Patent Description:
Magnetic resonance imaging (MRI) is an imaging modality that uses magnetic fields to reconstruct a structure of scanned objects of interest. An MRI scanner includes a magnet for generating a strong static magnetic field, such as a magnetic field in the range of <NUM> Tesla (T) to <NUM> T, and radio frequency (RF) transceivers for transmitting and/or receiving RF signals. When a body is placed in the generated static magnetic field, the Hydrogen protons within the body align to the magnetic field. An RF pulse is applied in the presence of an oscillating B1 field to tip the spins so that there is a bulk magnetization remaining in the transverse field. When the RF pulse is turned off, the Hydrogen protons return to alignment with the static magnetic field, the longitudinal component increasing and the transverse component decreasing. At a chosen time point, referred to as the sampling time, or echo time, or gradient echo time, data is collected and the received signal is used to reconstruct an image of the scanned body or a part thereof. In the current disclosure, various VFA techniques for reconstructing MR images based on collected MR datasets are described. <NPL>) relates to simultaneous estimation of B1(+) inhomogeneities and R1 values from uncorrected R1 maps in the human brain without the need for B1(+) mapping by employing a probabilistic framework for unified segmentation based correction of R1 maps for B1(+) inhomogeneities.

A magnetic resonance imaging (MRI) system can include a MRI scanner, at least one processor, and a memory, with computer code instructions stored thereon. The MRI scanner can be configured to acquire a first magnetic resonance (MR) dataset corresponding to a first flip angle and a second MR dataset corresponding to a second flip angle by imaging an anatomical region using at least one echo time. The computer code instructions, when executed by the at least one processor, cause the at least one processor to generate an apparent longitudinal relaxation time (T1app) map, representing a spatial distribution of T1app within the anatomical region using the first MR dataset and the second MR dataset. The at least one processor can estimate a first transmit RF field map by scaling the T1app map by a first constant value of longitudinal relaxation time (T<NUM>). The first constant value of T<NUM> can be associated with a first tissue type within the anatomical region. The at least one processor can estimate a second transmit RF field map by scaling the T1app map by a second constant value of T<NUM>. The second constant value of T<NUM> can be associated with a second tissue type within the anatomical region. The processor can generate a third transmit RF field map using the estimated first transmit RF field map and the estimated second transmit RF field map. The third transmit RF field map can represent a spatial distribution of the transmit RF field within the anatomical region.

The anatomical region can be a human brain where the first tissue type can be white matter, and the second tissue type can be gray matter. The at least one processor can estimate a fourth transmit RF field map by scaling the T1app map by a third constant value of T<NUM>. The third constant value of T<NUM> can be associated with a third tissue type within the anatomical region. Generating the third transmit RF field map can include using the estimated first transmit RF field map, the estimated second transmit RF field map, and the estimated fourth transmit RF field map. The anatomical region can be a human brain where the first tissue type can be white matter, the second tissue type can be gray matter, and the third tissue type can be cerebro-spinal fluid. The at least one processor can generate a first longitudinal relaxation time (T<NUM>) map by dividing the T1app map by the square of the third transmit RF field map. The first T<NUM> map can represent the spatial distribution of T<NUM> within the anatomical region.

The at least one processor can generate an apparent spin density (SDapp) map using the first MR dataset and the second MR dataset. The SDapp map can represent a spatial distribution of SDapp within the anatomical region. The at least one processor can generate a first spin density (SD) map by scaling the SDapp map by the third transmit RF field map. The at least one processor can synthesize, using the third transmit RF field map, the first T<NUM> map, and the first SD map, a third MR dataset corresponding to a third flip angle such that at least a first sub-region of the anatomical region corresponding to the first tissue type and a second sub-region of the anatomical region corresponding to the second tissue type are isointense. The at least one processor can estimate a receive RF field map using the synthesized third MR dataset. The receive RF field map can represent a spatial distribution of a receive RF field within the anatomical region. The at least one processor can generate a second SD map by scaling the first SD map by the estimated receive RF field map.

The at least one processor can scale the first MR dataset by the receive RF field map, scale the second MR data set by the receive MR dataset, and generate an image representing a weighted subtraction of the scaled first MR dataset from the scaled second MR dataset. The at least one processor can generate an image representing a weighted subtraction of the first MR dataset from the second MR dataset.

The at least one processor can estimate a plurality of transmit RF field maps for a plurality of subjects, and generate a transmit RF field template using an averaging of the estimated plurality of transmit RF field maps for the plurality of subjects. The at least one processor can generate a second T<NUM> map using the T1app map and the transmit RF field template. The at least one processor can generate a spin density (SD) map using an estimated SDapp map, the transmit RF field template, and an estimated receive RF field map.

According to at least one aspect, a method for magnetic resonance imaging (MRI) can include at least one processor receiving a first magnetic resonance (MR) dataset corresponding to a first flip angle and a second MR dataset corresponding to a second flip angle. The first MR dataset and the second MR dataset can be acquired by imaging an anatomical region using at least one echo time. The method can include the at least one processor generating an apparent longitudinal relaxation time (T1app) map, representing a spatial distribution of T1app within the anatomical region using the first MR dataset and the second MR dataset within the anatomical region. The method can include the at least one processor estimating a first transmit RF field map by scaling the T1app map by a first constant value of longitudinal relaxation time (T<NUM>). The first constant value of T<NUM> can be associated with a first tissue type within the anatomical region. The method can include the at least one processor estimating a second transmit RF field map by scaling the T1app map by a second constant value of T<NUM>. The second constant value of T<NUM> can be associated with a second tissue type within the anatomical region. The method can include the at least one processor generating a third transmit RF field map using the estimated first transmit RF field map and the estimated second transmit RF field map. The third transmit RF field map can represent a spatial distribution of the transmit RF field within the anatomical region.

