US 11,519,987 B2

11
titative parameters may be off-resonance (Af). Residual
changes in off-resonance may be used to predict the tem-
perature changes. In an example, the following equation
may be used to convert the off-resonance (Af) to temperature
(AT):

Afn-Afo
0.01 =y=Bo

5
AT, = ®

where y is the gyromagnetic ratio in MHz/T, By is the field
strength in Tesla, and n is the dynamic scan number. At block
210, a temperature map may be generated indicating the
temperature change determined at block 208. For example,
a map may be generated having a quantitative indication of
the determined temperature. At block 212, the temperature
map may be stored in memory or data storage of, for
example, the MRI system 100 shown in FIG. 1) or other
computer system. In an embodiment, the temperature map
may also be provided to and displayed on a display (e.g..
display 104, 1356 or 144 of MRI system 100 shown in FIG,
1) at block 214.

At block 216, it is determined whether there are any
additional time intervals in the acquisition for which a
temperature changes may be determined or if the acquisition
is complete. If there is an additional time interval, the
process returns to block 204 and the MRF data from the next
time interval (e.g., time interval 312 shown in FIG. 3) is
compared to the MRF dictionary. Blocks 204-214 are per-
formed for each time interval to determine a temperature
change at the corresponding point in time of the acquisition.
Accordingly, blocks 204-214 may be repeated dynamically.
As shown in FIG. 3, ata first time interval 306, quantitative
parameters are determined 308 based on the MRF data
acquired during the interval 306 and the identified param-
eters are converted to temperature change 310. At a second
time interval 312, quantitative parameters are determined
314 based on the MRF data acquired during the interval 312
and the identified parameters are converted to temperature
change 316. Ata third time interval 318, quantitative param-
eters are determined 320 based on the MRF data acquired
during the interval 318 and the identified parameters are
converted to temperature change 322. In the embodiment
shown in FIG. 3, quantitative parameters may be determined
for every 876 time points (approximately 9.6 seconds of
MRF data) by matching the acquired MRF data in the
corresponding time interval to the MRF dictionary and the
determined quantitative parameters are converted to tem-

perature (AT). Accordingly, in some embodiments a tem- 5

perature map may be generated approximately every 9.6
seconds,

Returning to FIG. 2, at block 216 if there are no remaining
time intervals and the acquisition is complete, the process

ends at block 218. As mentioned above, in some embodi- s

‘ments the method for temperature quantification using MRF
may be used to monitor the temperature of tissues in a
subject during thermal surgical procedures and guidance of
thermal therapies. In such embodiments, the total length of
the MRF acquisition (e.g, an MRFgRF acquisition as
described above) may be determined based on the duration
of the heating or cooling of the subject during the thermal
surgical procedure or thermal therapy. In some embodi-
ments, the thermal procedure, therapy or treatment may be
stopped (manually or automatically) based on the deter-
mined temperature change and temperature map (blocks 208
and 210), for example, if the determined temperature falls

20

&

&

12

above or below a predetermined threshold. In some embodi-
ments, the thermal therapy or treatment may be modified
(manually or automatically) based on the determined tem-
perature change and temperature map (blocks 208 and 210),
for example, if the determined temperature falls above or
below a predetermined threshold.

In some embodiments, it may be desirable to further
increase the temporal resolution for clinical applications and
to, for example, improve temperature measurements for
adipose tissues. To further increase the temporal resolution,
at each predetermined time interval, the temperature (e.g.,
AT) of a tissue or tissues in the region of interest in the
subject may be determined directly by matching the
acquired MR data from the region of interest to a thermal
signal MRF dictionary. FIG. 4 illustrates a method for
magnetic resonance fingerprinting (MRF) thermometry
using a thermal MRF dictionary in accordance with an
embodiment and FIG. § illustrates an example flip angle
pattern and time intervals for determining temperature
changes using the method of FIG. 4 in accordance with an
embodiment. At block 402, MRF data may be acquired from
a tissue (¢.g., aqueous and/or adipose tissue) in region of
interest ina subject using, for example, an MRI system (e.g.
MRI system 100 shown in FIG. 1). The MRF data can be

acquired by the MRI system using an MRF pulse sequence

with smoothly varied RF phase for MRF resonant frequen-
cies played out continuously during the acquisition or scan.
As discussed above with respect to block 202 of FIG. 2, in
an embodiment the MRF pulse sequence with smoothly
varying RF phase can be optimized to provide a faster
temporal resolution for the temperature quantification and
monitoring. For example, the number of time points per scan
can be reduced which results in an increase in temporal
resolution, a faster acquisition and increase sensitivity to By
or resonant frequency changes. In addition, a decrease in the
number of time points can result in reduced signal to noise
ratio (SNR). Reduction of the number of time points can
bring finer resolution sampling of the temperature curves
through time. In various embodiments, the MRF pulse
sequence may be configured to be sensitive to one or more
parameters such as T,, T>, T3**, By and off-resonance (Af). In
some embodiments, the T, sensitivity of the MRF pulse
sequence may be increased by periodically applying an
inversion RF pulse during the acquisition.

In some embodiments, the MRF pulse sequence is a MRF
with quadratic RF phase (MRFqRF) pulse sequence as
discussed above. While the following description will be
discussed in terms of embodiments using an MRFqRF
sequence and quadratic RF phase, it should be understood
that in other embodiments other sweep patterns that provide
a phase that changes smoothly in time (or smoothly varying
phase) may be used in the systems and methods described
herein to acquire the MRF data. As discussed above with
respect to FIG. 2, the MRFqRF pulse sequence can be
optimized to provide a faster temporal resolution for the
temperature quantification and monitoring. For example, the
number of time points per acquisition using the MRFqRF
pulse sequence can be reduced (compared to a conventional
MRFgRF acquisition) to provide a faster temporal resolution
for the temperature quantification. An example MRFqRF
flip angle sweep pattern is shown in FIG. 5. In FIG. 8, a flip
angle pattern 528 representing flip angles 502 over time
(e.g., time points 504) is shown.

‘As the acquisition at block 402 of FIG. 4 is played out, a
baseline measurement is made at a predetermined baseline
measurement time interval and then temperature changes are
determined at a plurality of predetermined temperature



US 11,519,987 B2

Sheet 3 of 6

Dec. 6, 2022

U.S. Patent

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US 11,519,987 B2

3

resonance signals from the subject using a coil array, and a
computer system. The computer system can be programmed
to acquire MRF data from a region of interest in a subject
using an MRF pulse sequence with smoothly varying RF
phase for MR resonant frequencies that is played out con-
tinuously. For each of a plurality of temperature measure-
ment time intervals during acquisition of the MRF data the
computer system can be further programmed to generate a
thermal signal MRF dictionary, compare a set of MRF data
associated with the temperature measurement time interval
to the thermal signal MRF dictionary to determine a tem-
perature change, and generate a quantitative map of the
temperature change in the region of interest.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will hereafter be described with
reference to the accompanying drawings, wherein like ref-
erence numerals denote like elements.

