of constriction) or to higher intensity values (in case of dilation), this compared to a control, normal or reference blood vessel signal intensity distribution curve. Furthermore, by fitting or scaling the blood vessel signal intensity distribution, big vessels, when present (depending on e.g. imaging plane / field of view or e.g. the state of a brain), can be easily suppressed in the distribution, and such suppression is according to a dynamic or adaptive threshold being dependent on the signal intensity distribution curve itself (e.g. Figure 8B and 8C).

[0048] In another step of the method, an experimental or test blood vessel signal intensity distribution curve is normalized towards a control, normal or reference blood vessel signal intensity distribution curve. This is easily done by applying a scaling factor. The resulting normalized intensity distribution curves perfectly overlap after this normalization (e.g. Figure 8A). Obviously, the fitting to remove large vessels can be part of the normalization process.

[0049] In a further step of the method, all voxels within an intensity range of interest of a normalized intensity distribution curve (reflecting a range of vessel diameters - an alternative adaptive threshold enabling elimination of large vessels from the distribution) can be selected to create a spatial mask to be applied on the original image. The method ensures that always the same distribution of vessels independently of the absolute intensity value can be assessed, therewith enabling assessment of and comparison between different physiological / perfusion states (such as occurring within a subject e.g. in case of failing CA; or such as occurring due to intersubject variation), which is crucial for diagnosis and monitoring. The images resulting from this method are then used to extract hemodynamic parameters as is done in e.g. standard functional ultrasound imaging (e.g. Mace et al. 2013, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 60:492-506).

[0050] Based on the foregoing, the following aspects and embodiments are defined. The support for these aspects and embodiment is based on experimental work on brain tissue which can be considered a more complex and difficult tissue. The hereinafter described aspects and embodiments therefore are also applicable to other than brain tissues.

[0051] In its essence, a main aspect relates to methods of or for determining a blood parameter or hemodynamic parameter, or methods of or for blood monitoring from ultrasound or ultrasonic signals, such methods comprising one or more steps of, or comprising:

[0052] - measuring, determining, detecting or quantifying the Doppler signals or echoes or the intensity of the Doppler signals or echoes obtained from an ultrasound or ultrasonic wave emitted in a tissue, the intensities of the Doppler signals or echoes correlating with the diameter of the blood vessels (or wherein the intensities of the Doppler signals or echoes correlate with the diameter of the blood vessels);

[0053] - determining, calculating, plotting or computing the distribution of the intensities of the measured, determined, detected or quantified Doppler signals or echoes or of the measured, determined, detected or quantified Doppler signal or echo intensities;

[0054] - normalizing the determined, calculated, plotted or computed Doppler signal or echo intensity distribution to or relative to a control, normal or reference Doppler signal or echo intensity distribution;

[0055] - selecting a range of Doppler signal or echo intensities within the normalized distribution, therewith selecting a set of blood vessels with a range of diameters correlating with the selected range of Doppler signal or echo intensities;

[0056] - measuring, determining or quantifying the blood or hemodynamic parameter, or monitoring the blood or hemodynamic parameter; or monitor the blood in the selected set of blood vessels.

[0057] In one embodiment to the above method, the measuring of the intensity of the Doppler signal includes filtering out the Doppler signal or echoes coming from the tissue, or, alternatively includes selecting the Doppler signal or echoes coming from the blood. Thus, the step of measuring, determining, detecting or quantifying the Doppler signals or echoes or the intensity of the Doppler signals or echoes obtained from an ultrasound or ultrasonic wave emitted in a tissue, the intensities of the Doppler signals or echoes correlating with the diameter of the blood vessels (or wherein the intensities of the Doppler signals or echoes correlate with the diameter of the blood vessels) can be formulated alternatively as:

[0058] - selecting from the Doppler signals or echoes obtained from an ultrasound or ultrasonic wave emitted in a tissue the Doppler signals or echoes coming from or associated with the blood and measuring, - measuring, determining, detecting or quantifying the intensity of the blood Doppler signal or echo, the intensity of the blood Doppler signal or echo correlating with the diameter of a blood vessel (or wherein the intensity of the blood Doppler signal or echo correlates with the diameter of a blood vessel). The intensity (or strength) of a Doppler signal or echo is also referred to as power Doppler, both are used interchangeably herein.

[0059] In one embodiment to the above method, a set of ultrasound images signals a ( r ,, tj) is acquired at times tj, j=l..n, wherein the positions n, i=l..N represents the spatial dimension.

[0060] The images a( n, tj), are a superposition of the echoes coming from the blood (b) and from the tissue (T): a(r,, tj) = b(n, tj) + T(rj, tj).

[0061] The dataset afn, tj) can be filtered to keep only the signal from the moving red blood cells b(rj, tj) = filter [a(n, tj)].

[0062] After the filtering step, in each position r, of the image, a signal is obtained of n time points representing the echoes of the red blood cells in such point. The resulting signal is used to compute multiple parameters such as the power Doppler and velocity. The power Doppler is defined as I (n) = £"=1b (n, tj)2. The computed value l(n) is proportional to the blood volume inside a voxel (three- dimensional pixel).

