U.S. patent application number 17/060860 was filed with the patent office on 2021-06-03 for automatic imaging plane planning and following for mri using artificial intelligence.
This patent application is currently assigned to Shanghai United Imaging Intelligence Co., LTD.. The applicant listed for this patent is Shanghai United Imaging Intelligence Co., LTD.. Invention is credited to Terrence Chen, Xiao Chen, Zhang Chen, Shanhui Sun.
Application Number | 20210161422 17/060860 |
Document ID | / |
Family ID | 1000005152899 |
Filed Date | 2021-06-03 |
United States Patent
Application |
20210161422 |
Kind Code |
A1 |
Chen; Xiao ; et al. |
June 3, 2021 |
AUTOMATIC IMAGING PLANE PLANNING AND FOLLOWING FOR MRI USING
ARTIFICIAL INTELLIGENCE
Abstract
A method includes acquiring initial scout images of a patient's
heart, using a neural network to establish a patient specific heart
model, and automatically plan imaging planes of the patient
specific heart model, performing an accelerated scan of the
patient's heart, using the neural network to determine a current
location and pose of the patient's heart from the accelerated scan,
and to reposition the imaging planes to correspond to the current
location and pose of the patient's heart, and using the
repositioned imaging planes to perform an acquisition scan and
generate an image of the patient's heart from the acquisition scan
according to a selected imaging protocol.
Inventors: |
Chen; Xiao; (Cambridge,
MA) ; Sun; Shanhui; (Cambridge, MA) ; Chen;
Zhang; (Cambridge, MA) ; Chen; Terrence;
(Cambridge, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Shanghai United Imaging Intelligence Co., LTD. |
Shanghai |
|
CN |
|
|
Assignee: |
; Shanghai United Imaging
Intelligence Co., LTD.
Shanghai
CN
|
Family ID: |
1000005152899 |
Appl. No.: |
17/060860 |
Filed: |
October 1, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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62941904 |
Nov 29, 2019 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01R 33/5612 20130101;
A61B 5/055 20130101; A61B 5/7264 20130101; G06N 3/084 20130101;
A61B 5/0044 20130101; G01R 33/5615 20130101 |
International
Class: |
A61B 5/055 20060101
A61B005/055; A61B 5/00 20060101 A61B005/00; G06N 3/08 20060101
G06N003/08; G01R 33/561 20060101 G01R033/561 |
Claims
1. A method comprising: acquiring initial scout images of a
patient's heart; using a neural network to establish a patient
specific heart model, and automatically plan imaging planes of the
patient specific heart model; performing an accelerated scan of the
patient's heart; using the neural network to determine a current
location and pose of the patient's heart from the accelerated scan,
and to reposition the imaging planes to correspond to the current
location and pose of the patient's heart; and using the
repositioned imaging planes to perform an acquisition scan and
generate an image of the patient's heart from the acquisition scan
according to a selected imaging protocol.
2. The method of claim 1, comprising acquiring the initial scout
images from standard MRI body views.
3. The method of claim 1, wherein the initial scout images comprise
2D or 3D images from one or more of axial sagittal and coronal
views.
4. The method of claim 1, wherein using the neural network to
determine a current location and pose of the patient's heart from
the accelerated scan comprises reconstructing an image from the
accelerated scan and comparing the reconstructed image from the
accelerated scan to the patient specific heart model.
5. The method of claim 1, further comprising comparing the current
location and pose of the patient's heart to the location and pose
of the patient specific heart model, and repositioning the imaging
planes obtained from the patient specific heart model to correspond
to the current location and pose of the patient's heart.
6. The method of claim 1, wherein the neural network comprises one
or more of a combination of CNN and RNN models, a GRU model, an
LSTM model, a fully convolutional neural network model, a
generative adversarial network, a back propagation neural network
model, a radial basis function neural network model, a deep belief
nets neural network model, an Elman neural network model.
7. The method of claim 1, wherein the accelerated scan comprises
one or more of compressed sensing; parallel imaging; or fast spin
echo techniques to allow for acquisition in less time of a reduced
amount of data than required to support a higher resolution or
larger field of view.
