A method and a system for ultrasound-based non-invasive temperature estimation using echo stretching

Abstract

“A METHOD AND A SYSTEM FOR ULTRASOUND-BASED NON-INVASIVE TEMPERATURE ESTIMATION USING ECHO STRETCHING” ABSTRACT The present disclosure discloses a method and a system for ultrasound-5 based non-invasive temperature estimation using echo stretching. The method comprises receiving pre-heating beamformed Radio Frequency (RF) data and post-heating beamformed RF data from a region of interest, using an ultrasound transducer. The method further comprises performing adaptive up-sampling on the pre-heating beamformed RF data and the post-heating beamformed RF data 10 using an adaptive sampling rate. Furthermore, the method comprises performing adaptive windowing on the adaptive up-sampled pre-heating beamformed RF data and the adaptive upsampled post-heating beamformed RF data using cross-correlation, based on a cumulative time shift. Further, the method comprising determining a stretch factor to obtain a thermal strain of the region of interest. Lastly, the method comprises estimating the non-invasive temperature 15 for the region of interest based on the thermal strain. [Figure 2]

Description

TECHNICAL FIELD

[001] The present invention generally relates to the field of temperature monitoring, and moreparticularly relates to a method and a system for non-invasive temperature estimation usingultrasound.BACKGROUND

[002] The following description includes information that may be useful in understanding thepresent invention. It is not an admission that any of the information provided herein is prior artor relevant to the presently claimed invention, or that any publication specifically or implicitlyreferenced is prior art.

[003] Thermal therapy encompasses a range of clinical interventions in which a user's tissueis mildly heated to therapeutic temperatures, typically between 39°C and 45°C, for applicationssuch as cancer treatment, drug delivery enhancement, and immune modulation. Accurate, realtime monitoring of temperature distribution during heating of the tissues is essential to ensuretherapeutic efficacy while preventing overheating of healthy tissues.

[004] Conventionally, magnetic resonance imaging and invasive thermometry probes arecommonly used for temperature tracking. However, the magnetic resonance imaging and theinvasive thermometry probes are associated with high costs, limited availability, and poor spatialresolution or invasiveness, resulting in less ideal for routine clinical use.

[005] Ultrasound thermometry offers a non-invasive, real-time, and cost-effective solutionfor temperature monitoring. Among acoustic parameters affected by temperature, speed of soundexhibits a relatively linear variation within the mild heating range (39°C to 45°C). Conventionalultrasound-based methods use speed of sound-based estimators for estimating the temperature.In the conventional ultrasound-based methods, a short time window of pre-heating signals andpost-heating signals (as shown in Fig. 1a) may be considered. Further, temperature is estimatedby first calculating a cumulative time shift of echoes along an A-line (as shown in Fig. 1b) forpre-heating signals and post-heating signals (as shown in Fig. 1a). Then, a spatial gradient isderived by the cumulative time shift to obtain thermal strain (as shown in Fig. 1c), that correlateswith temperature change. However, two-step process in the conventional ultrasound-basedmethods is highly sensitive to noise, often necessitating use of low-pass filtering, regularizationtechniques, or other forms of context-based smoothing to suppress artifacts.

[006] A critical limitation of such two-step process in the conventional ultrasound-basedmethods approach is that the estimation error arises as there is no pure time shift of the signalwithin the considered window, however the signals undergo compression. Consideration of theshort time windows may lead to large variation in the cumulative time shift, and the fluctuationsin the cumulative time shift are amplified by gradient operation, that requires filtering and filtersneed to be customized to particular scenario. Consequently, the accuracy of temperatureestimation becomes heavily dependent on the parameters chosen for filtering and regularization.For every different temperature profile, the filter must be optimized making these techniquesless generalizable and potentially prone to underestimating or over smoothing the temperatureestimates. As a result, finer spatial variations in temperature may be suppressed reducingaccuracy of the reconstructed temperature map.

[007] Therefore, there is a need for a method for accurately estimating the temperature inreal-time.SUMMARY

[008] The present disclosure overcomes one or more shortcomings of the prior art andprovides additional advantages. Embodiments and aspects of the disclosure described in detailherein are considered a part of the claimed disclosure.

[009] In one non-limiting embodiment of the present disclosure, a method for ultrasoundbased non-invasive temperature estimation using echo stretching, is disclosed. The methodcomprises receiving pre-heating beamformed Radio Frequency (RF) data and post-heatingbeamformed RF data from a region of interest, using an ultrasound transducer. The pre-heatingbeamformed RF data and the post-heating beamformed RF data relates to ultrasound signals ofthe region of interest. The method further comprises performing adaptive up-sampling on thepre-heating beamformed RF data and the post-heating beamformed RF data using an adaptivesampling rate. Furthermore, the method comprises performing adaptive windowing on theadaptive up-sampled pre-heating beamformed RF data and the adaptive up-sampled post-heatingbeamformed RF data using cross-correlation, based on a cumulative time shift. Further, themethod comprising determining a stretch factor to obtain a thermal strain of the region ofinterest. The stretch factor is required to stretch the adaptive windowed post-heatingbeamformed RF data for aligning with the adaptive windowed pre-heating beamformed RF data.Lastly, the method comprises estimating the non-invasive temperature for the region of interestbased on the thermal strain.

[0010] In another non-limiting embodiment of the present disclosure, wherein to perform theadaptive up-sampling on the pre-heating beamformed RF data and the post-heating beamformedRF data the method comprises performing windowing with predefined window size withpredefined overlapping on the pre-heating beamformed RF data and the post-heatingbeamformed RF data to split the pre-heating beamformed RF data and the post-heatingbeamformed RF data to plurality of windows. Further, the method comprises determining a sampleshift value for a last window from the plurality of windows of the pre-heating beamformed RFdata and the post-heating beamformed RF data by performing cross-correlation on the preheating beamformed RF data and the post-heating beamformed RF data. Furthermore, the methodcomprises estimating the adaptive sampling rate based on the sample shift value. Lastly, themethod comprises performing the adaptive up-sampling on the pre-heating beamformed RF dataand the post-heating beamformed RF data using the adaptive sampling rate.