The method can further include the at least one processor estimating a fourth transmit RF field map by scaling the T1app map by a third constant value of T<NUM>. The third constant value of T<NUM> can be associated with a third tissue type within the anatomical region. Generating the third transmit RF field map can include using the estimated first transmit RF field map, the estimated second transmit RF field map, and the estimated fourth transmit RF field map. The anatomical region can be a human brain where the first tissue type can be white matter, the second tissue type can be gray matter, and the third tissue type can be cerebro-spinal fluid. The method can further include the at least one processor generating a first longitudinal relaxation time (T<NUM>) map by dividing the T1app map by the square of the third transmit RF field map. The first T<NUM> map can represent the spatial distribution of T<NUM> within the anatomical region.

The method can further include the at least one processor generating an apparent spin density (SDapp) map using the first MR dataset and the second MR dataset. The SDapp map can represent a spatial distribution of SDapp within the anatomical region. The at least one processor can generate a first spin density (SD) map by scaling the SDapp map by the third transmit RF field map. The at least one processor can synthesize, using the third transmit RF field map, the first T<NUM> map, and the first SD map, a third MR dataset corresponding to a third flip angle such that at least a first sub-region of the anatomical region corresponding to the first tissue type and a second sub-region of the anatomical region corresponding to the second tissue type are isointense. The at least one processor can estimate a receive RF field map using the synthesized third MR dataset. The receive RF field map can represent a spatial distribution of a receive RF field within the anatomical region. The at least one processor can generate a second SD map by scaling the first SD map by the estimated receive RF field map.

The method can further include the at least one processor scaling the first MR dataset by the receive RF field map, scaling the second MR data set by the receive MR dataset, and generating an image representing a weighted subtraction of the scaled first MR dataset from the scaled second MR dataset. The method can further include the at least one processor generating an image representing a weighted subtraction of the first MR dataset from the second MR dataset.

The method can further include the at least one processor estimating a plurality of transmit RF field maps for a plurality of subjects, and generating a transmit RF field template using an averaging of the estimated plurality of transmit RF field maps for the plurality of subjects. The method can further include the at least one processor generating a second T<NUM> map using the T1app map and the transmit RF field template. The method can further include the at least one processor generating a spin density (SD) map using an estimated SDapp map, the transmit RF field template, and an estimated receive RF field map.

A computer-readable medium includes computer code instructions stored thereon. The computer code instructions when executed by at least one processor cause a method for magnetic resonance imaging (MRI) to be performed. The method can include the at least one processor receiving a first magnetic resonance (MR) dataset corresponding to a first flip angle and a second MR dataset corresponding to a second flip angle. The first MR dataset and the second MR dataset can be acquired by imaging an anatomical region using at least one echo time. The method can include the at least one processor generating an apparent longitudinal relaxation time (T1app) map, representing a spatial distribution of T1app within the anatomical region using the first MR dataset, the second MR dataset and a constant value for a transmit radio frequency (RF) field within the anatomical region. The method can include the at least one processor estimating a first transmit RF field map by scaling the T1app map by a first constant value of longitudinal relaxation time (T<NUM>). The first constant value of T<NUM> can be associated with a first tissue type within the anatomical region. The method can include the at least one processor estimating a second transmit RF field map by scaling the T1app map by a second constant value of T<NUM>. The second constant value of T<NUM> can be associated with a second tissue type within the anatomical region. The method can include the at least one processor generating a third transmit RF field map using the estimated first transmit RF field map and the estimated second transmit RF field map. The third transmit RF field map can represent a spatial distribution of the transmit RF field within the anatomical region.

Quantitative magnetic resonance imaging (qMRI) involves measuring and/or using signal intensities or relative signal intensities to distinguish between different types of tissues or to identify abnormal tissues. By quantifying the tissue properties, it becomes easier to develop methods to both segment and classify normal and abnormal tissue types. The use of quantitative spin density (SD), longitudinal relaxation time (T<NUM>), transverse magnetization decay rate (T<NUM>*), and quantitative susceptibility mapping (QSM) has important clinical applications today. For example, quantitative T<NUM> imaging has potential utility in studying atherosclerosis in cardiovascular imaging, tumors, stroke, and multiple sclerosis. Also, quantitative SD mapping plays a significant role and has been used to study edema and changes in tissue water content after treatment. Generally, this type of tissue quantification makes it possible to follow the response of the tissue to treatment in a more rigorous fashion.

Quantitative T<NUM> imaging and quantitative SD mapping can be achieved using variable flip angle (VFA) techniques. Using VFA methods involves acquisition of MR data for a plurality of different flip angles. In particular, a MRI scanner can acquire multiple spoiled gradient echo datasets with different nominal flip angles, and use the acquired datasets to appropriately quantify T<NUM> and/or SD. While VFA techniques are characterized by the ease of MR data collection, VFA-based techniques call for accurate knowledge of the radiofrequency (RF) excitation (or transmit) field (B1t) or flip angles (FA) as a function of position. In fact, the ability to accurately calculate the T<NUM> map based on VFA datasets depends on accurate knowledge of the spatial distribution of the transmit RF field (B1t) map. Also, reconstruction of the SD map involves the use of the receive RF field (B1r) map. In some implementations, a MRI scanner or a respective processor can compute the transmit RF field B1t by using acquired datasets for at least three different flip angles (FAs) including a dataset associated with a relatively large FA, for example, compared to the other two FAs. Using datasets corresponding to less than three FAs or relatively small FAs can render the problem of constructing the transmit RF field B1t ill-posed and can call for additional constraints to remedy the ill-posedness of the problem. Examples of such constraints to address the ill-posedness can include using a single tissue such as fat to determine the B1t field distribution for breast imaging, or using constraints on the relationship between T<NUM> and SD for white matter and gray matter.

