IP Library › Granted Patent US 12,094,037
Granted Patent B2
US 12,094,037 · App. 17/645,473 · Granted Sep 17, 2024

Medical image processing apparatus and medical image processing method

Inventors: Hisashi Takahashi (Kashiwa, JP); Taiga Goto (Kashiwa, JP); Kana Tanaka (Kashiwa, JP)
Assignee: FUJIFILM Healthcare Corporation
G06T11/008A61B6/032A61B6/5235A61B6/5258G06T5/70G06T7/0012G06V10/25G16H30/40G06T2207/10072G06T2207/30004G06V2201/03
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Quick Facts
Patent No.
US 12,094,037
App. No.
17/645,473
Granted
Sep 17, 2024
Kind
B2
Abstract

There are a medical image processing apparatus and a medical image processing method that are capable of maintaining the estimation accuracy of noise intensity even though a system noise ratio rises. A medical image processing apparatus includes a difference image generating unit that divides projection data obtained by applying radiation to an examinee to create a difference image between tomographic images reconstructed for every divided piece of projection data; a local variance computing unit that computes local variance in the difference image; and a noise estimation unit that corrects the local variance using a correction function found in advance to estimate noise intensity of a tomographic image of the examinee. The correction function includes system noise.

Claims (117)

1. A medical image processing apparatus comprising:

a difference image generating unit that divides projection data obtained by applying radiation to an examinee to create a difference image between tomographic images reconstructed for every divided piece of projection data;

a local variance computing unit that computes local variance in the difference image; and

a noise estimation unit that corrects the local variance using a correction function, the correction function having been found in advance, to estimate noise intensity of a tomographic image of the examinee,

wherein the correction function includes system noise.

2. The medical image processing apparatus according to claim 1 , wherein when the local variance is VarD, the noise intensity is VarC, a distance from a rotation center of a scanner is s, and the correction function is α(s) and β(s), the noise intensity VarC is computed from

VarC

=

VarD

α

⁢

(

s

)

+

α

⁡

(

s

)

2

+

β

⁡

(

s

)

·

VarD

.

3. The medical image processing apparatus according to claim 2 , wherein the correction functions α(s) and β(s) are found for each of imaging conditions based on projection data of a phantom filled with a uniform substance.

4. The medical image processing apparatus according to claim 1 , wherein the noise estimation unit creates a noise intensity map by mapping the noise intensity, and displays the noise intensity map together with a tomographic image of the examinee.

5. The medical image processing apparatus according to claim 4 , wherein a noise reduction process is applied to the tomographic image of the examinee based on noise intensity of a ROI set in the tomographic image of the examinee.

6. The medical image processing apparatus according to claim 1 , wherein the system noise is noise generated in a detector circuit in a course of detection of the radiation to conversion of a digital signal.

7. A medical image processing apparatus comprising:

a difference image generating unit that divides projection data obtained by applying radiation to an examinee to create a difference image between tomographic images reconstructed for every divided piece of projection data;

a local variance computing unit that computes local variance in the difference image; and

a noise estimation unit that corrects the local variance using a correction function found in advance to estimate noise intensity of a tomographic image of the examinee,

wherein the correction function includes system noise, and

wherein the system noise is noise generated in a detector circuit in a course of detection of the radiation to conversion of a digital signal.

8. The medical image processing apparatus according to claim 7 , wherein when the local variance is VarD, the noise intensity is VarC, a distance from a rotation center of a scanner is s, and the correction function is α(s) and β(s), the noise intensity VarC is computed from

Var

⁢

C

=

Var

⁢

D

α

⁡

(

s

)

+

α

⁡

(

s

)

2

+

β

⁡

(

s

)

·

Var

⁢

D

.

9. The medical image processing apparatus according to claim 8 , wherein the correction functions α(s) and β(s) are found for each of imaging conditions based on projection data of a phantom filled with a uniform substance.

10. The medical image processing apparatus according to claim 7 , wherein the noise estimation unit creates a noise intensity map by mapping the noise intensity, and displays the noise intensity map together with a tomographic image of the examinee.

11. The medical image processing apparatus according to claim 10 , wherein a noise reduction process is applied to the tomographic image of the examinee based on noise intensity of a ROI set in the tomographic image of the examinee.

12. A medical image processing method comprising:

a difference image creating step in which projection data obtained by applying radiation to an examinee is divided to create a difference image between tomographic images reconstructed for every divided piece of projection data;

a local variance computing step wherein local variance is computed in the difference image; and

a noise estimating step wherein the local variance is corrected using a correction function, the correction function having been found in advance, to estimate noise intensity of a tomographic image of the examinee,

wherein the correction function includes system noise.

13. The medical image processing method according to claim 12 , wherein the system noise is noise generated in a detector circuit in a course of detection of the radiation to conversion of a digital signal.

14. The medical image processing method according to claim 12 , wherein when the local variance is VarD, the noise intensity is VarC, a distance from a rotation center of a scanner is s, and the correction function is α(s) and β(s), the noise intensity VarC is computed from

Var

⁢

C

=

Var

⁢

D

α

⁡

(

s

)

+

α

⁡

(

s

)

2

+

β

⁡

(

s

)

·

Var

⁢

D

.

15. The medical image processing method according to claim 14 , wherein the correction functions α(s) and β(s) are found for each of imaging conditions based on projection data of a phantom filled with a uniform substance.

16. The medical image processing method according to claim 12 , further comprising:

creating a noise intensity map by mapping the noise intensity; and

displaying the noise intensity map together with a tomographic image of the examinee.

17. The medical image processing method according to claim 16 , further comprising:

applying a noise reduction process to the tomographic image of the examinee based on noise intensity of a ROI set in the tomographic image of the examinee.

Assignments (2)
MERGER Recorded Aug 9, 2024
From: FUJIFILM HEALTHCARE CORPORATION
To: FUJIFILM CORPORATION
Reel/Frame 068242/0301 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 22, 2021
From: TAKAHASHI, HISASHI; GOTO, TAIGA; TANAKA, KANA
To: FUJIFILM HEALTHCARE CORPORATION
Reel/Frame 058456/0228 →
Priority Claims (1)
JP 2021-015118 · Feb 2, 2021 · national
Continuity (1)
Related Publication 20220245871A1 · Aug 4, 2022
Cited By (4)
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