scholarly journals Fast Interleaved Multislice T1 Mapping: Model-Based Reconstruction of Single-Shot Inversion-Recovery Radial FLASH

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoqing Wang ◽  
Dirk Voit ◽  
Volkert Roeloffs ◽  
Martin Uecker ◽  
Jens Frahm
Keyword(s):  
High Speed ◽  
T1 Mapping ◽  
Inversion Recovery ◽  
Inversion Pulse ◽  
Breath Hold ◽  
Single Shot ◽  
Radial Sampling ◽  
Model Based ◽  
Inverse Reconstruction ◽  
Space Data

Purpose. To develop a high-speed multislice T1 mapping method based on a single-shot inversion-recovery (IR) radial FLASH acquisition and a regularized model-based reconstruction. Methods. Multislice radial k-space data are continuously acquired after a single nonselective inversion pulse using a golden-angle sampling scheme in a spoke-interleaved manner with optimized flip angles. Parameter maps and coil sensitivities of each slice are estimated directly from highly undersampled radial k-space data using a model-based nonlinear inverse reconstruction in conjunction with joint sparsity constraints. The performance of the method has been validated using a numerical and experimental T1 phantom as well as demonstrated for studies of the human brain and liver at 3T. Results. The proposed method allows for 7 simultaneous T1 maps of the brain at 0.5 × 0.5 × 4 mm3 resolution within a single IR experiment of 4 s duration. Phantom studies confirm similar accuracy and precision as obtained for a single-slice acquisition. For abdominal applications, the proposed method yields three simultaneous T1 maps at 1.25 × 1.25 × 6 mm3 resolution within a 4 s breath hold. Conclusion. Rapid, robust, accurate, and precise multislice T1 mapping may be achieved by combining the advantages of a model-based nonlinear inverse reconstruction, radial sampling, parallel imaging, and compressed sensing.

scholarly journals Improvement of Fast Model-Based Acceleration of Parameter Look-Locker T1 Mapping

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5371
Author(s):  
Michał Staniszewski ◽  
Uwe Klose
Keyword(s):  
T1 Mapping ◽  
Region Of Interest ◽  
Computational Time ◽  
Single Shot ◽  
Mean Values ◽  
Model Based ◽  
Fitting Procedure ◽  
Novel Method ◽  
Space Data ◽  
Brain Measurements

Quantitative mapping is desirable in many scientific and clinical magneric resonance imaging (MRI) applications. Recent inverse recovery-look locker sequence enables single-shot T1 mapping with a time of a few seconds but the main computational load is directed into offline reconstruction, which can take from several minutes up to few hours. In this study we proposed improvement of model-based approach for T1-mapping by introduction of two steps fitting procedure. We provided analysis of further reduction of k-space data, which lead us to decrease of computational time and perform simulation of multi-slice development. The region of interest (ROI) analysis of human brain measurements with two different initial models shows that the differences between mean values with respect to a reference approach are in white matter—0.3% and 1.1%, grey matter—0.4% and 1.78% and cerebrospinal fluid—2.8% and 11.1% respectively. With further improvements we were able to decrease the time of computational of single slice to 6.5 min and 23.5 min for different initial models, which has been already not achieved by any other algorithm. In result we obtained an accelerated novel method of model-based image reconstruction in which single iteration can be performed within few seconds on home computer.

Model-based reconstruction for T1 mapping using single-shot inversion-recovery radial FLASH

2016 ◽  
Vol 26 (4) ◽  
pp. 254-263 ◽  
Author(s):  
Volkert Roeloffs ◽  
Xiaoqing Wang ◽  
Tilman J. Sumpf ◽  
Markus Untenberger ◽  
Dirk Voit ◽  
...  
Keyword(s):  
T1 Mapping ◽  
Inversion Recovery ◽  
Single Shot ◽  
Model Based

scholarly journals The Assisted Positioning Technology for High Speed Train Based on Deep Learning