In the current disclosure, methods and systems for strategically acquired gradient echo (STAGE) imaging with improved image quality and quantitative data are described. STAGE imaging can allow for comprehensive rapid imaging using acquired datasets corresponding to two FAs. Also, the systems and methods for STAGE imaging described herein allow for reliable and accurate reconstruction of T1 and SD maps.

<FIG> is a block diagram illustrating a magnetic resonance imaging (MRI) system <NUM>, according to an aspect of the invention. In brief overview, the MRI system <NUM> includes a MRI scanner <NUM>, a processor <NUM>, a memory <NUM>, and a display device <NUM>. The processor <NUM> is communicatively coupled to the MRI scanner <NUM>, the memory <NUM> and the display device <NUM>.

The MRI scanner <NUM> can include a magnet (not shown in <FIG>) for generating a strong static magnetic field, such as a magnetic field in the range of <NUM> Tesla (T) to <NUM> T, and a plurality of radio frequency (RF) coils (not shown in <FIG>) for transmitting and/or receiving RF signals. The RF coils can include transmit RF coils and receive RF coils. The RF transmit coils can emit RF pulses to excite a subject, such as an anatomical region of a patient, according to a MRI pulse sequence. The receive RF coils can record MRI signals generated by the subject following the emission of the RF pulses. The RF coils may include RF transceivers capable of alternately transmitting and receiving RF signals. The RF coils can acquire MRI data according to an RF spoiled gradient data acquisition. The recorded MRI signals can be associated with two different FAs.

The imaging system <NUM> includes one or more processors <NUM>. The one or more processors <NUM> can include a processor integrated within the MRI scanner <NUM>, a processor of a computing device communicatively coupled to the MRI scanner <NUM>, or a combination thereof. The memory <NUM> can include a memory component of the MRI scanner <NUM>, a memory component of a computing device communicatively coupled to the MRI scanner <NUM>, or a combination thereof. The memory <NUM> includes computer executable instructions, which when executed by the one or more processors <NUM>, cause the one or more processors <NUM> to perform methods for STAGE imaging described herein. The memory <NUM> can store MRI data acquired by the MRI scanner <NUM>, and the processor(s) <NUM> can access such data from the memory <NUM>. The memory <NUM> can receive and store images generated by the processor(s) <NUM> based on the MRI data acquired by the scanner <NUM>.

The display device <NUM> can include a cathode ray tube (CRT) display, a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a plasma display panel (PDP), a liquid crystal display (LCD), or other display known to a person of ordinary skill in the art. The display device <NUM> may be a stand-alone device or a display of a computing device (e.g., a desktop, laptop, or tablet) communicatively coupled to the MRI scanner <NUM>. The display device <NUM> can include a touch screen. The display device <NUM> can receive image data from the processor <NUM> or the memory <NUM> and display the received image data. For example, upon reconstructing MRI images based on data acquired by the MRI scanner <NUM>, the processor <NUM> can provide the reconstructed images for display on the display device <NUM>. The signal intensity for an RF spoiled gradient echo data acquisition as a function of flip angle θ can be described as: <MAT> where T1 is the longitudinal relation time, SD is the spin density, TR is the repetition time, TE is the echo time, B1t is the transmit RF field, B1r is the receive RF field (also referred to as RF coil sensitivity or bias field), and E1 = exp(-TR/T<NUM>). Here we have assumed that the B1t term is normalized to unity when the correct flip angle is obtained. Multiplying both sides of equation (<NUM>) by <MAT>, equation (<NUM>) can be rewritten as: <MAT> where SDeff = SD · B<NUM>r. e-TE/T2* represents the effective spin density.

According to equation (<NUM>), <MAT> can be viewed as a linear function of <MAT>. Specifically, for data collected for different nominal excitation flip angles, one can fit the transformed data to a line with slope E<NUM> and ordinate intercept (or x-axis crossing) at SDeff ·(<NUM>-E<NUM>). As such, one can determine the value of E<NUM> by computing the slope of a line defined by at least two data points corresponding to at least two FAs in the <MAT> coordinate system. Also, using the determined E<NUM> value and the point of intersection between the x-axis and line formed by the at least two data points one can determine the value of SDeff. However, due to inhomogeneities of the B1t field, the measured flip angle may be different from the actual flip angle value chosen to run the scan and this can lead to significant error in the estimation of T<NUM>, especially at high magnetic fields. Therefore, an accurate estimation of T<NUM> calls for an accurate knowledge of the transmit RF field B1t.

For low FAs and where TR << T1, equation (<NUM>) can be approximated as: <MAT> where the angle θE is defined as cosθE = exp(-TR/T<NUM>). Using equation (<NUM>), the apparent spin density (SDapp) and the apparent longitudinal relaxation time (T1app) can be derived as: <MAT> <MAT> Considering equations (<NUM>)-(<NUM>), the apparent spin density SDapp is linearly proportional to B1t, and the apparent longitudinal relaxation time T1app is linearly proportional to B1t<NUM>. Thus a <NUM>% error in B1t (or a <NUM>% in FA if the actual FA is θ' = B1t θ), can result in a <NUM>% error in T<NUM> estimation and a <NUM>% error in SD estimation. Hence, accurate reconstruction of T<NUM> and/or SD maps calls for accurate estimation of the B1t map.