[0011] In another non-limiting embodiment of the present disclosure, wherein to perform adaptivewindowing on the adaptive up-sampled pre-heating beamformed RF data and the adaptive upsampled post-heating beamformed RF data, the method comprises determining the cumulativetime shift by performing the cross correlation on the adaptive up-sampled pre-heatingbeamformed RF data and the adaptive up-sampled post-heating beamformed RF data. Further,the method comprises shifting a plurality of adaptive windows associated with the adaptive upsampled post-heating beamformed RF data by the cumulative time shift for aligning with theadaptive up-sampled pre-heating beamformed RF data.

[0012] In yet another non-limiting embodiment of the present disclosure, wherein to determine thestretch factor, the method comprises stretching the adaptive windowed post-heating beamformedRF data using at least one stretch factor. Further the method comprises determining the stretchfactor from the at least one stretch factor required to stretch the adaptive windowed post-heatingbeamformed RF data for aligning plurality of windows associated with the adaptive windowedpost-heating beamformed RF data with corresponding plurality of windows associated with theadaptive up-sampled pre-heating beamformed RF data.

[0013] In yet another non-limiting embodiment of the present disclosure, wherein to estimate thenon-invasive temperature for the region of interest based on the thermal strain, the methodcomprises determining the thermal strain based on the stretch factor. Further, the method comprisesapplying median filtering on the thermal strain to estimate the non-invasive temperature for theregion of interest based on the thermal strain using a calibrated thermal model.

[0014] In one non-limiting embodiment of the present disclosure, a system for ultrasoundbased non-invasive temperature estimation using echo stretching, is disclosed. The systemcomprises a memory, at least one processor coupled with the memory. The at least one processoris configured to receive pre-heating beamformed Radio Frequency (RF) data and post-heatingbeamformed RF data from a region of interest, using an ultrasound transducer. The pre-heatingbeamformed RF data and the post-heating beamformed RF data relates to ultrasound signals ofthe region of interest. Further, the at least one processor is configured to perform adaptive upsampling on the pre-heating beamformed RF data and the post-heating beamformed RF datausing an adaptive sampling rate. Furthermore, the at least one processor is configured to performadaptive windowing on the adaptive up-sampled pre-heating beamformed RF data and theadaptive up-sampled post-heating beamformed RF data using cross-correlation, based on acumulative time shift. Further, the at least one processor is configured to determine a stretchfactor to obtain a thermal strain of the region of interest. The stretch factor is required to stretchthe adaptive windowed post-heating beamformed RF data for aligning with the adaptivewindowed pre-heating beamformed RF data. Lastly, the at least one processor is configured toestimate the non-invasive temperature for the region of interest based on the thermal strain.

[0015] In yet another embodiment of the present disclosure, wherein to perform the adaptive upsampling on the pre-heating beamformed RF data and the post-heating beamformed RF data, theat least one processor is configured to perform windowing with predefined window size withpredefined overlapping on the pre-heating beamformed RF data and the post-heatingbeamformed RF data to split the pre-heating beamformed RF data and the post-heatingbeamformed RF data to plurality of windows. Further, the at least one processor is configured todetermine a sample shift value for a last window from the plurality of windows of the pre-heatingbeamformed RF data and the post-heating beamformed RF data by performing cross-correlationon the pre-heating beamformed RF data and the post-heating beamformed RF data. Furthermore,the at least one processor is configured to estimate the adaptive sampling rate based on the sampleshift value. Lastly, the at least one processor is configured to perform the adaptive up-samplingon the pre-heating beamformed RF data and the post-heating beamformed RF data using theadaptive sampling rate.

[0016] In yet another embodiment of the present disclosure, to perform the adaptive windowing onthe adaptive up-sampled pre-heating beamformed RF data and the adaptive up-sampled postheating beamformed RF data, the at least one processor is configured to determine the cumulativetime shift by performing the cross correlation on the adaptive up-sampled pre-heatingbeamformed RF data and the adaptive up-sampled post-heating beamformed RF data. Further,the at least one processor is configured to shift a plurality of adaptive windows associated withthe adaptive up-sampled post-heating beamformed RF data by the cumulative time shift foraligning with the adaptive up-sampled pre-heating beamformed RF data.

[0017] In yet another embodiment of the present disclosure, wherein to determine the stretch factor,the at least one processor is configured to stretch the adaptive windowed post-heatingbeamformed RF data using at least one stretch factor. Further, the at least one processor isconfigured to determine the stretch factor from the at least one stretch factor required to stretchthe adaptive windowed post-heating beamformed RF data for aligning plurality of windowsassociated with the adaptive windowed post-heating beamformed RF data with correspondingplurality of windows associated with the adaptive up-sampled pre-heating beamformed RF data.

[0018] In yet another embodiment of the present disclosure, wherein to estimate the non-invasivetemperature for the region of interest based on the thermal strain, the at least one processor isconfigured to determine the thermal strain based on the stretch factor. Further, the at least oneprocessor is configured to apply median filtering on the thermal strain to estimate the noninvasive temperature for the region of interest based on the thermal strain using a calibratedthermal model.

[0019] The foregoing summary is illustrative only and is not intended to be in any way limiting.In addition to the illustrative aspects, embodiments, and features described above, furtheraspects, embodiments, and features will become apparent by reference to the drawings and thefollowing detailed description.BRIEF DESCRIPTION OF DRAWINGS

[0020] The features, nature, and advantages of the present disclosure will become moreapparent from the detailed description set forth below when taken in conjunction with thedrawings in which like reference characters identify correspondingly throughout. Someembodiments of system and / or methods in accordance with embodiments of the present subjectmatter are now described, by way of example only, and with reference to the accompanyingFigs., in which:

[0021] FIG. 1 shows an existing two-step approach method for non-invasive temperatureestimation using ultrasound, in accordance with the conventional technology.

[0022] FIG. 2 depicts an exemplary environment for ultrasound-based non-invasivetemperature estimation using echo stretching, in accordance with embodiments of the presentdisclosure.

