Khan et al., 2020 - Google Patents
Unsupervised deconvolution neural network for high quality ultrasound imagingKhan et al., 2020
- Document ID
- 5468689081964886131
- Author
- Khan S
- Huh J
- Ye J
- Publication year
- Publication venue
- 2020 IEEE International Ultrasonics Symposium (IUS)
External Links
Snippet
High quality US imaging demand large number of measurements that can increase the cost, size and power requirements. Therefore, low-powered, portable and 3D ultrasound imaging system require reconstruction algorithms that can produce high quality images using fewer …
- 238000002604 ultrasonography 0 title abstract description 30
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
- G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
- G01S15/8906—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques
- G01S15/895—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques characterised by the transmitted frequency spectrum
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
- G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
- G01S15/8906—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques
- G01S15/899—Combination of imaging systems with ancillary equipment
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
- G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
- G01S15/8906—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques
- G01S15/8909—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques using a static transducer configuration
- G01S15/8915—Short-range imaging systems; Acoustic microscope systems using pulse-echo techniques using a static transducer configuration using a transducer array
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/5269—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving detection or reduction of artifacts
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/48—Diagnostic techniques
- A61B8/488—Diagnostic techniques involving Doppler signals
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/5207—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of raw data to produce diagnostic data, e.g. for generating an image
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Khan et al. | Adaptive and compressive beamforming using deep learning for medical ultrasound | |
Perdios et al. | CNN-based image reconstruction method for ultrafast ultrasound imaging | |
Zhou et al. | High spatial–temporal resolution reconstruction of plane-wave ultrasound images with a multichannel multiscale convolutional neural network | |
Lu et al. | Complex convolutional neural networks for ultrafast ultrasound imaging reconstruction from in-phase/quadrature signal | |
Khan et al. | Unsupervised deconvolution neural network for high quality ultrasound imaging | |
Sharifzadeh et al. | Phase aberration correction: A convolutional neural network approach | |
CN104887266B (en) | Method for small-area three-dimensional passive cavitation imaging and three-dimensional composite imaging based on area array | |
US20250044446A1 (en) | Methods to maintain image quality in ultrasound imaging at reduced cost, size, and power | |
KR101610874B1 (en) | Module for Processing Ultrasonic Signal Based on Spatial Coherence and Method for Processing Ultrasonic Signal | |
Yoon et al. | Deep learning for accelerated ultrasound imaging | |
Afrakhteh et al. | Temporal super-resolution of echocardiography using a novel high-precision non-polynomial interpolation | |
Chen et al. | Reconstruction of enhanced ultrasound images from compressed measurements using simultaneous direction method of multipliers | |
Mamistvalov et al. | Deep unfolded recovery of sub-nyquist sampled ultrasound images | |
Wasih et al. | A robust cascaded deep neural network for image reconstruction of single plane wave ultrasound RF data | |
Khan et al. | Universal plane-wave compounding for high quality us imaging using deep learning | |
Jin et al. | Compressive dynamic aperture b-mode ultrasound imaging using annihilating filter-based low-rank interpolation | |
Koike et al. | Deep learning for hetero–homo conversion in channel-domain for phase aberration correction in ultrasound imaging | |
Khan et al. | Unsupervised deep learning for accelerated high quality echocardiography | |
Khan et al. | Phase aberration robust beamformer for planewave us using self-supervised learning | |
Shijo et al. | SwinIR for photoacoustic computed tomography artifact reduction | |
Khan et al. | Pushing the limit of unsupervised learning for ultrasound image artifact removal | |
Tierney et al. | Evaluating input domain and model selection for deep network ultrasound beamforming | |
Toffali et al. | Improving the quality of monostatic synthetic-aperture ultrasound imaging through deep-learning-based beamforming | |
Hahne et al. | Learning super-resolution ultrasound localization microscopy from radio-frequency data | |
Huh et al. | Unsupervised learning for acoustic shadowing artifact removal in ultrasound imaging |