8. The method of claim 1, wherein the selected imaging protocol
comprises one or more of obtaining anatomic images of the heart,
determining cardiac function, or determining myocardial
viability.
9. A system comprising: an MRI scanner; and a processing engine
coupled to the MRI scanner, the processing engine comprising a
processor and a memory comprising computer readable program code,
wherein the processor under control of the computer readable
program code is operable to: acquire initial scout images of a
patient's heart; use a neural network to establish a patient
specific heart model, and automatically plan imaging planes of the
patient specific heart model; perform an accelerated scan of the
patient's heart; use the neural network to determine a current
location and pose of the patient's heart from the accelerated scan,
and to reposition the imaging planes to correspond to the current
location and pose of the patient's heart; and cause the MRI scanner
to use the repositioned imaging planes to perform an acquisition
scan and generate an image of the patient's heart from the
acquisition scan according to a selected imaging protocol.
10. The system of claim 9, wherein the processor under control of
the computer readable program code is operable to cause the MRI
scanner to acquire the initial scout images from standard MRI body
views.
11. The system of claim 9, wherein the initial scout images
comprise 2D or 3D multi-slice images from one or more of axial
sagittal and coronal views.
12. The system of claim 9, wherein the processor under control of
the computer readable program code is operable to use the neural
network to determine a current location and pose of the patient's
heart from the accelerated scan by reconstructing an image from the
accelerated scan and comparing the reconstructed image from the
accelerated scan to the patient specific heart model.
13. The system of claim 9, wherein the processor under control of
the computer readable program code is operable to use the neural
network to compare the current location and pose of the patient's
heart to the location and pose of the patient specific heart model,
and reposition the imaging planes obtained from the patient
specific heart model to correspond to the current location and pose
of the patient's heart.
14. The system of claim 9, wherein the neural network comprises one
or more of a combination of CNN and RNN models, a GRU model, an
LSTM model, a fully convolutional neural network model, a
generative adversarial network, a back propagation neural network
model, a radial basis function neural network model, a deep belief
nets neural network model, an Elman neural network model.
15. The system of claim 9, wherein the accelerated scan comprises
one or more of compressed sensing; parallel imaging; or fast spin
echo techniques.
16. The system of claim 9, wherein the selected imaging protocol
comprises one or more of obtaining anatomic images of the heart,
determining cardiac function, or determining myocardial viability.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application claims the benefit of U.S. Provisional
Application No. 62/941,904, filed 29 Nov. 2019, which is
incorporated by reference herein in its entirety.
BACKGROUND
[0002] The aspects of the present disclosure relate generally to
Magnetic Resonance Imaging (MRI), and in particular to predicting
cardiac signals from MRI data. MRI is a widely used medical
technique which produces images of a region of interest using
magnetic and radio frequency energy. During an MRI scan, volume
coils (for example, body coils) and local coils (for example,
surface coils) may acquire MR signals produced by nuclear
relaxation inside the object being examined. Cardiac MR imaging is
widely regarded as one of the most complex examinations utilizing
magnetic resonance due to the patient's respiratory and cardiac
motions. In a conventional scanning workflow, obtaining target
heart views is typically performed through a multi-step approach
100 as illustrated in FIG. 1. As shown in block 102, scout images
and multi-slice localizer images, typically in the form of a set of
three plane, low resolution, large field of view images, may be
initially acquired to determine an approximate location of the
heart in the patient's body, including the standard heart views:
the short axis view corresponding to the echocardiographic
parasternal short axis plane; the horizontal long axis view
corresponding to the echocardiographic apical 4-chamber plane; and
the vertical long axis view corresponding to the echocardiographic
apical 2-chamber plane. The localizer images may be used to
manually plan slices through the standard heart views, as shown in
block 104.
[0003] The image plane planning of the subsequently performed
cardiac MR acquisition scans, for example, cine or functional
scans, relies on the views determined above, and the planning is
generally accomplished by performing an acquisition scan, as shown
in block 106, and referring or copying the slice locations from the
pre-determined standard heart views to the acquisition scan, as
shown in block 108. However, patient motion and inconsistency of
breath-holding positions in between scans may introduce
mis-registrations of the slices from scan to scan, and may
introduce difficulties in interpreting the images. In order to
overcome the mis-registration, an additional acquisition scan may
be performed, as shown in block 110, and a technician may
reposition the slices manually, as shown in block 112, and an
acquisition scan for the selected imaging protocol may be performed
as shown in block 114. Severe mis-registration may even require
patient repositioning, additional repeated scans, or additional
post-processing to register the images, any one of which may add
additional cost in the form of additional labor, time, computation,
etc. to the MR scanning processes.
