Low Magnetic Field MRI Texture Analysis

Abstract

A system and method for identifying a target region using a low-field magnetic resonance imaging (MRI) system are disclosed. The method includes obtaining a T2-weighted image including a slice from the low-field MRI system, annotating a first region corresponding to a suspicious region on the slice, and annotating a second region corresponding to a non-suspicious region on the slice. The second region is 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 with the second texture feature value.

Description

Technical Field

[0001] This application claims priority to U.S. Provisional Application No. 63 / 369,301, filed Jul. 25, 2022, which is incorporated by reference in its entirety for all purposes.

Background Art

[0002] Texture analysis of images can be used to identify various tissue types.

Summary of the Invention

[0003] In one general aspect, the present disclosure provides an exemplary method of identifying a target region using a low-field magnetic resonance imaging (MRI) system. The exemplary method may include obtaining a T2-weighted image, which may include slices, from the low-field MRI system; annotating a first region on the slice that may correspond to a suspicious region; and annotating a second region on the slice that may correspond to a non-suspicious region. The second region may be the same size as the first region. The method may further 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 with the second texture feature value.

[0004] In another aspect, the present disclosure provides a low-field MRI system. An exemplary system may include an array of magnets configured to generate a permanent non-uniform B0 magnetic field within a target region offset from the array of magnets, and a control circuit. The control circuit may be configured to generate a T2-weighted image from a single-sided low-field MRI, identify a first region on the T2-weighted image that may correspond to a suspicious region, and identify a second region on the T2-weighted image. The second region may correspond to an unsuspicious region and may be 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 to the second texture feature value. The system may further include a display configured to convey the comparison of the first texture feature value and the second texture feature value. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The novel features of the various aspects are set forth with particularity in the appended claims. However, aspects of the mechanisms and methods of operation may be best understood in conjunction with the following description taken in conjunction with the accompanying drawings.

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[0018] The accompanying drawings are not intended to be drawn to scale. Throughout the several views, corresponding reference numerals indicate corresponding parts. For clarity, not all components are labeled in each figure. The examples described herein illustrate certain embodiments of the invention in one form, and such examples should not be construed as limiting the scope of the invention in any way.

Embodiments for Carrying Out the Invention

[0019] The following international patent applications are hereby incorporated by reference in their entirety. · International Patent Application No. PCT / US2020 / 018352, filed on February 14, 2020, with the title "SYSTEMS AND METHODS FOR ULTRALOW FIELD RELAXATION DISPERSION", corresponding to the current International Publication No. WO2020 / 168233 · International Patent Application No. PCT / US2020 / 019530, filed on February 24, 2020, with the title "SYSTEMS AND METHODS FOR PERFORMING MAGNETIC RESONANCE IMAGING", corresponding to the current International Publication No. WO2020 / 172673 · International Patent Application No. PCT / US2020 / 019524, filed on February 24, 2020, with the title "PSEUDO-BIRDCAGE COIL WITH VARIABLE TUNING AND APPLICATIONS THEREOF", corresponding to the current International Publication No. WO2020 / 172672 · International Patent Application No. PCT / US2020 / 024776, filed on March 25, 2020, with the title "SINGLE-SIDED FAST MRI GRADIENT FIELD COILS AND APPLICATIONS THEREOF", corresponding to the current International Publication No. WO2020 / 198395 · Corresponding to the current International Publication No. WO2020 / 198396, the International Patent Application No. PCT / US2020 / 024778 filed on March 25, 2020, with the title of invention "SYSTEMS AND METHODS FOR VOLUMETRIC ACQUISITION IN A SINGLE-SIDED MRI SYSTEM" · Corresponding to the current International Publication No. WO2020 / 264194, the International Patent Application No. PCT / US2020 / 039667 filed on June 25, 2020, with the title of invention "SYSTEMS AND METHODS FOR IMAGE RECONSTRUCTIONS IN MAGNETIC RESONANCE IMAGING" · Corresponding to the current International Publication No. WO2021 / 150902, the International Patent Application No. PCT / US2021 / 014628 filed on January 22, 2021, with the title of invention "MRI-GUIDED ROBOTIC SYSTEMS AND METHODS FOR BIOPSY" · Corresponding to the current International Publication No. WO2021 / 168291, the International Patent Application No. PCT / US2021 / 018834 filed on February 19, 2021, with the title of invention "RADIO FREQUENCY RECEPTION COIL NETWORKS FOR SINGLE-SIDED MAGNETIC RESONANCE IMAGING" · Corresponding to the current International Publication No. WO2021 / 183484, the International Patent Application No. PCT / US2021 / 021464 filed on March 9, 2021, with the title of invention "PHASE ENCODING WITH FREQUENCY SWEEP PULSES FOR MAGNETIC RESONANCE IMAGING IN INHOMOGENEOUS MAGNETIC FIELDS" · International Patent Application No. PCT / US2021 / 021461, filed on March 9, 2021, with the title "PULSE SEQUENCES AND FREQUENCY SWEEP PULSES FOR SINGLE-SIDED MAGNETIC RESONANCE IMAGING", corresponding to the current International Publication No. WO2021 / 183482 · International Patent Application No. PCT / US2021 / 021464, filed on March 9, 2021, with the title "PHASE ENCODING WITH FREQUENCY SWEEP PULSES FOR MAGNETIC RESONANCE IMAGING IN INHOMOGENEOUS MAGNETIC FIELDS", corresponding to the current International Publication No. WO2021 / 183484 · International Patent Application No. PCT / US2022 / 071924, filed on April 26, 2022, with the title "LOCALIZATION GUIDE AND METHOD FOR MRI GUIDED PELVIC INTERVENTIONS", is also incorporated herein by reference in its entirety.

[0020] The following U.S. provisional patent applications are incorporated herein by reference in their entireties. - U.S. Provisional Patent Application No. 62 / 806,664, filed on February 15, 2019, with the title "SYSTEMS AND METHODS FOR ULTRALOW FIELD RELAXATION DISPERSION" - U.S. Provisional Patent Application No. 62 / 809,503, filed on February 22, 2019, with the title "PSEUDO-BIRDCAGE COIL WITH VARIABLE TUNING AND APPLICATIONS THEREOF" - U.S. Provisional Patent Application No. 62 / 823,521, filed on March 25, 2019, with the title "SINGLE-SIDED FAST MRI GRADIENT FIELD COILS AND APPLICATIONS THEREOF" - 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" - U.S. Provisional Patent Application No. 62 / 965,070, filed on January 23, 2020, entitled "GUIDED ROBOTIC SYSTEM, METHODS AND APPARATUS FOR BIOPSY" - U.S. Provisional Patent Application No. 62 / 979,332, filed on February 20, 2020, entitled "SYSTEM AND METHODS FOR UTILIZING A RADIO FREQUENCY RECEIVE NETWORK FOR SINGLE-SIDED MAGNETIC RESONANCE IMAGING" - U.S. Provisional Patent Application No. 62 / 987,286, filed on March 9, 2020, entitled "SYSTEMS AND METHODS FOR ADAPTING DRIVEN EQUILIBRIUM FOURIER TRANSFORM FOR SINGLE-SIDED MRI" - U.S. Provisional Patent Application No. 62 / 987,292, filed on March 9, 2020, entitled "SYSTEMS AND METHODS FOR LIMITING K-SPACE TRUNCATION IN A SINGLE-SIDED MRI SCANNER" - U.S. Provisional Patent Application No. 62 / 823,511, filed on March 25, 2019, entitled "SYSTEMS AND METHODS FOR VOLUMETRIC ACQUISITION IN A SINGLE-SIDED MRI SCANNER" - U.S. Provisional Patent Application No. 63 / 180,013, filed on April 26, 2021, entitled "INTERVENTIONAL LOCALIZATION GUIDE AND METHOD FOR MRI GUIDED PELVIC INTERVENTIONS" - U.S. Provisional Patent Application No. 63 / 266,383, filed on January 4, 2022, entitled "RELAXATION-BASED MAGNETIC RESONANCE THERMOMETRY WITH A LOW-FIELD SINGLE-SIDED MRI SCANNER" - U.S. Provisional Patent Application No. 63 / 367,787, filed on July 6, 2022, entitled "BIOPSY DEVICES AND METHODS"

