Download or read book Accelerating Pediatric Magnetic Resonance Imaging Using Deep Learning based Image Reconstruction written by Christopher Michael Sandino and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging (MRI) is a powerful diagnostic tool for visualizing soft tissue anatomy, but physical limits on data acquisition speed result in uncomfortably long MRI exams. This is problematic for many patient populations, but especially for pediatric patients, who often require general anesthesia (GA) to reduce anxiety and body motion. Many attempts at accelerating data acquisition have been made to reduce or eliminate use of GA for pediatric MRI. For example, compressed sensing (CS) methods have been used to iteratively reconstruct rapidly acquired measurements into high-quality images by leveraging sparse priors. More recently, deep learning (DL) methods have been used to train deep neural network models to map the rapidly acquired measurements into even higher-quality images. While DL reconstruction approaches may potentially accelerate data acquisition beyond CS, these approaches have several issues which impede their clinical adoption. First, DL reconstructions require large quantities of high-quality ground truth data for supervised training, which can be costly and time-consuming to acquire. Second, memory requirements during network training limit the applicability of DL reconstruction to low-dimensional MRI data, such as static or dynamic 2-D imaging with limited spatiotemporal resolution. In this thesis, a series of projects demonstrating robust DL reconstruction techniques for acceleration of high-dimensional pediatric MRI will be presented. First, physics-based models are incorporated into deep CNN architectures to enforce consistency between intermediate network outputs and the rapidly acquired measurements in a novel method called DL-ESPIRiT. Physics-based modeling allows DL-ESPIRiT to be trained end-to-end in a supervised fashion with relatively little training data compared to non-physics-driven DL reconstruction. DL-ESPIRiT is applied and validated on 12X prospectively accelerated dynamic 2-D MRI scans acquired at Lucile Packard Children's Hospital. Finally, DL-ESPIRiT is extended to leverage subspace methods within the network to address GPU memory limitations during training. This method, known as deep learning-based subspace reconstruction (DL-Subspace), reconstructs a compressed representation of the MRI data instead of the data directly, thereby reducing the memory footprint during training and accelerating DL inference times. DL-Subspace is demonstrated to reconstruct 2-D dynamic MRI data with 4X higher memory efficiency and inference speed.

Download or read book Deep Learning Based Image Reconstruction in Abdominal and Cardiac Magnetic Resonance Imaging MRI written by Chang Gao and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: MRI plays an important role in abdominal and cardiac imaging due to its excellent soft tissue contrast and high image resolution. Despite the benefit of excellent image quality, MRI acquisition is intrinsically slow, causing patient discomfort and slowing down the clinical workflow, which hinders its broad clinical use. For decades, undersampling reconstruction techniques have been investigated to accelerate MRI acquisition. Traditional parallel imaging and compressed sensing methods either have limited acceleration capability or require extensive computational and time resources. While the recent development of deep learning achieved unprecedented performance in image reconstruction and image enhancement tasks, there are challenges remaining to be solved. One challenge is the potential loss of image details due to network over-smooths or over-regularization. Another challenge is that networks may struggle to generalize well to diverse MRI data acquired under different conditions. In medical imaging, high-quality diverse datasets are challenging to acquire, especially for rare or specialized MRI applications. Lastly, for non-Cartesian sampling, the reconstruction can be challenging due to the need for time-consuming interpolation of non-Cartesian k-space onto a Cartesian basis. The overall goal of the dissertation is to contribute to the development of deep learning-based accelerated image reconstruction techniques and investigate the challenges in network development as mentioned above. Specifically, we aim to develop deep learning networks to improve image quality and reduce artifacts and noise for the application of (1) undersampled radial MRI reconstruction in the abdomen (aims 1 and 2), and (2) ferumoxytol-enhanced cardiac cine MRI reconstruction (aim 3). In aim 1, I developed a generative adversarial network using paired undersampled and ground truth images to reduce streaking artifacts and preserve image sharpness. In aim 2, I developed a radial k-space prediction framework by training an attention-based transformer network on k-space data. By combining the acquired and predicted k-space data, the reconstructed images will have an improved signal-to-noise ratio and fewer streaking artifacts. In aim 3, I developed an unrolled spatiotemporal deep learning network for ferumoxytol-enhanced cardiac cine MRI reconstruction. The network was trained using non-contrast-enhanced bSSFP cine images and can be successfully generalized to ferumoxytol-enhanced images.

