US 11,961,164 B2Granted

Continuous bed motion acquisition with axially short phantom for PET imaging system setup and quality control

AssigneeSiemens Medical Solutions USA, Inc.

InventorsVladimir Panin, Mehmet Aykac, Shikui Yan, Brian Kelly

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Abstract

An improved method for time alignment (TA) procedure and crystal efficiency (CE) normalization estimation procedure for a PET scanner system is disclosed. In the TA procedure modeled time-of-flight (TOF) data are compared against the measured TOF data from an axially short cylinder phantom in order to find individual detector's time offsets (TOs). Then the TOs are estimated simultaneously by matching the TOF center of mass between the modeled and measured TOF data. In the CE estimation, TOF reconstruction of CBM data on the axially short cylinder phantom is performed. Alternating between TOF image reconstruction and CE updates eventually lead to the correct estimation of activity and CE component.

Claims11

Dependent claims

A method for monitoring a positron emission tomography (PET) scanner performance during a continuous-bed-motion (CBM) acquisition using an axially short phantom to cover long axial PET scanner's field of view, wherein the PET scanner has a patient bed that moves a patient in an axial motion through the PET scanner during the CBM acquisition, the method comprising:

(a) generating time-of-flight (TOF) data of positron annihilation activity in the axially short phantom mounted on the patient bed during the CBM acquisition, while accounting for the axial motion of the patient bed during the CBM acquisition, as a first data set that comprises CBM data y with spatial projection index j=(ρ,θ,ζ), wherein ρ is transaxial radial coordinate, θ is azimuthal coordinate, and TOF bin index T is modeled by combining true events modeled projection p from emission object f, defined by voxel index k, corrected for scanner efficiency through a normalization array n for attenuation by α, and scatter estimation S, corrected for scanner efficiency, and mean random data r according to:
y
_
ρθζ
⁢
T
=
a
ρ
⁢
θ
⁢
ζ
⁢
n
ρθζ
-
1
(
ε
)
⁢
∑
k
C
ρθζ
⁢
T
,
k
⁢
f
k
+
n
ρθζ
-
1
⁢
S
ρθζ
⁢
T
+
r
_
ρθζ
⁢
T
=
a
ρ
⁢
θ
⁢
ζ
⁢
n
ρθ
⁢
ζ
-
1
(
ε
)
⁢
p
_
ρθζ
⁢
T
+
n
ρθζ
-
1
⁢
S
ρθζ
⁢
T
+
r
_
ρθζ
⁢
T
;
wherein C is the geometrical projection system matrix;
wherein ε is the crystal efficiency;
and
wherein coordinate ζ is the CBM data normalization;
(b) generating a complementary data set by integrating the TOF data over the axial motion of the patient bed during the CBM acquisition, wherein the complementary data set is modeled by the equation:
y
_
ρθ
⁢
zT
=
n
ρθ
⁢
z
-
1
(
ε
)
⁢
B
(
a
ρθζ
⁢
∑
k
C
ρθζ
⁢
T
,
k
⁢
f
k
)
+
n
ρθ
⁢
z
-
1
(
ε
)
⁢
B
⁡
(
S
ρθζ
⁢
T
)
+
r
_
ρθ
⁢
zT
=
n
ρθ
⁢
z
-
1
(
ε
)
⁢
p
_
ρθ
⁢
zT
+
⁢
n
ρθ
⁢
z
-
1
(
ε
)
⁢
S
ρθ
⁢
zT
+
r
_
ρθ
⁢
zT
,
wherein z is axial plane coordinate, B is a blurring operator defined as
B
⁡
(
p
_
ρθζ
⁢
T
)
=
∫
0
t
a
⁢
c
⁢
q
′
dt
′
⁢
∑
ζ
e
-
λ
⁢
t
⁢
d
ρθ
⁢
z
(
t
)
⁢
p
_
ρθζ
⁢
T
⁢
δ
z
,
Z
⁡
(
ζ
,
t
)
,
and Y ρθz, a motion blurring operator, is calculated by:
y
ρθ
⁢
z
=
B
⁡
(
y
ρ
⁢
θ
⁢
ζ
)
=
∫
0
t
a
⁢
c
⁢
q
′
dt
′
⁢
∑
ζ
e
-
λ
⁢
t
⁢
d
ρθ
⁢
z
(
t
)
⁢
y
ρθζ
⁢
δ
z
,
Z
⁡
(
ζ
,
t
)
,
wherein t′acq is CBM acquisition time;
and
(c) simultaneously reconstructing the positron annihilation activity and crystal efficiency normalization coefficient from the complementary data set.