[0023] FIG. 3 depicts a detailed block diagram illustrating a system for ultrasound-based noninvasive temperature estimation using echo stretching, in accordance with embodiments of thepresent disclosure.

[0024] FIG. 4 illustrates an exemplary flow diagram for ultrasound-based non-invasivetemperature estimation using echo stretching, in accordance with embodiments of the presentdisclosure.

[0025] FIG. 5 illustrates an exemplary signal diagram for ultrasound-based non-invasivetemperature estimation using echo stretching, in accordance with embodiments of the presentdisclosure.

[0026] FIG. 6a and 6b illustrates an exemplary graphical representation of adaptive upsampling in a temperature gradient of 10 degree C and 0.1 degree C, respectively, in accordancewith embodiments of the present disclosure.

[0027] FIG. 7a illustrates an exemplary graphical representation of cross correlation valueverses depth in a temperature gradient of 10 degree C in accordance with embodiments of thepresent disclosure.

[0028] FIG. 7b illustrates an exemplary graphical representation of an effect of temperatureon 0.1 degree C temperature gradient, in accordance with embodiments of the present disclosure.

[0029] FIG. 8a and 8b illustrates an exemplary graphical representation of spatial resolutionin a temperature gradient of 10 degree C and 0.1 degree C, respectively, in accordance withembodiments of the present disclosure.

[0030] FIG. 9a and 9b illustrates an exemplary graphical representation of temperatureresolution in a temperature gradient of 10 degree C and 0.1 degree C, respectively, in accordancewith embodiments of the present disclosure.

[0031] FIG. 10a and 10b illustrates an exemplary graphical representation of minimumdetectable hotspots for hotspot temperature gradient of 10 degree C and 0.1 degree C,respectively, in accordance with embodiments of the present disclosure.

[0032] FIG. 11a and 11b illustrates an exemplary graphical representation of temperatureestimation at signal to noise ratio (SNR) levels in a temperature gradient of 10 degree C and 0.1degree C, respectively, in accordance with embodiments of the present disclosure.

[0033] FIG. 12 represents a flowchart of an exemplary method for ultrasound-based noninvasive temperature estimation using echo stretching, in accordance with embodiments of thepresent disclosure.

[0034] It should be appreciated by those skilled in the art that any block diagrams hereinrepresent conceptual views of illustrative systems embodying the principles of the presentsubject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, statetransition diagrams, pseudo code, and the like represent various processes which may besubstantially represented in a computer readable medium and executed by a computer orprocessor, whether or not such computer or processor is explicitly shown.DETAILED DESCRIPTION

[0035] The foregoing has broadly outlined the features and technical advantages of the presentdisclosure in order that the detailed description of the disclosure that follows may be betterunderstood. It should be appreciated by those skilled in the art that the conception and specificembodiment disclosed may be readily utilized as a basis for modifying or designing otherstructures for carrying out the same purposes of the present disclosure.

[0036] The novel features which are believed to be characteristic of the disclosure, both as toits organization and method of operation, together with further objects and advantages will bebetter understood from the following description when considered in connection with theaccompanying figures. It is to be expressly understood, however, that each of the figures isprovided for the purpose of illustration and description only and is not intended as a definitionof the limits of the present disclosure.

[0037] As described earlier, the critical limitation of such two-step process in the conventionalultrasound-based methods approach is that the estimation error arises as there is no pure timeshift of the signal within the considered window, however the signals undergo compression.Consideration of the short time windows may lead to large variation in the cumulative time shift,and the fluctuations in the cumulative time shift are amplified by gradient operation, that requiresfiltering and filters need to be customized to particular scenario. Consequently, the accuracy oftemperature estimation becomes heavily dependent on the parameters chosen for filtering andregularization. For every different temperature profile, the filter must be optimized making thesetechniques less generalizable and potentially prone to underestimating or over smoothing thetemperature estimates. As a result, finer spatial variations in temperature may be suppressedreducing accuracy of the reconstructed temperature map.

[0038] The present disclosure describes a method and system for non-invasive temperatureestimation using ultrasound. In particular, the present disclosure comprises acquiring pre-heatingand post-heating beamformed RF A-line data from a region of interest. Then dividing the datainto overlapping windows and applying cross-correlation to determine sample shifts. Anadaptive up-sampling factor is calculated and applied, followed by adaptive windowing to alignsignals. Post-heating windows are stretched across various factors to identify thermal strain,which is then median filtered and converted into temperature estimates. This process is repeatedacross all A-lines to generate a smoothed 2D temperature map. Thus, the present disclosureaccuracy estimates the temperature.

[0039] FIG. 2 exemplary environment 200 for ultrasound-based non-invasive temperatureestimation using echo stretching, in accordance with embodiments of the present disclosure. Theexemplary environment 200 particularly depicts a system 205 and a heat source 202. The system205 may estimate temperature in a region of interest 203. The environment 100 is exemplifiedfor a scenario when the heat source 202 raises the temperature for the region of interest 203 of auser 201. In such scenario, an ultrasound transducer 204 may receive beamformed RadioFrequency (RF) data from the region of interest 203 and transmit the RF data to the system 205.Then, the system 205 needs to estimate the temperature of the region of interest 203. In anexemplary embodiment, the region of interest 203 may refer to a portion of the user body wherethe temperature may be raised. In an exemplary embodiment, the beamformed RF data may referto processed signals obtained by applying beamforming techniques to raw RF data. In anexemplary embodiment, the RF data may be obtained by an array of sensors such as ultrasoundimaging, radar, sonar, and wireless communications and the like. The beamforming techniquesmay refer to signal processing technique that combines signals from multiple sensors in a waythat enhances signals from a specific direction or location. In an exemplary embodiment, thebeamformed RF data may represent focused and enhanced signal from a specific direction orfocal point.

