From debeca55bdbe2dcc5b4b52a89eb02aa86a83eb34 Mon Sep 17 00:00:00 2001 From: Ateeb Ahmed Date: Mon, 24 Mar 2025 17:21:35 +0100 Subject: [PATCH] added details given by farzad. https://chat.informatik.uni-wuerzburg.de/group/magnet_website?msg=ScM43FauafXbSX4hx --- _data/spring.yml | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/_data/spring.yml b/_data/spring.yml index 9ea294f..49e5ac5 100644 --- a/_data/spring.yml +++ b/_data/spring.yml @@ -4,6 +4,7 @@ # knoll : 3 # wech : 4 # wolfgang : 5 +# cecilia : 6 # internal speaker to id mapping: # schreiber : 1 @@ -30,6 +31,7 @@ days: end_time: "09:30" color: "#fbc08f" id: 2 + internal_speaker_idx: 1 - title: "Keynote" start_time: "09:30" end_time: "10:30" @@ -45,6 +47,7 @@ days: end_time: "12:30" color: "#a5b6ca" id: 5 + external_speaker_idx: 6 - title: "Lunch Break" start_time: "12:30" end_time: "14:00" @@ -54,6 +57,7 @@ days: start_time: "14:00" end_time: "16:00" color: "#ffe698" + external_speaker_idx: 6 id: 7 - title: "Coffee Break" start_time: "16:00" @@ -79,6 +83,7 @@ days: end_time: "10:30" color: "#a5b6ca" id: 11 + internal_speaker_idx: 6 - title: "Coffee Break" start_time: "10:30" end_time: "11:00" @@ -88,6 +93,7 @@ days: start_time: "11:00" end_time: "12:30" color: "#a5b6ca" + internal_speaker_idx: 5 id: 13 - title: "Lunch Break" start_time: "12:30" @@ -99,6 +105,7 @@ days: end_time: "16:00" color: "#ffe698" id: 15 + internal_speaker_idx: 5 - title: "Coffee Break" start_time: "16:00" end_time: "16:30" @@ -125,6 +132,7 @@ days: end_time: "10:30" color: "#a5b6ca" id: 18 + internal_speaker_idx: 5 - title: "Coffee Break" start_time: "10:30" end_time: "11:00" @@ -134,6 +142,7 @@ days: start_time: "11:00" end_time: "12:30" color: "#a5b6ca" + internal_speaker_idx: 2 id: 20 - title: "Lunch Break" start_time: "12:30" @@ -169,6 +178,7 @@ days: end_time: "10:30" color: "#d7e4bc" id: 25 + description: The presentation focuses on physics-based machine learning for cardiac MRI reconstruction. We'll begin with a brief overview of cardiac MRI, outlining its potential and current challenges. We will then define physics-based machine learning in MR reconstruction, contrasting it with alternative approaches of transforming undersampled acquisitions into high-quality images. A key component of physics-based methods is ensuring data consistency, and we will discuss the critical role of transfer functions in modeling gradient behavior and accurately determining k-space trajectories. We will then explore diffusion probabilistic models, a powerful generative approach that has shown significant promise for physics-based MR reconstruction, and demonstrate its potential for high-quality cardiac MRI. - title: "Coffee Break" start_time: "10:30" end_time: "11:00" @@ -179,6 +189,7 @@ days: end_time: "12:30" color: "#ffe698" id: 27 + internal_speaker_idx: 4 - title: "Lunch Break" start_time: "12:30" end_time: "14:00" @@ -189,6 +200,7 @@ days: end_time: "15:30" color: "#d7e4bc" id: 29 + external_speaker_idx: 1 - title: "Coffee Break" start_time: "15:30" end_time: "16:00" @@ -214,6 +226,7 @@ days: end_time: "10:30" color: "#ffe698" id: 33 + internal_speaker_idx: 6 - title: "Coffee Break" start_time: "10:30" end_time: "11:00" @@ -224,6 +237,30 @@ days: end_time: "12:30" color: "#d7e4bc" id: 35 + external_speaker_idx: 3 + description: | + In 2016, machine learning techniques have been first introduced to solve the inverse problem of MR image generation from accelerated acquisitions (1,2,3). + Since then, the field has grown substantially, and a wide range of machine learning methods have been developed, applied to a wide range of imaging applications + and already rolled out as clinical products by all major scanner manufacturers. + + In this lecture, I will start with the background of an artificial intelligence process to generate MR images from the acquired measurement data. + In particular, I will discuss physics informed architectures that map iterative algorithms onto neural networks. + I will discuss their performance for a range of clinical applications (4,5,6) as well as ongoing challenges related to data availability (7), + generalizability and validation of the results. I will also include a discussion of the lessons learnt from the recent fastMRI image reconstruction + challenges organized jointly with Facebook AI research (8). + +

References:

+

1. Hammernik et al. Learning a variational model for compressed sensing MRI reconstruction. Proc. ISMRM p33 (2016).

+

2. Hammernik et al. Learning a Variational Network for Reconstruction of Accelerated MRI Data. MRM, 79:3055-3071 (2018).

+

3. Knoll et al. Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction. IEEE Signal Processing Magazine, 37:1:128-40 (2020).

+

4. Johnson et al. Deep learning reconstruction enables highly accelerated biparametric MR imaging of the prostate. JMRI 56: 184-195 (2022).

+

5. Johnson et al. Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI. Radiology 307:e220425 (2023).

+

6. Vornehm et al. CineVN: Variational network reconstruction for rapid functional cardiac cine MRI. Magnetic Resonance in Medicine 93:138-150 (2025)

+

7. Knoll et al. fastMRI: a publicly available raw k-space and DICOM dataset for accelerated MR image reconstruction using machine learning. + Radiology Artificial Intelligence (2:2020).

+

8. Knoll et al. Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. + MRM 84 (6), 3054-3070 (2020).

+ - title: "Lunch Break" start_time: "12:30" end_time: "14:00"