Low field MRI texture analysis

Abstract

A system and method for identifying a region of interest using a low field magnetic resonance imaging (MRI) system is disclosed. The method comprises: obtaining a T2 weighted image from a low-field MRI system, wherein the T2 weighted image comprises slices; annotating a first area on the slice, wherein the first area corresponds to the suspicious area; and annotating a second region on the slice, wherein the second region corresponds to the non-suspicious region. The second region includes the same size as the first region. The method further includes calculating a first texture feature value for the first region, calculating a second texture feature value for the second region, and comparing the first texture feature value to the second texture feature value.

Description

[0001] Cross-references

[0002] This application claims priority to U.S. Provisional Application No. 63 / 369,301, filed on July 25, 2022, which is incorporated herein by reference in its entirety for all purposes. Background Art

[0003] Texture analysis of images can be used to identify different tissue types. Summary of the invention

[0004] In one general aspect, the present disclosure provides an exemplary method for identifying a region of interest using a low-field magnetic resonance imaging (MRI) system. The exemplary method may include: obtaining a T2-weighted image from a low-field MRI system, wherein the T2-weighted image may include a slice; annotating a first region on the slice, wherein the first region may correspond to a suspicious region; and annotating a second region on the slice, wherein the second region may correspond to a non-suspicious region. The second region may include the same size as the first region. The method may also include calculating a first texture feature value for the first region, calculating a second texture feature value for the second region, and comparing the first texture feature value to the second texture feature value.

[0005] In another aspect, the present disclosure provides a low-field MRI system. An exemplary system may include: a magnet array configured to generate a permanent non-uniform B0 magnetic field in a region of interest offset from the magnet array; and a control circuit. The control circuit may be configured to: generate a T2-weighted image from a unilateral low-field MRI; identify a first region on the T2-weighted image, wherein the first region may correspond to a suspicious region; and identify a second region on the T2-weighted image. The second region may correspond to a non-suspicious region, and wherein the second region may include the same size as the first region. The control circuit may be configured to calculate a first texture feature value for the first region, calculate a second texture feature value for the second region, and compare the first texture feature value with the second texture feature value. The system may also include a display configured to convey a comparison of the first texture feature value with the second texture feature value. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The novel features of the various aspects are set forth with particularity in the appended claims.However, the described aspects, both as to organization and method of operation, may be best understood by reference to the following description taken in conjunction with the accompanying drawings.

[0007] Figure 1A fused low-field and high-field MR image according to various aspects of the present disclosure includes a co-registered T2-weighted image from a low-field magnetic resonance (MR) image and a high-field MR image and depicts a region annotated on the high-field MR image.

[0008] Figure 2 is a texture map array according to various aspects of the present disclosure.

[0009] Figure 3A and Figure 3B is a graphical representation of texture feature values ​​on high-field MR images and low-field MR images according to various aspects of the present disclosure.

[0010] Figure 4 is a flow chart depicting a validation study for comparing Haralick texture feature values ​​from high-field MR images and low-field MR images in accordance with various aspects of the present disclosure.

[0011] Figure 5 is a perspective view of an MRI scanner according to various aspects of the present disclosure.

[0012] Figure 6 According to various aspects of the present disclosure Figure 5 An exploded perspective view of an MRI scanner, exposing the permanent magnet assembly and gradient coil assembly within the housing.

[0013] Figure 7 According to various aspects of the present disclosure Figure 5 Elevation view of an MRI scanner.

[0014] Figure 8 According to various aspects of the present disclosure Figure 5 Elevation view of an MRI scanner.

[0015] Fig. 9 According to various aspects of the present disclosure Figure 5 A perspective view of the permanent magnet assembly of an MRI scanner.

[0016] Fig.10 According to various aspects of the present disclosure Figure 5 Elevation view of the gradient coil set and permanent magnet assembly of the MRI system shown in FIG.

[0017] Fig.11 is a control schematic diagram of a unilateral MRI system according to various aspects of the present disclosure.

[0018] Fig.12 is a schematic diagram of a magnetic gradient along the Z-axis according to various aspects of the present disclosure.

[0019] The accompanying drawings are not intended to be drawn to scale. In the several views, corresponding reference numerals indicate corresponding parts. For clarity, not every component is labeled in every figure. The examples listed herein illustrate certain embodiments of the present invention in one form, and such examples should not be construed as limiting the scope of the present invention in any way. DETAILED DESCRIPTION

[0020] The following international patent applications are incorporated herein by reference in their respective entireties:

[0021] International application number PCT / US2020 / 018352, filed on February 14, 2020, entitled “SYSTEMS AND METHODS FOR ULTRALOW FIELDRELAXATION DISPERSION,” now International publication number WO2020 / 168233;

[0022] International application number PCT / US2020 / 019530, entitled “SYSTEMS AND METHODS FOR PERFORMING MAGNETIC RESONANCE IMAGING,” filed on February 24, 2020, now International publication number WO2020 / 172673;

[0023] International Application No. PCT / US2020 / 019524, filed on February 24, 2020, entitled “PSEUDO-BIRDCAGE COIL WITH VARIABLE TUNING AND APPLICATIONS THEREOF,” now International Publication No. WO2020 / 172672;

[0024] International Application No. PCT / US2020 / 024776, filed on March 25, 2020, entitled “SINGLE-SIDED FAST MRI GRADIENT FIELD COILS AND APPLICATIONS THEREOF,” now International Publication No. WO2020 / 198395;

[0025] International application number PCT / US2020 / 024778, entitled “SYSTEMS AND METHODS FOR VOLUMETRICACQUISITION IN A SINGLE-SIDED MRI SYSTEM”, filed on March 25, 2020, now International publication number WO2020 / 198396; International application number PCT / US2020 / 039667, entitled “SYSTEMS AND METHODS FOR IMAGE RECONSTRUCTIONS IN MAGNETIC RESONANCE IMAGING”, filed on June 25, 2020, now International publication number WO2020 / 264194; International application number PCT / US2020 / 039667, entitled “SYSTEMS AND METHODS FOR IMAGE RECONSTRUCTIONS IN MAGNETIC RESONANCE IMAGING”, filed on January 22, 2021, now International publication number WO2020 / 264194; BIOPSY” with international application number PCT / US2021 / 014628, now international publication number WO2021 / 150902;

[0026] International application number PCT / US2021 / 018834, filed on February 19, 2021, entitled “RADIO FREQUENCY RECEPTION COIL NETWORKS FOR SINGLE-SIDED MAGNETIC RESONANCE IMAGING,” now International publication number WO2021 / 168291;

[0027] International Patent Application No. PCT / US2021 / 021464, filed on March 9, 2021, entitled “PHASE ENCODING WITH FREQUENCY SWEEP PULSESFOR MAGNETIC RESONANCE IMAGING IN INHOMOGENEOUS MAGNETIC FIELDS,” now International Publication No. WO2021 / 183484;

[0028] International Patent Application No. PCT / US2021 / 021461, entitled “PULSE SEQUENCES AND FREQUENCY SWEEP PULSE FOR SINGLE-SIDED MAGNETIC RESONANCE IMAGING,” filed on March 9, 2021, now International Publication No. WO / 2021183482;

[0029] ·International patent application No. PCT / US2021 / 021464, entitled “PHASE ENCODING WITH FREQUENCY SWEEP PULSESFOR MAGNETIC RESONANCE IMAGING IN INHOMOGENEOUS MAGNETIC FIELDS,” filed on March 9, 2021, now International Publication No. WO2021 / 183484; and ·International patent application No. PCT / US2022 / 071924, entitled “LOCALIZATION GUIDE AND METHOD FOR MRI GUIDED PELVIC INTERVENTIONS,” filed on April 26, 2022, are also incorporated herein by reference in their entirety.