Download or read book Magnetic Resonance Image Reconstruction written by Mehmet Akcakaya and published by Academic Press. This book was released on 2022-11-04 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. The book discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. This unique resource is suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI. Explains the underlying principles of MRI reconstruction, along with the latest research“/li> Gives example codes for some of the methods presented Includes updates on the latest developments, including compressed sensing, tensor-based reconstruction and machine learning based reconstruction

Download or read book Automating and Accelerating Magnetic Resonance Imaging written by Ke Lei and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging (MRI) can provide high-quality multi-contrast diagnostic images. It is non-invasive and does not use ionizing radiation. Therefore, it is safe for young patients. MRI exams follow a procedure consisting of preparation, scan prescription, data collection (i.e., the actual scanning), image reconstruction, and checking the result. Unfortunately, MRI has significantly longer scan times compared to other modalities such as computed tomography (CT). These long scan times are especially challenging for children who may struggle to stay still. In this dissertation, we aim to expedite the whole MRI exam procedure by automating and accelerating four parts of the scanning process: prescription, data collection, reconstruction, and the after-scan check. This is done through a series of three projects. First, we present a method for region-of-interest (ROI) prediction and field-of-view (FOV) prescription. Manual prescription of the field of view by MRI technologists is variable and prolongs the scanning process. Often, the FOV is either too large or crops critical anatomy. We propose a deep-learning framework, trained with radiologists' supervision, for automating FOV prescription. An intra-stack shared feature extraction network and an attention network are used to process a stack of 2D image inputs to generate scalars defining the location of a rectangular ROI. The attention mechanism is used to make the model focus on a small number of informative slices in a stack. Then the smallest FOV that makes the neural network predicted an ROI free of aliasing is calculated by an algebraic operation derived from MR sampling theory. The framework's performance is examined quantitatively with intersection over union (IoU) and pixel error on position, and qualitatively with a reader study. The framework's prescription is clinically acceptable 92\% of the time as rated by an experienced radiologist. Second, we present a learning-based model for reconstructing undersampled data using unpaired adversarial training. The lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this work leverages unpaired adversarial training for reconstruction networks, where the inputs are undersampled k-space data and naively reconstructed images from one dataset, and the labels are high-quality images from another dataset. The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality. The generator is an unrolled neural network -- a cascade of convolutional and data consistency layers. The discriminator is also a multilayer Convolutional Neural Network (CNN) that plays the role of a critic scoring the quality of reconstructed images based on the Wasserstein distance metric. Our experiments with knee MRI datasets demonstrate that the proposed unpaired training enables diagnostic-quality reconstruction when high-quality image labels are not available, or when the amount of label data is small. In addition, our adversarial training scheme can achieve better image quality (as rated by expert radiologists) compared with the paired training methods using pixel-wise loss. Finally, we present a no-reference image quality assessment (IQA) framework that checks the exam outcome. In clinical practice MR images are often first seen by radiologists long after the scan. If image quality is inadequate the patient may have to return for an additional scan, or a suboptimal interpretation is rendered. Automatic IQA would enable real-time remediation. Existing IQA methods for MRI give only a general quality score. These are agnostic to the cause of the low-quality scan and the solution for improvement. Furthermore, radiologists' image quality requirements vary with the scan type and diagnostic task. Therefore, the same score may have different implications for different scans. We propose a framework with a multi-task CNN model trained with calibrated labels and measured with image rulers. Labels calibrated by human inputs follow a well-defined and efficient labeling task. Image rulers address varying quality standards and provide a concrete way of interpreting raw scores from the CNN. The model supports assessments of perceptual noise level, rigid motion, and peristaltic motion. Our experiments show that label calibration, image rulers, and multi-task training improve the model's performance and ability to generalize.