306 more elements

A positron emission tomography (PET) calibration system comprising:

a PET scanner having an axially short phantom mounted on a patient bed that moves in an axial motion through the PET scanner during the CBM acquisition;
and
a scintillation crystal efficiency calibration system that performs a method comprising:
(a) generating time-of-flight (TOF) data of positron annihilation activity in the axially short phantom during the CBM acquisition, while accounting for the axial motion of the patient bed during the CBM acquisition, as a first data set that comprises CBM data y with spatial projection index j=(ρ,θ,ζ), wherein ρ is transaxial radial coordinate, θ is azimuthal coordinate, and TOF bin index T is modeled by combining true events modeled projection p from emission object f, defined by voxel index k, corrected for scanner efficiency through a normalization array n for attenuation by α, and scatter estimation S, corrected for scanner efficiency, and mean random data r according to:
y
_
ρθ
⁢
ζ
⁢
T
=
a
ρ
⁢
θ
⁢
ζ
⁢
n
ρθζ
-
1
(
ε
)
⁢
∑
k
C
ρθζ
⁢
T
,
k
⁢
f
k
+
n
ρθζ
-
1
⁢
S
ρθζ
⁢
T
+
r
_
ρθ
⁢
ζ
⁢
T
=
a
ρ
⁢
θ
⁢
ζ
⁢
n
ρθζ
-
1
(
ε
)
⁢
p
_
ρθζ
⁢
T
+
n
ρθζ
-
1
⁢
S
ρθζ
⁢
T
+
r
_
ρθζ
⁢
T
;
wherein C is the geometrical projection system matrix;
wherein ε is the crystal efficiency;
and
wherein coordinate ζ is the CBM data normalization;
(b) generating a complementary data set by integrating the TOF data over the axial motion of the patient bed during the CBM acquisition, wherein the complementary data set is modeled by the equation:
y
_
ρθ
⁢
zT
=
n
ρθ
⁢
z
-
1
(
ε
)
⁢
B
(
a
ρθζ
⁢
∑
k
C
ρθζ
⁢
T
,
k
⁢
f
k
)
+
n
ρθ
⁢
z
-
1
(
ε
)
⁢
B
⁡
(
S
ρθζ
⁢
T
)
+
r
_
ρθ
⁢
zT
=
n
ρθ
⁢
z
-
1
(
ε
)
⁢
p
_
ρθ
⁢
zT
+
⁢
n
ρθ
⁢
z
-
1
(
ε
)
⁢
S
ρθ
⁢
zT
+
r
_
ρθ
⁢
zT
,
wherein z is axial plane coordinate, B is a blurring operator defined as
B
⁡
(
p
_
ρθζ
⁢
T
)
=
∫
0
t
a
⁢
c
⁢
q
′
dt
′
⁢
∑
ζ
e
-
λ
⁢
t
⁢
d
ρθ
⁢
z
(
t
)
⁢
p
_
ρθζ
⁢
T
⁢
δ
z
,
Z
⁡
(
ζ
,
t
)
.
and Y ρθz, a motion blurring operator, is calculated by:
y
ρθ
⁢
z
=
B
⁡
(
y
ρ
⁢
θ
⁢
ζ
)
=
∫
0
t
a
⁢
c
⁢
q
′
dt
′
⁢
∑
ζ
e
-
λ
⁢
t
⁢
d
ρθ
⁢
z
(
t
)
⁢
y
ρθζ
⁢
δ
z
,
Z
⁡
(
ζ
,
t
)
,
wherein t′acq is CBM acquisition time;
and
(c) simultaneously reconstructing the positron annihilation activity and crystal efficiency normalization coefficient from the complementary data set.