[0021] The following U.S. patent applications are hereby incorporated by reference in their entirety. - U.S. Patent Application Publication No. 2018 / 0356480, published on December 13, 2018, entitled "UNILATERAL MAGNETIC RESONANCE IMAGING SYSTEM WITH APERTURE FOR INTERVENTIONS AND METHODOLOGIES FOR OPERATING SAME" - U.S. Patent Application Publication No. 2022 / 0146613, published on May 12, 2022, entitled "SYSTEMS AND METHODS FOR ULTRALOW FIELD RELAXATION DISPERSION" - U.S. Patent Application Publication No. 2022 / 0043084, published on February 10, 2022, entitled "PSEUDO-BIRDCAGE COIL WITH VARIABLE TUNING AND APPLICATIONS THEREOF" - U.S. Patent Application Publication No. 2022 / 0113361, published on April 14, 2022, entitled "SYSTEMS AND METHODS FOR PERFORMING MAGNETIC RESONANCE IMAGING" - U.S. Patent Application Publication No. 2022 / 0091207, published on March 24, 2022, entitled "SINGLE-SIDED FAST MRI GRADIENT FIELD COILS AND APPLICATIONS THEREOF" - U.S. Patent Application No. 17 / 596,610, filed on December 14, 2021, entitled "SYSTEMS AND METHODS FOR IMAGE RECONSTRUCTION IN MAGNETIC RESONANCE IMAGING" - U.S. Patent Application No. 17 / 596,610, filed on December 14, 2021, entitled "SYSTEMS AND METHODS FOR IMAGE RECONSTRUCTION IN MAGNETIC RESONANCE IMAGING" - U.S. Patent Application No. 17 / 660,709, filed on April 26, 2022, entitled "INTERVENTIONAL LOCALIZATION GUIDE AND METHOD FOR MRI GUIDED PELVIC INTERVENTIONS"

[0022] The following U.S. design applications are hereby incorporated by reference in their entirety. - U.S. Design Application No. 29 / 681,014, filed on February 21, 2019, entitled "ANALYTICAL DEVICE", corresponding to the current U.S. Design Patent D895,803 issued on September 8, 2020 - U.S. Design Application No. 29 / 744,371, filed on July 28, 2020, entitled "ANALYTICAL DEVICE", corresponding to the current U.S. Design Patent D942,012 issued on January 25, 2022 - U.S. Design Application No. 29 / 790,895, filed on December 20, 2021, entitled "ANALYTICAL DEVICE"

[0023] Before detailing various aspects of the MRI system and method, it should be noted that the exemplary embodiments are not limited to the application or use in the configuration and arrangement details of the components illustrated in the accompanying drawings and description. The exemplary embodiments may be implemented or incorporated in other aspects, variations, and modifications and may be implemented or executed in various ways. Further, unless otherwise specified, the terms and expressions used herein are selected for the purpose of explaining the exemplary embodiments for the convenience of the reader and are not intended for limitation. Also, it will be understood that one or more of the aspects, expressions of aspects, and / or embodiments described below can be combined with any one or more of the other aspects, expressions of aspects, and / or embodiments described below.

[0024] Radiomics is the quantitative extraction and analysis of extractable data from medical images. Radiomics can be used to identify various types of tissues. For example, radiomics can be used to detect and classify prostate lesions. Radiomics involves extracting normal quantitative features, i.e., radiomic features, from radiographic images that are not visible to the naked eye of a radiologist. For example, radiomic features can include texture features such as energy, entropy, correlation, homogeneity, and inertia. Haralick texture features are calculated from a gray level co-occurrence matrix (GLCM), which is a matrix defined for an image in which the co-occurring pixel values (gray scale values or colors) at a given offset are distributed. The GLCM is used as an approach to texture analysis and is used in particular with various applications in medical image analysis, and the features generated using this technique are commonly referred to as Haralick features after Robert Haralick. The Haralick features extract the frequency of local spatial variations in signal intensity within an image and quantify the pixel relationships within the target region of the image. For example, the Haralick features can be determined by identifying / counting the co-occurrence of neighboring gray levels within the image.

[0025] Research on the Haralick texture analysis of prostate MRI has been limited to cancer detection related to high-field MRI. High-field MRI is an electromagnetic field greater than 1.5T. Generally, high-field MRI is an electromagnetic field of 1.5T to 3T. The following documents are hereby incorporated by reference in their entirety. · Cutaia, Giuseppe et al. “Radiomics and Prostate MRI: Current Role and Future Applications.” Journal of imaging vol. 7,2 34. 11 Feb. 2021 · 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 vol. 11,1 2085. 22 Jan. 2021 · Baek, Tae Wook et al. “Texture analysis on bi-parametric MRI for evaluation of aggressiveness in patients with prostate cancer.” Abdominal radiology (New York) vol. 45,12 (2020): 4214-4222 · Niu, Xiang-Ke et al. “Clinical Application of Biparametric MRI Texture Analysis for Detection and Evaluation of High-Grade Prostate Cancer in Zone-Specific Regions.” AJR. American journal of roentgenology vol. 210, 3 (2018): 549-556, and · 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.” European radiology vol. 25, 10 (2015): 2840-50

[0026] In various cases, low-field MRI is preferred over high-field MRI, as further described herein. For example, a low-field MRI system may be preferred in certain cases because it has a smaller footprint and / or lower shielding requirements than a high-field MRI system. Additionally, low-field MRI can be an open concept compared to high-field MRI. For example, according to single-sided low-field MRI, the patient experience provided can be improved, and accessibility by clinicians and / or surgical robots can be enhanced. International Patent Application No. PCT / US2021 / 014628, entitled “MRI-GUIDED ROBOTIC SYSTEMS AND METHODS FOR BIOPSY,” filed on January 22, 2021, the entire contents of which are incorporated herein by reference, describes, for example, an MRI-guided biopsy procedure.