Download or read book Machine Learning for Medical Image Reconstruction written by Nandinee Haq and published by Springer Nature. This book was released on 2021-09-29 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

Download or read book Machine Learning for Medical Image Reconstruction written by Florian Knoll and published by Springer. This book was released on 2018-09-11 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 17 full papers presented were carefully reviewed and selected from 21 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography, and deep learning for general image reconstruction.

Download or read book Machine Learning for Medical Image Reconstruction written by Nandinee Haq and published by Springer Nature. This book was released on 2022-09-22 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2022, held in conjunction with MICCAI 2022, in September 2022, held in Singapore. The 15 papers presented were carefully reviewed and selected from 19 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

Download or read book Regularized Image Reconstruction in Parallel MRI with MATLAB written by Joseph Suresh Paul and published by CRC Press. This book was released on 2019-11-05 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regularization becomes an integral part of the reconstruction process in accelerated parallel magnetic resonance imaging (pMRI) due to the need for utilizing the most discriminative information in the form of parsimonious models to generate high quality images with reduced noise and artifacts. Apart from providing a detailed overview and implementation details of various pMRI reconstruction methods, Regularized image reconstruction in parallel MRI with MATLAB examples interprets regularized image reconstruction in pMRI as a means to effectively control the balance between two specific types of error signals to either improve the accuracy in estimation of missing samples, or speed up the estimation process. The first type corresponds to the modeling error between acquired and their estimated values. The second type arises due to the perturbation of k-space values in autocalibration methods or sparse approximation in the compressed sensing based reconstruction model. Features: Provides details for optimizing regularization parameters in each type of reconstruction. Presents comparison of regularization approaches for each type of pMRI reconstruction. Includes discussion of case studies using clinically acquired data. MATLAB codes are provided for each reconstruction type. Contains method-wise description of adapting regularization to optimize speed and accuracy. This book serves as a reference material for researchers and students involved in development of pMRI reconstruction methods. Industry practitioners concerned with how to apply regularization in pMRI reconstruction will find this book most useful.

Download or read book Deep Learning for Biomedical Image Reconstruction written by Jong Chul Ye and published by Cambridge University Press. This book was released on 2023-09-30 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the power of deep neural networks for image reconstruction with this state-of-the-art review of modern theories and applications. The background theory of deep learning is introduced step-by-step, and by incorporating modeling fundamentals this book explains how to implement deep learning in a variety of modalities, including X-ray, CT, MRI and others. Real-world examples demonstrate an interdisciplinary approach to medical image reconstruction processes, featuring numerous imaging applications. Recent clinical studies and innovative research activity in generative models and mathematical theory will inspire the reader towards new frontiers. This book is ideal for graduate students in Electrical or Biomedical Engineering or Medical Physics.