[0030] The following U.S. provisional patent applications are incorporated herein by reference in their respective entireties:

[0031] U.S. Provisional Patent Application No. 62 / 806,664, filed on February 15, 2019, entitled “SYSTEMS AND METHODS FOR ULTRALOW FIELDRELAXATION DISPERSION”;

[0032] U.S. Provisional Patent Application No. 62 / 809,503, filed on February 22, 2019, entitled “PSEUDO-BIRDCAGE COIL WITH VARIABLE TUNING AND APPLICATIONS THEREOF”;

[0033] U.S. Provisional Patent Application No. 62 / 823,521, filed on March 25, 2019, entitled “SINGLE-SIDED FAST MRI GRADIENT FIELD COILS AND APPLICATIONS THEREOF”;

[0034] U.S. Provisional Patent Application No. 62 / 866,533, filed on June 15, 2019, entitled “SYSTEMS AND METHODS FOR IMAGE RECONSTRUCTION IN MAGNETIC RESONANCE IMAGING”;

[0035] U.S. Provisional Patent Application No. 62 / 965,070, filed on January 23, 2020, entitled “GUIDED ROBOTIC SYSTEM, METHODS AND APPARATUS FOR BIOPSY”;

[0036] U.S. Provisional Patent Application No. 62 / 979,332, filed on February 20, 2020, entitled “SYSTEM AND METHODS FOR UTILIZING A RADIOFREQUENCY RECEIVE NETWORK FOR SINGLE-SIDED MAGNETIC RESONANCE IMAGING”;

[0037] U.S. Provisional Patent Application No. 62 / 987,286, filed on March 9, 2020, entitled “SYSTEMS AND METHODS FOR ADAPTING DRIVENEQUILIBRIUM FOURIER TRANSFORM FOR SINGLE-SIDED MRI”;

[0038] U.S. Provisional Patent Application No. 62 / 987,292, filed on March 9, 2020, entitled “SYSTEMS AND METHODS FOR LIMITING K-SPACETRUNCATION IN A SINGLE-SIDED MRI SCANNER”;

[0039] - U.S. Provisional Patent Application No. 62 / 823,511, filed on March 25, 2019, entitled “SYSTEMS AND METHODS FOR VOLUMETRICACQUISITION IN A SINGLE-SIDED MRI SCANNER”;

[0040] U.S. Provisional Patent Application No. 63 / 180,013, filed on April 26, 2021, entitled “INTERVENTIONAL LOCALIZATION GUIDE AND METHODFOR MRI GUIDED PELVIC INTERVENTIONS”;

[0041] U.S. Provisional Patent Application No. 63 / 266,383, filed on January 4, 2022, entitled “RELAXATION-BASED MAGNETIC RESONANCETHERMOMETRY WITH A LOW-FIELD SINGLE-SIDED MRI SCANNER”; and

[0042] - U.S. Provisional Patent Application No. 63 / 367,787, filed on July 6, 2022, entitled “BIOPSY DEVICES AND METHODS”; the following U.S. patent applications are incorporated herein by reference in their respective entireties:

[0043] - U.S. Patent Application Publication No. 2018 / 0356480, entitled “UNILATERAL MAGNETIC RESONANCE IMAGING SYSTEM WITH APERTURE FOR INTERVENTIONS AND METHODOLOGIES FOR OPERATING SAME,” published on December 13, 2018;

[0044] - U.S. Patent Application Publication No. 2022 / 0146613, entitled “SYSTEMS AND METHODS FOR ULTRALOW FIELDRELAXATION DISPERSION,” published on May 12, 2022;

[0045] U.S. Patent Application Publication No. 2022 / 0043084, published on February 10, 2022, entitled “PSEUDO-BIRDCAGE COIL WITH VARIABLE TUNING AND APPLICATIONS THEREOF”;

[0046] U.S. Patent Application Publication No. 2022 / 0113361, entitled “SYSTEMS AND METHODS FOR PERFORMING MAGNETIC RESONANCE IMAGING,” published on April 14, 2022;

[0047] U.S. Patent Application Publication No. 2022 / 0091207, published on March 24, 2022, entitled “SINGLE-SIDED FAST MRI GRADIENT FIELD COILS AND APPLICATIONS THEREOF”;

[0048] - U.S. patent application Ser. No. 17 / 596,610, filed Dec. 14, 2021, entitled “SYSTEMS AND METHODS FOR IMAGE RECONSTRUCTION IN MAGNETIC RESONANCE IMAGING”;

[0049] - U.S. Patent Application No. 17 / 596,610, filed on December 14, 2021, entitled “SYSTEMS AND METHODS FOR IMAGE RECONSTRUCTION IN MAGNETIC RESONANCE IMAGING”; and

[0050] -U.S. patent application Ser. No. 17 / 660,709, filed on April 26, 2022, entitled “INTERVENTIONAL LOCALIZATION GUIDE AND METHODFOR MRI GUIDED PELVIC INTERVENTIONS”

[0051] The following U.S. design applications are incorporated herein by reference in their respective entireties:

[0052] - U.S. Design Application No. 29 / 681,014, entitled “ANALYTICAL DEVICE,” filed on February 21, 2019, now U.S. Design Patent D895,803, issued on September 8, 2020;

[0053] - U.S. Design Application No. 29 / 744,371, entitled “ANALYTICAL DEVICE,” filed on July 28, 2020, now U.S. Design Patent D942,012, issued on January 25, 2022; and

[0054] -U.S. design application 29 / 790,895, entitled “ANALYTICAL DEVICE,” filed on December 20, 2021.

[0055] Before explaining various aspects of the MRI system and method in detail, it should be noted that the illustrative examples are not limited in application or use to the details of the construction and arrangement of the parts shown in the drawings and the specification. The illustrative examples may be implemented or incorporated in other aspects, variations and modifications, and may be practiced or implemented in various ways. In addition, unless otherwise stated, the terms and expressions used herein are selected for the purpose of describing the illustrative examples for the convenience of the reader and are not for the purpose of limiting the illustrative examples. In addition, it will be understood that one or more of the aspects, expressions of aspects, and / or examples described below may be combined with any one or more of the other aspects, expressions of aspects, and / or examples described below.

[0056] Radiomics is the quantitative extraction and analysis of mineable data from medical images. It can be used to identify different types of tissue. For example, it can be used to detect and classify prostate lesions. Radiomics involves extracting quantitative features, i.e., radiomic features, from radiological images that are usually not visible to the naked eye of a radiologist. For example, radiomic features can include texture features such as energy, entropy, correlation, uniformity, and inertia. Haralick texture features are calculated from a gray level co-occurrence matrix (GLCM), which is a matrix defined on an image with a distribution of co-occurring pixel values ​​(grayscale values ​​or colors) at a given offset. GLCM is used as a method for texture analysis, which has a variety of applications, especially in medical image analysis; features generated using this technique are often called Haralick features, named after Robert Haralick. Haralick features extract the frequency of local spatial variations in signal intensity in an image and quantify pixel relationships within a region of interest in an image. For example, Haralick features can be determined by determining / counting the co-occurrence of adjacent gray levels in an image.

[0057] Haralick texture analysis of prostate MRI has only been studied for cancer detection associated with high-field MRI. High-field MRI has an electromagnetic field greater than 1.5 T. Typically, high-field MRI has an electromagnetic field between 1.5 T and 3 T. The following articles are incorporated herein by reference in their respective entireties:

[0058] Cutaia, Giuseppe, et al., “Radiomics and Prostate MRI: Current Role and Future Applications.” Journal of imaging, vol. 7, 2 34, February 11, 2021;

[0059] ·Nketiah, Gabriel A, et al., “Utility of T2-weighted MRI texture analysis in assessment of peripheral zone prostate cancer aggressiveness: a single-arm, multicenter study.” Scientific reports, Volume 11, 1 2085, January 22, 2021;

[0060] ·Baek, Tae Wook, et al., "Texture analysis on bi-parametric MRI forevaluation of aggressiveness in patients with prostate cancer." Abdominalradiology (New York), Volume 45, 12 (2020): 4214-4222;

[0061] ·Niu, Xiang-Ke, et al., "Clinical Application of Biparametric MRI TextureAnalysis for Detection and Evaluation of High-Grade Prostate Cancer in Zone-Specific Regions." AJR. American journal of roentgenology, Volume 210, 3(2018):549-556; and

[0062] Wibmer, Andreas, et al., “Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores.” Europeanradiology, Vol. 25, 10(2015):2840-50.