[0004] Navigation techniques have been used to monitor respiratory
motion of objects being imaged, however, most navigation techniques
aim at compensating for the respiratory motion by using a brief MR
scan limited to the patient's diaphragm between k-space lines.
Thus, the technique may compensate for motion within one data
acquisition but cannot address patient motion between multiple data
acquisitions. Furthermore, the navigation signal is usually a beam
perpendicular to the diaphragm which has a one dimensional limited
view and description of the motion of the diaphragm, which may lead
to an erroneous respiratory motion estimation.
[0005] As a result, image quality and usability for diagnosis
depends on additional scans between acquisition scans for the
selected imaging protocol and on an operators' skills and
experience in relocating the slices between scans. This represents
one of the major barriers to cardiac MRI being widely applied in
clinical procedures.
SUMMARY
[0006] It would be advantageous to provide a method and system that
may automatically acquire and adjust planned image planes to
compensate for changes in a pose of a heart throughout an entire
scanning process without manual intervention.
[0007] According to the present disclosure, the method and system
may exploit artificial intelligence, for example, a neural network,
to: automatically estimate a pose of the heart; automatically
provide imaging slice planning; reconstruct highly accelerated
inter-acquisition scout imaging; and monitor and follow patient
motion to maintain consistency of planned slice locations for each
acquisition. This may advantageously allow for a more automatic and
efficient scanning workflow for cardiac MRI and facilitate
implementation in most clinical settings for cardiac diagnosis.
[0008] The disclosed embodiments are directed to a method including
acquiring initial scout images of a patient's heart, using a neural
network to establish a patient specific heart model, and
automatically plan imaging planes of the patient specific heart
model, performing an accelerated scan of the patient's heart, using
the neural network to determine a current location and pose of the
patient's heart from the accelerated scan, and to reposition the
imaging planes to correspond to the current location and pose of
the patient's heart, and using the repositioned imaging planes to
perform an acquisition scan and generate an image of the patient's
heart from the acquisition scan according to a selected imaging
protocol.
[0009] The method may include acquiring the initial scout images
from standard MRI body views.
[0010] The initial scout images may include 2D or 3D multi-slice
images from one or more of axial sagittal and coronal views.
[0011] Using the neural network to determine a current location and
pose of the patient's heart from the accelerated scan may include
reconstructing an image from the accelerated scan and comparing the
reconstructed image from the accelerated scan to the patient
specific heart model.
[0012] The method may include comparing the current location and
pose of the patient's heart to the location and pose of the patient
specific heart model, and repositioning the imaging planes obtained
from the patient specific heart model to correspond to the current
location and pose of the patient's heart.
[0013] The neural network may include one or more of a combination
of CNN and RNN models, a GRU model, an LSTM model, a fully
convolutional neural network model, a generative adversarial
network, a back propagation neural network model, a radial basis
function neural network model, a deep belief nets neural network
model, an Elman neural network model.
[0014] The accelerated scan may include one or more of compressed
sensing; parallel imaging; or fast spin echo techniques to allow
for acquisition in less time of a reduced amount of data than
required to support a higher resolution or larger field of
view.
[0015] The selected imaging protocol may include one or more of
obtaining anatomic images of the heart, determining cardiac
function, or determining myocardial viability.
[0016] The disclosed embodiments are further directed to a system
including an MRI scanner, and a processing engine coupled to the
MRI scanner, the processing engine comprising a processor and a
memory comprising computer readable program code, wherein the
processor under control of the computer readable program code is
operable to acquire initial scout images of a patient's heart, use
a neural network to establish a patient specific heart model, and
automatically plan imaging planes of the patient specific heart
model, perform an accelerated scan of the patient's heart, use the
neural network to determine a current location and pose of the
patient's heart from the accelerated scan, and to reposition the
imaging planes to correspond to the current location and pose of
the patient's heart, and cause the MRI scanner to use the
repositioned imaging planes to perform an acquisition scan and
generate an image of the patient's heart from the acquisition scan
according to a selected imaging protocol.