FIG. 1 is a schematic diagram of an example MRI system
in accordance with an embodiment;

FIG. 2 illustrates a method for magnetic resonance fin-
gerprinting (MRF) thermometry in accordance with an
embodiment;

FIG. 3 illustrates an example flip angle pattern and time
intervals for determining temperature changes using the
method of FIG. 2 in accordance with an embodiment;

FIG. 4 illustrates a method for magnetic resonance fin-
gerprinting (MRF) thermometry using a thermal MRF dic-
tionary in accordance with an embodiment;

FIG. § illustrates an example flip angle pattern a
intervals for determining temperature changes
method of FIG. 4 in accordance with an embodiment; and

FIG. 6 illustrates example thermal signal MRF dictionary
entries in accordance with an embodiment

DETAILED DESCRIPTION,

Magnetic resonance fingerprinting (“MRE”) is a tech-
nique that facilitates mapping of tissue or other material
properties based on random or pseudorandom measurements
of the subject or object being imaged. In particular, MRF can
be conceptualized as evolutions in different “resonant spe-
cies” to which the RF is applied. The term “resonant
species,” as used herein, refers to a material, such as water,
fat, bone, muscle, soft tissue, and the like, that can be made
to resonate using NMR. By way of illustration, when radio
frequency (“RE”) energy is applied to a volume that has both
bone and muscle tissue, then both the bone and muscle tissue
will produce a nuclear magnetic resonance (“NMR”) signal:
however, the “bone signal” represents a first resonant spe-
cies and the “muscle signal” represents a second resonant
species, and thus the two signals will be different. These
different signals from different species can be collected

simultaneously over a period of time to collect an overall 5

“signal evolution” for the volume.

The measurements obtained in MRF techniques are
achieved by varying the acquisition parameters from one
repetition time (“TR”) period to the next, which creates a
time series of signals with varying contrast. Examples of
acquisition parameters that can be varied include flip angle
(“FA”), RE pulse phase, TR, echo time (“TE”), and sampling
patterns, such as by modifying one or more readout encod-
ing gradients. The acquisition parameters are varied in a
random manner, pseudorandom manner, or other manner
that results in signals from different materials or tissues to be
spatially incoherent, temporally incoherent, or both. For

3

3

&

&

50

4

example, in some instances, the acquisition parameters can
be varied according to a non-random or non-pseudorandom
pattern that otherwise results in signals from different mate-
rials or tissues to be spatially incoherent, temporally inco-
herent, or both.

From these measurements, which as mentioned above
may be random or pseudorandom, or may contain signals
from different materials or tissues that are spatially incoher-
ent, temporally incoherent, or both, MRE processes can be
designed to map any of a wide variety of parameters.
Examples of such parameters that can be mapped may
include, but are not limited to, tissue parameters or proper-
ties such as longitudinal relaxation time (T,), transverse
relaxation time (T,), and proton density (p), and device
dependent parameters such as main or static magnetic field
map (By). MRF is generally described in U.S. Pat. No.
8,723,518 and Published U.S. Patent Application No. 2015/
0301141, each of which is incorporated herein by reference
in its entirety.

The data acquired with MRF techniques are compared
with a dictionary of signal models, or templates, that have
been generated for different acquisition parameters from
magnetic resonance signal models, such as Bloch equation-

S based physics simulations which can include terms for the

temperature dependence of the various terms. This compari-
son allows estimation of the physical properties, such as
those mentioned above. As an example, the comparison of
the acquired signals to a dictionary can be performed using
any suitable matching or pattern recognition technique. The
properties for the tissue or other material in a given voxel are
estimated to be the values that provide the best signal match.
For instance, the comparison of the acquired data with the
dictionary can result in the selection of a signal vector,
which may constitute a weighted combination of signal
vectors, from the dictionary that best corresponds to the
observed signal evolution. The selected signal vector
includes values for multiple different quantitative properties,
which can be extracted from the selected signal vector and
used to generate the relevant quantitative property maps.

The stored signals and information derived from reference
signal evolutions may be associated with a potentially very
large data space. The data space for signal evolutions can be
partially described by:

Ng Na Ne a

SE= ST] S Ro rRar sles OREM, To, DM

where SE is a signal evolution; N, is a number of spins; N,
is a number of sequence blocks; Ng, is a number of RF
pulses in a sequence block; ct is a flip angle; @ is a phase
angle; R,(@) is a rotation due to off resonance; Rr, (op) is
a rotation due to RF differences; R(G) is a rotation due to a
magnetic field gradient; T, is a longitudinal, or spin-lattice,
relaxation time; T, is a transverse, or spin-spin, relaxation
time; D is diffusion relaxation: E,(T,, T2, D) is a signal decay
due to relaxation differences; and M, is the magnetization in
the default or natural alignment to which spins align when
placed in the main magnetic field.

While E,(T,, T>, D) is provided as an example, in different
situations, the decay term, E,(T,, T;, D), may also include
additional terms, E,(T,, T;, D, . . . ) or may include fewer
terms, such as by not including the diffusion relaxation, as



US 11,519,987 B2

Sheet 5 of 6

Dec. 6, 2022

U.S. Patent

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U.S. Patent Dec. 6, 2022 Sheet 4 of 6 US 11,519,987 B2

ACQUIRE MRF DATA FROM A REGION OF |402
INTEREST IN A SUBJECT USING AN MRF wD
PULSE SEQUENCE WITH SMOOTHLY VARIED
RF PHASE FOR MR RESONANT
FREQUENCIES

404

AT BASELINE MEASUREMENT TIME INTERVAL wy

OF THE ACQUISITION, DETERMINE BASELINE
QUANTITATIVE PARAMETERS

406
AT TEMPERATURE MEASUREMENT TIME wD
INTERVAL OF ACQUISITION, GENERATE

THERMAL SIGNAL MRF DICTIONARY

COMPARE THE MRF DATA ACQUIRED — [408
DURING THE TEMPERATURE MEASUREMENT |/

TIME INTERVAL TO THE THERMAL SIGNAL
MRF DICTIONARY
410
DETERMINE TEMPERATURE CHANGE U
412
GENERATE TEMPERATURE MAP J
416
DISPLAY |
STORE TEMPERATURE MAP TEMPERATURE
wU MAP
la 418 414
TEMPERATURE ae
YES END LJ
OF ACQUISITION
7
NO
420
AT TEMPERATURE MEASUREMENT TIME |

INTERVAL OF ACQUISITION, UPDATE THERMAL
SIGNAL MRF DICTIONARY

FIG. 4


US 11,519,987 B2

1
MAGNETIC RESONANCE
FINGERPRINTING THERMOMETRY

CROSS-REFERENCE TO RELATED,
APPLICATIONS

This application is based on, claims priority to, and
incorporates herein by re! in its entirety U.S.
63/004,660 filed Apr. 3, 2020 and entitled “Magnetic Reso-
nance Fingerprinting Thermometry.”

FUNDING STATEMENT

This invention was made with government support under
JONANBI18H006 awarded by the National Institute of Stan-
dards and Technology. The government has certain rights in
the invention.

BACKGROUND

Magnetic resonance imaging (MRI) is a non-invasive
imaging method that allows detailed pictures to be obtained
within the human body. MM thermometry can be used to
quantitatively map temperature changes within the body in
near-real time for use in monitoring thermal surgical proce-
dures. However, current MR thermometry methods are
typically used to map temperature changes for aqueous
tissues (water-based tissues) and can be diflicult to use to
map temperature changes quickly and accurately in adipose
tissues (fat-based tissues).

