Applying MRI Intensity Normalization on Non-Bone Tissues to Facilitate Pseudo-CT Synthesis from MRI

Hou, Kuei-Yuan; Lu, Haoyuan; Yang, Ching-Ching

doi:10.3390/diagnostics11050816

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Random search based on intuition works quite well and is perhaps the most common approach, where researchers (LeCun et al 2015) have identified and provided several suggestions for successful implementation. Detailed reviews of the deep-learning research related to the lung image analysis applications, such as reconstruction, segmentation, registration and image synthesis have been recently published (Olberg et al 2018, Duan et al 2019, Astley et al 2021, Hou et al 2021. Nonetheless, the major challenge is the development of models that can leverage the full imaging data available in 3D, with further validation studies in different populations, and global standardization of the image processing methods without significantly increasing the computational complexity in order to facilitate successful clinical translation.…”

Section: Machine Learning and Texture Analysismentioning

confidence: 99%

Quantification of pulmonary functional MRI: state-of-the-art and emerging image processing methods and measurements

et al. 2022

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Pulmonary functional magnetic resonance imaging (PfMRI) provides a way to non-invasively map and measure the spatial distribution of pulmonary ventilation, perfusion and gas-exchange abnormalities with unprecedented detail of functional processes at the level of airways, alveoli and the alveolar-capillary membrane. Current PfMRI approaches are dominated by hyperpolarized helium-3 (3He) and xenon-129 (129Xe) gases, which both provide rapid (8-15s) and well-tolerated imaging examinations in patients with severe pulmonary diseases and pediatric populations, whilst employing no ionizing radiation. While a number of review papers summarize the required image acquisition hardware and software requirements needed to enable PfMRI, here we focus on the image analysis and processing methods required for reproducible measurements using hyperpolarized gas ventilation MRI. We start with the transition in the literature from qualitative and subjective scoring systems to quantitative and objective measurements which enable precise quantification of the lung’s critical structure-function relationships. We provide an overview of quantitative biomarkers and the relevant respiratory system parameters that may be measured using PfMRI methods, outlining the history of developments in the field, current methods and then knowledge gaps and typical limitations. We focus on hyperpolarized noble gas MR image processing methods used for quantifying ventilation and gas distribution in the lungs, and discuss the utility and applications of imaging biomarkers generated through these techniques. We conclude with a summary of the current and future directions to further the development of image processing methods, and discuss the remaining challenges for potential clinical translation of these approaches and their integration into standard clinical workflows.

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Section: Machine Learning and Texture Analysismentioning

confidence: 99%

Quantification of pulmonary functional MRI: state-of-the-art and emerging image processing methods and measurements

et al. 2022

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Deep Learning in Image Processing: Part 2—Image Enhancement, Reconstruction and Registration

Pauwels,

Iosifidis

2023

Artificial Intelligence in Dentistry

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A review of PET attenuation correction methods for PET-MR

Krokos,

MacKewn,

Dunn

et al. 2023

EJNMMI Phys

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Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.

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Applying MRI Intensity Normalization on Non-Bone Tissues to Facilitate Pseudo-CT Synthesis from MRI

Cited by 3 publications

References 25 publications

Quantification of pulmonary functional MRI: state-of-the-art and emerging image processing methods and measurements

Quantification of pulmonary functional MRI: state-of-the-art and emerging image processing methods and measurements

Deep Learning in Image Processing: Part 2—Image Enhancement, Reconstruction and Registration

A review of PET attenuation correction methods for PET-MR

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