Computational Medical Image Reconstruction Techniques: A Comprehensive Review

Gothwal, Ritu; Tiwari, Shailendra; Shivani, Shivendra

doi:10.1007/s11831-022-09785-w

Cited by 5 publications

(2 citation statements)

References 152 publications

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“…However, due to their functionality, they can harm patients; therefore, their radiation exposure is reduced from the standard dose, or the imaging process is done in an under-sampled or sparse-view manner [91,92,93,94]. To diminish these drawbacks, e.g., low Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), medical image reconstruction must overcome the challenges mentioned and solve this ill-posed inversion problem [95]. This section overviews the diffusion-based paradigms for medical image reconstruction and enhancement.…”

Section: Reconstructionmentioning

confidence: 99%

Diffusion Models for Medical Image Analysis: A Comprehensive Survey

Kazerouni¹,

Aghdam²,

Heidari³

et al. 2022

Preprint

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Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired noise-free data from noisy data samples. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples in spite of their known computational burdens. Capitalizing on the advances in computer vision, the field of medical imaging has also observed a growing interest in diffusion models. With the aim of helping the researcher navigate this profusion, this survey intends to provide a comprehensive overview of diffusion models in the discipline of medical image analysis. Specifically, we start with an introduction to the solid theoretical foundation and fundamental concepts behind diffusion models and the three generic diffusion modeling frameworks, namely, diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms. To this end, we cover extensive applications of diffusion models in the medical domain, including segmentation, anomaly detection, image-to-image translation, 2/3D generation, reconstruction, denoising, and other medically-related challenges. Furthermore, we emphasize the practical use case of some selected approaches, and then we discuss the limitations of the diffusion models in the medical domain and propose several directions to fulfill the demands of this field. Finally, we gather the overviewed studies with their available open-source implementations at our GitHub 1 . We aim to update the relevant latest papers within it regularly.

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Section: Reconstructionmentioning

confidence: 99%

Diffusion Models for Medical Image Analysis: A Comprehensive Survey

Kazerouni¹,

Aghdam²,

Heidari³

et al. 2022

Preprint

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“…Artificial Intelligence (AI) made significant progress in solving various problems of computer tomography, computer vision and recognition [2], especially when the Machine Learning (ML) and Deep Learning (DL) methods are used. DL methods demonstrate impressive results in the tasks of CT-reconstruction [3][4][5] and image analysis for medical applications. It is clear from the review [6] that artificial neural networks (ANN) are successfully used for the majority of digital document imaging tasks [7][8][9][10][11][12] Data is a critical element for the development of AI methods.…”

Section: Introductionmentioning

confidence: 99%

From tomographic reconstruction to automatic text recognition: the next frontier task for the artificial intelligence

Polevoy,

Kulagin,

Ingacheva

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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Virtual unrolling or unfolding, digital unwrapping, flattening or unfurling - all these terms are used to describe the process of surface straightening of a tomographically reconstructed digital object. For many objects of historical heritage, tomography is the only way to obtain a hidden image of the original object without its destruction. Digital flattening is no longer considered a unique met hodology. It being applied by many research group, but AI-based methods are used insignificantly in such projects, despite the amazing success of AI in computer vision, in particular optical text recognition. It can be explained by the fact that the success of AI depends on large, broad and high quality datasets, but there are very few published CT-based datasets relevant to the task of digital flattening. Accumulation of a sufficient amount of data necessary for training models is a key point for the next technological breakthrough. In this paper, we present open and cumulative dataset CT-OCR-2022. Dataset includes 6 packages data for different model objects that help to enrich tomographic solutions and to train machine learning models. Each package contains optically scanned image of model objects, 400 measured X-ray projections, 2687 CT- reconstructed cross-sections of 3D reconstructed image, segmentation markups. We believe that CT-OCR-2022 dataset will serve as a benchmark for reconstructed object digital flattening and recognition systems, and that it will prove invaluable for advancement of the field of CT-reconstruction, symbols analysis and recognition. The data presented are openly available in Zenodo at doi:10.5281/zenodo.7123495 and linked repositories.

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A Systematic Review of Modern Approaches in Healthcare Systems for Lung Cancer Detection and Classification

Pandey

Bhandari

2023

Arch Computat Methods Eng

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Computational Medical Image Reconstruction Techniques: A Comprehensive Review

Cited by 5 publications

References 152 publications

Diffusion Models for Medical Image Analysis: A Comprehensive Survey

Diffusion Models for Medical Image Analysis: A Comprehensive Survey

From tomographic reconstruction to automatic text recognition: the next frontier task for the artificial intelligence

A Systematic Review of Modern Approaches in Healthcare Systems for Lung Cancer Detection and Classification

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