Post-Acquisition Hyperpolarized <sup>29</sup>Silicon Magnetic Resonance Image Processing for Visualization of Colorectal Lesions Using a User-Friendly Graphical Interface
Medical imaging devices often use automated processing that creates and displays a self-normalized image. When improperly executed, normalization can misrepresent information or result in an inaccurate analysis. In the case of diagnostic imaging, a false positive in the absence of disease, or a nega...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/3/610 |
| _version_ | 1827649409483014144 |
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| author | Caitlin V. McCowan Duncan Salmon Jingzhe Hu Shivanand Pudakalakatti Nicholas Whiting Jennifer S. Davis Daniel D. Carson Niki M. Zacharias Pratip K. Bhattacharya Mary C. Farach-Carson |
| author_facet | Caitlin V. McCowan Duncan Salmon Jingzhe Hu Shivanand Pudakalakatti Nicholas Whiting Jennifer S. Davis Daniel D. Carson Niki M. Zacharias Pratip K. Bhattacharya Mary C. Farach-Carson |
| author_sort | Caitlin V. McCowan |
| collection | DOAJ |
| description | Medical imaging devices often use automated processing that creates and displays a self-normalized image. When improperly executed, normalization can misrepresent information or result in an inaccurate analysis. In the case of diagnostic imaging, a false positive in the absence of disease, or a negative finding when disease is present, can produce a detrimental experience for the patient and diminish their health prospects and prognosis. In many clinical settings, a medical technical specialist is trained to operate an imaging device without sufficient background information or understanding of the fundamental theory and processes involved in image creation and signal processing. Here, we describe a user-friendly image processing algorithm that mitigates user bias and allows for true signal to be distinguished from background. For proof-of-principle, we used antibody-targeted molecular imaging of colorectal cancer (CRC) in a mouse model, expressing human MUC1 at tumor sites. Lesion detection was performed using targeted magnetic resonance imaging (MRI) of hyperpolarized silicon particles. Resulting images containing high background and artifacts were then subjected to individualized image post-processing and comparative analysis. Post-acquisition image processing allowed for co-registration of the targeted silicon signal with the anatomical proton magnetic resonance (MR) image. This new methodology allows users to calibrate a set of images, acquired with MRI, and reliably locate CRC tumors in the lower gastrointestinal tract of living mice. The method is expected to be generally useful for distinguishing true signal from background for other cancer types, improving the reliability of diagnostic MRI. |
| first_indexed | 2024-03-09T19:57:20Z |
| format | Article |
| id | doaj.art-c8c6d7a51d4245e9baded9c6b6fa179d |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T19:57:20Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-c8c6d7a51d4245e9baded9c6b6fa179d2023-11-24T00:54:47ZengMDPI AGDiagnostics2075-44182022-03-0112361010.3390/diagnostics12030610Post-Acquisition Hyperpolarized <sup>29</sup>Silicon Magnetic Resonance Image Processing for Visualization of Colorectal Lesions Using a User-Friendly Graphical InterfaceCaitlin V. McCowan0Duncan Salmon1Jingzhe Hu2Shivanand Pudakalakatti3Nicholas Whiting4Jennifer S. Davis5Daniel D. Carson6Niki M. Zacharias7Pratip K. Bhattacharya8Mary C. Farach-Carson9Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USADepartment of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USADepartment of Bioengineering, Rice University, Houston, TX 77005, USADepartment of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USADepartment of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USADepartment of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USADepartment of BioSciences, Rice University, Houston, TX 77005, USADepartment of Urology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USADepartment of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USADepartment of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center, Houston, TX 77054, USAMedical imaging devices often use automated processing that creates and displays a self-normalized image. When improperly executed, normalization can misrepresent information or result in an inaccurate analysis. In the case of diagnostic imaging, a false positive in the absence of disease, or a negative finding when disease is present, can produce a detrimental experience for the patient and diminish their health prospects and prognosis. In many clinical settings, a medical technical specialist is trained to operate an imaging device without sufficient background information or understanding of the fundamental theory and processes involved in image creation and signal processing. Here, we describe a user-friendly image processing algorithm that mitigates user bias and allows for true signal to be distinguished from background. For proof-of-principle, we used antibody-targeted molecular imaging of colorectal cancer (CRC) in a mouse model, expressing human MUC1 at tumor sites. Lesion detection was performed using targeted magnetic resonance imaging (MRI) of hyperpolarized silicon particles. Resulting images containing high background and artifacts were then subjected to individualized image post-processing and comparative analysis. Post-acquisition image processing allowed for co-registration of the targeted silicon signal with the anatomical proton magnetic resonance (MR) image. This new methodology allows users to calibrate a set of images, acquired with MRI, and reliably locate CRC tumors in the lower gastrointestinal tract of living mice. The method is expected to be generally useful for distinguishing true signal from background for other cancer types, improving the reliability of diagnostic MRI.https://www.mdpi.com/2075-4418/12/3/610colorectal cancerdiagnostic imaginghyperpolarizationimage processingGUIMRI |
| spellingShingle | Caitlin V. McCowan Duncan Salmon Jingzhe Hu Shivanand Pudakalakatti Nicholas Whiting Jennifer S. Davis Daniel D. Carson Niki M. Zacharias Pratip K. Bhattacharya Mary C. Farach-Carson Post-Acquisition Hyperpolarized <sup>29</sup>Silicon Magnetic Resonance Image Processing for Visualization of Colorectal Lesions Using a User-Friendly Graphical Interface Diagnostics colorectal cancer diagnostic imaging hyperpolarization image processing GUI MRI |
| title | Post-Acquisition Hyperpolarized <sup>29</sup>Silicon Magnetic Resonance Image Processing for Visualization of Colorectal Lesions Using a User-Friendly Graphical Interface |
| title_full | Post-Acquisition Hyperpolarized <sup>29</sup>Silicon Magnetic Resonance Image Processing for Visualization of Colorectal Lesions Using a User-Friendly Graphical Interface |
| title_fullStr | Post-Acquisition Hyperpolarized <sup>29</sup>Silicon Magnetic Resonance Image Processing for Visualization of Colorectal Lesions Using a User-Friendly Graphical Interface |
| title_full_unstemmed | Post-Acquisition Hyperpolarized <sup>29</sup>Silicon Magnetic Resonance Image Processing for Visualization of Colorectal Lesions Using a User-Friendly Graphical Interface |
| title_short | Post-Acquisition Hyperpolarized <sup>29</sup>Silicon Magnetic Resonance Image Processing for Visualization of Colorectal Lesions Using a User-Friendly Graphical Interface |
| title_sort | post acquisition hyperpolarized sup 29 sup silicon magnetic resonance image processing for visualization of colorectal lesions using a user friendly graphical interface |
| topic | colorectal cancer diagnostic imaging hyperpolarization image processing GUI MRI |
| url | https://www.mdpi.com/2075-4418/12/3/610 |
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