Koçanaoğulları et al., 2021 - Google Patents
Learning the regularization in dce-mr image reconstruction for functional imaging of kidneysKoçanaoğulları et al., 2021
View PDF- Document ID
- 10484380663250730488
- Author
- Koçanaoğulları A
- Ariyurek C
- Afacan O
- Kurugol S
- Publication year
- Publication venue
- IEEE Access
External Links
Snippet
Kidney DCE-MRI aims at both qualitative assessment of kidney anatomy and quantitative assessment of kidney function by estimating the tracer kinetic (TK) model parameters. Accurate estimation of TK model parameters requires an accurate measurement of the …
- 210000003734 Kidney 0 title abstract description 35
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10104—Positron emission tomography [PET]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
- G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
- G01R33/48—NMR imaging systems
- G01R33/54—Signal processing systems, e.g. using pulse sequences, Generation or control of pulse sequences ; Operator Console
- G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
- G01R33/5601—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution involving use of a contrast agent for contrast manipulation, e.g. a paramagnetic, super-paramagnetic, ferromagnetic or hyperpolarised contrast agent
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7285—Specific aspects of physiological measurement analysis for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
- A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves involving electronic or nuclear magnetic resonance, e.g. magnetic resonance imaging
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2211/00—Image generation
- G06T2211/40—Computed tomography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/50—Clinical applications
- A61B6/507—Clinical applications involving determination of haemodynamic parameters, e.g. perfusion CT
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Özbey et al. | Unsupervised medical image translation with adversarial diffusion models | |
Gu et al. | AdaIN-based tunable CycleGAN for efficient unsupervised low-dose CT denoising | |
Xiang et al. | Deep-learning-based multi-modal fusion for fast MR reconstruction | |
Chen et al. | Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges | |
Wang et al. | 3D conditional generative adversarial networks for high-quality PET image estimation at low dose | |
Terpstra et al. | Deep learning-based image reconstruction and motion estimation from undersampled radial k-space for real-time MRI-guided radiotherapy | |
Yoon et al. | Motion adaptive patch-based low-rank approach for compressed sensing cardiac cine MRI | |
Wollny et al. | Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis | |
US20220222781A1 (en) | Deep generative modeling of smooth image manifolds for multidimensional imaging | |
Zhang et al. | A deep unrolling network inspired by total variation for compressed sensing MRI | |
US12106476B2 (en) | Technique for assigning a perfusion metric to DCE MR images | |
Feng et al. | Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction | |
Belov et al. | Towards ultrafast MRI via extreme k-space undersampling and superresolution | |
Koçanaoğulları et al. | Learning the regularization in dce-mr image reconstruction for functional imaging of kidneys | |
Usman et al. | Motion corrected multishot MRI reconstruction using generative networks with sensitivity encoding | |
Lv et al. | Reconstruction of undersampled radial free‐breathing 3D abdominal MRI using stacked convolutional auto‐encoders | |
Aetesam et al. | Deep variational magnetic resonance image denoising via network conditioning | |
El Naqa et al. | Automated breathing motion tracking for 4D computed tomography | |
Li et al. | ISP-IRLNet: Joint optimization of interpretable sampler and implicit regularization learning network for accerlerated MRI | |
Gu et al. | Deep-learning based T1 and T2 quantification from Undersampled magnetic resonance fingerprinting data to track tracer kinetics in small laboratory animals | |
Shao et al. | 3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR) | |
Zhang et al. | CAMP-Net: Context-aware multi-prior network for accelerated MRI reconstruction | |
Lyu | Deep Neural Networks for MRI Applications | |
Oh et al. | Unpaired Deep Learning for Pharmacokinetic Parameter Estimation from Dynamic Contrast-Enhanced MRI | |
Lahiri | Learning-based algorithms for inverse problems in MR image reconstruction and quantitative perfusion imaging |