Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation

Öksüz, İlkay; Clough, James R.; Ruijsink, Bram; Puyol‐Antón, Esther; Bustin, Aurélien; Cruz, Gastão; Prieto, Claudia; King, Andrew P.; Schnabel, Julia A.

doi:10.1109/tmi.2020.3008930

Cited by 61 publications

(31 citation statements)

References 26 publications

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“…(b) in the single-channel MRI setting. Similar multi-task learning strategies are also used in the MRI setting [26]; these studies show that when the tasks are complementary, the joint learning of them using a single network can facilitate the exploitation of the synergies.…”

Section: End-to-end Multi-task Training Approachesmentioning

confidence: 99%

Joint Calibrationless Reconstruction and Segmentation of Parallel MRI

Pramanik¹,

Wu²,

Jacob³

2021

Preprint

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The volume estimation of brain regions from MRI data is a key problem in many clinical applications, where the acquisition of data at high spatial resolution is desirable. While parallel MRI and constrained image reconstruction algorithms can accelerate the scans, image reconstruction artifacts are inevitable, especially at high acceleration factors. We introduce a novel image domain deep-learning framework for calibrationless parallel MRI reconstruction, coupled with a segmentation network to improve image quality and to reduce the vulnerability of current segmentation algorithms to image artifacts resulting from acceleration. The combination of the proposed image domain deep calibrationless approach with the segmentation algorithm offers improved image quality, while increasing the accuracy of the segmentations. The novel architecture with an encoder shared between the reconstruction and segmentation tasks is seen to reduce the need for segmented training datasets. In particular, the proposed few-shot training strategy requires only 10% of segmented datasets to offer good performance.

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Section: End-to-end Multi-task Training Approachesmentioning

confidence: 99%

Joint Calibrationless Reconstruction and Segmentation of Parallel MRI

Pramanik¹,

Wu²,

Jacob³

2021

Preprint

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“…Pairs of synthetically motion-corrupted k-space data and artifact-free reconstructed CMR images serve as training database for the proposed adversarial training strategy. Beyond image reconstruction, Oksuz et al ( 356 ) introduced a joint framework for motion artifact detection and correction in k-space and image segmentation. The motion artifact network detects motion-affected lines in k-space, influencing the data consistency term.…”

Section: Artificial Intelligence For Cardiovascular Mrmentioning

confidence: 99%

Cardiac MR: From Theory to Practice

Ismail

Strugnell

Coletti

et al. 2022

Front. Cardiovasc. Med.

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Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.

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“…Within the scope of the fracture classification performed by Tanzi et al, in 2453 proximal femur X-ray images using InceptionV3, VGG16 and ResNet50 models, the highest accuracies achieved for structures of grade three and grade five were 87% and 78%, respectively [ 20 ]. Öksüz et al, proposed a segmentation network in which training is carried out that optimizes three different tasks in cardiac MR images: image artefact detection, artefact correction and image segmentation [ 21 ]. A distance for structural similarity metric and Fuzzy C-Means algorithm were developed for image segmentation by Tang et al [ 22 ].…”

Section: Related Workmentioning

confidence: 99%

Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models

Hardalaç

Uysal

Peker

et al. 2022

Sensors

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Hospitals, especially their emergency services, receive a high number of wrist fracture cases. For correct diagnosis and proper treatment of these, images obtained from various medical equipment must be viewed by physicians, along with the patient’s medical records and physical examination. The aim of this study is to perform fracture detection by use of deep-learning on wrist X-ray images to support physicians in the diagnosis of these fractures, particularly in the emergency services. Using SABL, RegNet, RetinaNet, PAA, Libra R-CNN, FSAF, Faster R-CNN, Dynamic R-CNN and DCN deep-learning-based object detection models with various backbones, 20 different fracture detection procedures were performed on Gazi University Hospital’s dataset of wrist X-ray images. To further improve these procedures, five different ensemble models were developed and then used to reform an ensemble model to develop a unique detection model, ‘wrist fracture detection-combo (WFD-C)’. From 26 different models for fracture detection, the highest detection result obtained was 0.8639 average precision (AP50) in the WFD-C model. Huawei Turkey R&D Center supports this study within the scope of the ongoing cooperation project coded 071813 between Gazi University, Huawei and Medskor.

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Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation

Cited by 61 publications

References 26 publications

Joint Calibrationless Reconstruction and Segmentation of Parallel MRI

Joint Calibrationless Reconstruction and Segmentation of Parallel MRI

Cardiac MR: From Theory to Practice

Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models

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