[0040] In an exemplary embodiment, the system 205 may include but not limited to a computersystem, a laptop, a mobile, a tablet, a phone. In an exemplary embodiment, the beamformed RFdata may be received using the ultrasound probe 401, ultrasound transducer and the like. In anexemplary embodiment, the ultrasound transducer may be coupled with the system 205.

[0041] In some implementations, the system 205 may comprise a processor 206, a memory207 and modules 208. In some implementations, the system 205 may include other components(not shown in this fig.) to implement desired functions of the system 205. In an exemplaryembodiment, the modules 208 may trained to estimate the temperature. In an exemplaryembodiment, the processor 206 in the system 205 may use the modules 208 for estimating thetemperature.

[0042] In an exemplary embodiment, the system 205 may receive pre-heating beamformedRadio Frequency (RF) data and post-heating beamformed RF data from the region of interest203, using an ultrasound transducer 204. The pre-heating beamformed RF data and the postheating beamformed RF data may relate to ultrasound signals of the region of interest 203.Further, the system 205 may perform adaptive up-sampling on the pre-heating beamformed RFdata and the post-heating beamformed RF data using an adaptive sampling rate. Then, the system205 may perform adaptive windowing on the adaptive up-sampled pre-heating beamformed RFdata and the adaptive up-sampled post-heating beamformed RF data using cross-correlation,based on a cumulative time shift. Further, the system 205 may determining a stretch factor toobtain a thermal strain of the region of interest. The stretch factor may be required to stretch theadaptive windowed post-heating beamformed RF data for aligning with the adaptive windowedpre-heating beamformed RF data. Then, the system 205 may estimate the non-invasivetemperature for the region of interest based on the thermal strain. Thus, the present disclosureaccurately determines the non-invasive temperature. A detailed explanation of the system 103 isprovided in the forthcoming paragraphs in conjunction with FIG.s 3, 4-12.

[0043] Fig. 3 depicts an exemplary block diagram illustrating a system 205 for ultrasoundbased non-invasive temperature estimation using echo stretching, in accordance withembodiments of the present disclosure. In an exemplary embodiment, the system 205 may be acomputer system. In some implementations, the system 205 may further comprise the processor206, the memory 207 and the modules 208. In an exemplary embodiment, the modules 208 maycomprise a RF data receiver module 301, an adaptive up-sampling module 302, an adaptivewindowing module 303, a stretch factor determination module 304 and a temperature estimationmodule 305.

[0044] In one implementation, the processor 206 may be implemented as one or moremicroprocessors, microcomputers, microcontrollers, digital signal processors, central processingunits, state machines, logic circuitries, and / or any devices that manipulate signals based onoperational instructions. Among other capabilities, the processor 206 may be configured to fetchand execute computer-readable instructions and other information stored in the memory 207. Inan exemplary embodiment, the processor 206 may be used for execution instruction of a classicalcomputer. In another exemplary embodiment, the processor 206 may be a general processor usedfor executing the instructions.

[0045] In one implementation, the processor 206 may receive the pre-heating beamformedRadio Frequency (RF) data and the post-heating beamformed RF data from the region of interest203 from the ultrasound transducer 204. The ultrasound transducer 204 may be a device thatconverts electrical energy into high-frequency sound waves (ultrasound) and vice versa. Theultrasound transducer 204 may be used for both receiving and transmitting the ultrasoundsignals.

[0046] In an exemplary embodiment, the memory 207 may include one or more non-transitorycomputer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or morememory devices, flash memory devices, etc., and combinations thereof. In an exemplaryembodiment, the input data 207a may be stored within the memory 207 in the form of variousdata structures. In a non-limiting example, input data 207a refers as the at least the pre-heatingbeamformed Radio Frequency (RF) data and the post-heating beamformed RF data from a regionof interest and the like. The memory 207 may also store other data 207b such as temporary dataand temporary files, generated by the processor 206 or other any other parts of the system 205including the modules 208.

[0047] In an exemplary embodiment, the modules 208 may be a pretrained for performingparticular task use a set of technologies. A person of ordinary skill will appreciate that themodules 208 may use the input data to mimic human cognitive functions like learning, problemsolving, and reasoning. The modules 208 may be pre-trained to perform particular task. In anexemplary embodiment, the modules 208 may reside inside the processor 206. In anotherexemplary embodiment, the modules 208 may reside outside the processor 206. In an exemplaryembodiment, the modules 208 may comprise the RF data receiver module 301, the adaptive upsampling module 302, the adaptive windowing module 303, the stretch factor determinationmodule 304 and the temperature estimation module 305.

[0048] In a scenario when the heat source 202 raises the temperature in or over the region ofinterest 203 of the user 201. In such scenario, the ultrasound transducer 204 may receivebeamformed Radio Frequency (RF) data from the region of interest 203 and transmit the RF datato the system 205. Then, the processor 206 of the system 205 needs to estimate the temperatureof the region of interest 203. For estimating the temperature, the processor 206 may receive thepre-heating beamformed RF data and the post-heating beamformed RF data from the region ofinterest 203, using the ultrasound transducer 204. The pre-heating beamformed RF data and thepost-heating beamformed RF data may relate to ultrasound signals of the region of interest 203.

[0049] In an exemplary embodiment, the pre-heating beamformed RF data and the postheating beamformed RF data may be ultrasound signals received from the region of interest 203.The pre-heating beamformed RF data may be ultrasound signals received from the region ofinterest 203 before heating the region of interest 203. The post-heating beamformed RF datamay be ultrasound signals received from the region of interest 203 while heating the region ofinterest 203. Referring to FIG. 3, an ultrasound probe 401 may be used for sensing ultrasoundsignals from the region of interest 203 and transmit the sensed ultrasound signals to theultrasound transducer 204. In an embodiment, the ultrasound transducer 204 may receive thepre-heating beamformed RF data 403 before starting to heat the region of interest 203, frombeamformed RF data 402 received from the region of interest 203. In an embodiment, theultrasound transducer 204 may receive the post-heating beamformed RF data 404 while heatingthe region of interest 203, from the beamformed RF data 402 received from the region of interest203. In the present disclosure, the pre-heating beamformed RF data 403 and the post-heatingbeamformed RF data 404 may be an A-line data.