[0063] In various cases, low-field MRI is preferred over high-field MRI, as further described herein. For example, a low-field MRI system may have a smaller footprint and / or may require reduced shielding requirements than a high-field MRI system, which may be preferred in some cases. In addition, low-field MRI may be more open than high-field MRI. For example, unilateral low-field MRI may provide a better patient experience and allow better access by clinicians and / or surgical robots. For example, international application number PCT / US2021 / 014628, entitled “MRI-GUIDED ROBOTIC SYSTEMS AND METHODS FOR BIOPSY” filed on January 22, 2021 (which is incorporated herein by reference in its entirety) describes an MRI-guided biopsy procedure.

[0064] However, low-field MR images have significant differences from high-field MR images. For example, T2 contrast is affected by field strength. The noise pattern between low-field MR images and high-field MR images is also different. More specifically, the noise in high-field MRI scanners is usually dominated by the object being imaged, with additional noise coming from the hardware. At low fields, object noise can be neglected, and the overall noise can be dominated by hardware components (such as RF coils and spectrometers).

[0065] In various aspects of the present disclosure, Haralick texture analysis can be used to distinguish cancerous and non-cancerous areas in images from low-field unilateral MRI systems. For example, image processing for Haralick texture analysis can be applied to low-field images from low-field unilateral MRI, as described below. Regions of interest (ROIs) suspected of cancer (such as suspicious / cancerous areas in the prostate) can be annotated on T2-weighted images from low-field unilateral MRI. For each cancerous ROI, a secondary ROI of the same size can be drawn on the same slice in a clinically non-suspicious area (e.g., such as non-cancerous tissue also in the prostate), which can be assumed to be normal non-cancerous tissue. The image can be normalized and rescaled to n grayscale bins, where n is between 4 and 256. For each ROI, a GLCM can be calculated in four or eight directions on a transverse 2D slice. Four Haralick texture maps (contrast, energy, correlation, and uniformity) can be created to evaluate the relationship between pixels in suspicious and non-suspicious areas by calculating texture measurements in a local neighborhood using a sliding window technique over the entire prostate region of the image and then averaging the values ​​of the texture maps obtained in the suspicious ROI and non-suspicious ROI.

[0066] In other cases, Haralick texture measurements may be extracted within corresponding ROIs (cancerous and non-suspicious regions).

[0067] Applying Haralick texture analysis to low-field MRI images can distinguish between suspicious (e.g., cancerous) ROIs and non-suspicious (e.g., assumed to be non-cancerous) ROIs. More specifically, texture measurements within a suspicious ROI show a consistent relationship compared to texture measurements within a non-suspicious ROI. For example, energy and uniformity texture features can be elevated within a suspicious region compared to a non-suspicious region, while contrast and correlation texture features can be reduced within a suspicious region compared to a non-suspicious region, as further described herein.

[0068] The aforementioned texture analysis can allow differentiation and characterization of tissues using low-field MRI.

[0069] In one aspect of the present disclosure, T2-weighted images from low-field MRI may be analyzed for textural features indicative of cancerous tissue.

[0070] In one aspect of the present disclosure, cancerous areas in the prostate may be distinguished from normal tissue by applying Haralick texture analysis to low-field T2-weighted images.

[0071] In various aspects of the present disclosure, low-field MRI images may be analyzed using GLCM to obtain texture features, where the grayscale includes 4 to 256 bins, the window size is (5,5)-(49,49) pixels, and the sliding window step is one to ten pixels.

[0072] In one aspect, for the 3T dataset and the low-field dataset, the images can be normalized and rescaled into L grayscale bins using the following formula:

[0073]

[0074] Where I is the image and L is the gray level bins. In various cases, L may be 64, i.e. the image is normalized and rescaled to 64 gray level bins.

[0075] In an exemplary application of the present disclosure, Haralick texture analysis can be applied to distinguish suspicious prostate lesions from normal tissue on low-field MRI. Prostate cancer is the second most commonly diagnosed cancer and the fourth leading cause of cancer death in men. For accurate diagnosis and timely and effective treatment, it may be crucial to accurately identify suspicious areas to obtain biopsies. MR images have been used for targeted prostate biopsies to pre-evaluate whether patients should undergo prostate biopsies and where to undergo biopsies. For example, annotated preoperative MR images (with a certain degree of suspicion assigned (e.g., using the prostate imaging reporting and data system (PI-RADS), version 2 scoring system)) can be co-registered with real-time ultrasound images in a cognitive or electronic manner to provide guidance during biopsy. However, in some cases, ultrasound image fusion biopsy has shown significant shortcomings, such as glandular deformation, steep learning curve and inaccurate registration, which limits its adoption.

[0076] Alternatively, a low-field MRI system (such as the one provided by Promaxo Inc. (Oakland, CA)) can provide an office-based open unilateral scanner that operates at a low, non-uniform B0 field (58-74mT) with nonlinear x-axis and y-axis gradients and a permanent built-in z gradient. The system can be used to guide transperineal prostate biopsy interventions. In various cases, the low-field MRI scanner can acquire images in the transverse direction without a transrectal probe. In addition, the patient can be positioned similar to a high-field (1.5T-3T) MRI. An exemplary unilateral low-field MRI system is further described herein. During a biopsy performed under the guidance of a low-field MRI system, a high-field T2-weighted MR image annotated by a radiologist can be superimposed on the low-field T2-weighted image. The low-field and high-field images can be fused together to directly target abnormal areas seen and / or annotated on the high-field MR image.

[0077] As an example, a radiologist can annotate a suspicious ROI in the prostate on a 3T, T2-weighted image, and the suspicious ROI in the prostate can be assigned a Prostate Imaging Reporting and Data System (PI-RADS) score. The 3T MR image volume and the low-field MR image volume can be strictly co-registered, and the annotations performed by the radiologist are propagated from the 3T image to the co-registered low-field image. Then, for each cancerous ROI, a secondary ROI of the same size can be drawn on the same slice in a clinically non-suspicious area of ​​the prostate that is assumed to be normal tissue. Figure 1 An exemplary co-registered T2-weighted image 50 from 3T MR and low-field MR is shown. Figure 1 In this figure, the patient has Gleason score 4+5 prostate cancer. The cancerous lesion is represented by the circle on the right, and the non-suspicious area with the same radius is marked by the circle on the left.

[0078] Regardless of which imaging modality is used for guidance in targeted biopsy, locating the biopsy location can be challenging because the clinical analysis of MR images is largely qualitative, for example, cancerous areas are identified and annotated by radiologists. However, in various cases, texture analysis can be applied to medical images, as further described herein. Image texture analysis is a technique that is used to extract the local spatial variation frequency of signal intensity to quantify pixel relationships within a region of interest and capture image patterns that the human eye may (and usually does not) distinguish. A common image texture analysis technique is Haralick texture analysis, which can be applied to quantitatively characterize, for example, breast cancer, colon cancer, and rectal cancer, as further described herein. In addition, as further described herein, Haralick texture analysis has been studied for prostate cancer detection on T2-weighted 3T MR images.