<FIG> is a flowchart illustrating a method <NUM> for magnetic resonance imaging (MRI). In brief overview, the method <NUM> includes obtaining (or receiving) a first magnetic resonance (MR) dataset corresponding to a first flip angle and a second MR dataset corresponding to a second flip angle (step <NUM>), and generating an apparent longitudinal relaxation time (T1app) map using the first MR data, the second MR data and a constant value for a transmit radio frequency (RF) field within the anatomical region (step <NUM>). The method <NUM> also includes estimating a first transmit RF field map by scaling the T1app map by a first constant value of longitudinal relaxation time (T<NUM>) (step <NUM>), estimating a second transmit RF field map by scaling the T1app map by a second constant value of T<NUM> (step <NUM>), and generating a third transmit RF field map using the estimated first transmit RF field map and the estimated second transmit RF field map (step <NUM>).

The method <NUM> includes obtaining (or receiving) a first magnetic resonance (MR) dataset corresponding to a first flip angle and a second MR dataset corresponding to a second flip angle (step <NUM>). The scanner <NUM> can acquire the first MR dataset and the second MR dataset by imaging an anatomical region using at least one echo time TE. The processor <NUM> can provide a user interface (UI), e.g., on the display device <NUM>, to allow a user to select settings for MR data acquisition. The processor <NUM> can cause the MR scanner <NUM> to image the anatomical region according to the selected settings. The selected settings can indicate MR data acquisitions using two separate FAs. The selected settings may be indicative of spoiled gradient echo MR data acquisition (e.g., using one or more spoiled gradient echo MR sequences). The anatomical region to be imaged can include a human brain or other organ or part of a patient's body.

The processor <NUM> can cause the MR scanner <NUM> (or the respective RF coils) to excite the anatomical region with RF pulses and record MR signals generated by the anatomical region according to the selected settings. In particular, the receive RF coils of the MR scanner <NUM> can record a first set of MR signals associated with a first FA and a second set of MR signals associated with a second FA. The relationship between the recorded MR signals and the corresponding FAs satisfies equation (<NUM>) and/or (<NUM>). Obtaining the first and second MR data sets can include the processor <NUM> receiving the recorded signals from the MR scanner <NUM> and generating a respective MR image for each MR dataset (or set of MR signals).

Referring to <FIG>, two MR images of a brain corresponding to two different FAs are shown. For example, <FIG> shows an image <NUM> of MR data acquired using a FA equal to <NUM>° and <FIG> shows an image <NUM> of MR data acquired using a FA equal to <NUM>°. The repetition time TR used in acquiring the MR data illustrated in <FIG> is equal to <NUM> milliseconds (ms). The values of the FAs associated with the images in <FIG> are chosen for illustrative purposes and are not to be interpreted as limiting. For example, other angle values (other than <NUM>° and/or <NUM>°) can be used for MR data acquisition given that the low FA (among the two FAs) is less than the Ernst angle and the large FA is greater than the Ernst angle. The processor <NUM> can reconstruct the images shown in <FIG> by taking the inverse Fourier transform of the recorded signals associated with the corresponding FAs, respectively.

Referring back to <FIG>, the method <NUM> includes generating an apparent longitudinal relaxation time (T1app) map using the first MR dataset, the second MR dataset, and a constant value for a transmit RF field B1t within the anatomical region (step <NUM>). The T1app map represents a spatial distribution of T1app within the anatomical region. In general and considering equation (<NUM>), T1app can be spatially varying even within a region associated with a single tissue due to the spatial inhomogeneity of B1t. For instance, T1app values can have a relatively large variance within a region representing one tissue. If there is a standard B1t available for all people, then that can be used to find local T<NUM> values from person to person. However, the lack of such standard value (or standard map) of B1t calls for methods or techniques to accurately estimate the B1t map. Generating the T1app map (step <NUM>) can be viewed as determining a first estimate of the T1app map to be used to reconstruct the B1t map.

The processor <NUM> can calculate T1app on a pixel-by-pixel basis using equation (<NUM>) and assuming a normalized constant value for B1t, e.g., equal to <NUM>, within the anatomical region. Specifically, the processor <NUM> can first determine E1 for each pixel as the slope in equation (<NUM>) of a line defined based on the data points associated with the different FAs. Specifically, considering data points S(θ<NUM>) and S(θ<NUM>) corresponding to FAs θ<NUM> and θ<NUM>, respectively, E1 can be computed as <MAT> The processor <NUM> can determine an estimate of T<NUM> using the computed E1 and the repetition time TR since E1 = exp(-TR/T<NUM>). The processor <NUM> can then compute T1app for each pixel according to equation (<NUM>) using the estimated value of T<NUM> and the constant value of B1t, and therefore generate the T1app map. Referring to <FIG>, an image <NUM> of the T1app map is depicted. The T1app map shown in <FIG> is computed as described above on a pixel by pixel basis using the MR data shown in <FIG> and a normalized constant value of B1t equal to <NUM>. The MR data is acquired using a value of TR equal to <NUM>.

The method <NUM> includes the processor <NUM> estimating a first transmit RF field map by scaling the T1app map by a first constant value of T<NUM> (step <NUM>), and estimating a second transmit RF field map by scaling the T1app map by a second constant value of T<NUM> (step <NUM>). The first constant value of T<NUM> corresponds to a first tissue type within the anatomical region and the second constant value of T<NUM> corresponds to a second tissue type within the anatomical region. For example, the first tissue type can be white matter and the second tissue type can be gray matter. In general, there is no three-dimensional (3D) reliable and accurate standard for validating human brain T<NUM> measurements. Also, T<NUM> can be influenced by many factors, such as temperature, chemical exchange and perfusion. Values of T<NUM> values for both gray matter and white matter recorded by various researchers show substantial variations. However, despite such variations, the ratio of T<NUM> values for gray matter divided by T<NUM> values for white matter appears fairly stable across various measurements by different researchers. Specifically, the ratio is about <NUM>. Accordingly, the first constant value of T<NUM> can be set to <NUM>, whereas the second constant value of T<NUM> can be set to <NUM>. In some implementations, other values of T<NUM> for the white matter and gray matter can be selected. For example, the T<NUM> value for the white matter can be set to any value in the range of <NUM> to <NUM>, and the T<NUM> value for the gray matter can be set to be equal <NUM> times the T<NUM> value for the whit matter. Also, for other types of tissue (e.g., other than gray matter and/or white matter), different T<NUM> values can be used.