[0063] In one embodiment to the above method, the power Doppler I is proportional to the cerebral blood volume V according to the formula V = a I wherein a is an unknown parameter that changes depending on the tissue or brain (e.g. intersubject variation) and on the acquisition conditions (e.g. inter-operator variation). The distribution of the power Doppler I can be deduced from the distribution of V as and by replacing V by a I, pi(l)=pv(a l)a is obtained. As a conclusion, for a given image the distribution of intensity is constant but may vary between subject but only within a given range called a, which corresponds to a "dilation constant" or "scaling factor". Importantly, a can be computed by adjusting the shape of the intensity distribution with a control, normal or reference distribution. Therefore, by knowing the constant a, we can normalize the intensity as ln(ri)= l(h) / a, wherein lnis a normalized intensity. Such a normalized image always has the same intensity distribution independent of the tissue or brain and independent of the acquisition conditions. In short, by selecting the same range of values in the image ln, hemodynamic measurements in vessels of the same diameters can be performed independent of the tissue, subject, and operator.

[0064] In one embodiment to the above method, the step of normalizing the determined, calculated, plotted or computed distribution to a control, normal or reference Doppler signal intensity distribution is performed or is performed only if the shape of the computed Doppler signal intensity distribution and the shape of the control, normal or reference Doppler signal intensity distribution are different. In general, such normalization can be done e.g. by the minimal or least squares method / regression.

[0065] In one embodiment to the above method, the control, normal or reference Doppler signal or echo intensity distribution is empirically or experimentally determined e.g. as an average or calibration of a number of computed distributions of Doppler signal or echo intensities measured obtained from ultrasound waves emitted in a sufficient number of independent tissues (of the same type), in particular in a sufficient number of different brains in case of the tissue being the brain, more in particular in a sufficient number of different brains, of the species of interest (e.g. mammalian species such as a rodent, a primate, a human). The control, normal or reference Doppler signal or echo intensity distribution can in general be obtained from a calibration of intensity distributions obtained from one or multiple tissues. Alternatively it was observed that such control, normal or reference Doppler signal or echo intensity distribution can be empirically approximated or be defined by a simple analytical function, such as P(x) = xaebx. Such control, normal or reference Doppler signal or echo intensity distribution is further herein also referred to as "po( I )". In general, adjusting an experimental Doppler signal or echo intensity distribution (herein after referred to also as "p( I)") to a control, normal or reference Doppler signal or echo intensity distribution may include a step of determining a dilation or scaling factor to adjust the experimental signal intensity distribution to fit with a normal, control or reference signal intensity distribution. Such scaling or dilation factor is further herein also referred to as "a".

[0066] In one embodiment to the above method, the step of normalizing the computed distribution to a control, normal or reference Doppler signal or echo intensity distribution is including a step of ignoring outliers in the distribution, such as ignoring the distribution tail representing the large vessels. With the large vessels being situated in the distribution tail of the signal intensity distribution, the need to define what is a large vessel is rendered moot.

[0067] In one embodiment to the above method, the measured intensity of the Doppler signal or echo is converted in an ultrasound image (also termed the original ultrasound image) and / or the selected range of Doppler signal or echo intensities within the normalized distribution is converted in an ultrasound image (also termed the normalized ultrasound image). In one embodiment to the above method, the blood or hemodynamic parameter or the blood monitoring is determined based on the Doppler signal or echo.

[0068] In one embodiment to the above method, the determined blood or hemodynamic parameter is blood volume, blood flow, blood velocity, blood mean velocity, or blood mean velocity multiplied by the normalized intensity. Herein, the mean velocity can be computed, determined or measured as e.g. the average of the first order momentum of the spectrum of the blood signal, or as e.g. the average of the correlation of the blood signal with a log. In a further embodiment thereto, a spatial mask is applied for measuring the blood or hemodynamic parameter. Such spatial mask or spatial filtering is selecting a set of voxels with lnvalues comprised in a predefined range. More specifically, the set of points tk, k=l..Nk is selected for which lmm < ln(h) < Imax, wherein lmm and lmax define the range, and the blood or hemodynamic parameter is determined, measured, or computed from this set of points. E.g. the (average) blood velocity is computed as the average of the voxels rk. Alternatively, the (mean) blood velocity is computed as the average of the first-order momentum of the spectrum of the blood signal, or is computed as the average of the correlation of the blood signal with a log.

[0069] In one embodiment to the above method, the blood or hemodynamic parameter is measured, determined, detected or quantified or monitored continuously or in real-time, or the blood monitoring is continuous or in real-time; in particular it is measured, determined, detected or quantified or monitored continuously or in real-time based on continuously obtained Doppler signals.

[0070] In an alternative formulation, the methods of the main aspect are comprising one or more steps of, or comprising:

[0071] - acquiring a set of ultrasound images

[0072] - filtering the images to select the signals or echoes of the red blood cells

[0073] - computing the signal intensity distribution of all voxels in the filtered image

[0074] - determining the scaling factor to adjust the computed signal intensity distribution to (fit with) a normal, control or reference signal intensity distribution

[0075] - normalizing the computed signal intensity distribution with the determined scaling factor

[0076] - selecting the voxels with the desired range of normalized signal intensities

[0077] - quantification of the blood or hemodynamic parameter, or monitor the blood based on the selected voxels. In another alternative formulation, the methods of the main aspect are comprising one or more steps of, or comprising:

[0078] - obtaining a set of ultrasound signals a ( r,, tj) of a biological tissue comprising blood vessels wherein r,, i=l..N are a plurality of spatial points and tj, j=l..n is the acquisition time