Download or read book Development of Deep Learning Methods for Magnetic Resonance Imaging Reconstruction and Analysis written by Yuhua Chen and published by . This book was released on 2021 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance image (MRI) is a widely used non-invasive radiation-free imaging technique that uniquely provides structural and functional information for disease detection, diagnosis, and treatment planning. However, the conventional MRI imaging techniques are typically slow and low in spatial or time resolution, resulting in long scan times and more susceptibility to motion artifacts. Moreover, a fast MRI scan usually comes in a low spatial resolution, making it less desirable for clinical application. A recently proposed technique, Multi-tasking MRI (MTMRI), significantly improves the scan efficiency with high temporal resolution. Nevertheless, the iterative reconstruction requires a lot of computational resources and takes a long time to process, making it challenging to fit in the clinical routine. Additionally, when doing image post-processing with MRI, despite MRI providing a good contrast of soft tissues, the variety in weighted contrast MRI's intensity values makes it challenging to extract image features compared with other quantitative imaging techniques. The most significant contribution of this dissertation's work is to address the three limitations above by developing a unified multi-purpose structure with deep-learning (DL) techniques. We achieved three primary goals in three different areas: 1) A general framework for highly accelerated MRI scanning without sacrificing spatial resolution, 2) reduce reconstruction time for motion-resolved free-breathing MRI technique, 3) accurately fully automated segmentation for abdominal MRI for fast image post-processing. All technical improvements utilize DL techniques to improve MRI in different aspects: to improve image quality in fast MRI scans, reduce reconstruction time in motion-resolved MRI, and reduce tedious human labors in abdominal MRI. First, a DL-based Super-Resolution (SR) technique is developed and evaluated in both brain MRI and coronary MR Angiography (MRA). SR can recover the image quality and structural details from a 4x and 16x low-resolution fast MRI scan. For brain MRI, several SR networks have been developed. The proposed network (mDCSRN) has successfully recovered the brain structural details from a 4x low-resolution fast scan. It is developed and evaluated on an open access high-resolution T1w brain MRI with 1131 healthy volunteers. Quantitative results show that it can achieve 4x acceleration in scan while keeping similar image quality. For coronary MRA, introducing a domain adaptive network (DRAGAN) jointly trained on both coronary and brain MRA to overcome catastrophic failures commonly in training a GAN in a small dataset, we successfully accelerated the MRA acquisition by a factor of 16. Second, DL networks are developed to accelerate the reconstruction of a 5-dimensional (5D) Multitasking MRI (MTMRI). The MTMRI is a respiratory and cardiac-motion-resolved, high-temporal-resolution technique that provides quantitative T1 mapping. However, the massive size of many dynamic MRI problems prevents deep learning networks from directly exploiting global temporal relationships. By applying deep neural networks inside a priori calculated temporal feature spaces, we enable deep learning reconstruction with global temporal modeling even for image sequences with >40,000 frames. One proposed variation of our approach using dilated multi-level Densely Connected Network (mDCN) speeds up feature space coordinate calculation by 3000x compared to conventional iterative methods, from 20 minutes to 0.39 seconds. Thus, the combination of low-rank tensor and deep learning models makes large-scale dynamic MRI feasible and practical for routine clinical application. Third, we developed Automated deep Learning-based Abdominal Multi-Organ segmentation (ALAMO) technique based on 2D U-net and a densely connected network structure with tailored design in data augmentation and training procedures. The model takes in multi-slice MR images and generates the output of segmentation results. 3.0-Tesla T1 VIBE (Volumetric Interpolated Breath-hold Examination) images of 102 subjects were used in our study. Ten OARs were studied, including the liver, spleen, pancreas, left/right kidneys, stomach, duodenum, small intestine, spinal cord, and vertebral bodies. ALAMO generated segmentation labels in good agreement with the manual results. Specifically, among the 10 OARs, 9 achieved high Dice Similarity Coefficients (DSCs) in the range of 0.87-0.96, except for the duodenum with a DSC of 0.80. Overall, the ALAMO model matched the state-of-the-art techniques in performance.