[0079] Haralick features of energy, correlation, contrast, and uniformity can be extracted from MR images of the prostate using one or more methods. The first method involves extracting Haralick texture measurements within the corresponding ROIs (cancerous and non-suspicious regions). The second method involves creating four texture maps (contrast, energy, correlation, and uniformity) by calculating texture measurements within a local neighborhood using a sliding window technique over the entire prostate region of the image, and then averaging the values ​​of the texture maps obtained in the cancerous ROI and non-suspicious ROI.

[0080] The evaluated texture features showed consistency in texture measurements for cancerous regions, where energy and homogeneity were elevated and contrast and correlation were reduced in cancerous regions compared to non-suspicious regions within a ROI from the same patient. Thus, several Haralick texture features showed promise for cancer detection in low-field T2-weighted MR images.

[0081] Haralick texture analysis utilizes GLCM, which is a two-dimensional histogram that captures the frequency of co-occurrence of two pixel intensities at a specific offset. GLCM considers the relationship between groups of two pixels in the original image, called reference pixels and neighboring pixels. The values ​​in the GLCM are counts of the frequency of pairs of adjacent image pixel values. GLCM can be symmetric to obtain the best texture calculation performance and overcome the problem of window edge pixels. In this case, symmetry means that the matrix counts each reference pixel together with its neighboring pixels to the right and to the left, so each pixel pair is counted twice, once forward and once backward, with the reference pixel and neighboring pixel swapped in the second count. The GLCM can then be normalized by dividing by the total number of accumulated co-occurrences. In a normalized symmetric GLCM, the diagonal elements all represent pairs of pixels with no grayscale difference, and the farther away from the diagonal, the greater the difference between the pixel grayscales.

[0082] Texture measures are various single values ​​used to summarize the normalized symmetric GLCM in different ways. Robert Haralick proposed fourteen different measures, and these texture features are related to each other. They can be divided into three independent groups - contrast group, order group and descriptive statistics group. The contrast group includes contrast, dissimilarity and uniformity, using weights related to the distance from the diagonal of the GLCM. The order group measures the frequency of occurrence of a given pair of two gray levels within a window. Order features include angular second moment (ASM), energy, maximum probability and entropy. The descriptive statistics group includes GLCM mean, variance and correlation. Contrast, uniformity, energy and correlation are used to distinguish cancer by results in some cases.

[0083] In each case, these measurements can be calculated using the following equations:

[0084] Normalized equation:

[0085]

[0086] where i, j are the row and column numbers. V is the value of cell i,j of the image window. And Pi,j is the recorded value of cell i,j of the normalized GLCM.

[0087] energy:

[0088]

[0089] Contrast ratio:

[0090]

[0091] Uniformity:

[0092]

[0093] Relevance:

[0094]

[0095] where μ is the mean value:

[0096]

[0097] and σ is the variance:

[0098]

[0099] For symmetric GLCMs, the mean and variance calculated using i or j gave the same results.

[0100] A texture image or texture map can then be created. Figure 2 An exemplary texture map is shown in Figure 2 , the upper row 60 depicts a texture map from a high field MR image, and the lower row 62 depicts a texture map from a low field MR image. Cancerous areas are represented by circles on the right, and non-suspicious areas with the same radius are marked by circles on the left.

[0101] To see different pixel-to-pixel relationships in various parts of an image, texture measurements can be calculated using the GLCM derived from a small area on the image at a time. Texture measurements can then be calculated in another small area until the entire image is covered. Creating texture images in this way can help quantitatively assess how pixel relationships vary in different areas.

[0102] In various cases, the texture map can be created by following the steps below: Step one, determine the window size, which is the small area used to fill the GLCM and perform texture measurement calculations. The window size is square and has an odd number of pixels on the sides. Step two, place the window in the first position in the upper left corner of the image. Step three, create a GLCM for the window and normalize it. Step four, calculate the selected texture measurement, which is a single number that represents the entire window. Place this number in the location of the center pixel of the window. Step five, move the window to a predefined distance (usually one pixel) and repeat steps three and four. Step six, continue with all possible window positions until the texture map is complete.

[0103] In various cases, Haralick texture measurements on high-field and low-field MR images can be presented graphically and, in some cases, compared. Figure 3A and Figure 3B , energy, contrast, correlation and uniformity texture values ​​are depicted in graphical representations 70, 80, respectively, for comparing high-field MR images and low-field MR images. Texture values ​​are based on Figure 3A and Figure 3B Nevertheless, using both methods, for cancerous and non-suspicious areas from the same patient, in both high-field MR images and low-field MR images, the contrast texture values ​​and correlation texture values ​​were lower while the energy texture values ​​and homogeneity texture values ​​were higher in the cancerous areas than in the non-suspicious areas.

[0104] Reference now Figure 4, a flow chart 90 is shown, which depicts a validation study technique for comparing Haralick features calculated from 3T MR images and low-field MR images. In this example, the data set includes patients with Gleason score 4+3 prostate cancer (91). The example study includes five patients with a total of seven lesions. The patients underwent a 3T MRI scan (92) and a low-field (58mT-74mT) MRI scan (93) performed on their prostate. A radiologist annotated suspicious ROIs in the prostate on the 3T, T2-weighted images, and the suspicious ROIs in the prostate were assigned a Prostate Imaging Reporting and Data System (PI-RADS) score. The 3T MR image volume and the low-field MR image volume were strictly co-registered, and the annotations performed by the radiologist from the 3T MR image were propagated to the co-registered low-field MR image (94). Then, for each suspicious ROI, a secondary ROI of the same size was drawn on the same slice in a clinically non-suspicious area of ​​the prostate assumed to be normal tissue. The Haralick texture feature was calculated from both the 3T MR images (95) and the low-field MR images (96) in the suspicious ROI and the non-suspicious ROI. Although the values ​​of the Haralick texture feature varied from image to image, the relative texture values ​​showed a pattern. More specifically, referring again to Figure 3A and Figure 3B , in both the high-field MR images and the low-field MR images, the contrast texture values ​​and the correlation texture values ​​are lower while the energy texture values ​​and the homogeneity texture values ​​are higher in the cancerous areas than in the non-suspicious areas.

[0105] An exemplary low-field unilateral MRI system is further described herein. According to various aspects, an MRI system is provided that may include a unique imaging region that may be offset from the front of the magnet. Such offsets and unilateral MRI systems are less restrictive than conventional MRI scanners. In addition, the form factor may have a built-in or inherent magnetic field gradient that creates a range of magnetic field values ​​over the region of interest. In other words, the inherent magnetic field may be inhomogeneous. The inhomogeneity of the magnetic field intensity in the region of interest for a unilateral MRI system may exceed 200 parts per million (200 ppm). For example, the inhomogeneity of the magnetic field intensity in the region of interest for a unilateral MRI system may be between 200 ppm and 200,000 ppm. In various aspects of the present disclosure, the inhomogeneity in the region of interest may be greater than 1,000 ppm and may be greater than 10,000 ppm. In one case, the inhomogeneity in the region of interest may be 81,000 ppm.

[0106] Intrinsic magnetic field gradients can be generated by permanent magnets in MRI scanners. For example, the magnetic field strength in the region of interest for a unilateral MRI system can be less than 1 Tesla (T). For example, the magnetic field strength in the region of interest for a unilateral MRI system can be less than 0.5T. In other cases, for example, the magnetic field strength can be greater than 1T and can be 1.5T. Compared to a typical MRI system, the system can operate at a lower magnetic field strength, allowing relaxation of constraints on RX coil design and / or allowing additional mechanisms (e.g., robots) to be used with MRI scanners. An exemplary MRI-guided robotic system is further described in, for example, international application number PCT / US2021 / 014628, entitled “MRI-GUIDED ROBOTIC SYSTEMS AND METHODS FORBIOPSY”, filed on January 22, 2021.

