IP Library Granted Patent US 12,694,483
Granted Patent B2
US 12,694,483 · App. 18/416,567 · Granted Jul 28, 2026

Systems and methods for a task-specific deep-learning-based denoising approach for myocardial perfusion SPECT

Inventors: Abhinav Kumar Jha (St. Louis, MO); Md Ashequr Rahman (St. Louis, MO); Zitong Yu (St. Louis, MO); Barry Siegel (St. Louis, MO)
Assignee: Washington University
G06T5/70G06T7/0012G06T2207/10108G06T2207/20081
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,694,483
App. No.
18/416,567
Filed
Jan 18, 2024
Granted
Jul 28, 2026
Kind
B2
Art Unit
2674
USPC
382/275
Abstract

A system for single-photon emission computed tomography (SPECT) is provided. The system includes a computer device comprises at least one processor in communication with at least one memory device. The at least one processor is programmed to: a) store a model trained to denoise computer tomography (CT) scans of a subject being examined; b) receive a CT scan of a first subject being examined; c) execute the model with the CT scan of the first subject as an input, wherein the model performs denoising on the CT scan while accounting for an observer loss function; and d) output a denoised-CT scan of the first subject.

Claims (29)

1 . A system for single-photon emission computed tomography (SPECT) comprising a computer device comprises at least one processor in communication with at least one memory device, wherein the at least one processor is programmed to:

store a model trained to denoise computer tomography (CT) scans of a subject being examined, wherein the at least one processor is further programmed to train the model with a plurality of historical low-dose CT scans and a plurality of normal dose CT scans, wherein the plurality of historical low-dose CT scans are images of where the corresponding subject received a lower dose of a radioactive tracer than subjects of the normal dose CT scans;

receive a CT scan of a first subject being examined;

execute the model with the CT scan of the first subject as an input, wherein the model performs denoising on the CT scan while accounting for an observer loss function; and

output a denoised-CT scan of the first subject.

2 . The system of claim 1 , wherein the lower dose is one of 12.5% and 6.25% of a normal dose.

3 . The system of claim 1 , wherein the at least one processor is further programmed to execute the model for a plurality of CT scans.

4 . The system of claim 3 , wherein the plurality of CT scans is a part of a video.

5 . The system of claim 1 , wherein the CT scan was taken during a myocardial perfusion.

6 . The system of claim 1 , wherein the at least one processor is further programmed to train the model with an observer loss function.

7 . A method for single-photon emission computed tomography (SPECT), the method implemented by a computer device comprising at least one processor in communication with one or more memory devices, the method comprises:

storing a model trained to denoise computer tomography (CT) scans of a subject being examined;

receiving a CT scan of a first subject being examined, wherein the at least one processor is further programmed to train the model with a plurality of historical low-dose CT scans and a plurality of normal dose CT scans, wherein the plurality of historical low-dose CT scans are images of where the corresponding subject received a lower dose of a radioactive tracer than subjects of the normal dose CT scans;

executing the model with the CT scan of the first subject as an input, wherein the model performs denoising on the CT scan while accounting for an observer loss function; and

outputting a denoised-CT scan of the first subject.

8 . The method of claim 7 , wherein the lower dose is one of 12.5% and 6.25% of a normal dose.

9 . The method of claim 7 further comprising executing the model for a plurality of CT scans.

10 . The method of claim 9 , wherein the plurality of CT scans is a part of a video.

11 . The method of claim 7 , wherein the CT scan was taken during a myocardial perfusion.

12 . The method of claim 7 further comprising training the model with an observer loss function.

13 . A computer device for single-photon emission computed tomography (SPECT) comprising at least one processor in communication with at least one memory device, wherein the at least one processor is programmed to:

store a model trained to denoise computer tomography (CT) scans of a subject being examined, wherein the at least one processor is further programmed to train the model with a plurality of historical low-dose CT scans and a plurality of normal dose CT scans, wherein the plurality of historical low-dose CT scans are images of where the corresponding subject received a lower dose of a radioactive tracer than the subjects of normal dose CT scans;

receive a CT scan of a first subject being examined;

execute the model with the CT scan of the first subject as an input, wherein the model performs denoising on the CT scan while accounting for an observer loss function; and

output a denoised-CT scan of the first subject.

14 . The computer device of claim 13 , wherein the lower dose is one of 12.5% and 6.25% of a normal dose.

15 . The computer device of claim 13 , wherein the at least one processor is further programmed to execute the model for a plurality of CT scans.

16 . The computer device of claim 15 , wherein the plurality of CT scans is a part of a video.