[0050] Referring back to FIG. 2, upon receiving the pre-heating beamformed RF data and thepost-heating beamformed RF data, the processor 206 may perform adaptive up-sampling on thepre-heating beamformed RF data and the post-heating beamformed RF data using an adaptivesampling rate. The adaptive up-sampling may indicate signal processing to increase resolutionof signals by inserting additional samples that adapts to local characteristics of data.

[0051] For performing the adaptive up-sampling on the pre-heating beamformed RF data andthe post-heating beamformed RF data, the processor 206 may perform windowing withpredefined window size with predefined overlapping on the pre-heating beamformed RF dataand the post-heating beamformed RF data to split the pre-heating beamformed RF data and thepost-heating beamformed RF data to plurality of windows (as shown in step 405 of FIG. 4). Inan embodiment, the processor 206 may perform windowing by splitting the pre-heatingbeamformed RF data and the post-heating beamformed RF data to the plurality of windows.Each window of the plurality of windows may be of window size 40λ with 90% overlap with anext window of the plurality of windows. A person skilled in the art may use any window sizeand overlapping value on the plurality of windows.

[0052] Referring to FIG. 4, the processor 206 may receive RF data and perform windowing atstep 501. The pre-heating beamformed RF data 403 may be windowed into first window x1w[n]second window x2w[n] and the like with window size 40λ with 90% overlap between the firstwindow and the second window. The post-heating beamformed RF data 404 may be windowedinto first window y1w[n], second window y2w[n] and the like with window size 40λ with 90%overlap between the first window and the second window. Similarly, the pre-heatingbeamformed RF data 403 and the post-heating beamformed RF data 404 may be windowed.

[0053] In an exemplary embodiment, the pre-heating beamformed RF data 403 and the postheating beamformed RF data 404 may be represented using a mathematical representation. Awindowed signal from the A-line acquired from the pre-heating beamformed RF data may beexpressed as:Equation (1)Wherein, s(t) represents the scatterer distribution within the medium, I(t) is the system's impulseresponse, and n(t)denotes uncorrelated noise.

[0054] In the post-heating beamformed RF data, the signal from the same window is representedas:Equation (2)Wherein:T is the cumulative time shift caused by the virtual movement of scatterers due to temperatureinduced changes in the speed of sound,'a' is a scaling factor that accounts for echo compression or stretching arising from depthdependent variations in the speed of sound. This depth dependency results from temperaturegradients, which cause scatterers at different depths to experience varying virtual displacements.

[0055] Referring back to FIG. 2, the processor 206 may determining a sample shift value for alast window from the plurality of windows of the pre-heating beamformed RF data and the postheating beamformed RF data by performing cross-correlation on the pre-heating beamformedRF data and the post-heating beamformed RF data. Then, the processor 206 may estimate theadaptive sampling rate based on the sample shift value and perform the adaptive up-sampling onthe pre-heating beamformed RF data and the post-heating beamformed RF data using theadaptive sampling rate.

[0056] Referring back to FIG. 5, at the step 501 of receiving RF data and windowing, theprocessor 206 may identify the last window from the plurality of windows in both the pre-heatingbeamformed RF data 403 and the post-heating beamformed RF data 404. That is, from the preheating beamformed RF data 403, the last window xlw[n]may be identified and from the postheating beamformed RF data 404, the last window ylw[n] may be identified. Then, at the step502 of adaptive up-sampling, the processor 206 may determine the sample shift value (τl) bycorrelating the last window of the pre-heating beamformed RF data and the post-heatingbeamformed RF data. Then, the adaptive sampling rate Equationmay be estimated using the sampleshift value (τl). Then, the processor 206 may perform the adaptive up-sampling on the preheating beamformed RF data 403 and the post-heating beamformed RF data 404 the adaptivesampling rate Equation. The adaptive sampling rate Equation may be a factor for performing the adaptiveup-sampling. The adaptive up-sampling on a window x[n] may be represented as x [n / U] for thepre-heating beamformed RF data 403. The adaptive up-sampling on a window y[n] may berepresented as y [n / U] for the post-heating beamformed RF data 404. Similarly, the adaptive upsampling may be performed on the plurality of windows of the pre-heating beamformed RF data403 and the post-heating beamformed RF data 404.

[0057] Referring back to FIG. 2, upon performing adaptive up-sampling, the processor 206may performing adaptive windowing on the adaptive up-sampled pre-heating beamformed RFdata and the adaptive up-sampled post-heating beamformed RF data using cross-correlation,based on a cumulative time shift (as shown in step 407 of FIG, 4). For performing adaptivewindowing, the processor 206 may determine the cumulative time shift by performing the crosscorrelation on the adaptive up-sampled pre-heating beamformed RF data and the adaptive upsampled post-heating beamformed RF data. Then, the processor 206 may shift a plurality ofadaptive windows associated with the adaptive up-sampled post-heating beamformed RF databy the cumulative time shift for aligning with the adaptive up-sampled pre-heating beamformedRF data. The cumulative time shift may refer to total accumulated delay or advancement appliedto a signal over multiple processing windows. In an embodiment, the cumulative time shift maybe determined by performing cross correlation between the window of the adaptive up-sampledpre-heating beamformed RF data and corresponding window of the adaptive up-sampled postheating beamformed RF data. Then, the processor 206 may shift the respective adaptive windowof the adaptive up-sampled post-heating beamformed RF data by the cumulative time shift, toalign with the corresponding window of the adaptive up-sampled pre-heating beamformed RFdata.