[0107] Figures 5 to 11 An MRI scanner 100 and its components are depicted. Figure 5 and Figure 6 As shown in , the MRI scanner 100 includes a housing 120 having a recessed and concave front face or front surface 125. In other aspects, the front face of the housing 120 can be flat and planar. The front surface 125 can face the object being imaged by the MRI scanner. Figure 5 and Figure 6 As shown in , the housing 120 includes a permanent magnet assembly 130, an RF transmit coil (TX) 140, a gradient coil assembly 150, an electromagnet 160, and an RF receive coil (RX) 170. In other cases, the housing 120 may not include the electromagnet 160. In addition, in some cases, the RF receive coil 170 and the RF transmit coil 140 may be incorporated into a combined Tx / Rx coil array. In various cases, the MRI scanner 100 is a single-sided scanner, and the various components (e.g., the permanent magnet assembly 130, the RF transmit coil (TX) 140, the gradient coil assembly 150, the electromagnet 160, and the RF receive coil (RX) 170) are positioned on the same side of the field of view.

[0108] Main references Figures 7 to 9 The permanent magnet assembly 130 includes a magnet array. The magnet array forming the permanent magnet assembly 130 is configured to cover the MRI scanner 100 (see Figure 7 ) on a front surface 125 or patient-facing surface and Figure 8 The permanent magnet assembly 130 includes a plurality of cylindrical permanent magnets arranged in parallel. Fig. 9, the permanent magnet assembly 130 includes parallel plates 132 held together by brackets 134. The system can be attached to the housing 120 of the MRI scanner 100 at brackets 136. There can be a plurality of holes 138 in the parallel plates 132. For example, the permanent magnet assembly 130 can include any suitable magnetic material, including but not limited to rare earth-based magnetic materials (such as neodymium-based magnetic materials, for example).

[0109] The permanent magnet assembly 130 defines an access hole or aperture 135 that can enter the patient through the housing 120 from the opposite side of the housing 120. In other aspects of the present disclosure, the permanent magnet array forming the permanent magnet assembly in the housing 120 can be non-porous and define an uninterrupted or continuous arrangement of permanent magnets without defining holes through the permanent magnets. In other cases, the permanent magnet array in the housing 120 can form more than one hole / access hole through the permanent magnet array.

[0110] According to various aspects of the present disclosure, the permanent magnet assembly 130 provides a magnetic field B0 in a region of interest 190 along the Z axis, such as Figure 5 . The Z axis is perpendicular to the permanent magnet assembly 130. In other words, the Z axis extends from the center of the permanent magnet assembly 130 and defines the direction of the magnetic field B0 away from the front face of the permanent magnet assembly 130. The Z axis can define the direction of the main magnetic field B0. The main magnetic field B0 can be along the Z axis away from the front face of the permanent magnet assembly 130 (i.e., the inherent gradient) and in the use Figure 5 decreases in the direction indicated by the arrow.

[0111] In one aspect, the non-uniformity of the magnetic field in the region of interest 190 for the permanent magnet assembly 130 can be about 81,000 ppm. In another aspect, the non-uniformity of the magnetic field strength in the region of interest 190 for the permanent magnet assembly 130 can be between 200 ppm and 200,000 ppm, and can be greater than 1,000 ppm in some cases, and greater than 10,000 ppm in various cases.

[0112] In one aspect, the magnetic field strength of the permanent magnet assembly 130 can be less than 1 T. In another aspect, the magnetic field strength of the permanent magnet assembly 130 can be less than 0.5 T. In other cases, the magnetic field strength of the permanent magnet assembly 130 can be greater than 1 T, and can be, for example, 1.5 T. Figure 5 , the Y axis extends upward and downward from the Z axis, and the X axis extends left and right from the Z axis. The X axis, Y axis, and Z axis are all orthogonal to each other, and the positive direction of each axis is determined by Figure 5 The corresponding arrows in the figure indicate the

[0113] The RF transmit coil 140 may be configured to transmit an RF waveform and associated electromagnetic field. The RF pulses from the RF transmit coil 140 may be configured to rotate the magnetization produced by the permanent magnet 130 by generating an effective magnetic field referred to as B1, which is orthogonal to the direction (eg, orthogonal plane) of the permanent magnetic field.

[0114] Main references Figure 7 , the gradient coil assembly 150 may include two sets of gradient coils 152, 154. The sets of gradient coils 152, 154 may be positioned on the front face or front surface 125 of the permanent magnet assembly 130, intermediate the permanent magnet assembly 130 and the region of interest 190. Each set of gradient coils 152, 154 may include coil portions on opposite sides of the aperture 135. Figure 5 For example, the gradient coil set 154 may be a gradient coil set corresponding to the X axis, and for example, the gradient coil set 152 may be a gradient coil set corresponding to the Y axis. The gradient coils 152, 154 may enable encoding along the X axis and the Y axis, as further described herein.

[0115] Reference now Fig.11 , shows a control schematic diagram for a unilateral MRI system 300. In various aspects of the present disclosure, the unilateral MRI scanner 100 and / or its components ( Figures 5 to 10 ) may be incorporated into the MRI system 300. For example, the imaging system 300 includes a permanent magnet assembly 308, which may be similar to the permanent magnet assembly 130 (see Figures 6 to 9 ). The imaging system 300 may further include an RF transmit coil 310, which may be similar to the RF transmit coil 140 (see Figure 7 ). In addition, the imaging system 300 may include an RF receiving coil 314, which may be similar to the RF receiving coil 170 (see Figure 7 ). In various aspects, the RF transmit coil 310 and / or the RF receive coil may also be positioned in the housing of the MRI scanner, and in some cases, the RF transmit coil 310 and the RF receive coil 314 may be combined into an integrated Tx / Rx coil. The system 300 may also include a gradient coil 320 configured to generate a gradient field to facilitate imaging of an object in the field of view 312.

[0116] The unilateral MRI system 300 may also include a computer 302 in signal communication with a spectrometer 304 and configured to send and receive signals between the computer 302 and the spectrometer 304 .

[0117] The main magnetic field B0 generated by the permanent magnet 308 may extend away from the permanent magnet 308 and away from the RF transmit coil 310 into a field of view 312. The field of view 312 may contain an object being imaged by the MRI system 300.

[0118] During the imaging process, the main magnetic field B0 can extend into the field of view 312. The direction of the effective magnetic field B1 can change in response to RF pulses and associated electromagnetic fields from the RF transmit coil 310. For example, the RF transmit coil 310 is configured to selectively transmit RF signals or pulses to objects, such as tissue, in the field of view. These RF pulses can change the effective magnetic field experienced by spins in the sample (e.g., patient tissue). When the RF pulse is on, the effective field experienced by the spins on resonance can be only the RF pulse, effectively canceling the static B0 field. The RF pulse can be, for example, a chirped or frequency swept pulse, as further described herein.

[0119] In addition, when an object in the field of view 312 is excited with RF pulses from the RF transmit coil 310, the precession of the object can cause induced currents or MR currents, which are detected by the RF receive coil 314. The RF receive coil 314 can send the excitation data to the RF preamplifier 316. The RF preamplifier 316 can boost or amplify the excitation data signals and send them to the spectrometer 304. The spectrometer 304 can send the excitation data to the computer 302 for storage, analysis, and image construction. For example, the computer 302 can combine multiple stored excitation data signals to create an image.

[0120] From the spectrometer 304, the signal may also be relayed to the RF transmit coil 310 via the RF power amplifier 306 and to the gradient coil 320 via the gradient power amplifier 318. The RF power amplifier 306 may amplify the signal and send it to the RF transmit coil 310. The gradient power amplifier 318 may amplify the gradient coil signal and send it to the gradient coil 320.

[0121] For example, described herein are systems and methods for efficiently collecting nuclear magnetic resonance spectra and magnetic resonance images in an inhomogeneous field, such as using a unilateral MRI scanner 100 and system 300 .