Temperature monitoring in both adipose and aqueous
tissues is important for guidance of thermal therapies in
vivo. Conventional MR thermometry in aqueous tissues is
based on the proton resonant frequency-shift (PRES), which
is a negative shift in the water molecule’s proton resonant
frequency with heating. This shift appears in the phase of a
type of MRI image known as the gradient echo image. These
images are able to be acquired in a few seconds, enabling
near-real time mapping of temperature for aqueous tissues
with MM. However, adipose tissues do not exhibit this shift
in frequency due to the lack of free water protons, therefore.
PRES can be unreliable to map temperature changes in
adipose tissues. To obtain temperature maps in adipose
tissues, proton T, or T, relaxation mapping is typically
employed. Relaxation techniques rely on changes in T, or T,
to monitor changes in temperature. Magnetic relaxation of
fat is known to be related to temperature changes. However,
relaxation methods require accurate and precise mapping of
T, and T,, which can be difficult to achieve in the high

temporal resolution (~3 s) required for guidance of therapies 5

and on the time scale needed for near-real time imaging,
Additionally, the relationship between relaxation and tem-
perature must be calibrated prior to imaging. To obtain
temperature contrast in both water and fat, recent methods

combine PRFS with accelerated relaxation mapping. These

methods, however, require the acquisition sequence and/or
reconstruction to be modified to incorporate relaxation map-
ping which can slow the temporal resolution of the sequence
and force compromises in mapping the relaxation.
Magnetic resonance fingerprinting (MRF) is a framework
that enable quantitative mapping of multiple tissue proper-
ties simultaneously in any tissue type. Conventional MRF
sequences have been developed that can map both relaxation
and resonant frequency simultaneously. This is accom-
plished by combining a temporal acquisition with dictionary
matching reconstructions based on modeling of all possible
signal evolutions. However, conventional MRF requires at

&

2

least 30 seconds of acquisition to reconstruct accurate prop-
erty maps and is typically performed only once, not dynami-
cally through time. Additionally, the sensitivity of MRF
mapping changes in these properties has not been explored.

It would be desirable to provide a system and method for
quantitative mapping of temperature changes in aqueous and
adipose tissue using MRF that overcome the aforementioned
drawbacks.

SUMMARY OF THE DISCLOSURE

In accordance with an embodiment, a method for tem-
perature quantification using magnetic resonance finger-
printing (MRF) includes acquiring MRF data from a region

5 of interest in a subject using an MRF pulse sequence with

smoothly varying RF phase for MR resonant frequencies
that is played out continuously. For each of a plurality of
time intervals during acquisition of the MRF data the
method further includes comparing a set of the MRF data
associated with the time interval to an MRF dictionary to
determine at least one quantitative parameter of the acquired
MRF data, determining a temperature change based on the
at least one quantitative parameter and generating a quan-
titative map of the temperature change in the region of
interest.

In accordance with another embodiment, a magnetic
resonance imaging (MRI) system includes a magnet system
configured to generate a polarizing magnetic field about at
least a portion of a subject, a magnetic gradient system
including a plurality of magnetic gradient coils configured to
apply at least one magnetic gradient field to the polarizing
magnetic field, a radio frequency (RF) system configured to
apply an RF field to the subject and to receive magnetic
resonance signals from the subject using a coil array and a
computer system. The computer system can be programmed
to acquire MRF data from a region of interest in a subject
using an MRF pulse sequence with smoothly varying RF
phase for MR resonant frequencies that is played out con-
tinuously. For each of a plurality of time intervals during
acquisition of the MRF data the computer system can be
further programmed to compare a set of MRF data associ-
ated with the time interval to an MRF dictionary to deter-
mine at least one quantitative parameter of the acquired
MRF data, determine a temperature change based on the at
least one quantitative parameter, and generate a quantitative
map of the temperature change in the region of interest.

In accordance with another embodiment, a method for
temperature quantification using magnetic resonance finger-
printing (MRF) includes acquiring MRF data from a region
of interest in a subject using an MRF pulse sequence with
smoothly varying RF phase for MR resonant frequencies
that is played out continuously. For each of a plurality of
temperature measurement time intervals during acquisition
of the MRF data the method further includes generating a
thermal signal MRF dictionary, comparing a set of MRF
data associated with the temperature measurement time
interval to the thermal signal MRF dictionary to determine
a temperature change, and generating a quantitative map of
the temperature change in the region of interest.

In accordance with another embodiment, a magnetic
resonance imaging (MRI) system includes a magnet system
configured to generate a polarizing magnetic field about at
least a portion of a subject, a magnetic gradient system
including a plurality of magnetic gradient coils configured to
apply at least one magnetic gradient field to the polarizing
magnetic field, a radio frequency (RF) system configured to
apply an RF field to the subject and to receive magnetic


a2) United States Patent

Keenan et al.

USO1151

US 11,519,987 B2
Dec. 6, 2022

(10) Patent No.:
(45) Date of Patent:

(54) MAGNETIC RESONANCE
FINGERPRINTING THERMOMETRY

(71) Applicants:Case Western Reserve University,
Cleveland, OH (US); Government of
the United States, as represented by
the Secretary of Commerce,
Gaithersburg, MD (US); The Regents
of the University of Colorado, a body
corporate, Denver, CO (US)

(72) Inventors: Kathryn Keenan, Boulder, CO (US);
Megan Poorman, Boulder, CO (US);
Rasim Boyacioglu, Cleveland Heights,
OH (US); Mark A. Griswold, Shaker
Heights, OH (US)

(73) Assignees: Case Western Reserve University,
Cleveland, OH (US); The Regents of
the University of Colorado, Denver,
CO (US); Government of the United
States, As Represented by the
Seeretary of Commerce, Gaithersburg,
MD (US)

(*) Notice: Subject to any disclaimer, the term of this
patent is extended or adjusted under 35
USC. 154(b) by 0 days.

(21)

Appl. No.: 17/301,509

(22) Filed: Apr. 5, 2021

(65) Prior Publication Data

US 2021/0311146 Al Oct. 7, 2021

Related U.S. Application Data

(60) Provisional application No. 63/004,660, filed on Apr.

3, 2020.
(51) Int. Cl
GOIR 33/50
GOIR 33/56

(2006.01)
(2006.01)

(Continued)

be

Wee DTN .

iit

Te mar a
ida ecuador |

(52) US. Cl.
cpc

wee GOIR 33/50 (2013.01), AGIB 5/0037
(2013.01); A61B 5/055 (2013.01); GOIR
33/385 (2013.01); GOIR 33/5608 (2013.01)

(58) Field of Classification Search
CPC .. GOIR 33/50; GOIR 33/385; GOIR 33/5608;
A61B 5/055; AG1B 5/0037
See application file for complete search history.
(56) References Cited
U.S. PATENT DOCUMENTS
8,723,518 B2 5/2014 Seiberlich

2015/0301138 Al 10/2015 Griswold

(Continued)
OTHER PUBLICATIONS

Wang, Charlie Yi, et al. “Magnetic resonance fingerprinting with
quadratic RF phase for measurement of T2* simultaneously with 6f,
TI, and T2” Magnetic Resonance in Medicine, vol. 81, No. 3, 2018,
pp. 1849-1862,, https://doi.org/10.1002/mrm.27543. (Year: 2018).*

(Continued)

Primary Examiner
(74) Attorney, Agent, or Firm

Gregory H Curran
Quarles & Brady LLP

(87)

A method for temperature quantification using magnetic
resonance fingerprinting (MRF) includes acquiring MRF
data from a region of interest in a subject using an MRF
pulse sequence with smoothly varying RF phase for MR
resonant frequencies that is played out continuously. For
each of a plurality of time intervals during acquisition of the
MRF data the method further includes comparing a set of the
MRF data associated with the time interval to an MRF
dictionary to determine at least one quantitative parameter of
the acquired MRF data, determining a temperature change
based on the at least one quantitative parameter and gener-
ating a quantitative map of the temperature change in the
region of interest. The region of interest can include aqueous
and adipose tissue.

