What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. 
Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

An overview of deep learning in medical imaging focusing on MRI

Thank you for downloading magnetic resonance imaging physical principles and sequence design. As you may know, people have look numerous times for their chosen books like this magnetic resonance imaging physical principles and sequence design, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some malicious bugs inside their laptop.

Magnetic Resonance Imaging Physical Principles And Sequence Design

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Demonstrate a novel fast method for reconstruction of multi-dimensional MR fingerprinting (MRF) data using deep learning methods.A neural network (NN) is defined using the TensorFlow framework and trained on simulated MRF data computed with the extended phase graph formalism. The NN reconstruction accuracy for noiseless and noisy data is compared to conventional MRF template matching as a function of training data size and is quantified in simulated numerical brain phantom data and International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom data measured on 1.5T and 3T scanners with an optimized MRF EPI and MRF fast imaging with steady state precession (FISP) sequences with spiral readout. The utility of the method is demonstrated in a healthy subject in vivo at 1.5T.Network training required 10 to 74 minutes; once trained, data reconstruction required approximately 10 ms for the MRF EPI and 76 ms for the MRF FISP sequence. Reconstruction of simulated, noiseless brain data using the NN resulted in a RMS error (RMSE) of 2.6 ms for T1 and 1.9 ms for T2 . The reconstruction error in the presence of noise was less than 10% for both T1 and T2 for SNR greater than 25 dB. Phantom measurements yielded good agreement (R2  = 0.99/0.99 for MRF EPI T1 /T2 and 0.94/0.98 for MRF FISP T1 /T2 ) between the T1 and T2 estimated by the NN and reference values from the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom.Reconstruction of MRF data with a NN is accurate, 300- to 5000-fold faster, and more robust to noise and dictionary undersampling than conventional MRF dictionary-matching.

MR fingerprinting Deep RecOnstruction NEtwork (DRONE)

T1-weighted image (T1WI) and T2-weighted image (T2WI) are the two routinely acquired Magnetic Resonance Imaging (MRI) protocols that provide complementary information for diagnosis. However, the total acquisition time of ~10 min yields the image quality vulnerable to artifacts such as motion. To speed up MRI process, various algorithms have been proposed to reconstruct high quality images from under-sampled k-space data. These algorithms only employ the information of an individual protocol (e.g., T2WI). In this paper, we propose to combine complementary MRI protocols (i.e., T1WI and under-sampled T2WI particularly) to reconstruct the high-quality image (i.e., fully-sampled T2WI). To the best of our knowledge, this is the first work to utilize data from different MRI protocols to speed up the reconstruction of a target sequence. Specifically, we present a novel deep learning approach, namely Dense-Unet, to accomplish the reconstruction task. The Dense-Unet requires fewer parameters and less computation, but achieves better performance. Our results have shown that Dense-Unet can reconstruct a 3D T2WI volume in less than 10 s, i.e., with the acceleration rate as high as 8 or more but with negligible aliasing artefacts and signal-noise-ratio (SNR) loss.

/pdf/ultra-fast-t2-weighted-mr-reconstruction-using-complementary-1rlvyyqotl.pdf

Ultra-Fast T2-Weighted MR Reconstruction Using Complementary T1-Weighted Information

Magnetic resonance fingerprinting (MRF) is a relatively new imaging framework which allows rapid and simultaneous quantification of multiple tissue properties, such as T1 and T2 relaxation times, in one acquisition. To accelerate the data sampling in MRF, a variety of methods have been proposed to extract tissue properties from highly accelerated MRF signals. While these methods have demonstrated promising results, further improvement in the accuracy, especially for T2 quantification, is needed. In this paper, we present a novel deep learning approach, namely residual channel attention U-Net (RCA-U-Net), to perform the tissue quantification task in MRF. The RCA-U-Net combines the U-Net structure with residual channel attention blocks, to make the network focus on more informative features and produce better quantification results. In addition, we improved the preprocessing of MRF data by masking out the noisy signals in the background for improved quantification at tissue boundaries. Our experimental results on two in vivo brain datasets with different spatial resolutions demonstrate that the proposed method improves the accuracy of T2 quantification with MRF under high acceleration rates (i.e., 8 and 16) as compared to the state-of-the-art methods.

RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting

This study introduces a technique for simultaneous multislice (SMS) cardiac magnetic resonance fingerprinting (cMRF), which improves the slice coverage when quantifying myocardial T1, T2 , and M0 . The single-slice cMRF pulse sequence was modified to use multiband (MB) RF pulses for SMS imaging. Different RF phase schedules were used to excite each slice, similar to POMP or CAIPIRINHA, which imparts tissues with a distinguishable and slice-specific magnetization evolution over time. Because of the high net acceleration factor (R = 48 in plane combined with the slice acceleration), images were first reconstructed with a low rank technique before matching data to a dictionary of signal timecourses generated by a Bloch equation simulation. The proposed method was tested in simulations with a numerical relaxation phantom. Phantom and in vivo cardiac scans of 10 healthy volunteers were also performed at 3 T. With single-slice acquisitions, the mean relaxation times obtained using the low rank cMRF reconstruction agree with reference values. The low rank method improves the precision in T1 and T2 for both single-slice and SMS cMRF, and it enables the acquisition of maps with fewer artifacts when using SMS cMRF at higher MB factors. With this technique, in vivo cardiac maps were acquired from three slices simultaneously during a breathhold lasting 16 heartbeats. SMS cMRF improves the efficiency and slice coverage of myocardial T1 and T2 mapping compared with both single-slice cMRF and conventional cardiac mapping sequences. Thus, this technique is a first step toward whole-heart simultaneous T1 and T2 quantification with cMRF.

Simultaneous multislice cardiac magnetic resonance fingerprinting using low rank reconstruction

Purpose

To develop an ultrafast T1 mapping method for high-resolution, volumetric T1 measurements in the abdomen.



Methods

The Look-Locker method was combined with a stack-of-spirals acquisition accelerated using three-dimensional (3D) through-time spiral GRAPPA reconstruction for fast data acquisition. A segmented k-space acquisition scheme was proposed and the time delay between segments for the recovery of longitudinal magnetization was optimized using Bloch equation simulations. The accuracy of this method was validated in a phantom experiment and in vivo T1 measurements were performed with 35 asymptomatic subjects on both 1.5 Tesla (T) and 3T MRI systems.



Results

Phantom experiments yielded close agreement between the proposed method and gold standard measurements for a large range of T1 values (200 to 1600 ms). The in vivo results further demonstrate that high-resolution T1 maps (2 × 2 × 4 mm3) for 32 slices can be achieved in a single clinically feasible breath-hold of approximately 20 s. The T1 values for multiple organs and tissues in the abdomen are in agreement with the published literature.



Conclusion

A high-resolution 3D abdominal T1 mapping technique was developed, which allows fast and accurate T1 mapping of multiple abdominal organs and tissues in a single breath-hold. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

/pdf/rapid-volumetric-t1-mapping-of-the-abdomen-using-three-3545al4l2q.pdf

Rapid volumetric T1 mapping of the abdomen using three-dimensional through-time spiral GRAPPA.

Purpose To develop a rapid 2D MR fingerprinting technique with a submillimeter in-plane resolution using a deep learning-based tissue quantification approach. Methods A rapid and high-resolution MR fingerprinting technique was developed for brain T1 and T2 quantification. The 2D acquisition was performed using a FISP-based MR fingerprinting sequence and a spiral trajectory with 0.8-mm in-plane resolution. A deep learning-based method was used to replace the standard template matching method for improved tissue characterization. A novel network architecture (i.e., residual channel attention U-Net) was proposed to improve high-resolution details in the estimated tissue maps. Quantitative brain imaging was performed with 5 adults and 2 pediatric subjects, and the performance of the proposed approach was compared with several existing methods in the literature. Results In vivo measurements with both adult and pediatric subjects show that high-quality T1 and T2 mapping with 0.8-mm in-plane resolution can be achieved in 7.5 seconds per slice. The proposed deep learning method outperformed existing algorithms in tissue quantification with improved accuracy. Compared with the standard U-Net, high-resolution details in brain tissues were better preserved by the proposed residual channel attention U-Net. Experiments on pediatric subjects further demonstrated the potential of the proposed technique for fast pediatric neuroimaging. Alongside reduced data acquisition time, a 5-fold acceleration in tissue property mapping was also achieved with the proposed method. Conclusion A rapid and high-resolution MR fingerprinting technique was developed, which enables high-quality T1 and T2 quantification with 0.8-mm in-plane resolution in 7.5 seconds per slice.

Yong Chen

Papers

Ultra-Fast T2-Weighted MR Reconstruction Using Complementary T1-Weighted Information

RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting

Simultaneous multislice cardiac magnetic resonance fingerprinting using low rank reconstruction

Rapid volumetric T1 mapping of the abdomen using three-dimensional through-time spiral GRAPPA.

Submillimeter MR fingerprinting using deep learning–based tissue quantification