[0122] Imaging using unilateral or open MRI can present many challenges. Typically, two sets of gradient coils in a unilateral system (see Fig.10 ) is placed on the front face of the permanent magnet assembly. Thus, the amplitude of the gradient can decrease as one moves away from the front face of the permanent magnet assembly. Thus, for a given phase encoding array, the field of view can change as one moves along the axis of the permanent magnetic field B0. In other words, the pulsed gradient coils in a single-sided scanner can have a small component along the direction of the permanent gradient.

[0123] Fig.12 500 is a schematic diagram of a magnetic field gradient along the Z axis of an MRI scanner 100. The permanent magnet 130 may have an inherent gradient along the Z axis. The strength of the Z gradient may decrease as one moves away from the permanent magnet 130. As can be seen in the schematic diagram, the Z gradient bends as one moves away from the permanent magnet, causing the strength of the gradient to decrease. The MRI scanner 100 may image multiple slices to create a slab. Each slice may be excited at a different frequency for imaging. A lower frequency may excite tissue in slices that are farther from the permanent magnet, and a higher frequency may excite tissue in slices that are closer to the magnet. In the schematic diagram, the slab or axial image consists of slices 0 to 100. n Each slice can have a corresponding frequency f0 to f n , where f0 is less than f n frequency.

[0124] According to various aspects of the present disclosure, the added phase can be compensated by applying phase encoding during a frequency swept or chirped excitation pulse. A frequency swept pulse can affect spins at different frequencies at different times during the pulse. This means that different amounts of phase can also be imparted to different frequencies by applying phase encoding during the excitation pulse. Spins excited at the beginning of the pulse can accumulate more phase than spins excited at the end of the pulse, which can accumulate less phase.

[0125] According to various aspects, if spins farther away from the permanent magnet are excited first, and if phase encoding is applied during a frequency swept excitation pulse, those farther away spins can accumulate more phase than spins closer to the permanent magnet, which can be excited last. This can reverse the normal way that spins accumulate phase from the surface gradient coils, thereby allowing the normal variation in gradient strength along the Z axis to be offset. By precisely tuning the amount of phase accumulated during the frequency swept excitation and during the subsequent phase encoding, a uniform amount of phase can be applied to the XY plane along the Z axis of the permanent magnet.

[0126] Example

[0127] Various aspects of the subject matter described herein are set forth in the following numbered examples.

[0128] Embodiment 1 - A method for identifying a region of interest using a low-field magnetic resonance imaging (MRI) system. The method includes: obtaining a T2-weighted image from a low-field MRI system, wherein the T2-weighted image includes slices; annotating a first region on the slice, wherein the first region corresponds to a suspicious region; and annotating a second region on the slice, wherein the second region corresponds to a non-suspicious region. The second region includes the same size as the first region. The method also includes: calculating a first texture feature value of the first region, calculating a second texture feature value of the second region, and comparing the first texture feature value with the second texture feature value.

[0129] Embodiment 2 - The method of Embodiment 1, wherein the first texture feature value and the second texture feature value correspond to Haralick texture features selected from energy, uniformity, contrast, and correlation.

[0130] Embodiment 3 - The method of Embodiment 1 or 2, further comprising generating a graphical representation comparing the first texture feature value to the second texture feature value.

[0131] Embodiment 4 - The method according to Embodiment 1, 2 or 3 further includes: calculating a plurality of first texture feature values ​​for a first region; and calculating a plurality of second texture feature values ​​for a second region, wherein the plurality of first texture feature values ​​and the second texture feature values ​​correspond to Haralick texture features selected from energy, uniformity, contrast and correlation.

[0132] Embodiment 5 - The method according to embodiment 1, 2, 3 or 4 also includes generating a gray level co-occurrence matrix for the slice.

[0133] Embodiment 6 - A method according to Embodiment 5, wherein the gray level co-occurrence matrix is ​​calculated using 4 to 256 bins.

[0134] Embodiment 7 - The method according to embodiment 5 or 6 also includes generating a texture map from a gray level co-occurrence matrix.

[0135] Embodiment 8 - A method according to Embodiment 5, 6 or 7, wherein calculating the first texture feature value includes calculating an average first value using a sliding window technique in a gray level co-occurrence matrix.

[0136] Embodiment 9 - A method according to Embodiment 8, wherein the sliding window technique includes a sliding window size between 5×5 pixels and 49×49 pixels, and wherein the sliding window technique also includes a sliding window stride between one pixel and ten pixels.

[0137] Embodiment 10 - A system comprising: a unilateral low-field MRI system comprising a magnet array configured to generate a permanent non-uniform B0 magnetic field in a region of interest offset from the magnet array; and a control circuit. The control circuit is configured to: generate a T2-weighted image from the unilateral low-field MRI; identify a first region on the T2-weighted image, wherein the first region corresponds to a suspicious region; and identify a second region on the T2-weighted image. The second region corresponds to a non-suspicious region, and wherein the second region comprises the same size as the first region. The control circuit is configured to: calculate a first texture feature value for the first region, calculate a second texture feature value for the second region, and compare the first texture feature value with the second texture feature value. The system also includes a display configured to convey a comparison of the first texture feature value with the second texture feature value.

[0138] Example 11 - A system according to Example 10, wherein the unilateral low-field MRI system also includes a shell including a front side, wherein a first axis extends through the front side into the region of interest, and wherein a permanent, non-uniform B0 magnetic field extends from the permanent magnet array into the region of interest relative to the first axis.

[0139] Embodiment 12 - A system according to Embodiment 10 or 11, wherein the permanent non-uniform B0 magnetic field comprises a magnetic field strength of less than 100 mT in the region of interest.

[0140] Embodiment 13 - A system according to Embodiment 10 or 11, wherein the permanent non-uniform B0 magnetic field comprises a magnetic field strength between 58 mT and 74 mT in the region of interest.

[0141] Example 14 - A system according to Example 10, 11, 12 or 13, wherein the unilateral low-field MRI system further comprises: a gradient coil group, at least one radio frequency coil, a power supply circuit and a memory, wherein the control circuit communicates signals with the gradient coil group, at least one radio frequency coil, the power supply circuit and the memory.

[0142] Embodiment 15 - A system according to Embodiment 10, 11, 12, 13 or 14, wherein the first texture feature value and the second texture feature value correspond to Haralick texture features selected from energy, uniformity, contrast and correlation.

[0143] Embodiment 16 - A system according to Embodiment 10, 11, 12, 13, 14 or 15, wherein the control circuit is further configured to: calculate a plurality of texture feature values ​​for the first region, and calculate a plurality of texture feature values ​​for the second region. The plurality of texture feature values ​​correspond to Haralick texture features selected from energy, uniformity, contrast and correlation.

[0144] Example 17 - A system according to Example 10, 11, 12, 13, 14, 15 or 16, wherein the comparison includes a graphical representation.

[0145] Example 18 - A system according to Example 10, 11, 12, 13, 14, 15, 16 or 17, wherein the control circuit is also configured to generate a gray level co-occurrence matrix, and wherein the gray level co-occurrence matrix is ​​calculated using 4 to 256 bins.

[0146] Embodiment 19 - A system according to Embodiment 18, wherein the control circuit is further configured to: generate a texture map from a gray level co-occurrence matrix, and calculate an average first value in the gray level co-occurrence matrix using a sliding window technique. The sliding window technique includes a sliding window size between 5×5 pixels and 49×49 pixels, and wherein the sliding window technique also includes a sliding window stride between one pixel and ten pixels.