2020 ◽  
Vol 10 (23) ◽  
pp. 8625
Author(s):  
Yali Song ◽  
Yinghong Wen
Keyword(s):  
Deep Learning ◽  
High Speed ◽  
Detection Method ◽  
Generative Adversarial Networks ◽  
Single Shot ◽  
High Speed Train ◽  
Data Set ◽  
Detection Model ◽  
Model Based ◽  
Cumulative Error

In the positioning process of a high-speed train, cumulative error may result in a reduction in the positioning accuracy. The assisted positioning technology based on kilometer posts can be used as an effective method to correct the cumulative error. However, the traditional detection method of kilometer posts is time-consuming and complex, which greatly affects the correction efficiency. Therefore, in this paper, a kilometer post detection model based on deep learning is proposed. Firstly, the Deep Convolutional Generative Adversarial Networks (DCGAN) algorithm is introduced to construct an effective kilometer post data set. This greatly reduces the cost of real data acquisition and provides a prerequisite for the construction of the detection model. Then, by using the existing optimization as a reference and further simplifying the design of the Single Shot multibox Detector (SSD) model according to the specific application scenario of this paper, the kilometer post detection model based on an improved SSD algorithm is established. Finally, from the analysis of the experimental results, we know that the detection model established in this paper ensures both detection accuracy and efficiency. The accuracy of our model reached 98.92%, while the detection time was only 35.43 ms. Thus, our model realizes the rapid and accurate detection of kilometer posts and improves the assisted positioning technology based on kilometer posts by optimizing the detection method.

Real‐time Triggered RAdial Single‐Shot Inversion recovery for arrhythmia‐insensitive myocardial T1 mapping: motion phantom validation and in vivo comparison

2018 ◽  
Vol 81 (3) ◽  
pp. 1714-1725 ◽  
Author(s):  
Daniel Gensler ◽  
Tim Salinger ◽  
Markus Düring ◽  
Kristina Lorenz ◽  
Roland Jahns ◽  
...  
Keyword(s):  
Real Time ◽  
T1 Mapping ◽  
Inversion Recovery ◽  
Single Shot ◽  
Myocardial T1 ◽  
Motion Phantom ◽  
Phantom Validation

scholarly journals High-resolution myocardial T1 mapping using single-shot inversion recovery fast low-angle shot MRI with radial undersampling and iterative reconstruction

2016 ◽  
Vol 89 (1068) ◽  
pp. 20160255 ◽  
Author(s):  
Xiaoqing Wang ◽  
Arun A Joseph ◽  
Oleksandr Kalentev ◽  
Klaus-Dietmar Merboldt ◽  
Dirk Voit ◽  
...  
Keyword(s):  
High Resolution ◽  
Iterative Reconstruction ◽  
T1 Mapping ◽  
Inversion Recovery ◽  
Single Shot ◽  
Myocardial T1

scholarly journals Model‐based reconstruction for simultaneous multi‐slice mapping using single‐shot inversion‐recovery radial FLASH

2020 ◽  
Vol 85 (3) ◽  
pp. 1258-1271
Author(s):  
Xiaoqing Wang ◽  
Sebastian Rosenzweig ◽  
Nick Scholand ◽  
H. Christian M. Holme ◽  
Martin Uecker
Keyword(s):  
Inversion Recovery ◽  
Single Shot ◽  
Model Based

scholarly journals Single-shot Multi-slice T1 Mapping at High Spatial Resolution – Inversion-Recovery FLASH with Radial Undersampling and Iterative Reconstruction

2015 ◽  
Vol 9 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoqing Wang ◽  
Volkert Roeloffs ◽  
K. Dietmar Merboldt ◽  
Dirk Voit ◽  
Sebastian Schätz ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
T1 Mapping ◽  
High Spatial Resolution ◽  
Lattice Relaxation ◽  
Inversion Recovery ◽  
Spin Lattice Relaxation ◽  
Single Shot ◽  
Nonlinear Inversion ◽  
Spin Lattice ◽  
Single Slice