The processor <NUM> can generate the first estimate of the B1t map by scaling (or dividing) the T1app value at each pixel by the first constant value of T<NUM>. Referring to <FIG>, an image <NUM> of the first estimate of the B1t map generated using the T1app map shown in <FIG>. The first estimate of the B1t map shown in <FIG> is computed using a constant value of T<NUM> equal to <NUM> and corresponding to white matter. The processor <NUM> can generate the second estimate of the B1t map by scaling (or dividing) the T1app value at each pixel by the second constant value of T<NUM>. The processor <NUM> can also generate masks for the first issue type and the second issue type within the anatomical region using at least one of the first estimate and the second estimate of the B1t map. For instance, the processor <NUM> can use the first estimate of the B1t map determined using a constant value of T<NUM> corresponding to white matter to generate a mask of white matter and a mask of gray matter. The estimates of the B1t map are characterized by low spatial frequency content and can be adequate for segmenting the anatomical region into various regions corresponding to different tissue types. The processor <NUM> can apply a high-pass filter to the first estimate of the B1t map to distinguish between white matter and gray matter regions. The processor <NUM> may apply a low-pass filter to the first estimate of the B1t map to determine the white matter region, and apply a high-pass filter to the first estimate of the B1t map to determine the gray matter region. The processor <NUM> can also apply an erosion algorithm to each mask to avoid interference or overlap between regions associated with different tissue types. For each of the masks generated by the processor <NUM>, pixels associated with the corresponding tissue type can have a value equal to <NUM> (or a non-zero value) whereas other pixels can have a value equal to zero.

Referring to <FIG>, images representing masks for white matter and gray matter are shown. The image <NUM> represents a mask of the white matter, and the image <NUM> represents a mask of the gray matter. Both masks are generated using the first estimate of the B1t map shown in <FIG>.

At step <NUM>, the processor <NUM> generates a third B1t map using the estimated first B1t and the estimated second B1t map. Referring to <FIG>, plots of the first estimate of B1t and the second estimate of B1t across a line within the anatomical region are shown. The plot <NUM> represents the first estimate of B1t (or an estimate of B1t using a constant value of T<NUM> corresponding to white matter) across the line within the anatomical region. The plot <NUM> represents the second estimate of B1t (or an estimate of B1t using a constant value of T<NUM> corresponding to gray matter) across the line within the anatomical region. Comparing the two plots <NUM> and <NUM>, one can see that the first estimate of B1t and the second estimate of B1t are substantially shifted versions of one another. Generating the third B1t map can include the processor <NUM> shifting at least one of the first and second estimated maps of B1t until both the first and second estimated maps of B1t substantially overlap. For example, the processor <NUM> can shift the first estimated map of B1t by a shift value such that the mean square error between the shifted first estimated map of B1t and the second estimated map of B1t is minimized.

The processor <NUM> can generate the third B1t map using the shifted first estimated map of B1t, the second estimated map of B1t, and the masks corresponding to the first and second tissue types. In particular, the processor can (i) multiply (i.e., pixel by pixel multiplication) the mask corresponding to the first tissue type (e.g., white matter) with the shifted first estimated map of B1t, (ii) multiply (i.e., pixel by pixel multiplication) the mask corresponding to the second tissue type (e.g., gray matter) with the second estimated map of B1t, and (iii) merge the results of these multiplications to form a merged map of B1t (or the third B1t map). The processor <NUM> may apply local quadratic fitting to the merged map of B1t (or the third B1t map) to fill in (or assign values to) any pixels that do not belong to any of the generated masks.

Referring to <FIG>, plots associated with the merged B1t map with and without local quadratic fitting are depicted. The plot <NUM> represents values of the merged B1t across a line within the anatomical region. The plot <NUM> represents the same values of the merged B1t across the line within the anatomical region after applying local quadratic fitting. Local quadratic fitting can be achieved using a local quadratic fitting matrix of size m x m, where m is an integer. is used to create a smooth less noisy result throughout the entire n x n image. As illustrated in <FIG>, the local quadratic fitting operation leads to a smooth and less noise map of B1t.

Referring to <FIG>, an image <NUM> illustrating an example merged B1t map is shown. In particular, the image <NUM> represents the resulting image when merging the multiplicative product of the mask <NUM> corresponding to the white matter and the shifted first estimated map of B1t and the multiplicative product of the mask <NUM> corresponding to the gray matter and the second estimated map of B1t. The black regions in the image <NUM> represent pixels or regions that belong neither to mask <NUM> nor to mask <NUM>.

Referring to <FIG>, an image <NUM> of B1t map obtained after applying local quadratic fitting to image <NUM> is shown. As illustrated in image <NUM>, the local quadratic fitting allows for filling in the gaps in the merged B1t shown in FIG. The B1t map shown in image <NUM> provides a reliable estimate of B1t within the anatomical region and allows for accurate reconstruction of the T<NUM> map and/or the SD map.