[0027] However, low-field MR images are significantly different from high-field MR images. For example, T2 contrast is affected by the magnetic field strength. The noise pattern is also different between low-field MR images and high-field MR images. More specifically, in the noise of high-field MRI scanners, the object being imaged is usually dominant, and the noise from hardware is additive. In low fields, object noise can be ignored, and hardware components such as RF coils and spectrometers can become dominant in the overall noise.

[0028] In various aspects of the present disclosure, by using Haralick texture analysis, cancerous regions and non-cancerous regions can be distinguished in images from a low-field single-sided MRI system. For example, image processing for Haralick texture analysis can be applied to low-field images from a low-field single-sided MRI as follows. An annotation can be made on a T2-weighted image from a low-field single-sided MRI for a target region (ROI: region of interest) suspected of cancer (e.g., a suspected / cancerous region of the prostate, etc.). For each cancerous ROI, a secondary ROI of the same size can be drawn on the same slice in a region that is clinically not suspicious and can be presumed to be normal non-cancerous tissue (e.g., non-cancerous tissue of the prostate as well). By normalizing and rescaling the image, it can be made into n gray-level bins, where n is from 4 to 256. For each ROI, the GLCM can be calculated in 4 or 8 directions on a transverse 2D slice. By creating four Haralick texture maps (contrast, energy, correlation, and homogeneity), the pixel-to-pixel relationship between the suspicious region and the non-suspicious region can be evaluated, which is done by using a sliding window technique to calculate the texture scale within a local neighborhood over the entire prostate region of the image and then averaging the values of the resulting texture maps for the suspicious ROI and the non-suspicious ROI.

[0029] In other cases, the Haralick texture scale can be extracted within each ROI (cancerous region and non-suspicious region).

[0030] By applying Haralick texture analysis to low-field MRI images, suspicious (e.g., cancerous) ROIs and non-suspicious (e.g., presumed to be non-cancerous) ROIs can be distinguished. More specifically, by comparing the values of texture metrics within a suspicious ROI with the values of texture metrics of a non-suspicious ROI, a consistent relationship becomes apparent. For example, texture features such as energy and homogeneity increase within a suspicious region when compared to a non-suspicious region, and texture features such as contrast and correlation may decrease within a suspicious region when compared to a non-suspicious region, as further described herein.

[0031] According to the above texture analysis, it becomes possible to discriminate and characterize tissues using low-field MRI.

[0032] In one aspect of the present disclosure, T2-weighted images from low-field MRI can be analyzed for texture features indicative of cancerous tissue.

[0033] In one aspect of the present disclosure, by applying Haralick texture analysis to low-field T2-weighted images, cancerous regions within the prostate (one or more) can be distinguished from normal tissue.

[0034] In various aspects of the present disclosure, low-field MRI images can be analyzed for texture features using GLCM, where the gray levels include 4 to 256 bins, the window size is (5, 5) to (49, 49) pixels, and the stride of the sliding window is 1 to 10 pixels.

[0035] In one aspect, for both 3T and low-field datasets, the following formula:

Number

[0036] In one exemplary application of the present disclosure, by applying Haralick texture analysis, suspicious prostate lesions can be distinguished from normative 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 essential to accurately identify suspicious areas and obtain biopsies. MR images have been used in targeted prostate biopsies to pre-assess whether a patient should undergo a prostate biopsy and where to take the biopsy. (For example, using the scoring system of the Prostate Imaging Reporting and Data System (PI-RADS) version 2) By cognitively or electronically overlaying an annotated pre-procedure MR image with a score indicating the degree of suspicion onto a real-time ultrasound image, guidance can be provided, for example, during the biopsy. However, in some cases, fusing the biopsy with the ultrasound image has revealed significant drawbacks such as gland deformation, the steepness of the learning curve, and inaccurate alignment, so its adoption is limited.

[0037] Instead, a low-field MRI system, such as that provided by Promaxo Inc. (Oakland, CA), can provide an office-based open-sided single-sided scanner operating at a low non-uniform B0 magnetic field (58 to 74 mT), where the x and y-axis gradients are non-linear and the z-gradient is a permanent built-in type. This system can be used to guide transperineal prostate biopsy interventions. In various cases, a low-field MRI scanner can acquire images along the transverse direction without a transrectal probe. Further, patient positioning can be performed in the same manner as high-field (1.5T to 3T) MRI. An exemplary single-sided low-field MRI system is further described herein. During a biopsy using guidance from a low-field MRI system, a high-field T2-weighted MR image with annotations by a radiologist can be overlaid on the low-field T2-weighted image. By fusing the low-field image and the high-field image, abnormal regions that are visualized or annotated on the high-field MR image can be directly targeted.

[0038] As an example, for a suspicious ROI within the prostate, a radiologist can annotate it on a 3T T2-weighted image and assign a Prostate Imaging Reporting and Data System (PI-RADS) score. By precisely overlaying the 3T MR image volume and the low-field MR image volume, the radiologist's annotations can be transferred from the 3T image to the overlaid 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 unsuspicious region of the prostate predicted to be normal tissue. An exemplary T2-weighted image 50 overlaid from 3T MR and low-field MR is shown in FIG. 1. In FIG. 1, the patient's prostate cancer has a Gleason score of 4+5. The cancerous lesion is indicated by the right circle, and an unsuspicious region of the same radius is marked by the left circle.

[0039] Regardless of which imaging modality is used for guidance in a targeted biopsy, it can be challenging to localize the site from which the biopsy is taken. This is because clinical interpretation of MR images has hitherto been largely qualitative, for example, because a radiologist identifies and annotates cancerous regions. However, in various cases, texture analysis is applicable to medical images, as further described herein. Image texture analysis is a technique that quantifies pixel relationships within a target region and extracts the frequency of local spatial variations in signal intensity to capture image patterns that may (and usually are) indistinguishable to the human eye. 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. Further, as further described herein, Haralick texture analysis has been studied for prostate cancer detection on T2-weighted 3T MR images.

[0040] The Haralick features of energy, correlation, contrast, and homogeneity can be extracted from MR images of the prostate using one or more methods. The first method involves extracting Haralick texture measures within each ROI (cancerous region and regions not suspected of being cancerous). The second method involves using a sliding window technique across the entire prostate region of the image to calculate texture measures within local neighborhoods and then creating four (contrast, energy, correlation, and homogeneity) texture maps by averaging the values of the resulting texture maps in cancerous ROIs and ROIs not suspected of being cancerous.

[0041] By evaluating texture features, it is possible to demonstrate the consistency of texture measures in cancerous regions compared to regions not suspected of being cancerous within the ROI from the same patient, with energy and homogeneity increasing while contrast and correlation decreasing within cancerous regions compared to regions not suspected of being cancerous. As a result, some Haralick texture features are promising for cancer detection on low-field T2-weighted MR images.

[0042] 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 two groups of two pixels in the original image, called the reference pixel and the neighboring pixel. The values within GLCM are the counts of the frequency of neighboring pairs of image pixel values. Due to the symmetry of GLCM, the performance of texture calculation can be optimized, and the problem of window edge pixels can be overcome. In this context, symmetry means that each reference pixel in the matrix is counted together with its both right and left neighboring pixels, and each pixel pair is counted twice, that is, once in the forward direction and once in the reverse direction, and in the second count, the reference pixel and the neighboring pixel are swapped. Then, GLCM can be normalized by dividing by the total number of accumulated co-occurrences. In the normalized symmetric GLCM, all diagonal elements represent pixel pairs with no gray level difference, and the farther away from the diagonal, the greater the difference in the gray levels of the pixels.