[0058] Upon performing adaptive windowing, the processor 206 may determine a stretchfactor to obtain a thermal strain of the region of interest 203. The stretch factor may be requiredto stretch the adaptive windowed post-heating beamformed RF data for aligning with theadaptive windowed pre-heating beamformed RF data. In an exemplary embodiment, the stretchfactor may be determined by stretching the windowed post-heating beamformed RF data. Theprocessor 206 may stretch the adaptive windowed post-heating beamformed RF data using atleast one stretch factor. Further, the processor 206 may determine the stretch factor from the atleast one stretch factor required to stretch the adaptive windowed post-heating beamformed RFdata for aligning plurality of windows associated with the adaptive windowed post-heatingbeamformed RF data with corresponding plurality of windows associated with the adaptive upsampled pre-heating beamformed RF data. The stretch factor may be an optimal stretch factoridentifying from a plurality of stretch factors that are used to stretch the adaptive windowed postheating beamformed RF data. In an embodiment, the stretch factor determination module 304may be used for determining the stretch factor. In an embodiment, the stretch factor may bedetermined using the equation (3) below:Equation (3)

[0059] Referring back to FIG. 5, at step 503 of adaptive windowing and echo stretching, theprocessor 206 may determine the cumulative time shift δTP by performing the cross correlationon the windows of the adaptive up-sampled pre-heating beamformed RF data and the adaptiveup-sampled post-heating beamformed RF data. Then, the processor 206 may shift the windowsof the adaptive up-sampled post-heating beamformed RF data ypuw[γn] by the cumulative timeshift δTP to align with the window of the adaptive up-sampled pre-heating beamformed RF dataxpuw[m].

[0060] Then, the each of the shifted windows of the adaptive windowed post-heatingbeamformed RF data may be stretched by the stretch factor γ. Then, the stretch factor ypopt amongthe plurality of stretch factors γ may be determined as the optimal stretch factor that may berequired to stretch the adaptive windowed post-heating beamformed RF data for aligning withthe adaptive windowed pre-heating beamformed RF data.

[0061] Referring back to FIG. 2, upon determining the stretch factor, the processor 206 mayestimate the non-invasive temperature for the region of interest 203 based on the thermal strain.For estimating the non-invasive temperature, the processor 206 may determine the thermal strainbased on the stretch factor. The thermal strain (ε) may be determined using equation (4) below:Equation (4)

[0062] Then, the processor 206 may apply median filtering on the thermal strain to estimatethe non-invasive temperature for the region of interest 203 based on the thermal strain using acalibrated thermal model. In an embodiment, the temperature estimation module 305 may beused for determining the non-invasive temperature. The non-invasive temperature may bedetermined using equation (5) below:Equation (5)

[0063] Referring back to FIG. 4, at step 504 of estimating temperature, the processor 206 mayestimate the non-invasive temperature for the region of interest 203. The processor 206 maydetermine the thermal strain based on the stretch factor. Further, processor 206 may applymedian filtering on the thermal strain to estimate the non-invasive temperature for the region ofinterest 203 based on the thermal strain using the calibrated thermal model.

[0064] In the present disclosure, the adaptive up-sampling may address the low samplingfrequency (<40MHz) of diagnostic ultrasound systems, that limits accurate sub-sample timeshift estimation. Without adaptive up-sampling the temperature estimates exhibit ripple artifacts(as shown in Fig.6a), where the true temperature value passes through the median of the ripples.The median filter reduces this artifact, but the underlying issue stems from the insufficientsampling rate.

[0065] For instance, in Fig 6a, up to 23mm depth, the cumulative time shift may remain belowhalf a sample, causing the cumulative time shift to appear as zero when cross-correlated betweenthe pre-heating A-line data and the post-heating A-line data. The stretching algorithmcompensates for this sub-sample shift by increasing the stretch factor γ, leading to a monotonicrise in the temperature estimate up to 23mm. Beyond the depth, as T exceeds 0.5 samples, digitalcross-correlation forces T =1, resulting in under-stretching and a drop in the estimatedtemperature, followed by another increase.

[0066] The adaptive up-sampling may correct this issue by ensuring that the total sample shiftsalong the A-line approximately match the number of analysis windows (assuming each windowcan contribute one sample shift). Residual discrepancies are minimized using median filtering.As shown in Fig 6a, after the adaptive up-sampling, the temperature estimates align closely withthe true values leading an average error of < 2% (w.r.t peak temperature of 10°C)

[0067] For the 0.1°C temperature rise scenario, the total shift across the A-line is less than halfa sample, resulting in a monotonic rise without adaptive up-sampling. After applying adaptiveup-sampling, the temperature estimate closely follows the true profile as shown in Fig 6b withaverage error less than < 6%. This demonstrates that adaptive up-sampling is critical for accuratetemperature estimation using echo stretching.

[0068] The post-heated signals may undergo compression relative to pre-heated signals,causing the number of corresponding samples to decrease with depth. This reduction lowers thecross-correlation accuracy, thereby degrading temperature estimation. The adaptive windowingmitigates this issue by enhancing cross-correlation at larger depths, as demonstrated in Fig. 7afor a 10°C temperature gradient.

[0069] The impact of the adaptive windowing for a 0.1°C gradient is shown in Fig.7b, wherethe average temperature estimation error drops from 18% (without adaptive windowing) to <6%.This highlights that adaptive windowing is critical for accurately estimating small temperaturegradients. The higher the cross-correlation value, better is the temperature estimate.

[0070] Further, Fig.8 illustrates the impact of window lengths (40λ, 20λ, and 10λ) ontemperature estimation. Smaller window lengths improve spatial resolution for a centerfrequency of 6.6MHz, 10λ windows provide 4 samples / mm, compared to 1 sample / mm with40λ.

[0071] For a 10°C gradient as shown in Fig.8a, the average error remains <2% for 40λ and20λ, but increases to 3.5% for 10λ. For small temperature gradients as shown in Fig.8b, using20λ raises the error to 16%, which is impractical even under noise-free conditions. Therefore,the most achievable spatial resolution for small gradients is 1 sample / mm (40λ), which can beconsidered the minimum reliable spatial resolution.

[0072] Fig.9a demonstrates that the present disclosure can resolve two 10°C hotspotsseparated by 2.5mm when using a 10λ window. With a 40λ window, the temperature resolutiondecreases to approximately 3-3.5mm. Fig.9b shows that for two 0.1°C hotspots, a 40λ windowachieves resolution at separations of 4.5-5mm. Thus, in all cases, two hotspots can be resolvedby their full width at half maximum (FWHM) when separated by at least 5mm.