[0079] - applying a filter process to a(n, tj) to extract the signal of the moving red bloods cells to produce the blood signal b(rj, tj) = filter [a (n, tj)]

[0080] - computing the intensity of the blood signal l(rj) = "=1b (n, tj)2

[0081] - computing the distribution of values of the intensity p( I)

[0082] - determining the factor a for adjusting the computed intensity distribution to (fit with) a normal, control or reference intensity distribution according to p( la) / a=p0( I ) wherein p0(l) is the normal, control or reference intensity distribution

[0083] - normalizing the intensities as ln= a I

[0084] - selecting the points rk, k=l..Nk for which the normalized intensity is between a predefined range of values lmin< ln(ri) < Imax

[0085] - quantification of a blood or hemodynamic parameter from the blood signal of the selected points b(rk, tj).

[0086] In any of the above, the Doppler signal or echo in particular is a power Doppler signal or echo.

[0087] In any of the above, the Doppler signals or echoes are obtained by plane wave imaging, such as from ultrasound emitted in plane waves.

[0088] In any of the above methods, a further step can optionally be added, such step including generating a sharpened spatial image for each spatial component image of multiple spatial component images.

[0089] Any of these above-listed embodiments can be combined in the methods subject of the main aspect. Furthermore, any of the individual steps of the alternatively phrased methods subject of the main aspect is fully interchangeable between the alternative phrased methods, added or added in part to an alternative phrasing (if present in another alternative phrased method), or deleted or deleted in part from an alternative phrasing (if not present in another alternative phrased method).

[0090] In one particular case, the tissue is a brain or is brain tissue. In one embodiment to this particular case the ultrasound imaging of brain tissue is minimally invasive. In particular, the ultrasound imaging is through a cranial window, a burr hole, a trephination, trepanation or a thinned skull bone. In another embodiment to this particular case, the selected set of cerebral blood vessels includes penetrating arterioles, capillaries, and venules. Follow-up of the hemodynamic behavior of these is deemed crucial in the management and / or monitoring of clinical indications potentially affecting the cerebral perfusion pressure, the cerebral autoregulation or the cerebrovascular reactivity.

[0091] In this particular case relating to methods subject of the main aspect wherein the tissue is a brain or is brain tissue, the ultrasound waves can be transduced, and the Doppler signal can be received through a burr hole in case of the tissue being the brain. In a further embodiment to such methods, the shape or design of the ultrasound wave transducer / Doppler signal receiver is following the contours, shape, or delineation of the burr hole; or is more in particular following the contours, shape, delineation of a segment of the burr hole. More in particular the segment of the burr hole is e.g. a quadrant of the circle formed by the burr hole, or half of the circle formed by the burr hole. In some more detail, in the experimental work described herein the device in which the ultrasound wave transducer / Doppler signal receiver module is incorporated had a rectangular shape / design (see (102) in Figure 10 A). Such device can have any desirable form and the shape or design of the ultrasound wave transducer / Doppler signal receiver module of the device can be adapted or re-designed to fit over a burr hole, or over a part or segment of a burr hole. Such module can e.g. be circular, half-circular, or quarter-circular in shape or design (if not protruding from the device comprising the ultrasound transducer / Doppler signal receiver module) or e.g. cylindrical (Figure 10 B), half-cylindrical (Figure 10 C) or quarter-cylindrical (Figure 10 D) in shape or design (if protruding from the device comprising the ultrasound transducer / Doppler signal receiver module). An advantage of such module covering only part or a segment of a burr hole is that the same burr hole can then be used for introducing e.g. a probe for determining or measuring intracranial pressure (ICP) (such as used herein experimentally).

[0092] As described more elaborately by Evensen and Eide 2020 (Fluids Barriers CNS 17:34), mass brain lesions such as e.g. in TBI may lead to pressure gradients, themselves potentially causing disturbances in blood circulation (cerebral blood flow, CBF) and blood supply to the central nervous system (CNS). CNS impairments to the midbrain or brainstem may have life-threatening effects on vital functions (may result in respiratory and cardiovascular failure) and consciousness. Preventing high intracranial pressure (ICP) in such cases is of utmost importance to preserve CNS function. Most often, cerebral perfusion pressure (CPP) is used as parameter thereto: mean CPP= mean arterial blood pressure (mean ABP) - mean ICP (in general: CPP = ABP - ICP). The CBF, however, is also dependent on the cerebral autoregulatory capacity and the relationship between ICP and brain oxygenation is still not fully understood. Determining hemodynamic parameters with methods as described herein thus provides an additional layer of information independent of ICP, and / or may over time render determination of ICP unnecessary. Methodologies for measuring ICP are still mostly invasive (e.g. via via a cerebrospinal fluid (CSF) ventricular catheter, ICP sensors placed in the brain parenchyma). An ICP sensor may be part of a multi-modality catheters

[0093] Based hereon, a second main aspect of this disclosure relates to methods of assessing or monitoring brain / cerebral physiology or function or of cerebral monitoring, such methods comprising: determining, measuring, quantifying or monitoring a cerebral blood parameter or hemodynamic parameter according to any of the methods and embodiments of the main aspect as described hereinabove determining, measuring, quantifying or monitoring the cerebral perfusion pressure (CPP) or the mean CPP assessing or monitoring the brain or cerebral physiology or function or cerebral monitoring based on (assessing or monitoring) the correlation between the determined, measured, quantified or monitored cerebral blood parameter or hemodynamic parameter and the determined, measured, quantified or monitored CPP or mean CPP.