ABSTRACT

12 Claims, 6 Drawing Sheets

ea
‘aco |?

mone



US 11,519,987 B2

7

control the further performance of the scan, the data acqui-
sition server 112 may be programmed to produce such
information and convey it to the pulse sequence server 110.
For example, during pre-scans, magnetic resonance data
may be acquired and used to calibrate the pulse sequence
performed by the pulse sequence server 110. As another
example, navigator signals may be acquired and used to
adjust the operating parameters of the RF system 120 or the
gradient system 118, or to control the view order in which
k-space is sampled. In still another example, the data acqui-
sition server 112 may also process magnetic resonance
signals used to detect the arrival of a contrast agent in a
magnetic resonance angiography (“MRA”) scan. For
example, the data acquisition server 112 may acquire mag-
netic resonance data and processes it in real-time to produce
information that is used to control the scan.

The data processing server 114 receives magnetic reso-
nance data from the data acquisition server 112 and pro-
cesses the magnetic resonance data in accordance with

instructions provided by the operator workstation 102. Such 2

processing may include, for example, reconstructing two-
dimensional or three-dimensional images by performing a
Fourier transformation of raw k-space data, performing
other image reconstruction algorithms (e.g., iterative or
backprojection reconstruction algorithms), applying filters
to raw k-space data or to reconstructed images, generating
functional magnetic resonance images, or calculating
motion or flow images.

Images reconstructed by the data processing server 114
are conveyed back to the operator workstation 102 for
storage. Real-time images may be stored in a data base
memory cache, from which they may be output to operator
display 102 or a display 136. Batch mode images or selected
real time images may be stored in a host database on disc
storage 138. When such images have been reconstructed and
transferred to storage, the data processing server 114 may
notify the data store server 116 on the operator workstation
102. The operator workstation 102 may be used by an
operator to archive the images, produce films, or send the
images via a network to other facilities.

The MM system 100 may also include one or more
networked workstations 142. For example, a networked
workstation 142 may include a display 144, one or more
input devices 146 (¢.g.. a keyboard, a mouse), and a pro-
cessor 148. The networked workstation 142 may be located
within the same facility as the operator workstation 102, or
in a different facility, such as a different healthcare institu-
tion or clinic.

The networked workstation 142 may gain remote access

to the data processing server 114 or data store server 116 via 5

the communication system 140. Accordingly, multiple net-
worked workstations 142 may have access to the data
processing server 114 and the data store server 116. In this
manner, magnetic resonance data, reconstructed images, or

other data may be exchanged between the data processing 5

server 114 or the data store server 116 and the networked
workstations 142, such that the data or images may be
remotely processed by a networked workstation 142.

The present disclosure describes systems and methods for
temperature quantification using magnetic resonance finger-
printing (MRF). The described systems and methods may be
used for multi-contrast temperature mapping of aqueous and
adipose tissues. The temperature quantification systems and
methods can leverage multiple MRI contrast pathways to
accurately map changes in temperature in tissues. In some
embodiments, the systems and methods can be used to
determine temperature changes in aqueous and adipose

4

&

8

tissue simultaneously. The systems and methods for tem-
perature quantification using MRF can utilize an MRF pulse
sequence with smoothly varying RF phase for RF resonant
frequencies played out continuously during an acquisition of
MRF data. In some embodiments, the MRF pulse sequence
is an MRF with quadratic RF phase (MRFqRF) pulse
sequence although it should be understood that other sweep
patterns that provide an RF phase that changes smoothly in
time (or smoothly varying RF phase) may be used with the
systems and methods described herein. In some embodi-
ments, temperature changes may be determined at a plurality
of predetermined time intervals during an acquisition (or
scan) using the MRF pulse sequence with smoothly varying
RF phase for RF resonant frequencies played out continu-
ously. In an embodiment, at each predetermined time inter-
val, one or more parameters (¢.g., tissue properties or
parameters such as T, or T>, or device dependent parameters
such as By) of the acquired MR data for a region of interest
in a subject may be determined and the one or more
parameters can be converted to temperature (e.g., AT) for a
tissue or tissues in the region of interest. In another embodi-
ment, at each predetermined time interval, the temperature
(e.g.. AT) ofa tissue or tissues in the region of interest in the
subject may be determined directly by matching the
acquired MR data from the region of interest to a thermal
signal MRF dictionary. The thermal signal MRF dictionary
may be updated dynamically at each predetermined time
interval during the acquisition (or scan) using, for example,
information (e.g., the magnetization) from a previous time
interval. Accordingly, in some embodiments the thermal
signal MRF dictionary may be advantageously created and
updated on the fly during an acquisition.

In some embodiments, the systems and methods for
temperature quantification using MRF may be used to
continuously monitor the temperature of tissues in a subject
during thermal surgical procedures and guidance of thermal
therapies that include the application of heating or cooling to
tissues in a subject (e.g., procedures or therapies performed
witha laser ablation system, other systems for heating tissue
or other systems for cooling tissues). In an embodiment, the
MRF temperature quantification systems and methods may
be used to determine temperature changes due to heating of
‘one or more tissues (e.g., aqueous or adipose tissues) in a
region of interest in a subject. In another embodiment, the
MRF temperature quantification systems and methods may
be used to determine temperature changes due to cooling of
one or more tissues (e.g., aqueous or adipose tissues) in a
region of interest in a subject. Further advantages of the
described systems and methods are that no interleaving is
required and the systems and methods may be used in low
signal-to-noise ratio (SNR) scenarios.

FIG. 2 illustrates a method for magnetic resonance fin-
gerprinting (MRF) thermometry in accordance with an
embodiment and FIG. 3 illustrates an example flip angle
pattern and time intervals for determining temperature
changes using the method of FIG. 2 in accordance with an
embodiment. The method for MRF thermometry may be
used for temperature quantification and multi-contrast tem-
perature mapping. At block 202, MRF data may be acquired
from tissue (e.g., aqueous and/or adipose tissue) in region of
interest in a subject using, for example, an MM system (e,
MM system 100 shown in FIG. 1). The MRF data can be
acquired by the MRI system using an MRF pulse sequence
with a smoothly varying RF phase for MRF resonant fre-
quencies played out continuously during the acquisition or
scan. In an embodiment the MRF pulse sequence with
smoothly varying RF phase can be optimized to provide a



US 11,519,987 B2

5
B(T,, T.) or E,(T,, Ts, - . . ). Also, the summation on “”
could be replace by a product on “j”. The dictionary may
store signals described by,

SRE(S:1) Qs

where S, is the default, or equilibrium, magnetization; S, is
a vector that represents the different components of magne-
tization, M,, M,, and M. during the i” acquisition block; R,
is a combination of rotational effects that occur during the i”
acquisition block; and E, is a combination of effects that alter
the amount of magnetization in the different states for the i”
acquisition block. In this situation, the signal at the i”
acquisition block is a function of the previous signal at
acquisition block (i.e., the (i-1) acquisition block). Addi-
tionally or alternatively, the dictionary may store signals as
a function of the current relaxation and rotation effects and
of previous acquisitions. Additionally or alternatively, the
dictionary may store signals such that voxels have multiple
resonant species or spins, and the effects may be different for
every spin within a voxel. Further still, the dictionary may
store signals such that voxels may have multiple resonant
species or spins, and the effects may be different for spins
within a voxel, and thus the signal may be a function of the
effects and the previous acquisition blocks.

‘Thus, in MRE, a unique signal timecourse is generated for
each pixel. This timecourse evolves based on both physi-
ological tissue properties such as TI or T2 as well as
acquisition parameters like flip angle (FA) and repetition
time (TR). This signal timecourse can, thus, be referred to as
a signal evolution and each pixel can be matched to an entry
in the dictionary, which is a collection of possible signal
evolutions or timecourses calculated using a range of pos-
sible tissue property values and knowledge of the quantum
physics that govern the signal evolution. Upon matching the
measured signal evolution/timecourse to a specific diction-
ary entry, the tissue properties corresponding to that dic-
tionary entry can be identified. A fundamental criterion in
MRE jis that spatial incoherence be maintained to help
separate signals that are mixed due to undersampling. In
other words, signals from various locations should differ
from each other, in order to be able to separate them when
aliased.