[0043] Texture measures are various single values used to summarize symmetric GLCMs normalized in different ways. Robert Haralick proposed 14 different measures, and these texture features are correlated with each other. These features can be divided into three groups that are independent of each other, namely the contrast group, the orderliness group, and the description statistics group. In the contrast group, weights related to the distance from the GLCM diagonal are used, and contrast, dissimilarity, and homogeneity are included. In the orderliness group, the frequency of occurrence within a given pair of two gray levels in a window is measured. The features of orderliness include angular second moment (ASM), energy, maximum probability, and entropy. The description statistics group includes GLCM mean (mean), variance, and correlation. Contrast, homogeneity, energy, and correlation are useful for distinguishing cancer by the outcome in specific cases.

[0044] In various cases, these metrics can be calculated using the following equations:

[0045] Normalization equation: [Number] Here, i and j are the numbers of rows and columns. V is the value of cell i, j of the image window. And Pi,j is the value recorded for cell i, j of the normalized GLCM.

[0046] Energy: [Number]

[0047] Contrast: [Number]

[0048] Homogeneity: [Number]

[0049] Correlation: [Number] Here, μ is the mean, [Number] σ is the variance, [Number] In the case of symmetric GLCM, calculating the mean and variance using i or j gives the same result.

[0050] Next, a texture image or texture map can be created. An exemplary texture map is shown in FIG. 2. In FIG. 2, the upper row 60 shows a texture map from a high-field MR image, and the lower row 62 shows a texture map from a low-field MR image. The cancerous region is indicated by the right circle, and a non-suspect region of the same radius is marked by the left circle.

[0051] To examine the pixel-to-pixel relationship variations in various parts of the image, the texture metrics can be calculated by GLCM derived from a small area on the image at a time. Next, the calculation of the texture metrics can be done in another small area until the entire image is covered. In this way, a texture image can be created, which is useful for quantitatively evaluating how the pixel relationships vary in various regions.

[0052] In various cases, a texture map can be created by following these steps: Step 1, determine the window size, which is a small area for filling the GLCM and calculating the texture metrics. The window size is square and has an odd number of pixels on each side. Step 2, place the window at the first position covering the upper left of the image. Step 3, create and normalize the GLCM for this window. Step 4, calculate the selected texture metric, which is a single number representing the entire window. This number is replaced with the central pixel of the window. Step 5, move the window by a predetermined distance (usually 1 pixel) and repeat steps 3 and 4. Step 6, proceed to all possible window positions until the texture map is completed.

[0053] In various cases, the Haralick texture measures in high-field and low-field MR images can be graphed and compared in a particular case. For example, referring to FIGS. 3A and 3B, energy, contrast, correlation, and uniformity texture values are shown in graph displays 70, 80, respectively, to contrast high-field and low-field MR images. The texture values were calculated according to different methods in FIGS. 3A and 3B. Nevertheless, for cancerous and non-suspected regions from the same patient, using either method, in both high-field and low-field MR images, the contrast and correlation texture values were lower in the cancerous regions than in the non-suspected regions, while the energy and uniformity texture values were higher.

[0054] Referring now to FIG. 4, a flowchart 90 is shown that illustrates a validation investigation technique for comparing Haralick features calculated from 3T MR images and low-field MR images. In this example, the dataset included prostate cancer patients (91) with a Gleason score of 4+3. An exemplary investigation included five patients with a total of seven lesions. The prostate patients underwent a 3T MRI scan (92) and a low-field (58 mT to 74 mT) MRI scan (93). Suspected ROIs in the prostate were annotated on 3T, T2-weighted images by a radiologist and assigned a Prostate Imaging Reporting and Data System (PI-RADS) score. The 3T MR image volume and the low-field MR image volume were superimposed, and the radiologist's annotations were transferred to the superimposed low-field MR images from the 3T MR images (94). Next, for each suspected ROI, a secondary ROI of the same size was drawn on the same slice in a clinically unsuspicious region of the prostate predicted to be normal tissue. Haralick texture features were calculated from both the 3T MR images (95) and the low-field MR images (96) in the suspected and unsuspected ROIs. Although the numerical values of the Haralick texture features were different for different images, a pattern was confirmed due to the relativity of the texture values. More specifically, referring again to FIGS. 3A and 3B, in both high-field and low-field MR images, in cancerous regions compared to unsuspected regions, the texture values of contrast and correlation were lower, while the texture values of energy and homogeneity were higher.

[0055] An exemplary low-field single-sided MRI system is further described herein. According to various aspects, an MRI system is provided that can include a unique imaging region that can be offset from the face of the magnet. Such an offset and single-sided MRI system has fewer limitations compared to conventional MRI scanners. Further, this form factor can have an integrated or unique magnetic field gradient that creates a range of magnetic field values across the entire target region. In other words, the unique magnetic field may be non-uniform. The non-uniformity of the magnetic field strength in the target region of the single-sided MRI system can exceed 200 parts per million (ppm). For example, the non-uniformity of the magnetic field strength in the target region of the single-sided MRI system can be from 200 ppm to 200,000 ppm. In various aspects of the present disclosure, the non-uniformity in the target region can exceed 1,000 ppm and can exceed 10,000 ppm. In one example, the non-uniformity in the target region can be 81,000 ppm.

[0056] The unique magnetic field gradient can be generated by a permanent magnet within the MRI scanner. The magnetic field strength in the target region of the single-sided MRI system can be, for example, less than 1 tesla (T). For example, the magnetic field strength in the target region of the single-sided MRI system can be less than 0.5 T. In other examples, the magnetic field strength can exceed 1 T and can be, for example, 1.5 T. Since this system can operate at a lower magnetic field strength compared to a typical MRI system, the design constraints on the RX coil are relaxed and / or additional mechanisms such as, for example, robotics can be used with the MRI scanner. An exemplary MRI-guided robotic system is further described, for example, in International Application PCT / US2021 / 014628 filed on January 22, 2021, entitled "MRI-GUIDED ROBOTIC SYSTEMS AND METHODS FOR BIOPSY".

[0057] Figures 5 - 11 show the MRI scanner 100 and components of the MRI scanner 100. As shown in FIGS. 5 and 6, the MRI scanner 100 includes a housing 120 that includes a recessed or front face 125. In other embodiments, the face of the housing 120 can be flat and planar. The front face 125 can face an object to be imaged by the MRI scanner. As shown in FIGS. 5 and 6, the housing 120 includes a permanent magnet assembly 130, an RF transmit coil (TX) 140, a gradient coil set 150, an electromagnet 160, and an RF receive coil (RX) 170. In other examples, the housing 120 may not include the electromagnet 160. Further, in certain examples, the RF receive coil 170 and the RF transmit coil 140 may be combined and incorporated into a Tx / Rx coil array. In various examples, the MRI scanner 100 is a single-sided scanner, and various components such as, for example, the permanent magnet assembly 130, the RF transmit coil (TX) 140, the gradient coil set 150, the electromagnet 160, and the RF receive coil (RX) 170 are located on the same side of the field of view.