[0017] These and other aspects, implementation forms, and
advantages of the exemplary embodiments will become apparent from
the embodiments described herein considered in conjunction with the
accompanying drawings. It is to be understood, however, that the
description and drawings are designed solely for purposes of
illustration and not as a definition of the limits of the disclosed
invention, for which reference should be made to the appended
claims. Additional aspects and advantages of the invention will be
set forth in the description that follows, and in part will be
obvious from the description, or may be learned by practice of the
invention. Moreover, the aspects and advantages of the invention
may be realized and obtained by means of the instrumentalities and
combinations particularly pointed out in the appended claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] In the following detailed portion of the present disclosure,
the invention will be explained in more detail with reference to
the example embodiments shown in the drawings. These embodiments
are non-limiting exemplary embodiments, in which like reference
numerals represent similar structures throughout the several views
of the drawings, wherein:
[0019] FIG. 1 illustrates a conventional scanning workflow, where
obtaining target heart views is performed through a multi-step
approach including multiple scans and manual repositioning of
planned slices.
[0020] FIG. 2 illustrates an exemplary MRI apparatus according to
aspects of the disclosed embodiments;
[0021] FIG. 3 illustrates an exemplary architecture of a processing
engine according to the disclosed embodiments;
[0022] FIG. 4 illustrates an exemplary process flow according to
aspects of the disclosed embodiments;
[0023] FIGS. 5A-5C schematically illustrate usage of a patient
specific heart model according to aspects of the disclosed
embodiments; and
[0024] FIG. 6 depicts an exemplary simple neural network that may
be utilized to implement the disclosed embodiments.
DETAILED DESCRIPTION
[0025] In the following detailed description, numerous specific
details are set forth by way of examples in order to provide a
thorough understanding of the relevant disclosure. However, it
should be apparent to those skilled in the art that the present
disclosure may be practiced without such details. In other
instances, well known methods, procedures, systems, components,
and/or circuitry have been described at a relatively high-level,
without detail, in order to avoid unnecessarily obscuring aspects
of the present disclosure. Various modifications to the disclosed
embodiments will be readily apparent to those skilled in the art,
and the general principles defined herein may be applied to other
embodiments and applications without departing from the spirits and
scope of the present disclosure. Thus, the present disclosure is
not limited to the embodiments shown, but to be accorded the widest
scope consistent with the claims.
[0026] It will be understood that the term "system," "unit,"
"module," and/or "block" used herein are one method to distinguish
different components, elements, parts, section or assembly of
different level in ascending order. However, the terms may be
displaced by other expressions if they may achieve the same
purpose.
[0027] It will be understood that when a unit, module or block is
referred to as being "on," "connected to" or "coupled to" another
unit, module, or block, it may be directly on, connected or coupled
to the other unit, module, or block, or intervening unit, module,
or block may be present, unless the context clearly indicates
otherwise. As used herein, the term "and/or" includes any and all
combinations of one or more of the associated listed items.
[0028] Generally, the word "module," "unit," or "block," as used
herein, refers to logic embodied in hardware or firmware, or to a
collection of software instructions. A module, a unit, or a block
described herein may be implemented as software and/or hardware and
may be stored in any type of non-transitory computer-readable
medium or another storage device. In some embodiments, a software
module/unit/block may be compiled and linked into an executable
program. It will be appreciated that software modules can be
callable from other modules/units/blocks or from themselves, and/or
may be invoked in response to detected events or interrupts.
Software modules/units/blocks configured for execution on computing
devices may be provided on a computer-readable medium, such as a
compact disc, a digital video disc, a flash drive, a magnetic disc,
or any other tangible medium, or as a digital download (and can be
originally stored in a compressed or installable format that needs
installation, decompression, or decryption prior to execution).
