Intensity standardization methods in magnetic resonance imaging of head and neck cancer

Wahid, Kareem A.; He, Renjie; McDonald, Brigid A.; Anderson, Brian; Salzillo, Travis C.; Mulder, Samuel L.; Wang, Jarey; Sharafi, Christina Setareh; McCoy, Lance; Naser, Mohamed A.; Ahmed, Sara; Sanders, Kevin E.; Mohamed, Abdallah S.R.; Ding, Yao; Wang, Jihong; Hutcheson, K.A.; Lai, Stephen Y.; Fuller, Clifton D.; Dijk, Lisanne V. van

doi:10.1016/j.phro.2021.11.001

Cited by 19 publications

(15 citation statements)

References 36 publications

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“…In addition, the indirect assessment of the microarchitecture of the salivary glands with texture analysis has been recently hypothesized to be a useful tool in the identification of the severity of RTinduced xerostomia [21,34,37,42]. Overall, predictive models employing texture analysis alongside imaging techniques have shown very promising results in the assessment of head and neck disease [17,18,34,43,44]. Therefore, interest towards artificial intelligence and its applications to imaging is steadily growing.…”

Section: Discussionmentioning

confidence: 99%

“…The possibility of using MRI tools with the help of artificial intelligence to differentiate functional gland tissue from adipose tissue [17,19,48] intuitively suggested the possible benefit of these advanced techniques in the characterization of the gland radiosensitivity, albeit at the cost of a more complex standardization. It is well known that the voxel intensity on CT images relates to the intrinsic physical properties of a tissue; on the contrary, the voxel intensity on MRI acquisition techniques is highly dependent on machine-specific characteristics [44]. This makes quantitative assessments with radiomics more prone to variation based on hardwarespecific settings [44,49,50] and standardization as a whole more difficult.…”

Section: Discussionmentioning

confidence: 99%

“…It is well known that the voxel intensity on CT images relates to the intrinsic physical properties of a tissue; on the contrary, the voxel intensity on MRI acquisition techniques is highly dependent on machine-specific characteristics [44]. This makes quantitative assessments with radiomics more prone to variation based on hardwarespecific settings [44,49,50] and standardization as a whole more difficult. However, this is only partially true for quantitative functional MRI analyses, as those carried out in the present study on ADC and DCE-PWI; the hardware specifics are less impactful [44,50], which suggests other possible benefits to their implementation on texture analysis.…”

Section: Discussionmentioning

confidence: 99%

“…This makes quantitative assessments with radiomics more prone to variation based on hardwarespecific settings [44,49,50] and standardization as a whole more difficult. However, this is only partially true for quantitative functional MRI analyses, as those carried out in the present study on ADC and DCE-PWI; the hardware specifics are less impactful [44,50], which suggests other possible benefits to their implementation on texture analysis.…”

Section: Discussionmentioning

confidence: 99%

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Morphological, Functional and Texture Analysis Magnetic Resonance Imaging Features in the Assessment of Radiotherapy-Induced Xerostomia in Oropharyngeal Cancer

et al. 2023

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The aim of this single-center, observational, retrospective study was to investigate magnetic resonance imaging (MRI) biomarkers for the assessment of radiotherapy (RT)-induced xerostomia. Twenty-seven patients who underwent radiation therapy for oropharyngeal cancer were divided into three groups according to the severity of their xerostomia—mild, moderate, and severe—clinically confirmed with the Common Terminology Criteria for Adverse Events (CTCAE). No severe xerostomia was found. Conventional and functional MRI (perfusion- and diffusion- weighted imaging) performed both pre- and post-RT were studied for signal intensity, mean apparent diffusion coefficient (ADC) values, k-trans, and area under the perfusion curves. Contrast-enhanced T1 images and ADC maps were imported into 3D slicer software, and salivary gland volumes were segmented. A total of 107 texture features were derived. T-Student and Wilcoxon signed-rank tests were performed on functional MRI parameters and texture analysis features to identify the differences between pre- and post-RT populations. A p-value < 0.01 was defined as acceptable. Receiver operating characteristic (ROC) curves were plotted for significant parameters to discriminate the severity of xerostomia in the pre-RT population. Conventional and functional MRI did not yield statistically significant results; on the contrary, five texture features showed significant variation between pre- and post-RT on the ADC maps, of which only informational measure of correlation 1 (IMC 1) was able to discriminate the severity of RT-induced xerostomia in the pre-RT population (area under the curve (AUC) > 0.7). Values lower than the cut-off of −1.473 × 10−11 were associated with moderate xerostomia, enabling the differentiation of mild xerostomia from moderate xerostomia with a 73% sensitivity, 75% specificity, and 75% diagnostic accuracy. Therefore, the texture feature IMC 1 on the ADC maps allowed the distinction between different degrees of severity of RT-induced xerostomia in the pre-RT population. Accordingly, texture analysis on ADC maps should be considered a useful tool to evaluate salivary gland radiosensitivity and help identify patients at risk of developing more serious xerostomia before radiation therapy is administered.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Morphological, Functional and Texture Analysis Magnetic Resonance Imaging Features in the Assessment of Radiotherapy-Induced Xerostomia in Oropharyngeal Cancer

et al. 2023

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“…In addition, ground-truth images were N4 bias field corrected to ensure a fair comparison with the N4 bias field corrected synthetic images; N4 bias field post-processing was performed in ADMIRE v. 3.42 (Elekta AB, Stockholm, Sweden). Additionally, both images were z-score normalized before metric evaluation to ensure that the analysis was independent of the scale of intensity values, as suggested in previous literature ( 48 ), therefore MSE and PSNR calculations can be interpreted as a normalized MSE and PSNR.…”

Section: Methodsmentioning

confidence: 99%

Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy

Wahid

El-Habashy

et al. 2022

Front. Oncol.