[0058] Referring mainly to FIGS. 7 - 9, the permanent magnet assembly 130 includes an array of magnets. The array of magnets forming the permanent magnet assembly 130 is configured to cover the front face 125 of the MRI scanner 100 or the surface facing the patient (see FIG. 7) and is shown as a horizontal bar in FIG. 8. The permanent magnet assembly 130 includes a plurality of cylindrical permanent magnets configured in parallel. Referring mainly to FIG. 9, the permanent magnet assembly 130 includes parallel plates 132 integrally held by brackets 134. The system can be attached to the housing 120 of the MRI scanner 100 at brackets 136. The parallel plates 132 may have a plurality of holes 138. 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, by way of example, neodymium-based magnetic materials.

[0059] The permanent magnet assembly 130 defines an access opening or bore 135 that can access the patient through the housing 120 from the opposite side of the housing 120. In other aspects of the present disclosure, an array of permanent magnets forming the permanent magnet assembly within the housing 120 may be without a bore and may define a continuous or uninterrupted arrangement of permanent magnets where no through bore is defined. In yet other examples, the array of permanent magnets within the housing 120 can form more than one bore / access opening through the permanent magnets.

[0060] According to various aspects of the present disclosure, the permanent magnet assembly 130 provides a magnetic field B0 within a region of interest 190 along the Z-axis as shown in FIG. 5. The Z-axis is orthogonal to the permanent magnet assembly 130. Stated another way, 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 face of the permanent magnet assembly 130. The Z-axis can define the direction of the primary magnetic field B0. The primary magnetic field B0 can decrease with a characteristic gradient along the Z-axis, that is, further away from the face of the permanent magnet assembly 130 and in the direction indicated by the arrow in FIG. 5.

[0061] In one aspect, the magnetic field non-uniformity of the permanent magnet assembly 130 in the region of interest 190 can be about 81,000 ppm. In another aspect, the magnetic field strength non-uniformity in the region of interest 190 with respect to the permanent magnet assembly 130 can be from 200 ppm to 200,000 ppm, and in certain examples can be greater than 1,000 ppm, and in various examples can be greater than 10,000 ppm.

[0062] 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 examples, the magnetic field strength of the permanent magnet assembly 130 can be greater than 1 T, for example, 1.5 T. Referring mainly to FIG. 5, the Y-axis extends vertically from the Z-axis, and the X-axis extends horizontally 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 indicated by the corresponding arrow in FIG. 5.

[0063] The RF transmission coil 140 may be configured to transmit an RF waveform and an associated electromagnetic field. The RF pulse from the RF transmission coil 140 can be configured to rotate the magnetization generated by the permanent magnet 130 by generating an effective magnetic field called B1 that is orthogonal to the direction of the permanent magnetic field (e.g., the orthogonal plane).

[0064] Referring mainly to FIG. 7, the gradient coil set 150 may include two gradient coil sets 152, 154. The gradient coil sets 152, 154 may be installed on the face or front surface 125 of the permanent magnet assembly 130 between the permanent magnet assembly 130 and the target region 190. Each gradient coil set 152, 154 may include coil portions on both sides of the bore 135. Referring to the axes in FIG. 5, the gradient coil set 154 can be, for example, a gradient coil set corresponding to the X-axis, and the gradient coil set 152 can be, for example, a gradient coil set corresponding to the Y-axis. The gradient coils 152, 154 can enable encoding along the X-axis and Y-axis, as described in more detail herein.

[0065] Referring now to FIG. 11, a control schematic diagram of the single-sided MRI system 300 is shown. The single-sided MRI scanner 100 and / or components of the single-sided MRI scanner 100 (FIGS. 5-10) may be incorporated into the MRI system 300 in various aspects of the present disclosure. For example, the imaging system 300 may include a permanent magnet assembly 308 that may be similar to the permanent magnet assembly 130 (see FIGS. 6-9) in various examples. The imaging system 300 may further include an RF transmit coil 310 that may be similar to, for example, the RF transmit coil 140 (see FIG. 7). Further, the imaging system 300 may include an RF receive coil 314 that may be similar to, for example, the RF receive coil 170 (see FIG. 7). In various aspects, the RF transmit coil 310 and / or the RF receive coil may also be located within the housing of the MRI scanner, and in certain examples, the RF transmit coil 310 and the RF receive coil 314 may be integrated together into a Tx / Rx coil. The system 300 may further include a gradient coil 320 configured to generate a gradient field to facilitate imaging of an object within the field of view 312.

[0066] The single-sided MRI system 300 may further include a computer 302 that communicates with the spectrometer 304 in a signal communication, and is configured to transmit and receive signals between the computer 302 and the spectrometer 304.

[0067] The main magnetic field B0 generated by the permanent magnet 308 may extend into the field of view 312 in a direction away from the permanent magnet 308 and in a direction away from the RF transmit coil 310. The field of view 312 may include an object to be imaged by the MRI system 300.

[0068] During the imaging process, the main magnetic field B0 can extend within the field of view 312. The direction of the effective magnetic field (B1) can change in response to the RF pulse and the associated electromagnetic field from the RF transmission coil 310. For example, the RF transmission coil 310 is configured to selectively transmit an RF signal or pulse to an object within the field of view, such as tissue. These RF pulses can change the effective magnetic field experienced by the spins within the sample (e.g., the patient's tissue). When the RF pulse is on, the effective magnetic field experienced by the resonating spins may be only the RF pulse, effectively canceling out the static B0 magnetic field. The RF pulse can be, for example, a chirp or frequency sweep pulse, as further described herein.

[0069] Furthermore, when an object within the field of view 312 is excited by an RF pulse from the RF transmission coil 310, the precessional motion of the object can generate an induced current or MR current that is detected by the RF reception coil 314. The RF reception coil 314 can transmit the excitation data to the RF preamplifier 316. The RF preamplifier 316 can boost or amplify the excitation data signals and transmit them to the spectrometer 304. The spectrometer 304 can transmit the excitation data to the computer 302 for storage, analysis, and image construction. The computer 302 can, for example, combine a plurality of stored excitation data signals to generate an image.

[0070] From the spectrometer 304, the signal can be further relayed to the RF transmission 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 can amplify the signal and transmit it to the RF transmission coil 310. The gradient power amplifier 318 can amplify the gradient coil signal and transmit it to the gradient coil 320.

[0071] Systems and methods for effectively collecting nuclear magnetic resonance spectra and magnetic resonance images in a non-uniform field using, for example, a single-sided MRI scanner 100 and system 300 are described herein.

[0072] Imaging using single-sided or open-type MRI can present many challenges. Typically, two sets of gradient coils (see Figure 10) in a single-sided system are placed on the face of the permanent magnet assembly. As a result, the amplitude of the gradient can decrease as it moves away from the face of the permanent magnet assembly. Thus, for a given array of phase encoding, the field of view can change as it moves along the axis of the permanent magnetic field B0. In other words, the pulsed gradient coils within a single-sided scanner can have a small component along the direction of the permanent gradient.