Such software code may be stored, partially or fully, on a storage
device of the executing computing device, for execution by the
computing device. Software instructions may be embedded in
firmware, such as an Erasable Programmable Read Only Memory
(EPROM). It will be further appreciated that hardware
modules/units/blocks may be included in connected logic components,
such as gates and flip-flops, and/or can be included of
programmable units, such as programmable gate arrays or processors.
The modules/units/blocks or computing device functionality
described herein may be implemented as software
modules/units/blocks, but may be represented in hardware or
firmware. In general, the modules/units/blocks described herein
refer to logical modules/units/blocks that may be combined with
other modules/units/blocks or divided into
sub-modules/sub-units/sub-blocks despite their physical
organization or storage. The description may be applicable to a
system, an engine, or a portion thereof.
[0029] The terminology used herein is for the purposes of
describing particular examples and embodiments only, and is not
intended to be limiting. As used herein, the singular forms "a,"
"an," and "the" may be intended to include the plural forms as
well, unless the context clearly indicates otherwise. It will be
further understood that the terms "include," and/or "comprise,"
when used in this disclosure, specify the presence of integers,
devices, behaviors, stated features, steps, elements, operations,
and/or components, but do not exclude the presence or addition of
one or more other integers, devices, behaviors, features, steps,
elements, operations, components, and/or groups thereof.
[0030] These and other features, and characteristics of the present
disclosure, as well as the methods of operation and functions of
the related elements of structure and the combination of parts and
economies of manufacture, may become more apparent upon
consideration of the following description with reference to the
accompanying drawings, all of which form a part of this disclosure.
It is to be expressly understood, however, that the drawings are
for the purpose of illustration and description only and are not
intended to limit the scope of the present disclosure. It is
understood that the drawings are not to scale.
[0031] FIG. 2 shows a schematic block diagram of an exemplary MRI
apparatus 202 for providing MRI data according to the disclosed
embodiments. The MRI apparatus 202 may include an MRI scanner 204,
receive and control circuitry 206 and a display 208. The MRI
scanner 204 may include, as shown in cross section in FIG. 2, a
magnetic field generator 210, a gradient magnetic field generator
212, and a Radio Frequency (RF) generator 214, all surrounding a
table 216 on which subjects under study may be positioned. The MRI
scanner 204 may also include an ECG signal sensor 218 for capturing
MRI data in the form of ECG signals from the subject under study
during MRI scanning, a camera 220 for capturing MRI data in the
form of video images of the subject under study during MRI
scanning, and a pulse detector 222, for capturing MRI data in the
form of a subject's pulse during MRI scanning. In some embodiments,
the MRI scanner 204 may perform a scan on a subject or a region of
the subject. The subject may be, for example, a human body or other
animal body. For example, the subject may be a patient. The region
of the subject may include part of the subject. For example, the
region of the subject may include a tissue of the patient. The
tissue may include, for example, lung, prostate, breast, colon,
rectum, bladder, ovary, skin, liver, spine, bone, pancreas, cervix,
lymph, thyroid, spleen, adrenal gland, salivary gland, sebaceous
gland, testis, thymus gland, penis, uterus, trachea, skeletal
muscle, smooth muscle, heart, etc. In some embodiments, the scan
may be a pre-scan for calibrating an imaging scan. In some
embodiments, the scan may be an imaging scan for generating an
image.
[0032] The main magnetic field generator 210 may create a static
magnetic field B0 and may include, for example, a permanent magnet,
a superconducting electromagnet, a resistive electromagnet, or any
magnetic field generation device suitable for generating a static
magnetic field. The gradient magnet field generator 212 may use
coils to generate a magnetic field in the same direction as B0 but
with a gradient in one or more directions, for example, along X, Y,
or Z axes in a coordinate system of the MRI scanner 204.
[0033] In some embodiments, the RF generator 214 may use RF coils
to transmit RF energy through the subject, or region of interest of
the subject, to induce electrical signals in the region of
interest. The resulting RF field is typically referred to as the B1
field and combines with the B0 field to generate MR signals that
are spatially localized and encoded by the gradient magnetic field.