Download or read book Applying Machine Learning and Deep Learning for Improved Acquisition Reconstruction and Quantification in MRI written by Enhao Gong and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic Resonance Imaging, MRI, is a powerful imaging modality that is frequently used in both clinical and academic settings. With its advantages of flexibility in signal encoding, we can use MRI to non-invasively visualize various soft-tissue contrasts, showing not only anatomical but also metabolic and functional information. In addition, MRI is a radiation-free modality which makes it favorable in numbers of clinical applications because of the reduced radiation-risk compared with other radiology modalities such as X-ray, Computed Tomography (CT), Positron Emission Tomography (PET) etc. Despite the advantages of MRI techniques, there are still several challenges preventing MRI from becoming more efficient and accessible. First, the scan time for MRI is usually longer than other modalities such as X-ray and CT, since it requires enough measurements to resolve high-quality images for diagnostic tasks. In order to accelerate MRI, various fast-imaging techniques, such as Parallel Imaging (PI) and Compressed Sensing (CS) have been proposed to speed up MRI acquisition using under-sampling. However, it is still unclear what is the best approach to conduct the under-sampling as different under-sampling patterns may result in different reconstruction quality. Second, the reconstruction methods for under-sampled MRI need further improvement. The reconstruction algorithms are formed as nonlinear optimization problems using iterative optimization that can be time-consuming. Fixed and handcrafted penalty terms are usually used to regularize the optimization, which are hard to tune. There are often trade-offs between the speed of the algorithm and the quality of resulting images. In many cases, the imperfect artifact suppression or over-smoothing slows down the clinical adoption of these fast-imaging techniques. Third, MR images are typically not quantitative. Most clinical MRI protocols used nowadays are contrast-weighted sequences, which incorporate the tissue contrasts in qualitative ways. Therefore, the resulting MR images may vary a lot between different protocols and scanners, which makes it very difficult for radiologists to conduct quantitative analysis or longitudinal comparison. In this work, we propose to resolve these remaining challenges to further improve MRI technologies. We utilized state-of-the-art Machine Learning and Deep Learning algorithms to significantly improve these three essential components in MRI: faster acquisition, better reconstruction, and more accurate qualification. Specifically, we firstly propose a machine learning based method to optimize the undersampling pattern for accelerated acquisition. The results, validated on in-vivo multi-contrast brain and prostate MRI datasets, demonstrate that the proposed method can generalize well for different anatomy. It enables efficient (5sec-10sec) and adaptive under-sampling pattern optimization at per-subject/per-scan level, and achieves 30%-50% lower PI+CS reconstruction error at the same acceleration factor. To improve MRI acquisition with a safer protocol and lower contrast dose, a deep learning model is developed to enhance the MRI. The proposed Deep Learning method yielded significant (N=50, p 0.001) improvements over the low-dose (10%) images ( 5dB PSNR gains and > 11.0% SSIM). Ratings on image quality and contrast enhancement are significantly (N=20, p

Download or read book Machine Learning for Medical Image Reconstruction written by Florian Knoll and published by Springer Nature. This book was released on 2019-10-24 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction.

