Multimodal Medical Image Fusion Techniques – A Review

Tirupal, T.; Mohan, B. Chandra; Kumar, S. Srinivas

doi:10.2174/1574362415666200226103116

Cited by 34 publications

(14 citation statements)

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“…In modern RT, CT simulation is most often supported by PET and MR images which are co‐registered to the CT simulation 102,103 . These image fusion methods and their accuracy need to be carefully validated by experts, as they also significantly influence the contouring 104 . It is also important to understand the caveats of the image modalities, as they are not always optimised for geometric accuracy which in radiotherapy is highly important.…”

Section: Practical Treatment Planning Considerationsmentioning

confidence: 99%

“…102,103 These image fusion methods and their accuracy need to be carefully validated by experts, as they also significantly influence the contouring. 104 It is also important to understand the caveats of the image modalities, as they are not always optimised for geometric accuracy which in radiotherapy is highly important. In some radiological MR sequences, the placement of water and fat can be distorted geometrically to improve the signal to noise ratio.…”

Section: Practical Treatment Planning Considerationsmentioning

confidence: 99%

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Plan quality in radiotherapy treatment planning – Review of the factors and challenges

et al. 2022

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A high-quality treatment plan aims to best achieve the clinical prescription, balancing high target dose to maximise tumour control against sufficiently low organ-at-risk dose for acceptably low toxicity. Treatment planning (TP) includes multiple steps from simulation/imaging and segmentation to technical plan production and reporting. Consistent quality across this process requires close collaboration and communication between clinical and technical experts, to clearly understand clinical requirements and priorities and also practical uncertainties, limitations and compromises. TP quality depends on many aspects, starting from commissioning and quality management of the treatment planning system (TPS), including its measured input data and detailed understanding of TPS models and limitations. It requires rigorous quality assurance of the whole planning process and it links to plan deliverability, assessable by measurement-based verification. This review highlights some factors influencing plan quality, for consideration for optimal plan construction and hence optimal outcomes for each patient. It also indicates some challenges, sources of difference and current developments. The topics considered include: the evolution of TP techniques; dose prescription issues; tools and methods to evaluate plan quality; and some aspects of practical TP. The understanding of what constitutes a high-quality treatment plan continues to evolve with new techniques, delivery methods and related evidence-based science. This review summarises the current position, noting developments in the concept and the need for further robust tools to help achieve it.

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Section: Practical Treatment Planning Considerationsmentioning

confidence: 99%

Section: Practical Treatment Planning Considerationsmentioning

confidence: 99%

Plan quality in radiotherapy treatment planning – Review of the factors and challenges

et al. 2022

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“…The CT images are used to visualize bone structure, and the MR images are used to visualize the internal or soft structures of the organ where the CT image is more accurate than the MRI image. On the other hand, PET and SPECT images provide metabolic or functional information in low resolution for the organ and are more accurate in tumor detection [12,14,25]. Table 1 describes the advantages and disadvantages of multimodality medical image.…”

Section: Gfsmentioning

confidence: 99%

Multimodal medical image fusion algorithm based on pulse coupled neural networks and nonsubsampled contourlet transform

Ibrahim

Makhlouf

El-Tawel

2022

Med Biol Eng Comput

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Combining two medical images from different modalities is more helpful for using the resulting image in the healthcare field. Medical image fusion means combining two or more images coming from multiple sensors. This technology obtains an output image that presents more effective and useful information from two images. This paper proposes a multi-modal medical image fusion algorithm based on the nonsubsampled contourlet transform (NSCT) and pulse coupled neural networks (PCNN) methods. The input images are decomposed using the NSCT method into low- and high-frequency subbands. The PCNN is a fusion rule for integrating both low- and high-frequency subbands. The inverse of the NSCT method is to reconstruct the fused image. The results of medical image fusion help doctors with disease diagnosis and patient treatment. The proposed algorithm is tested on six groups of multi-modal medical images using 100 pairs of input images. The proposed algorithm is compared with eight fusion methods. We evaluate the performance of the proposed algorithm using the fusion metrics: peak signal to noise ratio (PSNR), mutual information (MI), entropy (EN), weighted edge information (Q$$^{AB/F}$$ A B / F ), nonlinear correlation information entropy (Q$$_{ncie}$$ ncie ), standard deviation (SD), and average gradient (AG). Experimental results show that the proposed algorithm can perform better than other medical image fusion methods and achieve promising results. Graphical abstract

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“…Medical image fusion aims at extracting more useful information of input images, improving the application of medical images, and helping doctors understand image contents better. Therefore, the study of medical image fusion is significant ( Tirupal et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Two-Scale Multimodal Medical Image Fusion Based on Structure Preservation

Liu

Wang

Yin

et al. 2022

Front. Comput. Neurosci.

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Medical image fusion has an indispensable value in the medical field. Taking advantage of structure-preserving filter and deep learning, a structure preservation-based two-scale multimodal medical image fusion algorithm is proposed. First, we used a two-scale decomposition method to decompose source images into base layer components and detail layer components. Second, we adopted a fusion method based on the iterative joint bilateral filter to fuse the base layer components. Third, a convolutional neural network and local similarity of images are used to fuse the components of the detail layer. At the last, the final fused result is got by using two-scale image reconstruction. The contrast experiments display that our algorithm has better fusion results than the state-of-the-art medical image fusion algorithms.

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Multimodal Medical Image Fusion Techniques – A Review

Cited by 34 publications

References 0 publications

Plan quality in radiotherapy treatment planning – Review of the factors and challenges

Plan quality in radiotherapy treatment planning – Review of the factors and challenges

Multimodal medical image fusion algorithm based on pulse coupled neural networks and nonsubsampled contourlet transform

Two-Scale Multimodal Medical Image Fusion Based on Structure Preservation

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