To achieve this process, a magnetic resonance imaging
(MRI) system or nuclear magnetic resonance (NMR) system
may be utilized. FIG. 1 shows an example of an MM system
100 that may be used to perform magnetic resonance fin-
gerprinting. In addition, MRI system 100 may be used to
implement the methods described herein. MRI system 100
includes an operator workstation 102, which may include a

display 104, one or more input devices 106 (e.g.,.a keyboard, 5

a mouse), and a processor 108. The processor 108 may
include a commercially available programmable machine
running a commercially available operating system. The
operator workstation 102 provides an operator interface that

facilitates entering scan parameters into the MM system

100. The operator workstation 102 may be coupled to
different servers, including, for example, a pulse sequence
server 110, a data acquisition server 112, a data processing
server 114, and a data store server 116. The operator
workstation 102 and the servers 110, 112, 114, and 116 may
be connected via a communication system 140, which may
include wired or wireless network connections.

The pulse sequence server 110 functions in response to
instructions provided by the operator workstation 102 to
operate a gradient system 118 and a radiofrequency (“RF”)
system 120. Gradient waveforms for performing a pre-
scribed scan are produced and applied to the gradient system

20

&

0

&

6

118, which then excites gradient coils in an assembly 122 to
produce the magnetic field gradients G,, G,. and G, that are
used for spatially encoding magnetic resonance signals. The
gradient coil assembly 122 forms part of a magnet assembly
124 that includes a polarizing magnet 126 and a whole-body
RF coil 128.

RF waveforms are applied by the RF system 120 to the RF
coil 128, or a separate local coil to perform the prescribed
magnetic resonance pulse sequence. Responsive magnetic
resonance signals detected by the RF coil 128, or a separate
local coil, are received by the RF system 120. The respon-
sive magnetic resonance signals may be amplified, demodu-
lated, filtered, and digitized under direction of commands
produced by the pulse sequence server 110. The RF system
120 includes an RF transmitter for producing a wide variety
of RF pulses used in MM pulse sequences. The RF trans-
mitter is responsive to the prescribed scan and direction
from the pulse sequence server 110 to produce RF pulses of
the desired frequency, phase, and pulse amplitude wave-
form. The generated RF pulses may be applied to the
whole-body RF coil 128 or to one or more local coils or coil
arrays.

The RF system 120 also includes one or more RF receiver
channels. An RF receiver channel includes an RF preampli-
fier that amplifies the magnetic resonance signal received by
the coil 128 to which it is connected, and a detector that
detects and digitizes the I and Q quadrature components of
the received magnetic resonance signal. The magnitude of
the received magnetic resonance signal may, therefore, be
determined at a sampled point by the square root of the sum
of the squares of the I and Q components:

Paleta

and the phase of the received magnetic resonance signal may
also be determined according to the following relationship:

GB):

yauwt(9) °

The pulse sequence server 110 may receive patient data
from a physiological acquisition controller 130. By way of
example, the physiological acquisition controller 130 may
receive signals from a number of different sensors connected
to the patient, including electrocardiograph (“ECG”) signals
from electrodes, or respiratory signals from a respiratory
bellows or other respiratory monitoring devices. These sig-
nals may be used by the pulse sequence server 110 to
synchronize, or “gate,” the performance of the scan with the
subject’s heart beat or respiration.

The pulse sequence server 110 may also connect to a scan
room interface circuit 132 that receives signals from various
sensors associated with the condition of the patient and the
magnet system. Through the scan room interface circuit 132,
a patient positioning system 134 can receive commands to
move the patient to desired positions during the scan.

The digitized magnetic resonance signal samples pro-
duced by the RF system 120 are received by the data
acquisition server 112. The data acquisition server 112
operates in response to instructions downloaded from the
operator workstation 102 to receive the real-time magnetic
resonance data and provide buffer storage, so that data is not
lost by data overrun. In some scans, the data acquisition
server 112 passes the acquired magnetic resonance data to
the data processor server 114. In scans that require infor-
mation derived from acquired magnetic resonance data to


US 11,519,987 B2

Sheet 6 of 6

Dec. 6, 2022

U.S. Patent

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U.S. Patent Dec. 6, 2022 Sheet 2 of 6 US 11,519,987 B2

ACQUIRE MRF DATA FROM A REGION OF |202
INTEREST IN A SUBJECT USING AN MRF B
PULSE SEQUENCE WITH SMOOTHLY VARIED
RF PHASE FOR MR RESONANT
FREQUENCIES

AT TIME INTERVAL OF THE ACQUISITION, 204
COMPARE THE MRF DATA ACQUIRED By
DURING THE TIME INTERVAL TO AN MRF

DICTIONARY
206
DETERMINE QUANTITATIVE PARAMETER(S) yy
208

CONVERT QUANTITATIVE PARAMETERS TO U
TEMPERATURE CHANGE

210
GENERATE TEMPERATURE MAP /
214
DISPLAY LJ
STORE TEMPERATURE MAP TEMPERATURE
MAP
216 212
LAST
TIME INTERVAL OF
ACQUISITION
YES
218
END U

FIG. 2


US 11,519,987 B2

13

measurement time intervals during an acquisition as shown
in FIG. 5. In the embodiment shown in FIG. 5, one baseline
measurement time interval 506 and three temperature mea-
surement time intervals 510, 516 and 522 (or “dynamics”)
are shown. However, it should be understood that an acqui-
sition according to the disclosed temperature quantification
method can include more than three temperature measure-
ment time intervals. The baseline measurement time interval
and the time measurement time intervals may be defined in
terms of, for example, a number of time points or seconds of
acquisition time. Fach temperature measurement time inter-
val can represent a separate continuous dynamic scan. In the
example shown in FIG. 5, the baseline measurement time
interval 506 contains 876 time points which may correspond
to, for example, approximately 9.6 seconds of acquisition
time. In addition, the MRFqRF scan can be implemented
with temperature change time intervals 510, 516, 522 that
each include 219 time points. In this example, a time
measurement time interval 510, 516, 522 with 219 time
points may correspond to approximately 2.6 seconds of
acquisition time. Accordingly, a temperature change may be
measured every 219 time points or approximately every 2.6
seconds during the MRFqRF acquisition of block 402. In
addition, in the example of FIG. 5, the off-resonance fre-
quencies may be swept 2.88 Hz/TR by applying quadratic
phase to the RF pulses. While the example shown in FIG. 5
illustrates 876 time points for the baseline measurement time
interval and 219 time points per time measurement time
interval, it should be understood that the time intervals may
include other numbers of time points. As mentioned above,
the number of time points are configured to provide a
sufficient temporal resolution for temperature quantification
and monitoring. As shown in FIG. 5, temperature of one or
more tissues is determined at each temperature measurement
time interval 510, 516, 522 by generating a thermal signal
MRF dictionary 512, 518, 524, respectively and determining
temperature (AT) 514, 520, 526, respectively. As described
further below, the temperature change at each temperature
measurement time interval may be determined directly by
comparing the MRF data for the particular time interval to
a thermal signal MRF dictionary.