[0094] In one embodiment to this second main aspect, the methods of assessing or monitoring brain physiology or of cerebral monitoring are methods of assessing or monitoring cerebral autoregulation, cerebrovascular reactivity, or intracranial compliance (ICC; the capacity of the intracranial constituents to compensate for changes in intracranial volume / pressure-volume relationship).

[0095] In a further embodiment to the methods of this first or second main aspect, these methods further incorporate a step of functional ultrasound imaging or a step of assessing or monitoring the neurovascular coupling. With this extension, such methods can concurrently determine a cerebral blood or hemodynamic parameter, or monitor cerebral blood, or assess or monitor brain physiology, this together with neurovascular coupling. With the assessment or monitoring of the neurovascular coupling it is e.g. possible to detect (cortical) spreading depolarization or (cortical) spreading depression episodes. Altogether, such extended method more in particular is then a method of or for assessing or monitoring brain or cerebral pathophysiology.

[0096] Computer implementation / computer / computer or computing system / data-processing system

[0097] As one or more steps of any of the methods with their embodiments subject of the main aspect rely on computational processing, such methods are in particular computer-implemented methods.

[0098] This disclosure therefore also relates to computer programs having instructions which when executed cause a computing or data processing system or device to perform a method subject of the main aspect, or to perform a step of a method subject of the main aspect. Likewise, the disclosure relates to computing or data processing systems or devices, or machine readable media comprising a means for carrying out or performing a method subject of the main aspect, or to perform a step of a method subject of the main aspect.

[0099] A computer, computer or computing system, or data-processing system as mentioned herein may utilize one or more subsystems. A computer or computer or computing system, or data-processing system may be a single apparatus comprising the one or more subsystems (e.g. internal components), or may be multiple apparatuses each being a subsystem, and optionally, each comprising one or more own subsystems. Desktops, laptops, mainframe servers, tablets, mobile phones etc. all are eligible as computer, computer or computing system, or data-processing system. The subsystems are usually interconnected and include a (central) processor (single-core processor, multi-core processor on a same integrated chip, or multiple processing units on a single circuit board or networked) capable of executing instructions, an input / output (I / O) controller, and a storage device (external, internal, peripheral, cloud, any medium readable by a computer or computer system). Input devices include keyboards, scanners, a computer mouse, camera, microphone, etc. In particular, the input device is a data collection or data generating device (which by itself may comprise a computer or computer or computing system, or data-processing system), such as an ultrasound receiver / detector or ultrasound receiving / detecting device (whether automated or not). Collected or generated data are fed to a computer, computer or computing system, or data-processing system designed to analyze the collected or generated data; this may be an ordinary on which data analyzing software is installed (on a storage device) or which is capable of accessing data analyzing software (e.g. installed in or transmitted from a network) and whereby the processor of the computer / computing / data-processing system is instructed by the data analysis software on how to process the collected or generated data fed to the computer / computing / data-processing system, and how to display these via a display adapter to an output device. Output devices are further subsystems and comprise printers, monitors, computer readable medium. Input and output devices are usually connected to a computer / computing / data-processing system via input / output ports to one another or via a network. The specific combination of hardware and software allows implementation of e.g. analysis of data generated by an ultrasound receiver / detector or ultrasound receiving / detecting device (such analysis being e.g. selecting from a tissue / blood composite Doppler signal or echo the blood Doppler signal or echo; or e.g. calculating or computing a Doppler signal or echo intensity distribution; or e.g. normalizing a calculated or computed Doppler signal or echo intensity distribution relative to a reference Doppler signal or echo intensity distribution; or e.g. selection of a subset of Doppler signals or echoes from a normalized Doppler signal or echo intensity distribution; or e.g. assessing or monitoring neurovascular coupling). Different software packages (proprietary or open source) can be run on a computer or computer system to achieve the desired degree of data analysis. Output of one computerized data analysis can be the input of a subsequent computerized data analysis step, hence creating an analysis pipeline. Software components can be written in different codes (e.g. Java, C, C++, Perl, Python) as long as the computer processor is able to execute the functions of the software component.

[0100] The methods of this disclosure may be computer-implemented methods, or methods that are assisted or supported (in part) by a computer or by a computer / computing / data-processing system. For instance, information generated by an ultrasound receiver / detector or ultrasound receiving / detecting device can be provided in user readable format by at least one / another processor. The same or a further processor may, e.g., be selecting from a tissue / blood composite Doppler signal or echo the blood Doppler signal or echo; or e.g. be calculating or computing a Doppler signal or echo intensity distribution; or e.g. be normalizing a calculated or computed Doppler signal or echo intensity distribution relative to a reference Doppler signal or echo intensity distribution; or e.g. be selecting of a subset of Doppler signals or echoes from a normalized Doppler signal or echo intensity distribution; or e.g. be assessing or monitoring neurovascular coupling. The one or more processors may be coupled to random access memory operating under control of or in conjunction with a computer operating system. The processors may be included in one or more servers, clusters, or other computers or hardware resources, or may be implemented using cloud-based resources. The operating system may be, for example, a distribution of the LinuxTM operating system, the UnixTM operating system, or other open- source or proprietary operating system or platform. Processors may communicate with data storage devices, such as a database stored on a hard drive or drive array or such as a computer or machine readable medium, to access or store program instructions other data. Processors may further communicate via a network interface, which in turn may communicate via the one or more networks, such as the Internet or other public or private networks, such that a query or other request may be received from a client, or other device or service. Such computer-implemented methods (or such methods that are assisted or supported by a computer / computing / data-processing system) may be provided as a kit or as part of a kit. The bioinformatics software required to perform (part of) the computer-implemented methods, i.e. a computer program product, may also be part of a kit, or may be provided as an individual product. A computer product may also consist of a computer or machine readable medium (in any form such as disks (hard, soft disks etc.), cards (memory cards etc.), tapes, sticks (memory, USB sticks etc.), microchips, DVDs, CDs, etc.) which is storing any of the instructions, computer program, or bioinformatics software enabling a computer system to perform at least one of the analysis of the herein described methods and / or to perform at least one calculation as described herein. Furthermore, the computer / computing / data-processing system can be set up or configurated such that it is compliant with GDPR.