Download or read book Comparing the Training Performance of a Deep Neural Network for Accelerated MRI Reconstruction Using Synthesized and Realistic K Space Data written by Anil Kemisetti and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality used as a diagnostic tool. There is a steady rise in the imagining examination. Trends from 2000 - 2016 showed that nearly 16 million to 21 million patients had enrolled annually in various US health care systems. The number of MRIs per 1000 increased from 62 per 1000 to 139 per 1000 patients from 2000 to 2016. MR images are usually stored in Picture Archiving and Communication Systems (PACS) in Digital Imaging and Communication in Medicine (DICOM). DICOM format includes a header and imaging data. MRI k-space is the raw data obtained during the MR signal acquisition. The file size of complex MR data is huge. It is generally transformed into the anatomical imaging data, and raw data is discarded and not transferred to the PACS. The abundant DICOM data has the potential to be used for training neural networks. Deep Neural Network models depend on the extensive training datasets. DICOM images are magnitude images without the image phase. It is essential to understand the effect of missing image phase information to use the DICOM data for this training task effectively.My thesis attempts to compare a deep neural network's performance for accelerated MRI reconstruction using the k-space to DICOM only data. MR imaging offers a great deal of control to the user to acquire the data and reconstruct the clinical images. All this comes at the cost of an increase in the acquisition time. Typical scan times are between 30 to 40 mins. Scan times go up to 60 mins if a contrast agent needs to be administered. Such long acquisition times are not only expensive but a cause of inconvenience to the subject as it is impossible to stay motionless in the bore during the whole duration. Two areas are of interest to reduce the scan time, (i) accelerated acquisition and (ii) fast and efficient reconstruction.Methods like compressed sensing and parallel imaging are used to accelerate MRI acquisition. Compressed sensing achieves scan acceleration by overcoming the requirement of Nyquist sampling criteria. An undersampling pattern like the Poisson Disk undersampling pattern is used to acquire an incoherent random sparse signal instead of the full k-space. The "sigpy.mri" python library's "Poisson" API was used to simulate this undersampling. This Python API generates a variable-density Poisson-disc sampling pattern. Compressed Sensing theory mentions that image reconstruction would be possible using signals less than the number indicated by Nyquist as long as the k-space undersampling is done incoherently, which does not lead to structural aliasing when the anatomical image is constructed. This algorithm combines the undersampling with partial Fourier imaging. This API uses a fully sampled calibration region at the center of the k-space in addition to the acceleration factor. The acceleration factor is used for undersampling the region outside the fully sampled center region. Poisson disk undersampling does random sampling while constraining the maximum and minimum distance. This scheme leads to incoherent sampling and avoids structural artifacts.After the image acquisition comes, the reconstruction of the fully sampled k-space or the anatomical image with good SNR. A deep-learning neural network was trained to perform the reconstruction of the retrospectively undersampled data. The undersampled raw k-space data's training performance is compared with that of the undersampled k-space data obtained from the DICOM data.Our experiments have shown that the magnitude obtained from raw k-space data has consistently shown better initial training performance and faster convergence when compared to the magnitude image obtained from the DICOM image. It is also observed that after training enough epochs, the performance of the model trained using raw data is comparable to that of the DICOM images. The significance of this finding is in the fact that the abundantly available DICOM data can be used to train a deep neural network to perform reconstruction of the undersampled k-space.FastMRI is a research project from Facebook AI(FAIR) and NYU Langone Health. The dataset for this project is publicly available. This dataset has two types of scans, knee MRI and brain MRI. For this work, we have used single coil knee MRI data. For performing the training, 2D slices from these images are used from the training dataset's single-coil knee MRI volumes. The training dataset has 973 volumes and a total of 34,742 slices.

Download or read book Deep Neural Networks for Cardiovascular Magnetic Resonance Imaging written by Vahid Ghodrati Kouzehkonan and published by . This book was released on 2022 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic Resonance Imaging (MRI) is a powerful diagnostic imaging modalities known to provide high soft-tissue contrast and spatial resolution. Much of the versatility of MRI stems from the fact that the signal from different tissue types can be weighted differently through manipulation of the sequence in which radiofrequency (RF) and gradient events are played out during the data acquisition phase. However, data acquisition for most MRI measurements is sequential, limiting its speed and increasing its susceptibility to motion artifacts. This is particularly the case for cardiovascular applications, where cardiac and respiratory motion complicate all aspects of the data acquisition and signal processing pathways. Moreover, following data acquisition and image reconstruction, clinically relevant post-processing may require substantial time and effort, increasing the burden on clinical centers and medical staff. Thus, general algorithms should be customized to accelerate image acquisition, image reconstruction and image post-processing with the goal of expanding the speed, scope and reliability of cardiovascular MRI applications. This dissertation describes several deep learning-based methods applying tailored image reconstruction, respiratory motion correction, blood vessel segmentation, and instance T1 mapping calculation.

Download or read book Machine Learning for Medical Image Reconstruction written by Farah Deeba and published by Springer Nature. This book was released on 2020-10-21 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually. The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