[0073] Figure 12 is a schematic diagram 500 of the magnetic field gradient along the Z-axis of the MRI scanner 100. The permanent magnet 130 can provide an inherent gradient along the Z-axis. The intensity of the Z-gradient can decrease as it moves away from the permanent magnet 130. The Z-gradient bends as it moves away from the permanent magnet, and it can be confirmed in the schematic diagram that the intensity of the gradient decreases. The MRI scanner 100 can image multiple slices to generate a slab. Each slice can be excited at a different frequency for imaging. The low frequency can excite the tissue of the slice far from the permanent magnet, and the high frequency can excite the tissue of the slice close to the magnet. In the schematic diagram, the slab or axial image is composed of multiple slices going from Slice0 to Slice n towards. Each slice can correspond to frequencies from f0 to f n up to, where f0 is a frequency smaller than f n .

[0074] According to various aspects of the present disclosure, by applying phase encoding during a frequency sweep or chirp excitation pulse, the added phase can be compensated. A frequency sweep pulse can potentially affect the spins at various frequencies at various times during the pulse. This means that by applying phase encoding during the excitation pulse, it may also be possible to give different amounts of phase to different frequencies. The spins excited at the start of the pulse can accumulate more phase than the spins excited at the end of the pulse, which can hardly accumulate phase.

[0075] According to various aspects, when spins farther from a permanent magnet are excited first and phase encoding is applied during a frequency-swept excitation pulse, those more distant spins can accumulate more phase than spins closer to the permanent magnet that are excited last. This can reverse the normal method by which spins accumulate phase from a surface gradient coil and counteract the normal variation in gradient strength along the Z-axis. By accurately adjusting the amount of phase accumulated during frequency-swept excitation and subsequent phase encoding, it may be possible to apply a uniform amount of phase to the X-Y plane relative to the Z-axis of the permanent magnet.

[0076] Example Various aspects of the subject matter described herein are set forth in the following numbered examples.

[0077] Example 1 - A method of identifying a target region using a low-field magnetic resonance imaging (MRI) system. The method includes obtaining a T2-weighted image including a slice from the low-field MRI system, annotating a first region corresponding to a suspicious region on the slice, and annotating a second region corresponding to an unsuspicious region on the slice. The second region is the same size as the first region. The method 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 with the second texture feature value.

[0078] Example 2 - The method of Example 1, wherein the first texture feature value and the second texture feature value correspond to Haralick texture features selected from the group consisting of energy, homogeneity, contrast, and correlation.

[0079] Example 3 - The method of Example 1 or Example 2, further including generating a graphical display comparing the first texture feature value with the second texture feature value.

[0080] Example 4 - further including 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 plurality of second texture feature values correspond to Haralick texture features selected from the group consisting of energy, uniformity, contrast, and correlation, the method according to Example 1, Example 2, or Example 3.

[0081] Example 5 - the method according to Example 1, Example 2, Example 3, or Example 4, further including generating a gray-level co-occurrence matrix for a slice.

[0082] Example 6 - the method according to Example 5, wherein the gray-level co-occurrence matrix is calculated with 4 bins to 256 bins.

[0083] Example 7 - the method according to Example 5 or Example 6, further including generating a texture map from the gray-level co-occurrence matrix.

[0084] Example 8 - the method according to Example 5, Example 6, or Example 7, wherein calculating the first texture feature value includes calculating the average of the first values using a sliding window technique in the gray-level co-occurrence matrix.

[0085] Example 9 - the method according to Example 8, wherein in the sliding window technique, the sliding window size is 5×5 pixels to 49×49 pixels, and further in the sliding window technique, the sliding window stride is 1 to 10 pixels.

[0086] Example 10 - A system comprising a single-sided low-field MRI system, the single-sided low-field MRI system comprising an array of magnets configured to generate a permanent non-uniform B0 magnetic field within a target region offset from the array of magnets, the system further comprising a control circuit. The control circuit is configured to generate a T2-weighted image from the single-sided low-field MRI, identify a first region corresponding to a suspicious region on the T2-weighted image, and identify a second region on the T2-weighted image. The second region corresponds to a non-suspicious region and the second region is 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 further comprises a display configured to convey the comparison of the first texture feature value and the second texture feature value.

[0087] Example 11 - A single-sided low-field MRI system further comprising a housing having a face, the first axis extending through the face into the target region, the permanent non-uniform B0 magnetic field extending from an array of permanent magnets into the target region with respect to the first axis, the system of Example 10.

[0088] Example 12 - The system of Example 10 or Example 11, wherein the permanent non-uniform B0 magnetic field has a magnetic field strength of less than 100 mT within the target region.

[0089] Example 13 - The system of Example 10 or Example 11, wherein the permanent non-uniform B0 magnetic field has a magnetic field strength of 58 mT to 74 mT within the target region.

[0090] Example 14 - A single-sided low-field MRI system further comprising a gradient coil set, at least one radio frequency coil, a power circuit, and a memory, the control circuit being in signal communication with the gradient coil set, at least one radio frequency coil, the power circuit, and the memory, the system of Example 10, Example 11, Example 12, or Example 13.

[0091] Example 15 - The first texture feature value and the second texture feature value correspond to Haralick texture features selected from the group consisting of energy, uniformity, contrast, and correlation, and the system according to Example 10, Example 11, Example 12, Example 13, or Example 14.

[0092] Example 16 - The control circuit is further configured to calculate a plurality of texture feature values for a first region and calculate a plurality of texture feature values for a second region, and the system according to Example 10, Example 11, Example 12, Example 13, Example 14, or Example 15. The plurality of texture feature values correspond to Haralick texture features selected from the group consisting of energy, uniformity, contrast, and correlation.

[0093] Example 17 - The comparison includes a graph display, and the system according to Example 10, Example 11, Example 12, Example 13, Example 14, Example 15, or Example 16.

[0094] Example 18 - The control circuit is further configured to generate a gray-level co-occurrence matrix, and the gray-level co-occurrence matrix is calculated in 4 bins to 256 bins, and the system according to Example 10, Example 11, Example 12, Example 13, Example 14, Example 15, Example 16, or Example 17.

[0095] Example 19 - The control circuit is further configured to generate a texture map from the gray-level co-occurrence matrix and calculate an average first value using a sliding window technique in the gray-level co-occurrence matrix, and the system according to Example 18. In the sliding window technique, the sliding window size is 5×5 pixels to 49×49 pixels, and in the sliding window technique, the sliding window stride is 1 to 10 pixels.

[0096] Although several forms have been shown and described, it is not the applicant's intention to limit or restrict the scope of the appended claims in such detail. Many changes, variations, modifications, substitutions, combinations, and equivalents to those forms may be made and may be contemplated by those skilled in the art without departing from the scope of the present disclosure. Further, the structure of each element related to the forms described can alternatively be described as a means for providing the function performed by the element. Further, where a particular material is disclosed for a particular component, other materials may be used. Accordingly, it is understood that the foregoing description and the appended claims are intended to cover all such changes, combinations, and variations as fall within the scope of the disclosed forms. The appended claims are intended to cover all such changes, variations, modifications, substitutions, changes, and equivalents.

