About this Research Topic
Image Reconstruction is becoming more and more important for biomedical imaging with methods exploiting improved optimization methods and machine learning and more powerful computer hardware. In tandem, this inverse problem setting is becoming increasingly challenging, due to a larger size of measured data, multi-sequence and/or multi-model data and applications in a high-noise context.
This Research Topic will provide recent research advances in rapid image reconstruction with primary focus on emission tomography. The topic is linked to the Positron Emission Tomography (PET) rapid image reconstruction challenge and the corresponding workshop which took place during the 2024 IEEE Nuclear Science Symposium & Medical Imaging Conference, Tampa, Florida, USA.
The primary aim of the Topic is to stimulate research into the design and validation of fast tomographic image reconstruction algorithms applicable to real world data. Recent developments in scanner hardware such as fast time of flight and long axial field of view PET scanners allow measuring significantly more data than previously, providing additional opportunities for extracting more information from one scan.
However, this makes image reconstruction significantly more computationally intensive, and its acceleration vital. Recent developments of new reconstruction algorithms, for instance using advanced optimization techniques and/or machine learning, provide exciting avenues that this Research Topic envisages to explore.
If they wish, the Research Topic participants are invited to access a sizeable set of phantom data acquired on a range of clinical scanners that were created as part of the PET rapid image reconstruction challenge (PETRIC): https://github.com/SyneRBI/PETRIC/wiki. They could also consider using the associated software if it is helpful. However, this is not a requirement, and researchers can submit their own research independent of PETRIC.
Furthermore, we welcome review articles, mini reviews, methods, perspectives, brief research reports, data reports, general commentary, as well as technology & code. These types of articles are described in the journal page: https://www.frontiersin.org/journals/nuclear-medicine/for-authors/article-types. The range of research topics is not limited to PET imaging, but we also invite SPECT, CT and MRI rapid reconstruction algorithms, or promising generic theoretical methods that have the potential to be translated to clinical practice.
In the spirit of open science, all relevant articles are encouraged to make their software publicly available under an open-source license.
Topic Editors Prof. Charalampos Tsoumpas and Prof. Kris Thielemans received financial support from GE Healthcare, Siemens Healthineers, Positrigo. The other Topic Editors report no competing interests related to this Research Topic.
This Research Topic will provide recent research advances in rapid image reconstruction with primary focus on emission tomography. The topic is linked to the Positron Emission Tomography (PET) rapid image reconstruction challenge and the corresponding workshop which took place during the 2024 IEEE Nuclear Science Symposium & Medical Imaging Conference, Tampa, Florida, USA.
The primary aim of the Topic is to stimulate research into the design and validation of fast tomographic image reconstruction algorithms applicable to real world data. Recent developments in scanner hardware such as fast time of flight and long axial field of view PET scanners allow measuring significantly more data than previously, providing additional opportunities for extracting more information from one scan.
However, this makes image reconstruction significantly more computationally intensive, and its acceleration vital. Recent developments of new reconstruction algorithms, for instance using advanced optimization techniques and/or machine learning, provide exciting avenues that this Research Topic envisages to explore.
If they wish, the Research Topic participants are invited to access a sizeable set of phantom data acquired on a range of clinical scanners that were created as part of the PET rapid image reconstruction challenge (PETRIC): https://github.com/SyneRBI/PETRIC/wiki. They could also consider using the associated software if it is helpful. However, this is not a requirement, and researchers can submit their own research independent of PETRIC.
Furthermore, we welcome review articles, mini reviews, methods, perspectives, brief research reports, data reports, general commentary, as well as technology & code. These types of articles are described in the journal page: https://www.frontiersin.org/journals/nuclear-medicine/for-authors/article-types. The range of research topics is not limited to PET imaging, but we also invite SPECT, CT and MRI rapid reconstruction algorithms, or promising generic theoretical methods that have the potential to be translated to clinical practice.
In the spirit of open science, all relevant articles are encouraged to make their software publicly available under an open-source license.
Topic Editors Prof. Charalampos Tsoumpas and Prof. Kris Thielemans received financial support from GE Healthcare, Siemens Healthineers, Positrigo. The other Topic Editors report no competing interests related to this Research Topic.
Keywords: Medical Imaging, Positron Emission Tomography, Artificial Intelligence, Data Processing
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