[0147] Although several forms have been illustrated and described, the applicant has no intention of limiting or restricting the scope of the appended claims to these details. Without departing from the scope of the present disclosure, it will be apparent to those skilled in the art that many modifications, variations, changes, substitutions, combinations and equivalents to these forms may be realized and will occur. In addition, the structure of each element associated with the described form may alternatively be described as a means for providing the function performed by the element. In addition, in the case of materials for certain components being disclosed, other materials may be used. Therefore, it should be understood that the foregoing description and the appended claims are intended to cover all such modifications, combinations and variations within the scope of the disclosed forms. The appended claims are intended to cover all such modifications, variations, changes, substitutions, modifications and equivalents.

[0148] The above detailed description illustrates various forms of devices and / or processes by using block diagrams, flow charts and / or examples. To the extent that such block diagrams, flow charts and / or examples contain one or more functions and / or operations, those skilled in the art will understand that each function and / or operation in such block diagrams, flow charts and / or examples can be implemented individually and / or collectively by a wide range of hardware, software, firmware, or any combination thereof. Those skilled in the art will recognize that some aspects, all or part of the forms disclosed herein can be equivalently implemented in an integrated circuit as one or more computer programs operating on one or more computers (e.g., implemented as one or more programs operating on one or more computer systems), implemented as one or more programs operating on one or more processors (e.g., implemented as one or more programs operating on one or more microprocessors), implemented as firmware, or implemented as any combination thereof, and those skilled in the art will recognize that, according to the present disclosure, designing circuits and / or writing codes for software and / or firmware will be completely within the skill of those skilled in the art. Furthermore, those skilled in the art will appreciate that the mechanisms of the subject matter described herein can be distributed in a variety of forms as one or more program products and that the illustrative forms of the subject matter described herein apply regardless of the specific type of signal bearing medium used to actually perform the distribution.

[0149] Instructions for programming the logic to perform various disclosed aspects may be stored in a memory (such as a dynamic random access memory (DRAM), cache, flash memory, or other memory) in the system. In addition, the instructions may be distributed via a network or through other computer-readable media. Therefore, a machine-readable medium may include any mechanism for storing or transmitting information in a machine (e.g., a computer) readable form, but is not limited to floppy disks, optical disks, optical disks, read-only memories (CD-ROMs) and magneto-optical disks, read-only memories (ROMs), random access memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), magnetic or optical cards, flash memory, or tangible machine-readable memories for transmitting information via the Internet via electrical, optical, acoustic, or other forms of propagation signals (e.g., carrier waves, infrared signals, digital signals, etc.). Therefore, a non-transitory computer-readable medium includes any type of tangible machine-readable medium suitable for storing or transmitting electronic instructions or information in a machine (e.g., a computer) readable form.

[0150] As used in any aspect herein, the term "control circuitry" may refer to, for example, hard-wired circuitry, programmable circuitry (e.g., a computer processor including one or more separate instruction processing cores, a processing unit, a processor, a microcontroller, a microcontroller unit, a controller, a digital signal processor (DSP), a programmable logic device (PLD), a programmable logic array (PLA), or a field programmable gate array (FPGA)), a state machine circuit, firmware storing instructions executed by the programmable circuitry, and any combination thereof. The control circuitry may be embodied collectively or individually as circuitry forming part of a larger system, such as an integrated circuit (IC), an application specific integrated circuit (ASIC), a system on a chip (SoC), a desktop computer, a laptop computer, a tablet computer, a server, a smart phone, etc. Thus, as used herein, "control circuitry" includes, but is not limited to, circuitry having at least one discrete circuit, circuitry having at least one integrated circuit, circuitry having at least one application specific integrated circuit, circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program that at least partially performs the processes and / or devices described herein, or a microprocessor configured by a computer program that at least partially performs the processes and / or devices described herein), circuitry forming a memory device (e.g., in the form of random access memory), and / or circuitry forming a communication device (e.g., a modem, a communication switch, or an optoelectronic device). Those skilled in the art will recognize that the subject matter described herein may be implemented in an analog or digital manner, or some combination thereof.

[0151] As used in any aspect herein, the term "logic" may refer to an application, software, firmware, and / or circuitry configured to perform any of the above operations. Software may be embodied as a software package, code, instructions, instruction sets, and / or data recorded on a non-transitory computer-readable storage medium. Firmware may be embodied as code, instructions, instruction sets, and / or data hard-coded (e.g., non-volatile) in a memory device.

[0152] As used in any aspect herein, the terms "component," "system," "module" and the like may refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution.

[0153] As used in any aspect herein, an "algorithm" refers to a self-consistent sequence of steps leading to a desired result, where a "step" refers to manipulations of physical quantities and / or logical states which may (although need not) take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. These signals are often referred to as bits, values, elements, symbols, characters, terms, numbers, etc. These and similar terms may be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities and / or states.

[0154] The network may include a packet switching network. The communication devices may be able to communicate with each other using a selected packet switching network communication protocol. An example communication protocol may include an Ethernet communication protocol, which may allow communication using a transmission control protocol / Internet protocol (TCP / IP). The Ethernet protocol may conform to or be compatible with the Ethernet standard entitled "IEEE 802.3 Standard" issued by the Institute of Electrical and Electronics Engineers (IEEE) in December 2008 and / or subsequent versions of the standard. Alternatively or additionally, the communication devices may communicate with each other using an X.25 communication protocol. The X.25 communication protocol may conform to or be compatible with standards promulgated by the International Telecommunication Union-Telecommunication Standardization Sector (ITU-T). Alternatively or additionally, the communication devices may be able to communicate with each other using a frame relay communication protocol. The frame relay communication protocol may conform to or be compatible with standards promulgated by the International Telegraph and Telephone Consultative Committee (CCITT) and / or the American National Standards Institute (ANSI). Alternatively or additionally, the transceivers may be able to communicate with each other using an asynchronous transfer mode (ATM) communication protocol. The ATM communication protocol may conform to or be compatible with the ATM standard entitled "ATM-MPLS Network Interworking 2.0" released by the ATM Forum in August 2001 and / or subsequent versions of the standard. Of course, different and / or later developed connection-oriented network communication protocols are also contemplated herein.

[0155] Unless otherwise clearly indicated from the foregoing disclosure, it should be understood that throughout the foregoing disclosure, discussions using terms such as "process," "compute," "calculate," "determine," "display," etc. refer to the actions and processes of a computer system or similar electronic computing device that manipulates data represented as physical (electronic) quantities within the computer system's registers and memories and transforms that data into other data similarly represented as physical quantities within the computer system's memories or registers or other such information storage, transmission, or display devices.

[0156] One or more components may be referred to herein as being "configured to," "configurable to," "operable / operable to," "adaptive / adaptable," "capable of," "suitable / compliant with," etc. Those skilled in the art will recognize that "configured to" may generally include active state components and / or inactive state components and / or standby state components, unless the context requires otherwise.

[0157] The terms "proximal" and "distal" are used herein with respect to the handle portion or housing through which a clinician manipulates a surgical instrument. The term "proximal" refers to the portion closest to the clinician and / or the robotic arm, and the term "distal" refers to the portion positioned away from the clinician and / or the robotic arm. It should also be understood that for convenience and clarity, spatial terms such as "vertical," "horizontal," "upward," and "downward" may be used herein with respect to the accompanying drawings. However, robotic surgical tools are used in many orientations and positions, and these terms are not intended to be limiting and / or are not absolute.

[0158] Those skilled in the art will recognize that, in general, the terms used herein, and especially in the appended claims (e.g., the bodies of the appended claims), are generally intended to be “open-ended” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “comprising” should be interpreted as “including but not limited to,” etc.). Those skilled in the art will further understand that if a specific number of an introduced claim recitation is intended, such intent will be explicitly recited in the claim, and in the absence of such recitation, no such intent is present. For example, as an aid to understanding, the following appended claims are The use of the introductory phrases "at least one" and "one or more" may be included to introduce claim recitations. However, the use of such phrases should not be interpreted as implying that a claim recitation introduced by the indefinite article "a" or "an" limits any particular claim containing such introduced claim recitation to claims containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and an indefinite article such as "a" or "an" (e.g., "a" and / or "an" should generally be interpreted as meaning "at least one" or "one or more"); the same applies to the use of definite articles used to introduce claim recitations.