Returning to FIG. 4, baseline values for quantitative
parameters such as, for example, T,, T, and B, are deter-
mined at block 404. In an embodiment where the disclosed
method is used for monitoring a thermal surgical procedure
or thermal therapy, the baseline quantitative parameters may
be determined before heating or cooling is applied to tissue
of the subject. At the predetermined baseline measurement
time interval (e.g., interval 506 shown in FIG. 5), the

baseline quantitative parameters can be determined by com- 5

paring the MRF data acquired during the baseline measure-
ment time interval to an MRF dictionary. As described above
with respect to block 204 of FIG. 2, the MRF dictionary can
include known signal evolutions (e.g., simulated signal

evolutions generated using a Bloch simulation). Each entry

(or signal evolution) in the MRF dictionary corresponds
with t of quantitative parameters, for example, tissue
properties or parameters such as T,, T,, and proton density,
and device dependent parameters such as By. The MRF
dictionary may be stored in and retrieved from memory or
data storage of, for example, the MRI system 100 shown in
FIG. 1) or other computer system. In an embodiment, the
MRF dictionary may be a compressed MRF dictionary. For
example, the MRF dictionary may be compressed using a
known compression method such as, for example, singular
value decomposition (SVD) or randomized SVD (rSVD). In
an embodiment, the compressed MRF dictionary may also

20

14

be partially undersampled in the tissue dimension. The low
resolution in the tissue dimension may be recovered with
quadrative interpolation after comparison and matching. In
an embodiment, the acquired MRF data may also be com-
pressed using a known compression method such as, for
example, rSVD.

In some embodiments, the MRF data may be recon-
structed before comparison with the MRF dictionary using
known methods (¢.g., a non-uniform Fast Fourier Transform
(NUFFT)). In an embodiment, Independent Component
Analysis (ICA) may be used on the MRF data or recon-
structed images) to detect and estimate frequency drift. A
component that describes the linear frequency drift may then
be removed from all voxels in the MRF data (or recon-
structed images). The MRF data and/or reconstructed
images may be stored in memory or data storage of, for
example, the MRI system 100 shown in FIG. 1) or other
computer system. The MRF data (or reconstructed images)
are compared to the MRF dictionary at block 404 to match
the MRF data with the signal evolutions stored in the MRF
dictionary as discussed above. Comparing the MRF data (or
reconstructed images) to the MRF dictionary may be per-
formed in a number of ways such as, for example, using a
pattern matching, template matching, or other matching
algorithm, including the use of non-linear fitting, or the use
of deep learning or neural network structures. In some

* embodiments, the baseline quantitative parameters deter-

&

&

5

mined at block 404 include, but are not limited to, T,, T; and
By. In an embodiment, a quantitative map may be generated
for each determined baseline quantitative parameter identi-
fied. The identified baseline quantitative parameters may be
stored in memory or data storage of, for example, the MM
system 100 shown in FIG. 1) or other computer system.

At block 406, at the predetermined temperature measure-
ment time interval (e.g., interval 510 shown in FIG. 5), a
thermal signal MRF dictionary is generated. The generated
thermal signal MRF dictionary may be stored in memory or
data storage of, for example, the MM system 100 shown in
FIG. 1) or other computer system. The thermal signal MRF
dictionary includes signal evolutions based on changes in
temperature and may be generated using the baseline param-
eters determined at block 404 and known relationships
between the parameters (e.g., relaxation, resonant fre-
quency) and temperature. In an embodiment, the thermal
signal MRF dictionary may be generated using Bloch simu-
lations (which can include terms for the temperature depen-
dence of the various terms) for a range of temperature steps
from baseline. Accordingly, the baseline parameters (and
magnetization) determined from the baseline measurement
time interval may be used as input for the simulations. In
some embodiments, the thermal signal MRF dictionary
includes signal evolutions for both aqueous and adipose
tissues, allowing the temperature to be fit directly regardless
of tissue type. FIG. 6 illustrates example thermal signal
MRF dictionary entries in accordance with an embodiment.
In FIG. 6, signal evolutions for water 604 and fat 606 are
shown for a range of temperature steps. Each signal evolu-
tion for water 604 and fat 606 corresponds to a temperature
step, namely, AT=0 (curve 608), AT=10 (curve 610), AT=20
(curve 612), AT=30 (curve 614), and AT=40 (curve 616). In
the example dictionary entries of FIG. 6, frequency, T,, and
T,, may be updated according to the following linear equa-
tions using known constants for both water and fat:

ahh, oo BoAP 6

Th, =Thy BAT a)

2, T2y.49OT 8)


US 11,519,987 B2

17

information such as computer readable instructions, data
structures, program modules or other data. Computer read-
able media includes, but is not limited to, random access
memory (RAM), read-only memory (ROM), electrically
erasable programmable ROM (EEPROM), flash memory or
other memory technology, compact disk ROM (CD-ROM),
digital volatile disks (DVD) or other optical storage, mag-
netic cassettes, magnetic tape, magnetic disk storage or other
magnetic storage devices, or any other medium which can be
used to store the desired instructions and which may be
accessed by a system (e.g., a computer), including by
internet or other computer network form of access.

‘The present invention has been described in terms of one
or more preferred embodiments, and it should be appreciated
that many equivalents, alternatives, variations, and mod:
cations, aside from those expressly stated, are possible and
within the scope of the invention.

The invention claimed is:

1. A method for temperature quantification using mag-
netic resonance fingerprinting (MRF) comprising:

acquiring, using a magnetic resonance imaging (MRI)

system, MRF data from a region of interest in a subject
using an MRF pulse sequence with smoothly varying
RF phase for MR resonant frequencies that is played
out continuously; and

for each of a plurality of temperature measurement time

intervals during acquisition of the MRF data:

generating a thermal signal MRF dictionary;

comparing a set of MRF data associated with the
temperature measurement time interval to the ther-
mal signal MRF dictionary to determine a tempera-
ture change; and

generating a quantitative map of the temperature
change in the region of interest.

2. The method according to claim 1, wherein the plurality
of temperature measurement time intervals includes a first
temperature measurement time interval and a second tem-
perature measurement time interval and generating the ther-
mal signal MRE dictionary for the second temperature
measurement time interval is based on at least information
related to a determined temperature change for the first
temperature measurement time interval.

3. The method according to claim 1, further comprising:

for a baseline measurement time interval, comparing a set

of the MRF data associated with the baseline measure-
ment time interval to an MRF dictionary to determine
at least one baseline quantitative parameter of the set of
MRF data.

4. The method according to claim 3, wherein generating
the thermal signal MRF dictionary is based on at least the at

0

&

0

&

18

least one baseline quantitative parameter of the set of MRF
data associated with the baseline measurement time interval.

5. The method according to claim 1, wherein the region of
interest comprises a region of aqueous tissue.

6. The method according to claim 1, wherein the region of
interest comprises a region of adipose tissue.

7. The method according to claim 1, wherein the RF pulse
sequence is an MRF with quadratic RF phase (MRFqRF)
pulse sequence.

8. The method according to claim 1, wherein the MRF
pulse sequence is configured to be sensitive to T,, T, and
resonant frequency and the set of MRF data associated with
the time measurement time interval includes at least one
signal evolution.

9. The method according to claim 1, further comprising
modifying a thermal treatment based on the determined
temperature change.

10. A magnetic resonance imaging (MRI) system com-
prisin,

a magnet system configured to generate a polarizing
magnetic field about at least a portion of a subject;

a magnetic gradient system including a plurality of mag-
netic gradient coils configured to apply at least one
magnetic gradient field to the polarizing magnetic field;

a radio frequency (RF) system configured to apply an RF
field to the subject and to receive magnetic resonance
signals from the subject using a coil array; and

a computer system programmed to:
acquire MRF data from a region of interest in a subject

using an MRF pulse sequence with smoothly varying

RF phase for MR resonant frequencies that is played

out continuously; and

for each of a plurality of temperature measurement time

intervals during acquisition of the MRF data:

generate a thermal signal MRF dictionary;

compare a set of MRF data associated with the
temperature measurement time interval to the ther-
mal signal MRF dictionary to determine a tem-
perature change; and

generate a quantitative map of the temperature
change in the region of interest.