While the method <NUM> is described above with respect to two tissue types, the anatomical region can include more than two different tissue types. For instance, the anatomical region can include three distinct tissue types. In such instance, the processor <NUM> can further determine another estimate of the B1t map by scaling the T1app map (generated at step <NUM>) by a third constant value of T<NUM> associated with the third tissue type within the anatomical region. The processor <NUM> can also generate a third mask representative of the region occupied by the third tissue type within the anatomical region. For example, the processor <NUM> can use different filters (e.g., low-pass filter(s), band-pass filter(s), and/or a high-pass filter(s)) to generate each of the three masks. The processor <NUM> can update the third B1t map using the map of B1t estimated based on the third constant value of T<NUM> (e.g., according to the third mask). Alternatively, the processor <NUM> can (initially) generate the third B1t map using the three estimates of the B1t map (e.g., based on the three masks corresponding to the three tissue types). For instance, the third tissue type can be cerebro-spinal fluid and the processor <NUM> can (i) multiply the first B1t map estimated based the constant T<NUM> value for white matter by the first mask, (ii) multiply the second B1t map estimated using the constant T<NUM> value for gray matter by the second mask, (iii) multiply the B1t map estimated using the constant T<NUM> value for cerebro-spinal fluid by the third mask, and (iv) so on for other tissues. The constant T<NUM> value for cerebro-spinal fluid can be equal to <NUM>. The processor <NUM> can then merge the resulting images (from the multiplications with masks) to create a single more accurate B1t map image.

In some implementations, the processor <NUM> may remove local noise spikes by using, for example, a sliding window to calculate the mean and standard deviation in the window and remove those points (or pixels) that lie beyond a given threshold value (e.g., three standard deviations of the noise) above or below the mean. The processor <NUM> can apply local quadratic fitting as described above with regard to <FIG> to the merged B1t map using, for example, a local quadratic fitting matrix of size m x m to provide a smooth and less noisy B1t map image, where m is an integer. In some implementations, the integer m can be equal to <NUM>. However, other values for m may be used. While in the case of the human brain the tissue types can include white matter, gray matter, and cerebro-spinal fluid, the methods and systems are to be interpreted as limited to the human brain or to these tissue types. In general, the methods described herein are not to be restricted to a specific anatomical region or to a specific number of tissue types within the anatomical region.

The method <NUM> can further include the processor <NUM> using the B1t map determined at step <NUM> to generate a first estimate of the T<NUM> map. The processor <NUM> can use equation (<NUM>) and divide the T1app map determined at step <NUM> by the square of the B1t map determined at step <NUM>. Specifically, for each pixel or voxel x of the anatomical region, the processor <NUM> can compute the corresponding T<NUM> value as <MAT>. As such, this first estimate of the T<NUM> map accounts for the variation in the distribution of B1t within the anatomical region, and may be referred to as the corrected T<NUM> map.

The processor <NUM> can further generate an apparent spin density (SDapp) map using the first MR dataset and the second MR dataset. The SDapp map represents the spatial distribution of SDapp within the anatomical region. The processor <NUM> can determine the SDapp map using equation (<NUM>) and the constant value of B1t assumed at step <NUM>. For example, the processor <NUM> can solve equation (<NUM>) for SDapp at each pixel using data points from the datasets corresponding to the FAs θ<NUM> or θ<NUM> and the constant value of B1t assumed at step <NUM>. Alternatively, based on equation (<NUM>) and referring back to step <NUM>, the processor <NUM> can determine SDeff · (<NUM> - E1) at each pixel based on the intersection of the line defined by the data points <MAT> and <MAT> with the x-axis in the <MAT> coordinate system when assuming a normalized constant value of B1t (e.g., B1t = <NUM>). Since E<NUM> is already determined at step <NUM>, the processor <NUM> can generate a map of SDeff by dividing the value corresponding to the intersection with the x-axis by <NUM> - E<NUM> at each pixel, and multiply the SDeff map by the constant value of B1t assumed at step <NUM> to generate the SDapp map.

The processor <NUM> can generate a first estimate of the SD map by dividing the determined SDapp map by the B1t map determined at step <NUM>. According to Equation (<NUM>), for each pixel or voxel within the anatomical region, the processor <NUM> can scale (or divide) the SDapp value at that pixel by the B1t value at the same pixel (from the B1t map generated at step <NUM>). The generated first estimate of the SD map is an estimate of the SDeff map since SDeff = SD · B1r · e-TE/T2*. This first estimate of the SD map accounts for the variation in the distribution of B1t (but not the variation in the distribution of B1r). Once the B1r data are obtained, a corrected SD map can be obtained via SDcor = SD · e-TE/T2* and may be referred to as the corrected SD map. Finally, using multiple echoes to calculate T2*, the absolute SD can be obtained from the formula SD = SDcor · eTE/T2*.

Referring to <FIG>, images <NUM>, <NUM> and <NUM> illustrate examples of an estimate of a T<NUM> map, an estimate of a SDapp map, and an estimate of a SD map, respectively. The T<NUM> map shown in image <NUM> is generated by scaling (on a pixel by pixel basis) the T1app map in in image <NUM> (shown in <FIG>) by the square of B1t shown in image <NUM> (<FIG>). The estimate of the SDapp map in image <NUM> is generated using Equation (<NUM>), one of the datasets illustrated in images <NUM> or <NUM> (<FIG>). The estimate of the SD map (or SDeff map) of image <NUM> is generated by dividing the SDapp map of image <NUM> by the B1t map of image <NUM> (<FIG>).

The processor <NUM> can use the estimated B1t map (determined at step <NUM>), the corrected T<NUM> map and the corrected SD map to estimate (or generate) a map of B1r. In particular, the processor <NUM> can employ the estimated B1t map as well as the corrected T<NUM> and SD maps to synthesize a MR dataset corresponding to a specific flip angle that can make a first region associated with a first tissue type (e.g., white matter) and a second region associated with a second tissue type (e.g., gray matter) isointense. The processor <NUM> can synthesize such a MR dataset without additional MR data acquisition via the MRI scanner <NUM>, but rather by using Equation (<NUM>). The processor <NUM> may further vary the echo time TE to create a MR dataset where three regions corresponding to three different tissue types (e.g., white matter, gray matter, and cerebro-spinal fluid) are isointense. The processor <NUM> can scale the SD map estimate (e.g., the SD map shown in image <NUM> of <FIG>) by the estimated B1r map to alleviate the effect of the spatial variation of B1r within the anatomical region and achieve an improved estimate of the SD map.