311 more elements

A non-transitory, machine readable storage medium encoded with computer program software, such that when a processor executes the computer program software, the processor performs a method for monitoring a positron emission tomography (PET) scanner performance during a continuous-bed-motion (CBM) acquisition mode, wherein the PET scanner has a patient bed that moves the axially short phantom in an axial motion through the PET scanner during the CBM acquisition, the method comprising:

(a) generating time-of-flight (TOF) data of positron annihilation activity in the axially short phantom during the CBM acquisition, while accounting for the axial motion of the patient bed during the CBM acquisition, as a first data set that comprises CBM data y with spatial projection index j=(ρ,θ,ζ), wherein ρ is transaxial radial coordinate, θ is azimuthal coordinate, and TOF bin index T is modeled by combining true events modeled projection p from emission object f, defined by voxel index k, corrected for scanner efficiency through a normalization array n for attenuation by α, and scatter estimation S, corrected for scanner efficiency, and mean random data r according to:
y
_
ρθζ
⁢
T
=
a
ρ
⁢
θ
⁢
ζ
⁢
n
ρθζ
-
1
(
ε
)
⁢
∑
k
C
ρθζ
⁢
T
,
k
⁢
f
k
+
n
ρθζ
-
1
⁢
S
ρθζ
⁢
T
+
r
_
ρθζ
⁢
T
=
a
ρ
⁢
θ
⁢
ζ
⁢
n
ρθ
⁢
ζ
-
1
(
ε
)
⁢
p
_
ρθζ
⁢
T
+
n
ρθζ
-
1
⁢
S
ρθζ
⁢
T
+
r
_
ρθζ
⁢
T
;
wherein C is the geometrical projection system matrix;
wherein ε is the crystal efficiency;
and
wherein coordinate ζ is the CBM data normalization;
(b) generating a complementary data set by integrating the TOF data over the axial motion of the patient bed during the CBM acquisition, wherein the complementary data set is modeled by the equation:
y
_
ρθ
⁢
zT
=
n
ρθ
⁢
z
-
1
(
ε
)
⁢
B
(
a
ρθζ
⁢
∑
k
C
ρθζ
⁢
T
,
k
⁢
f
k
)
+
n
ρθ
⁢
z
-
1
(
ε
)
⁢
B
⁡
(
S
ρθζ
⁢
T
)
+
r
_
ρθ
⁢
zT
=
n
ρθ
⁢
z
-
1
(
ε
)
⁢
p
_
ρθ
⁢
zT
+
⁢
n
ρθ
⁢
z
-
1
(
ε
)
⁢
S
ρθ
⁢
zT
+
r
_
ρθ
⁢
zT
,
wherein z is axial plane coordinate, B is a blurring operator defined as
B
⁡
(
p
_
ρθζ
⁢
T
)
=
∫
0
t
a
⁢
c
⁢
q
′
dt
′
⁢
∑
ζ
e
-
λ
⁢
t
⁢
d
ρθ
⁢
z
(
t
)
⁢
p
_
ρθζ
⁢
T
⁢
δ
z
,
Z
⁡
(
ζ
,
t
)
,
and y ρθz, a motion blurring operator, is calculated by:
y
ρθ
⁢
z
=
B
⁡
(
y
ρ
⁢
θ
⁢
ζ
)
=
∫
0
t
a
⁢
c
⁢
q
′
dt
′
⁢
∑
ζ
e
-
λ
⁢
t
⁢
d
ρθ
⁢
z
(
t
)
⁢
y
ρθζ
⁢
δ
z
,
Z
⁡
(
ζ
,
t
)
,
wherein t′acq is CBM acquisition time;
and
(c) simultaneously reconstructing the positron annihilation activity and crystal efficiency normalization coefficient from the complementary data set.

306 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/596,829Filed 29 Sep 2020 · us provisional application
Priority: 9 Oct 2019
Published: US 2022/0309718 A129 Sep 2022
Granted: 16 Apr 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification7 codes

G06T 12/00
Tomographic reconstruction from projections
A61B 6/037
Computed tomography [CT]: emission tomography
G06T 12/20
Tomographic reconstruction from projections: inverse problem, i.e. transformations from projection space into object space
G16H 30/40
ICT specially adapted for the handling or processing of medical images: for processing medical images, e.g. editing