Brain Image Computation for Diagnosis and Treatment

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

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 3153

Special Issue Editor


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Guest Editor
Biomedical image analysis group, A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, FI-70211 Kuopio, Finland
Interests: brain image analysis; machine learning; neuroinformatics

Special Issue Information

Dear Colleagues,

As imaging is the only technique that can quantify the brain’s structure and function in living humans at the system level, the analysis of brain imaging data is uniquely placed to facilitate brain disease diagnosis, as well as treatment planning and follow-ups. Rapid advances in non-invasive neuroimaging methods have increased the possibilities for the use of imaging tools in the clinic and in the development of treatments. These advances can largely be attributed to the development of dedicated computational algorithms, which are used to extract quantitative information from images, aid in the diagnosis of various brain diseases, and make imaging-based predictions at the level of the individual.

This Special Issue of the Journal of Imaging aims to feature reports on recent advances in brain image computation that specifically aim to improve diagnoses and treatment. One particular area of focus is that of imaging-based personalized medicine, whereby novel computational methods can facilitate diagnostics and treatment planning.

Prof. Dr. Jussi Tohka
Guest Editor

Manuscript Submission Information

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Keywords

  • segmentation
  • registration
  • machine learning
  • neuroinformatics
  • imaging-based diagnostics
  • magnetic resonance imaging
  • positron emission tomography
  • image quantification
  • brain image analysis

Published Papers (1 paper)

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Research

16 pages, 4419 KiB  
Article
The Dose Optimization and Evaluation of Image Quality in the Adult Brain Protocols of Multi-Slice Computed Tomography: A Phantom Study
by Thawatchai Prabsattroo, Kanokpat Wachirasirikul, Prasit Tansangworn, Puengjai Punikhom and Waraporn Sudchai
J. Imaging 2023, 9(12), 264; https://doi.org/10.3390/jimaging9120264 - 28 Nov 2023
Cited by 1 | Viewed by 2651
Abstract
Computed tomography examinations have caused high radiation doses for patients, especially for CT scans of the brain. This study aimed to optimize the radiation dose and image quality in adult brain CT protocols. Images were acquired using a Catphan 700 phantom. Radiation doses [...] Read more.
Computed tomography examinations have caused high radiation doses for patients, especially for CT scans of the brain. This study aimed to optimize the radiation dose and image quality in adult brain CT protocols. Images were acquired using a Catphan 700 phantom. Radiation doses were recorded as CTDIvol and dose length product (DLP). CT brain protocols were optimized by varying parameters such as kVp, mAs, signal-to-noise ratio (SNR) level, and Clearview iterative reconstruction (IR). The image quality was also evaluated using AutoQA Plus v.1.8.7.0 software. CT number accuracy and linearity had a robust positive correlation with the linear attenuation coefficient (µ) and showed more inaccurate CT numbers when using 80 kVp. The modulation transfer function (MTF) showed a higher value in 100 and 120 kVp protocols (p < 0.001), while high-contrast spatial resolution showed a higher value in 80 and 100 kVp protocols (p < 0.001). Low-contrast detectability and the contrast-to-noise ratio (CNR) tended to increase when using high mAs, SNR, and the Clearview IR protocol. Noise decreased when using a high radiation dose and a high percentage of Clearview IR. CTDIvol and DLP were increased with increasing kVp, mAs, and SNR levels, while the increasing percentage of Clearview did not affect the radiation dose. Optimized protocols, including radiation dose and image quality, should be evaluated to preserve diagnostic capability. The recommended parameter settings include kVp set between 100 and 120 kVp, mAs ranging from 200 to 300 mAs, SNR level within the range of 0.7–1.0, and an iterative reconstruction value of 30% Clearview to 60% or higher. Full article
(This article belongs to the Special Issue Brain Image Computation for Diagnosis and Treatment)
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