[0101] Ultrasound (US)

[0102] The application of ultrasound scanning on biological tissues relies on the detection of differential reflection, scatter or echoes of emitted ultrasound waves by different tissue components. The echoes from stationary cells (such as tissue surrounding a blood vessel in case the tissue is perfectly stationary which is seldom the case when scanning living subjects) are the same from ultrasound pulse to ultrasound pulse. The time needed for echoes from moving cells to reach the receiver differs slightly from ultrasound pulse to ultrasound pulse. These differences can be measured directly (time difference), or can be expressed in terms of Doppler frequency (or Doppler frequency shift), which can, together with the strength of the echoed signal, be converted in e.g. an "ultrasound image". Medical ultrasound procedures include anatomical ultrasound (imaging organs etc.) and functional ultrasound (combining anatomy with information such as blood movement, blood flow direction and blood flow velocity in a blood vessel or in the heart). The power Doppler value is proportional to blood volume / to the number of moving red blood cells (RBCs) in the sample volume producing the Doppler echoes (e.g. Mace et al. 2013, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 60:492-506, and references cited therein). By using the Doppler effect, the velocity of a blood flow (CBFv) can be measured. Contrary to conventional US, functional ultrasound (fUS) uses plane-wave imaging. When the plane wave is emitted at different tilted angles, the combination of these different images constructs a new image with higher spatial resolution. fUS is easily applicable in soft tissues, it is safe and can be used for continuous monitoring; this has led to numerous new applications for this tool, like monitoring of brain activity in neonates.

[0103] EXAMPLES

[0104] EXAMPLE 1. Animals

[0105] Seven 6-week old pigs were brought under general anesthesia by intravenous injections of propofol, pancuronium, midazolam and fentanyl, and ventilated. Ventilation was performed by a volume- controlled ventilator with the following settings: tidal volume (TV) of 10 ml / kg, peak end expiratory pressure (PEEP) of 5 cm H2O, l / E %, peak pressure of 30 cm H2O and respiratory rate (RR) of 20-26 / min adjusted to maintain an end-tidal carbon dioxide (ETCO2) tension of 40 mmHg.

[0106] Arterial blood pressure (ABP), intracranial pressure (ICP), brain temperature and tissue oxygen pressure (PbtO2) or brain tissue oxygen pressure (PbO2) were monitored continuously. Heart rate was monitored simultaneously by a 3 lead ECG and by arterial pulse wave analysis. Blood oxygen level was monitored using a pulse oximeter and kept at 99-100% during the entire experiment. Inspired and expired concentrations of CO2 and 02 were monitored with a gas analyzer (Phillips M1026B, Philips Medical Systems, The Netherlands). ABP of CO2 (paCO2) was sampled for verification of continuously monitored end-tidal CO2. pH was kept in normal range 7.35-7.45, PaO2 at 200 mmHg and paCO2 at 38 mmHg. Rectal temperature was maintained at 38-39°C by a warming mattress and blankets. Continuously monitored signals were stored using ICM+ software (Cambridge University, Cambridge, United Kingdom). ABP, ICP and LDF signals were sampled at 250 Hz. CPP was calculated as the difference between ABP and ICP.

[0107] All animal care and procedures were approved by the Ethical Committee Animal Research Center, KULeuven (Ethical Approval: P107-2019) in compliance with the Belgian Royal Decree (29 May 2013) and European Directive 2010 / 63 / EU on the protection for animals used for scientific purposes. All animal procedures were conducted under veterinarian supervision according to the guidelines imposed by the Ethical Committee.

[0108] EXAMPLE 2. Non-pharmacological manipulation of the arterial blood pressure (ABP)

[0109] An arterial line was placed 5 cm above the diaphragm in the left femoral artery for continuous arterial blood pressure (ABP) monitoring. ABP was manipulated by non-pharmacological means using a balloon catheter. For hypotensive experiments, a balloon catheter was introduced in the right femoral vein and placed at the level of the diaphragm. Inflating the balloon in the inferior caval vein induced hypotension by decreasing the venous return. For hypertensive experiments, a balloon catheter is placed in the right femoral artery and inflating a balloon in the descending aorta elicits hypertension by increasing the afterload.