Download or read book Improved Data Representations and Data efficient Methods in Deep Learning for MRI Applications written by Elizabeth Katherine Cole and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging (MRI) is a medical imaging modality which provides high-quality non-invasive soft tissue visualization. The resulting images are used to assess patient health and diagnose various diseases, such as coronary heart disease, brain tumors, and liver disease. Unlike positron emission tomography and computed tomography, MRI does not use harmful ionizing radiation, which makes it a preferable modality in pediatric patients. However, MRI scans are traditionally very slow, requiring patients to lie still for long periods of time to avoid motion artifacts. This is especially difficult and uncomfortable for young children. Therefore, imaging speed remains a main limitation of MRI. Scan times can be significantly reduced by collecting less measurements in the frequency domain; however, this leads to low-quality images. Image reconstruction addresses this by converting undersampled raw data to high-quality images. Deep learning (DL) methods have recently provided rapid and robust image reconstruction compared to traditional iterative methods. However, these DL methods still have several issues. First, most approaches split the complex-valued MRI data into separate real and imaginary channels within some kind of convolutional neural network (CNN). This approach does not accurately represent the underlying complex-valued structure of the data. Second, the vast majority of DL methods for MR image reconstruction are supervised, requiring large amounts of ground truth data. However, ground truth data cannot be acquired for many types of MRI sequences, making it impossible to train existing DL models for reconstruction. In this thesis, both of these issues are addressed in a series of projects. First, work on formulating and analyzing complex-valued CNNs for supervised MR image reconstruction is shown. Complex-valued convolutions, as opposed to real-valued convolutions, are shown to more accurately represent MRI data and thus perform superior reconstructions, especially in terms of phase information. Additionally, it is shown that the superior phase recovery of these complex-valued networks provides more accurate fat-water separation, which is important for applications such as liver fat quantification, as well as more accurate blood flow estimation, an important cardiovascular application. Second, work is presented on unsupervised MR image reconstruction. A framework using generative adversarial networks is formulated to produce high quality reconstructions without ever using any ground truth images during training. Our unsupervised method is compared to compressed sensing (CS), which, being a traditional signal processing method, also requires no ground truth data. The reconstructions from our unsupervised method are superior compared to CS in terms of quantitative image quality metrics, especially at higher accelerations. This method also runs up to 7 times faster compared to CS. An additional reconstruction-related problem in MRI lies in the intrinsic high-dimensional nature of MRI datasets. In MRI, using multiple radio frequency (RF) coil arrays can increase parallel imaging (PI) acceleration and improve signal-to-noise (SNR) ratio. The large number of coils creates prohibitively large MRI datasets in space and infeasible computation time for reconstruction. Additionally, these datasets often contain redundant information across the various acquired images. Coil compression algorithms are effective in mitigating this problem by compressing the datasets to convert the original set of coil images into a smaller set of virtual coil images. This enables smaller datasets and faster computation time. However, traditional iterative coil compression methods are lossy and time-consuming. In this work, we construct an encoder-based neural network for the purposes of dimensionality reduction and apply it to the coil compression task in pursuit of higher reconstruction accuracy and faster coil compression. The learned compression method achieves up to 1.5x lower NRMSE and up to 10 times runtime speed compared to traditional methods on a benchmark test dataset.

Download or read book Advances in Clinical Radiology E Book 2022 written by Frank H. Miller and published by Elsevier Health Sciences. This book was released on 2022-09-19 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Clinical Radiology reviews the year’s most important findings and updates within the field in order to provide radiologists with the current clinical information they need for everyday practice. A distinguished editorial board, led by Dr. Frank H. Miller, identifies key areas of major progress and controversy and invites preeminent specialists to contribute original articles devoted to these topics. These insightful overviews in radiology inform and enhance clinical practice by bringing concepts to a clinical level and exploring their everyday impact on patient care. Contains a variety of articles on such topics as accelerating abdominopelvic MRI; image-guided biopsy: an algorithmic approach for optimizing results in the age of precision medicine; COVID in the abdomen; and advances in imaging of cystic renal masses: appraisal of emerging evidence from Bosniak version 2019 to artificial intelligence. Provides in-depth, clinical reviews in radiology, providing actionable insights for clinical practice. Presents the latest information in the field under the leadership of an experienced editorial team. Authors synthesize and distill the latest research and practice guidelines to create these timely topic-based reviews.