[0073] Fig.10a and Fig. 10b demonstrate that the present disclosure may detect hotspots ofsizes 2.5, 5, 10, and 15mm using a 40λ window. With the applied median filtering, the minimumreliably detectable hotspot size is 5-10 mm in diameter, with less than 50% bias in peaktemperature estimation.

[0074] Fig.11a and Fig. 11b illustrates the impact of system noise (SNR=40dB and 20dB)on temperature estimation using the proposed algorithm. For large temperature gradients asshown in Fig.11a, noise influence is minimal, with an average error of <2% of peak temperatureat 40dB SNR and <8% at 20dB SNR. In contrast, small temperature estimates are more sensitiveto noise, showing an average error of ~13% even at 40dB SNR as shown in Fig. 11b. Thereby,improving robustness with increasing temperature, supporting the use of a common pre-heatedreference frame during heating, as thermal strain grows with temperature rise.

[0075] FIG. 12 represents flowchart of an exemplary method for non-invasive temperatureestimation using ultrasound, in accordance with embodiments of the present disclosure. Theorder in which the method 1200 is described is not intended to be construed as a limitation, andany number of the described method blocks may be combined in any order to implement themethod. Additionally, individual blocks may be deleted from the methods without departingfrom the spirit and scope of the subject matter described. Furthermore, the method can beimplemented in any suitable hardware, software, firmware, or combination thereof. However,for ease of explanation, in the embodiments described below, the method 1200 may beimplemented by the respective components and / or by the processor 206 using the RF datareceiver module 301, the adaptive up-sampling module 302, the adaptive windowing module303, the stretch factor determination module 304 and the temperature estimation module 305 ofFIG. 3.

[0076] At step 1201, the method may include receiving the pre-heating beamformed RF dataand the post-heating beamformed RF data from the region of interest 203, using the ultrasoundtransducer. The pre-heating beamformed RF data and the post-heating beamformed RF data mayrelate to the ultrasound signals of the region of interest 203. In one implementation, the processor206 may receive the pre-heating beamformed RF data and the post-heating beamformed RF datafrom the region of interest 203.

[0077] At step 1202, the method may include performing the adaptive up-sampling on the preheating beamformed RF data and the post-heating beamformed RF data using the adaptivesampling rate. In one implementation, the processor 206 may perform the adaptive up-sampling.In another implementation, the adaptive up-sampling module 302 may perform the adaptive upsampling.

[0078] At step 1203, the method may include performing the adaptive windowing on theadaptive up-sampled pre-heating beamformed RF data and the adaptive up-sampled post-heatingbeamformed RF data using the cross-correlation, based on the cumulative time shift. In oneimplementation, the processor 206 may perform the adaptive windowing. In anotherimplementation, the adaptive windowing module 303 may perform the adaptive windowing.

[0079] At step 1204, the method may include determining the stretch factor to obtain thethermal strain of the region of interest 203. The stretch factor may be required to stretch theadaptive windowed post-heating beamformed RF data for aligning with the adaptive windowedpre-heating beamformed RF data. In one implementation, the processor 206 may determine thestretch factor. In another implementation, the stretch factor determination module 304 maydetermine the stretch factor.

[0080] At step 1205, the method may include estimating the non-invasive temperature for theregion of interest based on the thermal strain. In one implementation, the processor 206 mayestimate the non-invasive temperature. In another implementation, the temperature estimationmodule 305 may estimate the non-invasive temperature.

[0081] The order in which the method 1200 is described is not intended to be construed as alimitation, and any number of the described method blocks may be combined in any order toimplement the method. Additionally, individual blocks may be deleted from the methods withoutdeparting from the spirit and scope of the subject matter described.

[0082] The illustrated steps are set out to explain the exemplary embodiments shown, and itshould be anticipated that ongoing technological development will change the manner in whichparticular functions are performed. These examples are presented herein for purposes ofillustration, and not limitation. Further, the boundaries of the functional building blocks havebeen arbitrarily defined herein for the convenience of the description. Alternative boundariescan be defined so long as the specified functions and relationships thereof are appropriatelyperformed.

[0083] Advantageous- The Adaptive up-sampling may overcome the limitations of low sampling frequenciesin diagnostic ultrasound systems in echo stretching method for ultrasound thermometryapplication.- The proposed disclosure provides ultrasound thermometry framework that leverages amodified echo stretching method to directly estimate thermal strain and convert it intotemperature measurements. Thus, the present disclosure provides a single-step process that isinherently less sensitive to noise and eliminates error-prone gradient calculations.- The median filtering may suppress residual noise from up-sampling artifacts,improving temperature accuracy in the process of echo stretching for temperature estimation.- The adaptive windowing may improve cross-correlation at larger depths, significantlyreducing temperature estimation errors (from 18% to <6% for small gradients) in the echostretching based temperature estimation.- The optimized window-length selection for echo stretch analysis (e.g., 40λ for smalltemperature gradients) may balance spatial resolution and accuracy, enabling reliable hotspotdetection down to 5mm diameter and at least 1 temperature sample per 1 mm spatial resolutionalong the axial direction.

[0084] Alternatives (including equivalents, extensions, variations, deviations, etc., of thosedescribed herein) will be apparent to persons skilled in the relevant art(s) based on the teachingscontained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.Furthermore, one or more computer-readable storage media may be utilized in implementingembodiments consistent with the present disclosure. A computer-readable storage medium refersto any type of physical memory on which information or data readable by a processor may bestored. Thus, a computer-readable storage medium may store instructions for execution by oneor more processors, including instructions for causing the processor(s) to perform steps or stagesconsistent with the embodiments described herein. The term "computer- readable medium"should be understood to include tangible items and exclude carrier waves and transient signals,i.e., are non-transitory. Examples include random access memory (RAM), read-only memory(ROM), volatile memory, non-volatile memory, hard drives, CD ROMs, DVDs, flash drives,disks, and any other known physical storage media.