[0110] EXAMPLE 3. RBC flux measurement by laser Doppler flow (optical method)

[0111] Two small cranial burr holes were made posterior to the coronal suture on the right side, one for laser Doppler flow (LDF) monitoring (probe in contact with the dura) and one for combined intracranial pressure (ICP) and brain tissue oxygen pressure (PbO2) monitoring (intraparenchymal probe, requiring prior perforation of the dura). Anterior to the coronal suture, a round bony craniotomy was performed. Next, the dura was opened (under optical magnification to avoid damaging the underlying structures), and a steel cranial window was cemented onto the skull over the hole (cemented with dental acrylic cement (G-CEM LinkAceR, GC Europe, Belgium). The stainless-steel ring was fitted with 3 injection ports and a central 15 mm diameter opening sealed with a coverslip glass using acrylic glue (HistoacryIR, B. Braun, Germany) to prevent cerebrospinal fluid leakage. The space underneath the glass slip was filled with artificial cerebrospinal fluid (NaCI 132 mM / l, KCI 3.0 mM / l, MgCI2 1.5 mM / l, CaCI2 1.5 mM / l, urea 6.6 mM / l glucose 3.7 mM / l, NaHCO3 24.6 mM / l warmed to 37 °C and equilibrated with 6% 02 and 6% C02 in N2 to a pH 7.35-7.45, pCO2 40-42 mmHg and p02 42-50 mmHg) via the injection ports before being closed by caps.

[0112] Red blood cells (RBCs) were isolated and fluorescently labelled with CFSE and re-injected in the arterial compartment before the start of the non-pharmacological ABP manipulation. Pial arterioles were observed through the cranial window using an epifluorescence microscope (SMZ18 with P2-SHR Plan Apo lx, Nikon), illuminated with a solid-state light engine (SOLA SM2, Lumencor), and captured with a high-speed digital CMOS camera (Orca Flash 4.0 V2, Hamamatsu) controlled by NIS-Elements software (Nikon). A green, fluorescent filter (P2-EFL GFP-B Filter Cube 470 - 535nm, Nikon) was used. Images were acquired at 170 - 200 frames per second and digitally stored for offline analysis.

[0113] Pial arteorial RBC flux (“F") was calculated based on vessel diameter and RBC velocity (tracking of RBC labeled with CFSE) detected by the microscope with the following formula:

[0114] F = V * A = V * Tt * r2= V * n * (D / 2)2.

[0115] EXAMPLE 4. Cerebral perfusion determination with functional ultrasound (fUS)

[0116] After a midline scalp incision and removal of the periosteum the surface of the skull was exposed. A first right frontal craniotomy was performed on the right hemisphere for measurements of intracranial pressure (ICP), Laser Doppler flow (LDF) and red blood cells velocity (RBCv) measurements as described above.

[0117] A second cranial window was performed on the left hemisphere for fUS measurements. A ~7x25-mm2window was opened without removing the dura. A dedicated custom-made 3D printed plastic transducer-holder was cemented to the skull over the window, to keep the probe in the exact same position during the experiment. The dura was covered with a layer of 1.5% agarose (Sigma-Aldrich, USA) and a layer of ultrasound transmission gel (Aquasonic ClearR, Parker Laboratories Inc, USA) to ensure the acoustic coupling between the tissue and the ultrasound transducer. Finally, the ultrasound transducer was inserted and fixed with screws into the transducer holder. Piglets were allowed to recover for 2 hrs after this procedure.

[0118] A 12MHz ultrasound transducer (Imasonic, Voray-sur-l'Ognon, France), with a resolution of 0.125mm, a penetration depth of 15mm and acquisition time 2s. The head of the probe measured 20 x 7.5 mm. The casing of the probe had a tapered shape that enlarges from the active head to a size of 30 x 7.5mm. The ultrasound linear transducer linked to a 128-channel emission-reception electronics (Vantage, Verasonics, USA) and controlled by a high-performance computing workstation equipped with 4 GPUs (AUTC, fUSI-2, Estonia) EXAMPLE 5. Measurement of red blood cell (RBC) flux in pigs by microscopy.

[0119] The methodology as described in detail by Klein et al. 2019 (Scientific Reports 9:13333) was used. The RBC flux was calculated based on vessel diameter and RBC velocity through the microscope positioned above the cranial window of 7 animals. Combined microscopy data of seven experiments were plotted into one graph (Figure 1). The Y-axis refers to the RBC flux (V * TT * r2) acquired by processing of the microscopy data, which is displayed in percentages change to baseline. For both graphs, there is a moderate decline in flux when CPP was decreased to 60mmHg and there is a steeper decline when CPP reached 40mmHg and below.

[0120] EXAMPLE 6. Measurement of cerebral blood volume (CBV) and cerebral blood flow velocity (CBFv) in pigs by standard functional ultrasound imaging (fUS).

[0121] We were able to collect simultaneous data on CBV and CBfv from fUS in penetrating arterioles in seven animals. fUS could visualize parenchyma with a penetration depth of 15mm. Both quantitative CBV and CBFv could be captured throughout the experiment in all animals, as well as CBF direction, which is labeled negative or positive, as illustrated in Figure 2. The convention of flow is that the direction towards the cortex is labelled as negative, the direction towards the center is labelled as positive. Both arterioles and veins contribute to the flow, and discrimination between them with the resolution of standard fUS is not possible.