[0097] The foregoing detailed description has described various forms of devices and / or processes by use of block diagrams, flowcharts, and / or examples. To the extent that such block diagrams, flowcharts, and / or examples include one or more functions and / or operations, it will be understood by those skilled in the art that each function and / or operation within such block diagrams, flowcharts, and / or examples can be individually and / or collectively implemented by a wide variety of hardware, software, firmware, or substantially any combination thereof. Those skilled in the art will recognize that some aspects of the forms disclosed herein may be implemented in whole or in part in an integrated circuit, as one or more computer programs operating in one or more computers (e.g., as one or more programs operating in one or more computer systems), as one or more programs operating on one or more processors (e.g., as one or more programs operating in one or more microprocessors), as firmware, or as substantially any combination thereof, and that designing the circuitry and / or writing the code for the software and / or firmware is well within the skill of those in the art in light of this disclosure. Additionally, those skilled in the art will understand 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 particular type of signal-bearing medium used to actually effect the distribution.

[0098] Instructions used to program logic for implementing various disclosed aspects may be stored in memory within a system, such as, for example, dynamic random access memory (DRAM), cache, flash memory, or other storage. Further, the instructions may be distributed via a network or by other computer-readable media. Accordingly, a machine-readable medium can include any mechanism for storing or transmitting information in a machine (e.g., a computer) readable form, including but not limited to floppy disks, optical disks, compact disks, read only memory (CD-ROM), and magneto-optical disks, read only memory (ROM), random access memory (RAM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), magnetic or optical cards, flash memory, or tangible machine-readable storage such as that used in the transmission of information via the Internet through electrical, optical, acoustic, or other forms of propagated signals (such as carrier waves, infrared signals, digital signals, etc.). Thus, 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.

[0099] When used in any aspect herein, the term "control circuit" may represent, for example, a hardwired circuit, a programmable circuit (e.g., a computer processor including one or more individual 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 a programmable circuit, and any combination thereof. The control circuit may be embodied together or individually as a circuit forming part of a larger system, such as, for example, an integrated circuit (IC), an application specific integrated circuit (ASIC), a system on chip (SoC), a desktop computer, a laptop computer, a tablet computer, a server, a smartphone, etc. Thus, when used herein, "control circuit" includes, but is not limited to, an electrical circuit including at least one discrete electrical circuit, an electrical circuit including at least one integrated circuit, an electrical circuit including at least one application specific integrated circuit, an electrical circuit 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 implements the processes and / or devices described herein, or a microprocessor configured by a computer program that at least partially implements the processes and / or devices described herein), an electrical circuit forming a memory device (e.g., in the form of a random access memory), and / or an electrical circuit forming a communication device (e.g., a modem, a communication switch, or an optoelectronic device). One of ordinary skill in the art will recognize that the subject matter described herein may be implemented by analog or digital techniques or some combination thereof.

[0100] When used in any aspect herein, the term "logic" can represent an app, software, firmware, and / or circuitry configured to perform any of the foregoing operations. The software can be embodied as a software package, code, instructions, instruction sets, and / or data recorded on a non-transitory computer-readable storage medium. The firmware can be embodied as code, instructions, or instruction sets, and / or data hard-coded (e.g., non-volatile) in a memory device.

[0101] When used in any aspect herein, terms such as "component", "system", "module", etc. can represent an entity associated with a computer of either hardware, a combination of hardware and software, software, or software in execution.

[0102] When used in any aspect herein, "algorithm" represents a non-self-contradictory sequence of steps that produce a desired result, where "step" represents an operation on a physical quantity and / or logical state that can, although not necessarily, take the form of electrical or magnetic signals that can be stored, transmitted, combined, compared, and otherwise manipulated. It is common usage to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, etc. These terms and similar terms can be related to appropriate physical quantities and are nothing more than convenient labels applied to these quantities and / or states.

[0103] The network may include a packet-switching network. Communication devices may be able to communicate with each other using a selected packet-switching network communication protocol. One exemplary communication protocol may include an Ethernet communication protocol that permits communication using the Transmission Control Protocol / Internet Protocol (TCP / IP). The Ethernet protocol may comply with and / or be conformant to the Ethernet standard published by the Institute of Electrical and Electronics Engineers (IEEE) under the name of "IEEE 802.3 Standard" published in December 2008 and / or subsequent versions of this standard. Alternatively, or additionally, communication devices may be able to communicate with each other using an X.25 communication protocol. The X.25 communication protocol may comply with or be conformant to the standard published by the International Telecommunication Union - Telecommunication Standardization Sector (ITU-T). Alternatively, or additionally, communication devices may be able to communicate with each other using a frame relay communication protocol. The frame relay communication protocol may comply with or be conformant to the standard published by the International Telegraph and Telephone Consultative Committee (CCITT) and / or the American National Standards Institute (ANSI). Alternatively, or additionally, transceivers may be able to communicate with each other using an Asynchronous Transfer Mode (ATM) communication protocol. The ATM communication protocol may comply with and / or be conformant to the ATM standard published by the ATM Forum under the name of "ATM-MPLS Network Interworking 2.0" published in August 2001 and / or subsequent versions of this standard. Of course, different and / or later connection-oriented network communication protocols are also contemplated herein.

[0104] As is apparent from the above disclosure, unless explicitly stated otherwise, throughout the above disclosure, descriptions using terms such as "processing", "calculating", "computing", "identifying", "displaying", etc. represent actions and processes of a computer system or a similar electronic computing device that operate on and transform data represented as physical (electronic) quantities in the registers and memories of the computer system into other data similarly represented as physical quantities in the computer system memory or registers or other such information storage, transmission, or display devices.

[0105] One or more components may be expressed herein as "configured to", "configurable to", "operable to / operates", "adapted / adaptable", "capable of", "adapted to / conformed to", etc. One of ordinary skill in the art will recognize that "configured to" generally encompasses components in an active state and / or components in a non-active state and / or components in a standby state, unless the context requires otherwise.

[0106] The terms "proximal" and "distal" are used herein with reference to a clinician operating the handle portion or housing of 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 located away from the clinician and / or the robotic arm. It is further understood that, for convenience and clarity, spatial terms such as "vertical", "horizontal", "up", and "down" may be used herein with reference to the drawings. However, robotic surgical tools are used in many orientations and positions, and these terms are not intended to be limiting and / or absolute.

[0107] Those skilled in the art will generally recognize that the terms used herein, and particularly in the appended claims (e.g., the body of the appended claims), are generally intended to be "open" terms (e.g., the term "comprising" should be interpreted as "including but not limited to", the term "comprising" should be interpreted as "including at least ~", the term "comprising" should be interpreted as "including but not limited to", etc.). When a specific number of elements of a claimed item is intended, such intention is explicitly stated in the claim, and those skilled in the art will further understand that such intention does not exist if such statement is not present. For example, for the sake of assistance in understanding, the following appended claims may include the use of the modifying expressions "at least one" and "one or more" to modify the elements of the claim. However, the use of such expressions should not be construed as limiting any particular claim including an element so modified to a claim including only one such element, even when the same claim includes a modifying expression such as "one or more" or "at least one" and an expression corresponding to the English indefinite article "a" or "an" (e.g., an expression corresponding to the English indefinite article "a" or "an" should typically be construed as meaning "at least one" or "one or more"). The same holds true for the use of definite articles used to modify the elements of a claim.