[0159] Furthermore, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should generally be interpreted to mean at least the recited number (e.g., a mere recitation of "two recitations" without other modifiers generally means at least two recitations, or two or more recitations). Furthermore, where a convention similar to "at least one of A, B, and C, etc." is used, generally such construction is intended in the sense that those skilled in the art will understand the convention (e.g., "a system having at least one of A, B, and C" would include, but is not limited to, systems having A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). Where a convention similar to "at least one of A, B, or C, etc." is used, generally such construction is intended in the sense that those skilled in the art will understand the convention (e.g., "a system having at least one of A, B, or C" would include, but is not limited to, systems having A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). Those skilled in the art will further understand that, whether in the specification, claims or drawings, disjunctive words and / or phrases generally indicating two or more optional terms should be understood to include the possibility of one, any or both of these terms, unless the context dictates otherwise. For example, the phrase "A or B" will generally be understood to include the possibility of "A" or "B" or "A and B".

[0160] With respect to the appended claims, it will be appreciated by those skilled in the art that the operations described therein may generally be performed in any order. In addition, although various operational flow charts are presented in (one or more) sequences, it will be understood that the various operations may be performed in other orders than the order shown, or may be performed simultaneously. Examples of such alternating orderings may include overlapping, interleaving, interrupting, reordering, increasing, preparatory, supplementing, simultaneous, reverse or other variations of ordering, unless the context otherwise dictates. In addition, terms such as "responsive to," "related to," or other past tense adjectives are generally not intended to exclude such variations, unless the context otherwise dictates.

[0161] It is worth noting that any reference to "one aspect," "an aspect," "an example," "an example," etc. means that a particular feature, structure, or characteristic described in conjunction with that aspect is included in at least one aspect. Thus, the appearance of the phrases "in one aspect," "in an aspect," "in an example," and "in an example" in various places throughout the specification are not necessarily all referring to the same aspect. Furthermore, in one or more aspects, the particular features, structures, or characteristics may be combined in any suitable manner.

[0162] Any patent application, patent, non-patent publication, or other public material referred to in this specification and / or listed in any Application Data Sheet is incorporated herein by reference to the extent that the incorporated material is not inconsistent with this document. Therefore, to the extent necessary, the disclosure explicitly set forth herein supersedes any conflicting material incorporated herein by reference. Any material or portion thereof that is stated to be incorporated herein by reference but conflicts with existing definitions, statements, or other public material set forth herein will be incorporated only to the extent that there is no conflict between the incorporated material and the existing public material.

[0163] In summary, many benefits resulting from the adoption of the concepts described herein have been described. The above description of one or more forms has been given for purposes of illustration and description. It is not intended to be exhaustive or limited to the precise forms disclosed. Modifications or variations are possible in light of the above teachings. One or more forms are selected and described in order to illustrate the principles and practical applications, thereby enabling one of ordinary skill in the art to utilize various forms and make various modifications suitable for the intended specific use. The claims submitted herein are intended to define the entire scope.

Claims

1. A method for identifying a region of interest using a low-field magnetic resonance imaging (MRI) system, the method comprising: obtaining a T2-weighted image from the low-field MRI system, wherein the T2-weighted image comprises slices; annotating a first region on the slice, wherein the first region corresponds to a suspicious region; annotating a second region on the slice, wherein the second region corresponds to a non-suspicious region, and wherein the second region comprises the same size as the first region; Calculating a first texture feature value of the first area; Calculating a second texture feature value of the second area; as well as The first texture feature value is compared with the second texture feature value. 2 . The method of claim 1 , wherein the first texture feature value and the second texture feature value correspond to Haralick texture features selected from energy, uniformity, contrast, and correlation.

3. The method of any one of claims 1 and 2, further comprising generating a graphical representation comparing the first texture feature value to the second texture feature value.

4. The method according to claim 1, further comprising: Calculating a plurality of first texture feature values ​​of the first area; as well as A plurality of second texture feature values ​​of the second region are calculated, wherein the plurality of first texture feature values ​​and second texture feature values ​​correspond to Haralick texture features selected from energy, uniformity, contrast, and correlation. The method of claim 1 , further comprising generating a gray level co-occurrence matrix for the slices. The method according to claim 5 , wherein the gray level co-occurrence matrix is ​​calculated using 4 to 256 bins.

7. The method according to any one of claims 5 and 6, further comprising generating a texture map from the gray level co-occurrence matrix.

8. The method of claim 7, wherein calculating the first texture feature value comprises calculating an average first value in the gray level co-occurrence matrix using a sliding window technique.

9. The method of claim 8, wherein the sliding window technique comprises a sliding window size between 5x5 pixels and 49x49 pixels, and wherein the sliding window technique further comprises a sliding window stride between one pixel and ten pixels.

10. A system comprising: A unilateral low-field MRI system comprising a magnet array configured to generate a permanent non-uniform B0 magnetic field in a region of interest offset from the magnet array; A control circuit, the control circuit being configured to: generating a T2-weighted image from the unilateral low-field MRI; identifying a first region on the T2-weighted image, wherein the first region corresponds to a suspicious region; identifying a second region on the T2-weighted image, wherein the second region corresponds to a non-suspicious region, and wherein the second region comprises the same size as the first region; Calculating a first texture feature value of the first area; Calculating a second texture feature value of the second area; and comparing the first texture feature value with the second texture feature value; as well as A display configured to communicate a comparison of the first texture feature value and the second texture feature value.

11. The system of claim 10, wherein the unilateral low-field MRI system further comprises a housing comprising a front face, wherein a first axis extends through the front face into the region of interest, and wherein the permanent, non-uniform B0 magnetic field extends from the permanent magnet array into the region of interest relative to the first axis.

12. The system according to any one of claims 10 and 11, wherein the permanent inhomogeneous B0 magnetic field comprises a magnetic field strength of less than 100 mT in the region of interest.

13. The system according to any one of claims 10 and 11, wherein the permanent inhomogeneous B0 magnetic field comprises a magnetic field strength between 58 mT and 74 mT in the region of interest.

14. The system according to any one of claims 10 and 11, wherein the unilateral low-field MRI system further comprises: Gradient coil assembly; at least one radio frequency coil; Power supply circuit; as well as Memory; The control circuit is in signal communication with the gradient coil assembly, the at least one radio frequency coil, the power supply circuit, and the memory.

15. The system of any one of claims 10 and 11, wherein the first texture feature value and the second texture feature value correspond to Haralick texture features selected from energy, uniformity, contrast, and correlation.

16. The system according to any one of claims 10 and 11, wherein the control circuit is further configured to: Calculating a plurality of texture feature values ​​of the first region; and A plurality of texture feature values ​​of the second region are calculated, wherein the plurality of texture feature values ​​correspond to Haralick texture features selected from energy, uniformity, contrast, and correlation.

17. The system of any one of claims 10 and 11, wherein the comparison comprises a graphical representation.

18. The system of any one of claims 10 and 11, wherein the control circuit is further configured to generate a gray level co-occurrence matrix, and wherein the gray level co-occurrence matrix is ​​calculated using 4 to 256 bins.

19. The system of claim 18, wherein the control circuit is further configured to: generating a texture map from the gray level co-occurrence matrix; and The average first value is calculated in the gray level co-occurrence matrix using a sliding window technique, wherein the sliding window technique includes a sliding window size between 5×5 pixels and 49×49 pixels, and wherein the sliding window technique also includes a sliding window step between one pixel and ten pixels.

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