17 . The computer device of claim 13 , wherein the CT scan was taken during a myocardial perfusion.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 29, 2024
From: JHA, ABHINAV KUMAR; RAHMAN, MD ASHEQUR; YU, ZITONG; SIEGEL, BARRY
To: WASHINGTON UNIVERSITY
Reel/Frame 066596/0216 →
Continuity (2)
Provisional Application 63480572 · Jan 19, 2023
Related Publication 20240249396A1 · Jul 25, 2024
References Cited (54)
US 9589374B1 · Gao · 2017 [cited by examiner]
US 10792006B2 · Zhu · 2020 [cited by examiner]
US 11790492B1 · Ahmad · 2023 [cited by examiner]
US 11844636B2 · Zaharchuk · 2023 [cited by examiner]
US 12064281B2 · Qi · 2024 [cited by examiner]
US 12100075B2 · Massanes Basi · 2024 [cited by examiner]
US 12106452B2 · Kaethner · 2024 [cited by examiner]
US 12148073B2 · Fuchs · 2024 [cited by examiner]
US 20110164799A1 · Miao · 2011 [cited by examiner]
US 20170071562A1 · Suzuki · 2017 [cited by examiner]
US 20180042564A1 · Zhou · 2018 [cited by examiner]
US 20190156524A1 · Park · 2019 [cited by examiner]
US 20200118306A1 · Ye · 2020 [cited by examiner]
US 20200294288A1 · Smith · 2020 [cited by examiner]
US 20200311490A1 · Lee · 2020 [cited by examiner]
US 20200311914A1 · Zaharchuk · 2020 [cited by examiner]
US 20200330626A1 · Ting et al. · 2020 [cited by applicant]
US 20210052233A1 · Kaplan · 2021 [cited by examiner]
US 20210073950A1 · Vija · 2021 [cited by examiner]
US 20210118098A1 · Chan · 2021 [cited by examiner]
US 20210279918A1 · Koga · 2021 [cited by examiner]
US 20210366169A1 · Liu · 2021 [cited by examiner]
US 20210390668A1 · Ren · 2021 [cited by examiner]
US 20220092742A1 · Pei · 2022 [cited by examiner]
US 20220207791A1 · Shi et al. · 2022 [cited by applicant]
US 20220284643A1 · Jha · 2022 [cited by examiner]
US 20220375038A1 · Nagare · 2022 [cited by examiner]
US 20230083935A1 · Lu · 2023 [cited by examiner]
US 20230237638A1 · Li · 2023 [cited by examiner]
US 20230326101A1 · Lakshminarasimha et al. · 2023 [cited by examiner]
US 20230342999A1 · Liu et al. · 2023 [cited by applicant]
US 20230390583A1 · Kadoya · 2023 [cited by examiner]
US 20240029246A1 · Keshwani · 2024 [cited by examiner]
US 20240046534A1 · Yang · 2024 [cited by examiner]
US 20240046535A1 · Carmi · 2024 [cited by applicant]
US 20240104700A1 · Wang · 2024 [cited by examiner]
US 20240122566A1 · Cziria · 2024 [cited by examiner]
US 20240144442A1 · Wang · 2024 [cited by examiner]
US 20240169608A1 · Liu · 2024 [cited by examiner]
US 20240169610A1 · Li · 2024 [cited by examiner]
US 20240303815A1 · Oh · 2024 [cited by examiner]
US 20240307018A1 · Xiang · 2024 [cited by examiner]
US 20250014265A1 · O'Connor · 2025 [cited by examiner]
US 20250057499A1 · Wuelker · 2025 [cited by examiner]
US 20250078215A1 · Bergner · 2025 [cited by examiner]
US 20250245820A1 · Zainulina · 2025 [cited by examiner]
US 20250345010A1 · Hu · 2025 [cited by examiner]
CN 115082342A · 2022 [cited by applicant]
CN 117813055A · 2024 [cited by applicant]
Rahman, Ashequr, et al. “A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT.” Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment. vol. 12467. SPIE,… [cited by applicant]
Zitong Yu, Md Ashequr Rahman, Thomas Schindler, Richard Laforest, Abhinav K. Jha, A physics and learning-based transmission-less attenuation compensation method for SPECT, Proc. SPIE Medical Imaging, 2021. [cited by applicant]
Chen, Xiongchao et al., “Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac Spect”; HHS Public Access Eur J Nucl Med Mol Imaging: Jul. 2022; 3046-3060.… [cited by applicant]
Robert D. Johnson, Navkanwal Kaur Bath, Jeffrey Rinker, Stephen Fong, Sara St. James, Miguel Hernandez Pampaloni, Thomas A . Hope. Introduction to the D-SPECT for Technologists: Workflow Using a Dedicated Digital Cardia… [cited by applicant]
Shi, L., Onofrey, J.A., Liu, H. et al. Deep learning-based attenuation map generation for myocardial perfusion SPECT. Eur J Nucl Med Mol Imaging 47, 2383-2395 (2020). (Year: 2020). [cited by applicant]