11. The MRI system according to claim 10, wherein the
RF pulse sequence is an MRF with quadratic RF phase
(MRF RF) pulse sequence.

12. The MRI system according to claim 10, wherein the
temperature change is associated with one of heating or
cooling of a tissue in the region of interest of the subject.

+e Ree



US 11,519,987 B2

Sheet 1 of 6

Dec. 6, 2022

U.S. Patent

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US 11,519,987 B2
Page 2

(51) Int. Cl.

AGIB 5/055 (2006.01)
A6IB 5/00 (2006.01)
GOIR 33/385 (2006.01)
(56) References Cited

U.S. PATENT DOCUMENTS

2015/0301141 Al
2016/0033604 AL*

10/2015 Griswold

2/2016 Grodzki GOLR 33/5608

324/309

OTHER PUBLICATIONS,

Ishihara Y, et al. A precise and fast temperature mapping use water
proton chemical shift. Magnetic Resonance in Medicine 1995
34:814-823.

Ma D, et al. Magnetic resonance fingerprinting. Nature 2013
495:187-192.

McGivney, D., et al. “Towards continuous dictionary resolution in
MR fingerprinting using a quadratic inner product model.” Proceed-
ings of the 25th Annual Meeting of ISMRM, Montréal, QC, Canada.
vol. 27. 2019.

Odeen H et al. Non-Invasive Thermometry with Magnetic Reso-
nance Imaging. Theory and Applications of Heat Transfer in Humans,
vol. 1, 2018.

Rieke, V. et al. “MR thermometry.” J Magn Reson Imaging 27
2008): 376-390

Wang, C. Y., et al. “Magnetic resonance fingerprinting with qua-
dratic RF phase for measurement of T2* simultaneously with df, TI,
and T2.” Magnetic resonance in medicine 81.3 (2019): 1849-1862
Yang, M., et al. “Low rank approximation methods for MR finger-
printing with large scale dictionaries.” Magnetic resonance in
medicine 79.4 (2018): 2392-2400.

* cited by examiner


US 11,519,987 B2

9

faster temporal resolution for the temperature quantification
and monitoring. For example, the number of time points per
scan can be reduced which results in an increase in temporal
resolution, a faster acquisition and increase sensitivity to By
or resonant frequency changes. In addition, a decrease in the
number of time points can result in reduced signal to noise
ratio (SNR). Reduction of the number of time points can
bring finer resolution sampling of the temperature curves
through time. In various embodiments, the MRF pulse
sequence may be configured to be sensitive to one or more
parameters such as T,, T>, T,*, B and off-resonance (Af). In
some embodiments, the T, sensitivity of the MRF pulse
sequence may be increased by periodically applying an
inversion RF pulse during the acquisition.

In some embodiments, the MRF pulse sequence is a MRF
with quadratic RF phase (MRFqRF) pulse sequence. While
the following description will be discussed in terms of
embodiments using an MRFgRF sequence and quadratic RF
phase, it should be understood that in other embodiments

other sweep patterns that provide an RF phase that changes 2

smoothly in time (or smoothly varying RF phase) may be
used in the systems and methods described herein to acquire
the MRF data. An MRFqRF pulse sequence may be used to
simultaneously quantify T,, T>, Tz", By and off-resonance
(Af). An MRFQRF pulse sequence can be configured to, for
example, sweep the on-resonance frequency linearly
between -1/(2*TR) to 1/(2*TR) in time by modulating the
RF phase with a quadratic function. In some embodiments,
a larger or smaller range for the sweep of the on-resonance
frequency may be used. As mentioned above, in an embodi-
ment the MRFqRF sequence can be optimized to provide a
faster temporal resolution for the temperature quantification
and monitoring. For example, the number of time points per
acquisition using the MRFqRF pulse sequence can be
reduced (compared to a conventional MREgRF acquisition)
to provide a faster temporal resolution for the temperature
quantification. An example MRFqRF flip angle sweep pat-
tern is shown in FIG. 3. In FIG. 3, a flip angle pattern 324
representing flip angles 302 over time (e.g., time points 304)
is shown.

As the acquisition at block 202 of FIG. 2 is played out,
temperature changes are determined at a plurality of prede-
termined time intervals during an acquisition as shown in
FIG. 3. In the embodiment shown in FIG. 3, three time
intervals 306, 312 and 318 are shown, however, it should be
understood that an acquisition according to the disclosed
temperature quantification method can include more than
three time intervals. The time interval at which the tempera-
ture changes are determined may be defined in terms of, for

example, a number of time points or seconds of acquisition 5

time. Each time interval can represent a separate continuous
dynamic scan. In the example shown in FIG. 3, the MRFqRF
scan can be implemented with time intervals 306, 312, 318
that each include 876 time points. In this example, a time

interval 306, 312, 318 with 876 time points may correspond s

to approximately 9.6 seconds of acquisition time. Accord-
ingly, a temperature change may be measured approximately
every 9.6 seconds during the MRFqRF acquisition of block
202. In addition, in the example of FIG. 3, the off-resonance
frequencies may be swept 2.88 Hz/TR by applying quadratic
phase to the RF pulses. While the example shown in FIG. 3
illustrates 876 time points per time interval, it should be
understood that the time intervals may include other num-
bers of time points. As mentioned above, the number of time
points are configured to provide a sufficient temporal reso-
lution for temperature quantification and monitoring. As
shown in FIG. 3, temperature of one or more tissues is

4

&

10
determined at each time interval 306, 312, 318 by determin-
ing quantitative properties or parameters (e.g. T,, T,, Bo)
308, 314, 320, respectively and converting 310, 316, 322,
respectively, the determined properties or parameters to a
change in temperature (AT).

Returning to FIG. 2, at block 204, at the predetermined
time interval (e.g. time interval 306) the MRF data acquired
during the time interval is compared to an MRF dictionary.
The MRF dictionary includes known signal evolutions (e.g.
simulated signal evolutions). In an embodiment, the MRF
dictionary may be generated using a Bloch simulation. Each
entry (or signal evolution) in the MRF dictionary corre-
sponds with a set of quantitative parameters, for example,
tissue properties or parameters such as T,, T>, and proton
density, and device dependent parameters such as By. The
MRF ‘dictionary may be stored in and retrieved from
memory or data storage of, for example, the MM system 100
shown in FIG. 1) or other computer system. In an embodi-
ment, the MRF dictionary may be a compressed MRF
dictionary. For example, the MRF dictionary may be com-
pressed using a known compression method such as, for
example, singular value decomposition (SVD) or random-
ized SVD (rSVD). In an embodiment, the compressed MRF
dictionary may also be partially undersampled in the tissue

5 dimension. The low resolution in the tissue dimension may

be recovered with quadrative interpolation after comparison
and matching. In an embodiment, the acquired MRF data
may also be compressed using a known compression method
such as, for example, SVD.