Referring to <FIG>, example signals corresponding to different tissue types over a range of FAs are shown. The signal <NUM> corresponds to white matter (or WM), signal <NUM> corresponds to gray matter (or GM), and signal <NUM> corresponds to cerebro-spinal fluid (or CSF). The signal <NUM> represents the difference between signals <NUM> and <NUM>, the signal <NUM> represents the difference between signals <NUM> and <NUM>, and the signal <NUM> represents the difference between signals <NUM> and <NUM>. The signals <NUM>, <NUM>, and <NUM> were generated using constant T<NUM> values equal to <NUM> within white matter, <NUM> within gray matter, and <NUM> within cerebro-spinal fluid. The repetition time TR is equal to <NUM>, the white matter SD is equal to <NUM> and the SD of gray matter is equal to <NUM>. Also, the T<NUM>* values of <NUM> and <NUM> were used for white matter and gray matter, respectively. signals <NUM> and <NUM> intersect at a FA about (or slightly above) <NUM>°. By making different tissues isointense, the processor <NUM> can avoid the need to determine the local spin density. Since all signals are isointense, the amplitude variation in the synthesized dataset represents the variation in the B1r field. Accordingly, the processor <NUM> can determine (or generate) a B1r map based on the synthesized data set. The processor <NUM> may also employ local quadratic fitting in estimating the B1r map. The B1r map used to correct the initial images can be defined as the signal from the isointense image normalized to the value of B1r in the center of the image.

Referring to <FIG>, images illustrating an example synthesized MR dataset, an example receive RF field, and an example scaled spin density map are shown. Image <NUM> represents a double isointense MR dataset where the FA is selected such that the white matter and the gray matter are isointense. The image <NUM> represents a B1r map generated (or estimated) based on the isointense MR dataset of image <NUM>. The isointense MR dataset in image <NUM> includes gaps (black regions or pixels). Such gaps are eliminated in the B1r map of image <NUM> by using local quadratic fitting. In some implementations, the processor <NUM> may scale the SD map estimate (e.g., the SD map shown in image <NUM> of <FIG>) by the B1r map estimated based on the synthesized dataset to alleviate the effect of the spatial variation of B1r within the anatomical region on the estimated SD map. The image <NUM> represents a scaling (pixel-by-pixel scaling) of the SD map in image <NUM> by the B1r map of image <NUM>. Comparing the images <NUM> and <NUM>, one can see that the SD map in image <NUM> provides an improved representation of the various sub-regions within the anatomical region compared to image <NUM>.

In some implementations, the processor <NUM> (or some other computing device) can estimate a plurality of B1t maps for a plurality of subjects (e.g., patients or volunteers). The processor <NUM> may estimate the B1t map for each subject as described above with regard to method <NUM>. For each subject, the processor <NUM> can transform the corresponding B1t map for each subject into a respective template of the anatomical region (e.g., brain). The processor <NUM> can accomplish this by first transforming the magnitude image of the first dataset into a template brain (e.g., using Insight Segmentation, and Registration Toolkit (ITK)). The processor <NUM> can use the resulting transformation to map the B1t field into the template space. The processor <NUM> can then average the B1t values in the templates associated with the plurality of subjects into a common B1t template. In some implementations, the averaging can be a weighted averaging. For example, some templates associated with some subjects may be weighted more or less than other templates. The processor <NUM> can use the common (or average) B1t template as an estimate of the B1t map for various patients by performing an inverse transform back to the patient space. For instance, the common (or average) B1t template can be computed offline and used in reconstructing T<NUM> maps or SD maps (as described above with regard to the B1t map generated at step <NUM>) for various patients. By using the common (or average) B1t template, the processor <NUM> may avoid generating a B1t map for each new scanned subject and instead use the pre-computed common (or average) B1t template. In some implementations, the processor can use both the precomputed common (or average) B1t template and an estimate of the B1t map specific to the patient to generate separate images of the anatomical region (e.g., separate images of the T<NUM> map).

The processor <NUM> (or some other computing device) can also generate a common (or average) B1r template. The processor <NUM> can estimate a plurality of B1r maps for a plurality of subjects (e.g., patients or volunteers), for example, based on synthesized MR datasets with isointense regions for separate subjects as described above. For each subject, the processor <NUM> can transform the corresponding B1r map into a respective template of the anatomical region (e.g., brain), and average (e.g., weighted or non-weighted averaging) the templates for various subjects to generate a common (or average) B1r template. The common (or average) B1r template can be computed offline, and the processor <NUM> can use the common (or average) B1r template as an estimate of the B1r map for various patients. The availability of a precomputed common (or average) B1r template can allow avoiding the construction (or computation) of a respective B1r map for each newly scanned patient. In some implementations, the processor can use both the precomputed common (or average) B1r template and an estimate of the B1r map specific to the patient to generate separate images of the anatomical region.

Referring to <FIG>, examples of brain templates for the B1t and B1r map are shown. The upper row of images illustrates an example common (or average) brain template for the B1t map. This common (or average) template represents an estimate of the B1t map that can be used for various subjects. The lower row of images illustrates an example common (or average) brain template for the B1r map. This common (or average) template represents an estimate of the B1r map that can be used for various subjects.