[0122] As the spatial resolution was not high enough to measure vessel diameters, CBF itself could not be directly measured. In order to combine volumetric and velocity data, CBV and CBFv were multiplied (CBV*CBFv, called fUS product hereafter). CBFv, CBV and fUS product demonstrate a marked deflection point during gradual blood pressure lowering consistent with the lower limit of autoregulation (Figure 3).

[0123] Parenchymal regions were divided into different regions of interest (ROI) to study regional hemodynamic differences. When comparing those different regions, there were subtle differences as is illustrated in Figure 4. The infliction points for CBFv and fUS product are quite similar for the different ROIs, however for CBV the shape of the curve are different in the various regions. CBFv showed a rapid decline when CPP is lower than 30mmHg but was quite stable above 30mmHg in all ROIs. CBV showed a similar breakpoint at 30mmHg with a rapid decline when CPP lowered further. When increasing CPP, there was a gradual increase in CBV, which is translated to the fUS product, where also a gradual increase can be seen when CPP increased. The data obtained from the individual animals (n=7) were pooled together and analyzed. The curve picturing the fUS product (CBFv*CBV) plotted against CPP (Figure 5) has a similar shape as the curve of calculated RBC flux plotted against CPP (Figure 1). The fUS curve is characterized by a steep slope; it also illustrates the gradual increase of CBF with increasing CPP. For both graphs, there is a moderate decline in flux when CPP decreases to 60 mmHg and a steeper decline when CPP reaches 40 mmHg and below. Both methods (fUS and RBC flux measurement) revealed a clear lower limit of autoregulation, characterized by a sharp decrease of CBF for CPP, and a gradual increase of CBF for CPP beyond the autoregulation plateau.

[0124] All comparisons between RBC flux and fUS product were based on the percentage change with respect to baseline values of a given experiment. The absolute values of pooled RBCv ranged from 5 to 20 mm / s and for CBFv between 5 and 6 mm / s. The interclass correlation was very good for fUS product (UfD) vs. calculated RBC flux (cranial window) with a correlation coefficient of 0.71+-0.07.

[0125] Arterioles and veins are both taken into consideration by CBFv, since they cannot be distinguished by the resolution of fUS; furthermore, as is obviated by Figure 4, measurement of hemodynamic parameters is position-dependent.

[0126] EXAMPLE 7. Method to quantify blood flow continuously and independent of the brain region and comparable between different individuals

[0127] Any solution enabling quantitation of blood flow or perfusion thus must ensure i) that data sampling in the same brain is equivalent, and ii) that data sampling in different brains is comparable.

[0128] Ultrasound can image the blood volume and blood velocity. From a mathematical point of view, a trivial solution should have been to multiply both measures to provide the perfusion. However, this measure is unstable and not constant inside the brain; therefore, it is impossible to compare different brains.

[0129] To explain this problem, Figure 6 shows an example of 3 images of the same brain but taken in different planes; in these images the signal intensity is proportional to the blood volume. The signal intensity values inside the image range between 1 to 1000 on a linear scale. Quantifying the blood volume in these images faces problems, primarily due to the extensive distribution of the signal intensity values. Inside a given volume, different kinds of vessels can be differentiated: small vessels perfuse the blood locally, and larger vessels transport blood to other brain regions. To measure the perfusion specifically, the big vessels need to be rejected. Indeed these big vessels have signal intensities 100 to 1000 times higher than the small ones and these dominating signals create a considerable error in the quantification of the blood volume in all vessels. A simple solution would be to remove / filter the contribution of all large vessels.

[0130] 11 A simple threshold can, however not be used. Indeed, a fixed and predefined threshold must also be avoided because it depends on the physiological conditions that should be measured. If, for example, a brain has low perfusion, the threshold would need to be reduced. But as the perfusion state is a priori not known, it is impossible to apply a simple threshold. Furthermore, the spatial resolution of the ultrasound is not sufficient to measure the actual diameter of all vessels.

[0131] In working toward a suitable solution, a method was developed to classify the vessels independent of the absolute value of the micro-Doppler ultrasound signal intensity. In a first step, the brain vasculature was considered as a fractal, with some big vessels that split into smaller vessels multiple times until it reaches the smallest capillary size (Figure 7A). Such distribution of vessel diameters can plausibly be assumed to be the same in brains of different subjects or individuals and even in different parts of the same brain. Critically, such vessel diameter distribution can be measured directly within the ultrasound image without having to measure the actual diameter of each of the vessels. With the ultrasound image intensity being proportional to the blood volume, there is a direct link between the distribution of vessel diameters (via the blood volume) and the distribution of intensities. Furthermore, neither changes in vessel diameters (by constriction or dilation) nor the absence or presence of big vessels (depending on e.g. imaging pla ne / field of view or e.g. the state of a brain) is affecting the basic shape of the distribution (see Figures 7B and 7C). The intensity distribution curve as a whole is shifting, however, to lower intensity values (in case of constriction) or to higher intensity values (in case of dilation), this compared to a control / normal intensity distribution curve (see Figure 7C illustrating normal vs dilated blood vessels). Figure 7D illustrates the effect of applying a constant threshold (leaving out the larger blood vessels based on the blood signal intensity distribution) on blood vessel signal intensity distribution of images of 4 different pig brains. The blood vessel intensity distributions are similar in shape but shifted one towards another due to underlying brain to brain variation in blood vessel dilation (cfr. Figure 7C), anatomy or physiology. Applying the same fixed threshold to these 4 blood vessel signal distribution results in the selection of a different set of blood vessels from each individual blood vessel signal distribution and data on these individual selections cannot be compared faithfully. Figure 7E is a further illustration of variation in blood vessel signal distribution as observed in images of 2 different pig brains, further indicating that a constant threshold as in Figure 7D is leading to selection of a different set of blood vessels.