In some embodiments, the MRF data may be recon-
structed before comparison with the MRF dictionary using
known methods (e.g., a non-uniform Fast Fourier Transform
(NUFFT)). In an embodiment, Independent Component
Analysis (ICA) may be used on the MRF data or recon-
structed images) to detect and estimate frequency drift. A
component that describes the linear frequency drift may then
be removed from all voxels in the MRF data (or recon-
structed images). The MRF data and/or reconstructed
images may be stored in memory or data storage of, for
example, the MRI system 100 shown in FIG. 1) or other
computer system. The MRF data (or reconstructed images)
are compared to the MRF dictionary at block 204 to match
the MRF data with the signal evolutions stored in the MRF
dictionary. “Match” as used herein refers to the result of
comparing signals but does not refer to an exact match,
which may or may not be found. A match may be the signal
evolution that most closely resembles another signal evolu-
tion. Comparing the MRF data (or reconstructed images) to
the MRF dictionary may be performed in a number of ways
such as, for example, using a pattern matching, template
matching, or other matching algorithm, including the use of
non-linear fitting, or the use of deep learning or neural
network structures.

At block 206, one or more quantitative parameters of the
MRF data acquired during the time interval are determined
based on the comparison and matching at block 204 to
identify matching dictionary entry or entries (i.e., matching
signal evolution(s)). In one embodiment, the determined
quantitative parameters may include T,, T;, Bo. and off-
resonance (Af). In an embodiment, a quantitative map may
be generated for each identified quantitative parameter. The
identified quantitative parameters may be stored in memory
or data storage of, for example, the MRI system 100 shown,
in FIG. 1) or other computer system. At block 208, the one
or more quantitative parameters identified at block 208 may
be converted to a temperature change (AT) using known
methods. In one embodiment, one of the determined quan-



US 11,519,987 B2

15

In the example shown in FIG. 6, for water 604 the dictionary
peaks change eft to right due to changes in B, and for fat 606
the peaks shift from left to right in relative magnitude due to
changes in T, and T;. Combining both water 604 and fat 606
in the dictionary enables direct fitting for temperature for
both types of tissues (aqueous and adipose) simultaneously.

Returning to FIG. 4, at block 408 at the temperature
measurement time interval (e.g., interval 510 shown in FIG.
5), the MRF data acquired during the temperature measure-
ment time interval is compared to the thermal signal MRF
dictionary generated at block 406. In an embodiment, the
MRF dat quired in the temperature measurement time
interval may be concatenated with the baseline data acquired
during the baseline measurement time interval before com-
parison to the thermal signal MRF dictionary. In some
embodiments, the MRF data may be reconstructed before
comparison with the thermal signal MRF dictionary using
known methods (e.g., a non-uniform Fast Fourier Transform
(NUFFT)). The MRF data and/or reconstructed images may
be stored in memory or data storage of, for example, the
MRI system 100 shown in FIG. 1) or other computer system.
The MRF data (or reconstructed images) are compared to
the thermal signal MRF dictionary at block 408 to match the
MRF data acquired during the temperature measurement

time interval with the signal evolutions stored in the thermal 2

signal MRF dictionary. Comparing the MRF data (or recon-
structed images) to the thermal signal MRF dictionary may
be performed in a number of ways such as, for example,
using a pattem matching, template matching, or other
matching algorithm, including the use of non-linear fitting,
or the use of deep learning or neural network structures. In
an embodiment, the comparison of the acquired MRF data
to the thermal signal MRF dictionary includes matching
acquired MRF data from each voxel to the thermal signal
MRF dictionary.

At block 410, a temperature change for the MRF data is
determined based on the comparison and matching at block
408 to identify matching dictionary entry or entries (i.e.,
matching signal evolution(s)). The identified temperature
change may be stored in memory or data storage of, for
example, the MM system 100 shown in FIG. 1) or other
computer system. At block 412, a temperature map may be
generated indicating the temperature change determined at
block 410. For example, a map may be generated having a
quantitative indication of the determined temperature. At
block 414, the temperature map may be stored in memory or
data storage of, for example, the MM system 100 shown in
FIG. 1) or other computer system. In an embodiment, the
temperature map may be provided to and displayed on a
display (c.g., display 104, 1356 or 144 of MRI system 100
shown in FIG. 1) at block 416.

At block 418, it is determined whether there are any
additional temperature measurement time intervals in the
acquisition for which a temperature change may be deter-
mined or if the acquisition is complete. If there is an
additional temperature measurement time interval, an
updated thermal signal MRF dictionary is generated at block
420 for the next temperature measurement time interval
(e.g. time interval 516 shown in FIG. 5). The updated
thermal signal MRF dictionary includes signal evolutions
based on changes in temperature and may be generated
using the magnetization (e.g., the matched signal evolution
and associated parameters such as temperature) from the
prior temperature measurement time interval (e.g. the prior
dynamic) and known relationships between the parameters
(eg., relaxation, resonant frequency) and temperature. For
example, the thermal signal MRF dictionary may be gener-

20

3

4

4

5

s

at

16

ated using Bloch simulations for a range of temperature
steps from baseline and the magnetization determined from
the prior time measurement time interval may be used as
input for the simulations.

The process then returns to block 408 and the MRF data
from the next temperature measurement time interval (e.g.,
time interval 516 shown in FIG. 5) is compared to the
updated thermal signal MRF dictionary from block 420.
Blocks 408-420 are performed at each time measurement
time interval to update the thermal signal MRF dictionary
and to determine a temperature change at the corresponding
point in time of the acquisition. Advantageously, the thermal
signal MRF dictionaries generated at blocks 408 and 420
may be generated on the fly during the temperature quanti-

5 fication and monitoring process using information from the

previous time interval (or dynamic). As shown in FIG. 5, at
a first temperature measurement time interval 510, a thermal
signal MRF dictionary is generated 512 and a temperature
change is determined 514 based on the MRF data acquired
during the interval 510 and, as described above, the mag-
netization from the baseline measurement at interval 506. At
a second temperature measurement time interval 516, a
thermal signal MRF dictionary is generated 518 and a
temperature change is determined 520 based on the MRF
data acquired during the interval 518 and, as described
above, the magnetization from the previous temperature
measurement interval 510. At a third temperature measure-
ment time interval 522, a thermal signal MRF dictionary is
generated 524 and a temperature change is determined 526
based on the MRF data acquired during the interval 522 and,
as described above, the magnetization from the previous
temperature measurement interval 518. In the embodiment
shown in FIG. 5, a temperature change may be determined
for a time intervals of 219 time points (approximately 2.6
seconds of MRF data) by directly matching the acquired
MRF data in the corresponding time interval to the thermal
signal MRF dictionary. Accordingly, in some embodiments
a temperature map may be generated approximately every
2.6 seconds and advantageously enables monitoring of the
temperature every 2.4 seconds.

Returning to FIG. 4, at block 418 if the acquisition is
complete, the process ends at block 422. As mentioned
above, in some embodiments the method for temperature
quantification using MRF may be used to monitor the
temperature of tissues in a subject during thermal surgical
procedures and guidance of thermal therapies. In such
embodiments, the total length of the MRF acquisition (e.g.,
an MRFgRF acquisition as described above) may be deter-
mined based on the duration of the heating or cooling of the
subject during the thermal surgical procedure or thermal
therapy. In some embodiments, the thermal procedure,
therapy or treatment may be stopped (manually or automati-
cally) based on the determined temperature change and
temperature map (blocks 410 and 412), for example, if the
determined temperature falls above or below a predeter-
mined threshold. In some embodiments, the thermal therapy
or treatment may be modified (manually or automatically)
based on the determined temperature change and tempera-
ture map (blocks 410 and 412), for example, if the deter-
mined temperature falls above or below a predetermined
threshold.

Computer-executable instructions for magnetic resonance
fingerprinting thermometry according to the above-de-
scribed methods may be stored on a form of computer
readable media. Computer readable media includes volatile
and nonvolatile, removable, and non-removable media
implemented in any method or technology for storage of