For each scanned subject, the processor <NUM> can inverse transform the average B1t template (e.g., using ITK) and/or the average B1r template for that specific subject. For instance, the processor can adjust the common (or average) B1t template and/or the common (or average) B1r template to conform with the size of the anatomical region (e.g., brain) for a specific patient. The processor <NUM> may use the inverse transformed common (or average) B1t template to estimate the T<NUM> map for that specific patient. In particular, the processor <NUM> can scale (or divide on a pixel-by-pixel or voxel-by-voxel basis) the T1app map for the patient by the square of the inverse transformed common (or average) B1t template to generate an estimate of the T<NUM> map. Such estimate can either substitute the estimate based on an estimated B1t map specific to the patient or can be an additional (or second) estimate of the T<NUM> map. The processor <NUM> may also use the common (or average) B1t when estimating the SD (or SDeff) map for the patient. In particular, the processor <NUM> may scale (or divide on a pixel-by-pixel or voxel-by-voxel basis) the SDapp map for the patient by the inverse transformed common (or average) B1t template to generate an estimate of the SD map.

The processor <NUM> can also scale the original datasets (corresponding to FAs θ<NUM> and θ<NUM>) acquired at step <NUM> by the B1r map estimated based on the synthesized dataset, and generate a MR image of the anatomical region by subtracting the scaled datasets. The generated MR image can illustrate the various tissue types within the anatomical regions based on the respective T<NUM> values. The processor <NUM> can employ a weighted subtraction when subtracting a first scaled MR dataset corresponding to the first FA from the other scaled dataset corresponding to the second FA. In some implementations, the processor <NUM> can scale the original datasets (corresponding to FAs θ<NUM> and θ<NUM>) acquired at step <NUM> by the common (or average) B1r template computed or reconstructed offline (or an inverse transformation thereof). In some implementations, the processor <NUM> may scale the SD map estimate (e.g., the SD map shown in image <NUM> of <FIG>) by the common (or average) B1r template to alleviate the effect of the spatial variation of B1r within the anatomical region on the estimated SD map.

In some implementations, the processor <NUM> can subtract (without scaling) images corresponding to the original datasets (corresponding to FAs θ<NUM> and θ<NUM>) acquired at step <NUM> to generate another (or an alternative) MR image of the anatomical region. Such MR images can illustrate the various tissue types within the anatomical regions based on the respective T<NUM> values.

Referring to <FIG>, images of scaled MR datasets and subtraction MR images are illustrated. Images <NUM> and <NUM> represent scalings of images <NUM> and <NUM>, respectively, by the B1r map in image <NUM>. The images <NUM> and <NUM> represent an improvement over the corresponding original images <NUM> and <NUM> since the scaling by the B1r map alleviates the effect of the spatial variation of B1r within the anatomical region. However, images <NUM> and <NUM> still suffer from the effect of the spatial variation of B1t within the anatomical region. The image <NUM> represents a subtraction between the images <NUM> and <NUM> (e.g., subtracting the image corresponding to the low FA from the image corresponding to the large FA) corresponding to the original acquired datasets for the two FAs. The image <NUM> represents a subtraction between the scaled images <NUM> and <NUM> (e.g., subtracting the image corresponding to the low FA from the image corresponding to the large FA). While both images <NUM> and <NUM> provide an improved representation of the various tissues within the anatomical region compared to the images <NUM> and <NUM> corresponding to the original acquired data, the image <NUM> represents an improvement over image <NUM> since the effect of the spatial variation of B1r within the anatomical region is eliminated (or alleviated) in the former image <NUM>.

The methods and system described herein provide various techniques for generating improved images of anatomical regions scanned using two flip angles. These methods and system are not to be interpreted as limited to human brain and can be used for other anatomical regions. Also, while this disclosure describes various techniques described for generating various MR images (e.g., of the estimated T<NUM> map, the estimated SD map, the scaled SD map, the subtraction MR image, the scaled subtraction MR image, the estimated B1t map, the estimated B1r map, the common B1t template, or the common B1r template), this disclosure should be interpreted as encompassing various combinations of such techniques or corresponding MR images. Furthermore, the process <NUM> can apply the techniques for generating various MR images described herein for multiple echo times. For instance, the processor <NUM> can apply averaging or weighted averaging to MR images (e.g., B1t maps, B1r maps, T1app maps, T<NUM> maps, SDapp maps, SD maps, etc.) generated based on datasets associated with the various echo times to provide improved images of anatomical regions.

Claim 1:
A method for magnetic resonance imaging (MRI), comprising:
receiving (<NUM>), by at least one processor (<NUM>), a first magnetic resonance (MR) gradient echo dataset corresponding to a first flip angle and a first spoiled gradient echo MR sequence and a second MR gradient echo dataset corresponding to a second flip angle and a second spoiled gradient echo MR sequence, the first MR gradient echo dataset and the second MR gradient echo dataset acquired by imaging an anatomical region using at least one echo time, the first flip angle different from the second flip angle;
generating (<NUM>), by at least one processor (<NUM>), an apparent longitudinal relaxation time (T1app) map, representing a spatial distribution of T1app within the anatomical region using the first MR gradient echo dataset, the second MR gradient echo dataset and a constant value for a transmit radio frequency (RF) field within the anatomical region;
estimating (<NUM>), by the at least one processor (<NUM>), a first transmit RF field map by scaling the T1app map by a first constant value of longitudinal relaxation time (T<NUM>), the first constant value of T<NUM> associated with a first tissue type within the anatomical region;
estimating (<NUM>), by the at least one processor (<NUM>), a second transmit RF field map by scaling the T1app map by a second constant value of T<NUM>, the second constant value of T<NUM> associated with a second tissue type within the anatomical region; and
generating (<NUM>), by the at least one processor (<NUM>), a third transmit RF field map using a first region of the estimated first transmit RF field map corresponding to the first tissue type in the anatomical region and a second region of the estimated second transmit RF field map corresponding to the second tissue type in the anatomical region, the third transmit RF field map representing a spatial distribution of the transmit RF field within the anatomical region.