[0132] Based on the observations of the first step, a second step was developed. A control, normal or reference blood vessel intensity distribution curve was obtained by experimental acquisition in a sufficient number of different brains. Alternatively it was observed that such control, normal or reference blood vessel intensity distribution can be empirically approximated by a simple analytical function such as P(x) = xaebx.

[0133] Subsequent blood vessel signal intensity distributions calculated or computed from test ultrasound images were then normalized towards the control, normal or reference blood vessel intensity distribution such as by applying a suitable scaling factor. This resulted in the normalized blood vessel signal intensity distribution curves to overlap perfectly. Within the normalized blood vessel signal intensity distribution curves, all voxels within an intensity range of interest (the selected voxels; reflecting a range of vessel diameters) were selected to create a spatial mask to be applied on the original ultrasound images. The voxels associated with the same selection within the blood signal intensity distributions were subsequently selected in the original ultrasound images, thus giving rise to the "selected voxel" images of the original ultrasound images. The "selected voxel" images clearly are much more similar to each other (effectively reflecting the same set of selected blood vessels) compared to the original ultrasound images. This is illustrated in Figure 8A.

[0134] In case of large vessels appearing in the blood vessel signal intensity distribution (e.g. Figure 8B, right panel), the intensity distribution can be fitted ignoring the distribution tail representing the large vessels, as illustrated in Figure 8B. This is done independent of the blood vessel signal intensity distribution, as illustrated in Figure 8C for a normal and constricted vascular bed. Such fitted distributions can, as in Figure 8A, then be normalized. Alternatively, such fitting and normalization is performed in a single step.

[0135] Furthermore, this provides an adaptive threshold enabling elimination of large vessels from the distribution. The resulting image is then used to extract hemodynamic parameters. The resulting method ensures that always the same distribution of vessels independently of the absolute intensity value can be assessed, therewith enabling assessment of and comparison between different physiological / perfusion states (such as occurring within a subject e.g. in case of failing CA; or such as occurring due to intersubject variation), which is crucial for diagnosis and monitoring.

[0136] This method can be combined with assessment of neurovascular coupling. As an example, the progression of a spreading depression (SD) was monitored as illustrated in Figure 9.

Claims

CLAIMS1. A method for determining a hemodynamic parameter, the method comprising: measuring the Doppler signals obtained from an ultrasound wave emitted in a tissue, the intensity of the Doppler signals correlating with the diameter of blood vessels in the tissue; computing the distribution of intensities of the measured Doppler signals; normalizing the computed distribution to a reference Doppler signal intensity distribution; select a range of Doppler signal intensities within the normalized distribution, therewith selecting a set of blood vessels with a range of diameters correlating with the selected range of Doppler signal intensities; determining the hemodynamic parameter in the selected set of blood vessels.

2. The method according to claim 1 wherein the hemodynamic parameter is blood volume, blood flow, blood velocity, blood mean velocity, or blood mean velocity multiplied by the normalized intensity.

3. The method according to claim 1 or 2 wherein the hemodynamic parameter is continuously quantified based on continuously obtained Doppler signals.

4. The method according to any one of the foregoing claims wherein the ultrasound waves are ultrasound plane waves.

5. The method according to any one of the foregoing claims wherein the Doppler signals are obtained from plane wave ultrasound imaging.

6. The method according to any one of the foregoing claims further including discrimination of tissue and blood cell movement.

7. The method according to any one of the foregoing claims further including generating a sharpened image for each component image of multiple component images.

8. The method according to any one of the foregoing claims which is a real-time method.

9. The method according to any one of the foregoing claims wherein the tissue is brain tissue.

10. The method according to claim 9 wherein emission of the ultrasound wave and the measurement of the Doppler signals is minimally invasive, such as through a cranial window, a burr hole, a trepanation or a thinned skull bone.

11. The method according to claim 10 wherein the design of the ultrasound wave transducer / Doppler signal receiver is following the contours, shape, or delineation of a burr hole; or is more in particular following the contours, shape, delineation of a segment of a burr hole.

12. A method of assessing or monitoring cerebral physiology or function, such methods comprising: measuring or monitoring a cerebral hemodynamic parameter with a method according to any one of claims 9 to 11 measuring or monitoring the cerebral perfusion pressure (CPP) assessing or monitoring cerebral physiology or function based on the correlation between the measured or monitored cerebral hemodynamic parameter and the measured or monitored CPP.

13. An ultrasound wave transducer / Doppler signal receiver having a design following the contours, shape, or delineation of a burr hole, or having a design following the contours, shape, or delineation of a segment of a burr hole.

14. A computer program having instructions which when executed cause a computing or data processing system or device to carry out or perform a method according to any one of claims 1 to 12, or to carry out or perform a step of a method according to any one of claims 1 to 12.

15. A computing or data processing system or device, or machine readable medium comprising a means for carrying out or performing a method according to any one of claims 1 to 12, or for carrying out or performing a step of a method according to any one of claims 1 to 12.

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