The MRI scanner 204 may further include an RF detector 224
implemented using, for example, an RF coil, where the RF detector
operates to sense the RF field and convey a corresponding output to
the receive and control circuitry 206. The RF detector may also
include one or more coil arrays for parallel imaging. The function,
size, type, geometry, position, amount, or magnitude of the MRI
scanner 204 may be determined or changed according to one or more
specific conditions. For example, the MRI scanner 204 may be
designed to surround a subject (or a region of the subject) to form
a tunnel type MRI scanner, referred to as a closed bore MRI
scanner, or an open MRI scanner, referred to as an open-bore MRI
scanner. As another example, the MRI scanner may be portable and
transportable down hallways and through doorways to a patient,
providing MR scanning services to the patient as opposed to
transporting the patient to the MRI scanner. In some examples, a
portable MRI scanner may be configured to scan a region of interest
of a subject, for example, the subject's brain, spinal cord, limbs,
heart, blood vessels, and internal organs.
[0034] The ECG signal sensor 218 may operate to capture ECG signals
from the subject under study during MRI scanning for use in
subsequently identifying cardiac cycles and cardiac phases of the
subject. The camera 220 may operate to capture video images of the
subject under study during MRI scanning for use in subsequently
identifying cardiac cycles and cardiac phases of the subject.
During MRI scanning the subject may be requested to hold their
breath and to stay still in order to provide accurate MRI cardiac
data while scanning. However, this may be difficult for any number
of reasons, and video images of the subject may be used to
compensate for subject movement or breathing patterns during
scanning that may adversely affect the acquired MRI data. The pulse
detector 222 may provide pulse data from the subject during MRI
scanning which may also be used to enhance cardiac cycle and phase
predictions.
[0035] The receive and control circuitry 206 may control overall
operations of the MRI scanner 204, in particular, the magnetic
field generator 210, the gradient magnetic field generator 212, the
RF generator 214, and the RF detector 224. For example, the receive
and control circuitry 206 may control the magnet field gradient
generator to produce gradient fields along one or more of the X, Y,
and Z axes, and the RF generator to generate the RF field. In some
embodiments, the receive and control circuitry 206 may receive
commands from, for example, a user or another system, and control
the magnetic field generator 210, the gradient magnetic field
generator 212, the RF generator 214, and the RF detector 224
accordingly. The receive and control circuitry 206 may be connected
to the MRI scanner 204 through a network 226. The network 226 may
include any suitable network that can facilitate the exchange of
information and/or data for the MRI scanner 204. The network 226
may include one or more of a public network (e.g., the Internet), a
private network (e.g., a local area network (LAN), a wide area
network (WAN)), etc.), a wired network (e.g., an Ethernet network),
a wireless network (e.g., an 802.11 network, a Wi-Fi network,
etc.), a cellular network (e.g., a Long Term Evolution (LTE)
network), a frame relay network, a virtual private network ("VPN"),
a satellite network, a telephone network, routers, hubs, switches,
server computers, and/or any combination thereof. Merely by way of
example, the network 418 may include a cable network, a wireline
network, a fiber-optic network, a telecommunications network, an
intranet, a wireless local area network (WLAN), a metropolitan area
network (MAN), a public telephone switched network (PSTN), a
Bluetooth.RTM. network, a ZigBee.RTM. network, a near field
communication (NFC) network, or the like, or any combination
thereof. In some embodiments, the network 226 may include one or
more network access points. For example, the network 226 may
include wired and/or wireless network access points such as base
stations and/or internet exchange points through which one or more
components of the MRI scanner 204 may be connected with the network
226 to exchange data and/or information.
[0036] According to some embodiments, the receive and control
circuitry 206 may operate the MRI scanner 204 to perform operations
according to the disclosed embodiments, including automatically
estimating a pose of the heart; automatically providing imaging
slice planning; performing a highly accelerated scout scan between
acquisition scans, and automatically adjusting the image slices to
maintain consistency of planned slice locations for each
acquisition in spite of movement which may arise as a result of
heart movement, breathing, patient movement, or other factors that
cause changes in heart position between acquisition scans. The
receive and control circuitry 206 may include a processing engine
300 for operating the MRI scanner 204 to perform the operations and
workflows according to the disclosed embodiments.