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BackgroundQuick magnetic resonance imaging (MRI) scans with low contrast-to-noise ratio are typically acquired for daily MRI-guided radiotherapy setup. However, for patients with head and neck (HN) cancer, these images are often insufficient for discriminating target volumes and organs at risk (OARs). In this study, we investigated a deep learning (DL) approach to generate high-quality synthetic images from low-quality images.MethodsWe used 108 unique HN image sets of paired 2-minute T2-weighted scans (2mMRI) and 6-minute T2-weighted scans (6mMRI). 90 image sets (~20,000 slices) were used to train a 2-dimensional generative adversarial DL model that utilized 2mMRI as input and 6mMRI as output. Eighteen image sets were used to test model performance. Similarity metrics, including the mean squared error (MSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) were calculated between normalized synthetic 6mMRI and ground-truth 6mMRI for all test cases. In addition, a previously trained OAR DL auto-segmentation model was used to segment the right parotid gland, left parotid gland, and mandible on all test case images. Dice similarity coefficients (DSC) were calculated between 2mMRI and either ground-truth 6mMRI or synthetic 6mMRI for each OAR; two one-sided t-tests were applied between the ground-truth and synthetic 6mMRI to determine equivalence. Finally, a visual Turing test using paired ground-truth and synthetic 6mMRI was performed using three clinician observers; the percentage of images that were correctly identified was compared to random chance using proportion equivalence tests.ResultsThe median similarity metrics across the whole images were 0.19, 0.93, and 33.14 for MSE, SSIM, and PSNR, respectively. The median of DSCs comparing ground-truth vs. synthetic 6mMRI auto-segmented OARs were 0.86 vs. 0.85, 0.84 vs. 0.84, and 0.82 vs. 0.85 for the right parotid gland, left parotid gland, and mandible, respectively (equivalence p<0.05 for all OARs). The percent of images correctly identified was equivalent to chance (p<0.05 for all observers).ConclusionsUsing 2mMRI inputs, we demonstrate that DL-generated synthetic 6mMRI outputs have high similarity to ground-truth 6mMRI, but further improvements can be made. Our study facilitates the clinical incorporation of synthetic MRI in MRI-guided radiotherapy.

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Compensation cycle consistent generative adversarial networks (Comp‐GAN) for synthetic CT generation from MR scans with truncated anatomy

Zhao

Wang

et al. 2023

Medical Physics

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Background MR scans used in radiotherapy can be partially truncated due to the limited field of view (FOV), affecting dose calculation accuracy in MR‐based radiation treatment planning. Purpose We proposed a novel Compensation‐cycleGAN (Comp‐cycleGAN) by modifying the cycle‐consistent generative adversarial network (cycleGAN), to simultaneously create synthetic CT (sCT) images and compensate the missing anatomy from the truncated MR images. Methods Computed tomography (CT) and T1 MR images with complete anatomy of 79 head‐and‐neck patients were used for this study. The original MR images were manually cropped 10–25 mm off at the posterior head to simulate clinically truncated MR images. Fifteen patients were randomly chosen for testing and the rest of the patients were used for model training and validation. Both the truncated and original MR images were used in the Comp‐cycleGAN training stage, which enables the model to compensate for the missing anatomy by learning the relationship between the truncation and known structures. After the model was trained, sCT images with complete anatomy can be generated by feeding only the truncated MR images into the model. In addition, the external body contours acquired from the CT images with full anatomy could be an optional input for the proposed method to leverage the additional information of the actual body shape for each test patient. The mean absolute error (MAE) of Hounsfield units (HU), peak signal‐to‐noise ratio (PSNR), and structural similarity index (SSIM) were calculated between sCT and real CT images to quantify the overall sCT performance. To further evaluate the shape accuracy, we generated the external body contours for sCT and original MR images with full anatomy. The Dice similarity coefficient (DSC) and mean surface distance (MSD) were calculated between the body contours of sCT and original MR images for the truncation region to assess the anatomy compensation accuracy. Results The average MAE, PSNR, and SSIM calculated over test patients were 93.1 HU/91.3 HU, 26.5 dB/27.4 dB, and 0.94/0.94 for the proposed Comp‐cycleGAN models trained without/with body‐contour information, respectively. These results were comparable with those obtained from the cycleGAN model which is trained and tested on full‐anatomy MR images, indicating the high quality of the sCT generated from truncated MR images by the proposed method. Within the truncated region, the mean DSC and MSD were 0.85/0.89 and 1.3/0.7 mm for the proposed Comp‐cycleGAN models trained without/with body contour information, demonstrating good performance in compensating the truncated anatomy. Conclusions We developed a novel Comp‐cycleGAN model that can effectively create sCT with complete anatomy compensation from truncated MR images, which could potentially benefit the MRI‐based treatment planning.

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Intensity standardization methods in magnetic resonance imaging of head and neck cancer

Cited by 19 publications

References 36 publications

Morphological, Functional and Texture Analysis Magnetic Resonance Imaging Features in the Assessment of Radiotherapy-Induced Xerostomia in Oropharyngeal Cancer

Morphological, Functional and Texture Analysis Magnetic Resonance Imaging Features in the Assessment of Radiotherapy-Induced Xerostomia in Oropharyngeal Cancer

Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy

Compensation cycle consistent generative adversarial networks (Comp‐GAN) for synthetic CT generation from MR scans with truncated anatomy

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