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J. Imaging
Special Issues
Multimodal Imaging for Radiotherapy: Latest Advances and Challenges
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Multimodal Imaging for Radiotherapy: Latest Advances and Challenges

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 2177

Special Issue Editor

Dr. Arif Gulzar

E-Mail Website
Guest Editor
Center for Advanced Imaging, The University of Queensland, Brisbane, QLD 4072, Australia
Interests: novel radiothepeutic isotopes for PET imaging and therapy; multimodal imaging; targetted radiotherapy; upconversion luminescence

Special Issue Information

Dear Colleagues,

Nanoparticles have emerged as a promising tool for multimodal imaging in radiotherapy due to their ability to be engineered with multiple imaging modalities and therapeutic agents. Multimodal imaging is an approach that combines different imaging techniques to obtain a more complete and accurate representation of a patient's anatomy and pathology. In radiotherapy, multimodal imaging is used to precisely locate and target cancerous tissue, while minimizing exposure to healthy tissue. The following are some of the ways in which nanoparticles are used for multimodal imaging in radiotherapy:

  1. Computed tomography (CT): This modality is commonly used to obtain detailed images of a patient's anatomy, which can help in planning the radiotherapy treatment.
  2. Magnetic resonance imaging (MRI): MRI provides a high-resolution image of soft tissue structures, making it useful in the identification of tumors and other abnormalities.
  3. Positron emission tomography (PET): PET uses a radioactive tracer to detect metabolic activity in tissues. This can help in identifying areas of cancerous activity.
  4. Single-photon emission computed tomography (SPECT): SPECT is a nuclear medicine imaging technique that uses a radioactive tracer to produce 3D images of organs and tissues.
  5. Ultrasound: Ultrasound is commonly used to guide the placement of radiation therapy, as it can provide real-time images of the targeted area.

By combining these imaging modalities, radiation oncologists can obtain a more comprehensive understanding of a patient's anatomy and the extent of the cancerous tissue, allowing for a more precise and effective treatment plan. This approach can also help to reduce the risk of complications and side effects associated with radiotherapy, as healthy tissue is less likely to be affected.

Dr. Arif Gulzar
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • MRI
  • CT
  • PET
  • radiotherapy
  • isotopes

Published Papers (1 paper)

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Research

15 pages, 3172 KiB  
Article
MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor
by Farhana Khan, Shahnawaz Ayoub, Yonis Gulzar, Muneer Majid, Faheem Ahmad Reegu, Mohammad Shuaib Mir, Arjumand Bano Soomro and Osman Elwasila
J. Imaging 2023, 9(8), 163; https://doi.org/10.3390/jimaging9080163 - 16 Aug 2023
Cited by 16 | Viewed by 1466
Abstract
The diagnosis of brain tumors at an early stage is an exigent task for radiologists. Untreated patients rarely survive more than six months. It is a potential cause of mortality that can occur very quickly. Because of this, the early and effective diagnosis [...] Read more.
The diagnosis of brain tumors at an early stage is an exigent task for radiologists. Untreated patients rarely survive more than six months. It is a potential cause of mortality that can occur very quickly. Because of this, the early and effective diagnosis of brain tumors requires the use of an automated method. This study aims at the early detection of brain tumors using brain magnetic resonance imaging (MRI) data and efficient learning paradigms. In visual feature extraction, convolutional neural networks (CNN) have achieved significant breakthroughs. The study involves features extraction by deep convolutional layers for the efficient classification of brain tumor victims from the normal group. The deep convolutional neural network was implemented to extract features that represent the image more comprehensively for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classifications. In this paper, we experimented with five machine learnings (ML) to heighten the understanding and enhance the scope and significance of brain tumor classification. Further, we proposed an ensemble of three high-performing individual ML models, namely Extreme Gradient Boosting, Ada-Boost, and Random Forest (XG-Ada-RF), to derive binary class classification output for detecting brain tumors in images. The proposed voting classifier, along with convoluted features, produced results that showed the highest accuracy of 95.9% for tumor and 94.9% for normal. Compared to individual methods, the proposed ensemble approach demonstrated improved accuracy and outperformed the individual methods. Full article
(This article belongs to the Special Issue Multimodal Imaging for Radiotherapy: Latest Advances and Challenges)
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