[0037] FIG. 3 illustrates an example implementation of the
processing engine 300 according to the disclosed embodiments. The
processing engine 300 may include computer readable program code
stored on at least one computer readable medium 302 for carrying
out and executing the process steps described herein. The computer
readable program code for carrying out operations for aspects of
the present disclosure may be written in any combination of one or
more programming languages, including an object-oriented
programming language such as Java, Scala, Smalltalk, Eiffel, JADE,
Emerald, C++, C#, VB. NET, Python or the like, conventional
procedural programming languages, such as the "C" programming
language, Visual Basic, Fortran 2103, Perl, COBOL 2102, PHP, ABAP,
dynamic programming languages such as Python, Ruby, and Groovy, or
other programming languages. The computer readable program code may
execute entirely on the processing engine 300, partly on the
processing engine 300, as a stand-alone software package, partly on
the processing engine 300 and partly on a remote computer or server
or entirely on the remote computer or server. In the latter
scenario, the remote computer may be connected to the processing
engine 300 through any type of network, including those mentioned
above with respect to network 226.
[0038] The computer readable medium 302 may be a memory of the
processing engine 300. In alternate aspects, the computer readable
program code may be stored in a memory external to, or remote from,
the processing engine 300. The memory may include magnetic media,
semiconductor media, optical media, or any media which is readable
and executable by a computer. The processing engine 300 may also
include a computer processor 304 for executing the computer
readable program code stored on the at least one computer readable
medium 302. In at least one aspect, the processing engine 300 may
include one or more input or output devices, generally referred to
as a user interface 306 which may operate to allow input to the
processing engine 306 or to provide output from the processing
engine 300, respectively. The processing engine 300 may be
implemented in hardware, software or a combination of hardware and
software. According to one or more embodiments, the processing
engine 300 may be part of the receive and control circuitry 206,
while in other embodiments the processing engine 300 may be located
remotely from the receive and control circuitry 206.
[0039] FIG. 4 shows an exemplary work flow that may be implemented
using the exemplary MRI apparatus 202. As shown in block 402,
initial scout images may be acquired before performing subsequent
scans. The scout images may be 2D multi-slice images from all three
standard body views (axial, sagittal and coronal) at a low spatial
resolution. The scout images may also be low resolution true 3D
image volumes. As shown in block 404, from the scout images, the
location, pose, shape, and other aspects of a patient's heart may
be estimated using a neural network, an example of which is
illustrated as item 600 in FIG. 6. Referring to block 406, the
neural network 600 may utilize the aspects the heart to establish a
patient specific heart model, as schematically illustrated in FIG.
5A. Referring to block 408, the neural network 600 may be used to
estimate standard heart views according to clinical standards from
the patient specific heart model, including the short axis view,
horizontal long axis view, and vertical long axis view. Referring
to block 410, the estimated standard heart views may optionally be
updated and refined by one or more MR technicians and the updates
and refinements may be used to update the patient specific heart
model. Referring to block 412, the standard heart views may be used
to automatically plan imaging planes, as schematically illustrated
in FIG. 5B. Blocks 414 and 416 represent operations that may be
referred to as an Artificial Intelligence (AI) scout scan. As shown
in block 414, an accelerated scan, for example, one or more of a
multi-slice, multi-view, 2D or 3D, scan may be performed to acquire
cardiac positioning data to determine the location of the heart
before the next acquisition scan. The accelerated scan techniques
may include using compressed sensing where data is undersampled in
the K-space, parallel imaging where data is individually obtained
from multiple receiver coils, and fast spin echo where multiple
echoes are acquired during each sequence pulse to allow for
acquisition in less time of a reduced amount of data than required
to support a higher resolution or larger field of view.
[0040] The neural network 600 may be used to reconstruct the highly
accelerated data as shown in block 416, and may be used to compare
the heart location and pose to those of the patient specific heart
model from the initial scouting, as shown in block 418. Referring
to block 420, the prescribed imaging planes may then be
automatically adjusted to correspond to the heart location and
pose, as illustrated in FIG. 5C, and used for an acquisition scan
for a selected imaging protocol, as shown in block 422. Thus, the
AI scout scan 414, 416 may be used to determine the location and
pose of the heart immediately before the acquisition scan 422 and
may be used to represent the location and pose of the heart for
that particular acquisition scan.