[0108] In addition, even if a specific number of elements of a claimed element is explicitly recited, one of ordinary skill in the art would recognize that such elements are typically meant to be present in at least the recited number (e.g., the bare recitation of "two elements" without other modifying language typically means at least two or more elements). Further, in instances where a convention similar to "at least one of A, B, and C" is used, generally one of ordinary skill in the art would assume that such a construction is intended based on how that convention is understood (e.g., a system including "at least one of A, B, and C" includes, but is not limited to, a system including only A, only B, only C, a combination of A and B, a combination of A and C, a combination of B and C, and / or a combination of A, B, and C). In instances where a convention similar to "at least one of A, B, or C" is used, generally one of ordinary skill in the art would assume that such a construction is intended based on how that convention is understood (e.g., a system including "at least one of A, B, or C" includes, but is not limited to, a system including only A, only B, only C, a combination of A and B, a combination of A and C, a combination of B and C, and / or a combination of A, B, and C). Typically, one of ordinary skill in the art further understands that disjunctive language and / or expressions presenting two or more alternative terms are to be understood as encompassing one, any, or both of those terms, absent a contrary indication in the context in which it appears, whether included in the description, claims, or drawings. For example, the expression "A or B" is typically understood to include the possibilities of "A" or "B" or "A and B".

[0109] In connection with the appended claims, one of ordinary skill in the art will understand that the operations recited therein can generally be performed in any order. Further, although various operation flow diagrams are presented in a certain order, it must be understood that the various operations can be performed in other orders than the order in which they are shown, or can be performed simultaneously. Examples of such alternative orders can include overlapping, alternating, interrupted, rearranged, progressive, preliminary, supplementary, simultaneous, reversed, or other variant orders, unless the context indicates otherwise. Further, terms such as "in response to," "associated with," or other past-tense adjectives generally are not intended to exclude such variants unless the context indicates otherwise.

[0110] It is important to note that all references to "one aspect," "aspect," "exemplification," "one exemplification," etc. mean that the particular features, structures, or characteristics described in connection with the aspect are included in at least one aspect. Thus, the use of the phrases "in one aspect," "in an aspect," "in an example," and "in one example" in various places throughout this specification are not necessarily all referring to the same aspect. Further, the particular features, structures, or characteristics can be combined in any suitable manner in one or more aspects.

[0111] Any patent application, patent, unpatented publication, or other disclosure document referred to herein and / or listed in any application data sheet is incorporated herein by reference to the extent that the incorporated content is not inconsistent with this specification. Thus, and to the extent necessary, the disclosure expressly set forth herein prevails over any conflicting content incorporated herein by reference. Although it is referred to as being incorporated herein by reference, any document, or portion thereof, that conflicts with an existing definition, description, or other disclosure content set forth herein is incorporated only to the extent that no conflict arises between the incorporated document and the existing disclosure content.

[0112] In summary, many advantages resulting from using the concepts described herein are explained. The above description of one or more forms is presented for purposes of illustration and explanation. It is not intended to be exhaustive or to limit the disclosure 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 and thereby enable one of ordinary skill in the art to use the various forms and with various modifications as are suited to the particular use contemplated. The claims submitted herewith are intended to define the full scope.

Claims

1. A method for identifying a target region using a low-field magnetic resonance imaging (MRI) system, comprising: obtaining a T2-weighted image including slices from the low-field MRI system; annotating a first region corresponding to a suspicious region on the slice; and annotating a second region corresponding to an unsuspicious region and having the same size as the first region on the slice; 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 with the second texture feature value.

2. The method according to claim 1, wherein the first texture feature value and the second texture feature value correspond to Haralick texture features selected from the group consisting of energy, homogeneity, contrast, and correlation.

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

4. The method according to claim 1, further comprising calculating a plurality of first texture feature values for the first region and calculating a plurality of second texture feature values for the second region, wherein the plurality of first texture feature values and the plurality of second texture feature values correspond to Haralick texture features selected from the group consisting of energy, homogeneity, contrast, and correlation.

5. The method according to claim 1, further comprising generating a gray-level co-occurrence matrix for the slice.

6. The method according to claim 5, wherein the gray-level co-occurrence matrix is calculated with 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 according to claim 7, wherein calculating the first texture feature value includes calculating an average of first values using a sliding window technique in the gray-level co-occurrence matrix.

9. ​ In the sliding window technique, the sliding window size is from 5×5 pixels to 49×49 pixels, and in the sliding window technique, further, the sliding window stride is from 1 to 10 pixels, the method according to claim 8.

10. A system, comprising a single-sided low-field MRI system, the single-sided low-field MRI system comprising an array of magnets configured to generate a permanent non-uniform B0 magnetic field within a target region offset from the array of magnets, the system further comprising a control circuit, the control circuit generating a T2-weighted image from the single-sided low-field MRI system, identifying a first region corresponding to a suspicious region on the T2-weighted image, identifying a second region corresponding to a non-suspicious region on the T2-weighted image and having the same size as the first region, calculating a first texture feature value for the first region, calculating a second texture feature value for the second region, configured to compare the first texture feature value with the second texture feature value, the system further comprising a display configured to convey the comparison of the first texture feature value and the second texture feature value, a system.

11. The single-sided low-field MRI system further comprises a housing having a face, the first axis extending through the face and into the target region, the housing, the permanent non-uniform B0 magnetic field extending from the array of permanent magnets into the target region with respect to the first axis, the system according to claim 10.

12. The permanent non-uniform B0 magnetic field has a magnetic field strength of less than 100 mT within the target region, the system according to any one of claims 10 and 11.

13. The permanent non-uniform B0 magnetic field has a magnetic field strength of 58 mT to 74 mT within the target region, the system according to any one of claims 10 and 11.

14. The single-sided low-field MRI system further comprises a gradient coil set, at least one radio frequency coil, a power circuit, and a memory, The control circuit communicates with the gradient coil set, the at least one radio frequency coil, the power circuit, and the memory in signal communication, the system according to any one of claims 10 and 11.

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

16. The control circuit further calculates a plurality of texture feature values for the first region, configured to calculate a plurality of texture feature values for the second region, the plurality of texture feature values corresponding to Haralick texture features selected from the group consisting of energy, uniformity, contrast, and correlation, the system according to any one of claims 10 and 11.

17. The comparison includes a graphical display, the system according to any one of claims 10 and 11.

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

19. The control circuit further generates a texture map from the gray level co-occurrence matrix, configured to calculate an average of a first value using a sliding window technique in the gray level co-occurrence matrix, in the sliding window technique, the sliding window size is 5×5 pixels to 49×49 pixels, and in the sliding window technique, the sliding window stride is 1 to 10 pixels, the system according to claim 18.

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