Claims

1. A method for ultrasound-based non-invasive temperature estimation using echo stretching, comprising: receiving, using an ultrasound transducer, pre-heating beamformed Radio Frequency (RF) data and post-heating beamformed RF data from a region of interest, wherein the preheating beamformed RF data and the post-heating beamformed RF data relates to ultrasound signals of the region of interest; performing adaptive up-sampling on the pre-heating beamformed RF data and the postheating beamformed RF data using an adaptive sampling rate; performing adaptive windowing on the adaptive up-sampled pre-heating beamformed RF data and the adaptive up-sampled post-heating beamformed RF data using cross-correlation, based on a cumulative time shift; determining a stretch factor to obtain a thermal strain of the region of interest, wherein the stretch factor is required to stretch the adaptive windowed post-heating beamformed RF data for aligning with the adaptive windowed pre-heating beamformed RF data; and estimating the non-invasive temperature for the region of interest based on the thermal strain.

2. The method as claimed in claim 1, wherein performing the adaptive up-sampling on the preheating beamformed RF data and the post-heating beamformed RF data, comprising: performing windowing with predefined window size with predefined overlapping on the pre-heating beamformed RF data and the post-heating beamformed RF data to split the preheating beamformed RF data and the post-heating beamformed RF data to plurality of windows; determining a sample shift value for a last window from the plurality of windows of the pre-heating beamformed RF data and the post-heating beamformed RF data by performing crosscorrelation on the pre-heating beamformed RF data and the post-heating beamformed RF data; estimating the adaptive sampling rate based on the sample shift value; and performing the adaptive up-sampling on the pre-heating beamformed RF data and the post-heating beamformed RF data using the adaptive sampling rate.

3. The method as claimed in claim 1, wherein performing adaptive windowing on the adaptive up-sampled pre-heating beamformed RF data and the adaptive up-sampled post-heating beamformed RF data, further comprising: determining the cumulative time shift by performing the cross correlation on the adaptive upsampled pre-heating beamformed RF data and the adaptive up-sampled post-heating beamformed RF data; and shifting a plurality of adaptive windows associated with the adaptive up-sampled postheating beamformed RF data by the cumulative time shift for aligning with the adaptive upsampled pre-heating beamformed RF data.

4. The method as claimed in claim 1, wherein determining the stretch factor, comprising: stretching the adaptive windowed post-heating beamformed RF data using at least one stretch factor; and determining the stretch factor from the at least one stretch factor required to stretch the adaptive windowed post-heating beamformed RF data for aligning plurality of windows associated with the adaptive windowed post-heating beamformed RF data with corresponding plurality of windows associated with the adaptive up-sampled pre-heating beamformed RF data.

5. The method as claimed in claim 1, wherein estimating the non-invasive temperature for the region of interest based on the thermal strain, comprising: determining the thermal strain based on the stretch factor; and applying median filtering on the thermal strain to estimate the non-invasive temperature for the region of interest based on the thermal strain using a calibrated thermal model.

6. A system for ultrasound-based non-invasive temperature estimation using echo stretching, comprises: a memory; at least one processor coupled with the memory configured to: receive pre-heating beamformed Radio Frequency (RF) data and post-heating beamformed RF data from a region of interest, using an ultrasound transducer, wherein the preheating beamformed RF data and the post-heating beamformed RF data relates to ultrasound signals of the region of interest; perform adaptive up-sampling on the pre-heating beamformed RF data and the postheating beamformed RF data using an adaptive sampling rate; perform adaptive windowing on the adaptive up-sampled pre-heating beamformed RF data and the adaptive up-sampled post-heating beamformed RF data using cross-correlation, based on a cumulative time shift; determine a stretch factor to obtain a thermal strain of the region of interest, wherein the stretch factor is required to stretch the adaptive windowed post-heating beamformed RF data for aligning with the adaptive windowed pre-heating beamformed RF data; and estimate the non-invasive temperature for the region of interest based on the thermal strain.

7. The system as claimed in claim 6, wherein to perform the adaptive up-sampling on the preheating beamformed RF data and the post-heating beamformed RF data, the at least one processor is configured to: perform windowing with predefined window size with predefined overlapping on the pre-heating beamformed RF data and the post-heating beamformed RF data to split the preheating beamformed RF data and the post-heating beamformed RF data to plurality of windows; determine a sample shift value for a last window from the plurality of windows of the pre-heating beamformed RF data and the post-heating beamformed RF data by performing crosscorrelation on the pre-heating beamformed RF data and the post-heating beamformed RF data; estimate the adaptive sampling rate based on the sample shift value; and perform the adaptive up-sampling on the pre-heating beamformed RF data and the postheating beamformed RF data using the adaptive sampling rate.

8. The system as claimed in claim 6, wherein to perform the adaptive windowing on the adaptive up-sampled pre-heating beamformed RF data and the adaptive up-sampled post-heating beamformed RF data, the at least one processor is configured to: determine the cumulative time shift by performing the cross correlation on the adaptive up-sampled pre-heating beamformed RF data and the adaptive up-sampled post-heating beamformed RF data; and shift a plurality of adaptive windows associated with the adaptive up-sampled postheating beamformed RF data by the cumulative time shift for aligning with the adaptive upsampled pre-heating beamformed RF data.

9. The system as claimed in claim 6, wherein to determine the stretch factor, the at least one processor is configured to: stretch the adaptive windowed post-heating beamformed RF data using at least one stretch factor; and determine the stretch factor from the at least one stretch factor required to stretch the adaptive windowed post-heating beamformed RF data for aligning plurality of windows associated with the adaptive windowed post-heating beamformed RF data with corresponding plurality of windows associated with the adaptive up-sampled pre-heating beamformed RF data.

10. The system as claimed in claim 6, wherein to estimate the non-invasive temperature for the region of interest based on the thermal strain, the at least one processor is configured to: determine the thermal strain based on the stretch factor; and apply median filtering on the thermal strain to estimate the non-invasive temperature for the region of interest based on the thermal strain using a calibrated thermal model.