Purpose: To develop a method for T1 mapping at high spatial resolution and for multiple slices. Methods: The proposed method emerges as a single-shot inversion-recovery experiment which covers the entire spin-lattice relaxation process by serial acquisitions of highly undersampled radial FLASH images, either in single-slice or multi-slice mode. Serial image reconstructions are performed in time-reversed order and first involve regularized nonlinear inversion (NLINV) to estimate optimum coil sensitivity profiles. Subsequently, the coil profiles are fixed for the calculation of differently T1-weighted frames and the resulting linear inverse problem is solved by a conjugate gradient (CG) technique. T1 values are obtained by pixelwise fitting with a Deichmann correction modified for multi-slice applications. Results: T1 accuracy was validated for a reference phantom. For human brain, T1 maps were obtained at 0.5 mm resolution for single-slice acquisitions and at 0.75 mm resolution for up to 5 simultaneous slices (5 mm thickness). Corresponding T1 maps of the liver were acquired at 1 mm and 1.5 mm resolution, respectively. All T1 values were in agreement with literature data. Conclusion: Inversion-recovery sequences with highly undersampled radial FLASH images and NLINV/CG reconstruction allow for fast, robust and accurate T1 mapping at high spatial resolution and for multiple slices.

scholarly journals Accelerated cardiac T1 mapping in four heartbeats with inline MyoMapNet: a deep learning-based T1 estimation approach

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
Rui Guo ◽  
Hossam El-Rewaidy ◽  
Salah Assana ◽  
Xiaoying Cai ◽  
Amine Amyar ◽  
...  
Keyword(s):  
Neural Networks ◽  
T1 Mapping ◽  
Inversion Recovery ◽  
Inversion Pulse ◽  
Mapping Data ◽  
Post Contrast ◽  
Recovery Times ◽  
Similar Accuracy ◽  
Fully Connected

Abstract Purpose To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses fully connected neural networks (FCNN) to estimate T1 values from four T1-weighted images collected after a single inversion pulse in four heartbeats (Look-Locker, LL4). Method We implemented an FCNN for MyoMapNet to estimate T1 values from a reduced number of T1-weighted images and corresponding inversion-recovery times. We studied MyoMapNet performance when trained using native, post-contrast T1, or a combination of both. We also explored the effects of number of T1-weighted images (four and five) for native T1. After rigorous training using in-vivo modified Look-Locker inversion recovery (MOLLI) T1 mapping data of 607 patients, MyoMapNet performance was evaluated using MOLLI T1 data from 61 patients by discarding the additional T1-weighted images. Subsequently, we implemented a prototype MyoMapNet and LL4 on a 3 T scanner. LL4 was used to collect T1 mapping data in 27 subjects with inline T1 map reconstruction by MyoMapNet. The resulting T1 values were compared to MOLLI. Results MyoMapNet trained using a combination of native and post-contrast T1-weighted images had excellent native and post-contrast T1 accuracy compared to MOLLI. The FCNN model using four T1-weighted images yields similar performance compared to five T1-weighted images, suggesting that four T1 weighted images may be sufficient. The inline implementation of LL4 and MyoMapNet enables successful acquisition and reconstruction of T1 maps on the scanner. Native and post-contrast myocardium T1 by MOLLI and MyoMapNet was 1170 ± 55 ms vs. 1183 ± 57 ms (P = 0.03), and 645 ± 26 ms vs. 630 ± 30 ms (P = 0.60), and native and post-contrast blood T1 was 1820 ± 29 ms vs. 1854 ± 34 ms (P = 0.14), and 508 ± 9 ms vs. 514 ± 15 ms (P = 0.02), respectively. Conclusion A FCNN, trained using MOLLI data, can estimate T1 values from only four T1-weighted images. MyoMapNet enables myocardial T1 mapping in four heartbeats with similar accuracy as MOLLI with inline map reconstruction.

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