[0041] Cardiac MRI imaging protocols may be generally tailored to
specific clinical indications, for example, anatomic images of the
heart and great vessels, including axial, coronal, sagittal, long
axis, and short axis views, and views of coronary arteries, and
valves. Other cardiac MRI imaging protocols may be directed to
cardiac function, for example, motion of the ventricular walls
during systole and diastole, turbulence created by valvular
stenosis, and cine studies obtained by repeatedly imaging the heart
at a single slice location throughout the cardiac cycle. Still
other cardiac MRI imaging protocols may be directed to myocardial
viability, utilizing for example, segmented, T1-weighted,
inversion-prepared fast gradient echo sequences.
[0042] While the AI scout scan 414, reconstruction of AI scout scan
data 416, comparison of the heart location and pose 418, automatic
repositioning 420, and acquisition scan 422, are described in the
context of being performed by a single neural network 600, it
should be understood that the scan 414, reconstruction 416,
comparison 418, automatic repositioning 420, and acquisition scan
422, may be performed individually by different neural networks or
performed in groups by different neural networks.
[0043] It should be noted that utilizing the neural network 600
advantageously ensures reconstruction quality and reduces the time
required for establishing the patient specific heart model,
planning and repositioning the image planes, computing the
reconstructions, and repositioning the image planes. For example,
because the position, pose, and short and long axes are defined by
the patient specific heart model, the neural network may utilize
this information to automatically plan the imaging planes, instead
of having a technician manually plan the imaging planes.
Furthermore, because the disclosed embodiments establish a patient
specific heart model, a technician is no longer required to perform
additional scans to relocate the heart position, pose, and short
and long axes, because the position, pose, and short and long axes
are defined by the patient specific heart model. Thus, the desired
slice location relative to the structure of the heart as defined
during the initial scouting may be maintained regardless of changes
in the heart location and pose throughout the imaging protocol
scans. Still further, use of the neural network 600 enables
completion of the AI scout scan and in particular, reconstruction
of the accelerated data, in significantly less time than technician
controlled rescans, reducing the time required for the patient to
stop breathing or remain immobile, or both. It should also be noted
that while the disclosed embodiments are described in the context
of utilizing a neural network, other computational methods that
meet the speed and accuracy requirements may also be utilized.
[0044] FIG. 6 depicts an example of the neural network 600 that may
be utilized to implement the disclosed embodiments. While a simple
neural network is shown, it should be understood that the disclosed
embodiments may be implemented utilizing a deep learning method or
deep learning model including one or more gated recurrent units
(GRUs), long short term memory (LSTM) networks, fully convolutional
neural network (FCN) models, generative adversarial networks
(GANs), back propagation (BP) neural network models, radial basis
function (RBF) neural network models, deep belief nets (DBN) neural
network models, Elman neural network models, or any deep learning
or machine learning model capable of performing the operations
described herein.
[0045] Techniques that train to learn or to select a particular
neural network structure can be used to learn the hyperparameter of
the neural network 600 for optimal performance. One example
following a reinforcement learning framework can be a searching
neural network that can act on the tested neural network by
changing the hyperparameters and observing the resulting
performance. The searching network can continuously perform trials
of acting and observing, and accumulate experiences through the
trials. The target of the searching network is to maximize some
reward, which can be defined as achieving better performance. The
searching network will eventually reach an optimal performance
point, at which the operations of the searching network may be
terminated.
[0046] Thus, while there have been shown, described and pointed
out, fundamental novel features of the invention as applied to the
exemplary embodiments thereof, it will be understood that various
omissions, substitutions and changes in the form and details of
devices and methods illustrated, and in their operation, may be
made by those skilled in the art without departing from the spirit
and scope of the presently disclosed invention. Further, it is
expressly intended that all combinations of those elements, which
perform substantially the same function in substantially the same
way to achieve the same results, are within the scope of the
invention. Moreover, it should be recognized that structures and/or
elements shown and/or described in connection with any disclosed
form or embodiment of the invention may be incorporated in any
other disclosed or described or suggested form or embodiment as a
general matter of design choice. It is the intention, therefore, to
be limited only as indicated by the scope of the claims appended
hereto.
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