A novel loss function to reproduce texture features for deep learning‐based MRI‐to‐CT synthesis

Yuan, Siqi; Liu, Yuxiang; Wei, Ran; Zhu, Ji; Men, Kuo; Dai, Jianrong

doi:10.1002/mp.16850

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…However, because of the often insufficient CT image contrast for soft tissues, several studies recommended the integration of complementary magnetic resonance (MR) modality. 4,33 This is important especially from the perspective of OAR contouring, 4,34,35 synthetic MR image generation for MR-aided RT, 36,37 synthetic CT image generation for MR-only RT [38][39][40][41] and MR-guided RT. 42,43 While some OARs can be accurately and reliably contoured in CT images (i.e., bone structures such as, e.g., the mandible), MR images are often used to better visualize soft tissues.…”

Section: Introductionmentioning

confidence: 99%

vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images

Podobnik,

Ibragimov,

Peterlin

et al. 2024

Medical Physics

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BackgroundAccurate and consistent contouring of organs‐at‐risk (OARs) from medical images is a key step of radiotherapy (RT) cancer treatment planning. Most contouring approaches rely on computed tomography (CT) images, but the integration of complementary magnetic resonance (MR) modality is highly recommended, especially from the perspective of OAR contouring, synthetic CT and MR image generation for MR‐only RT, and MR‐guided RT. Although MR has been recognized as valuable for contouring OARs in the head and neck (HaN) region, the accuracy and consistency of the resulting contours have not been yet objectively evaluated.PurposeTo analyze the interobserver and intermodality variability in contouring OARs in the HaN region, performed by observers with different level of experience from CT and MR images of the same patients.MethodsIn the final cohort of 27 CT and MR images of the same patients, contours of up to 31 OARs were obtained by a radiation oncology resident (junior observer, JO) and a board‐certified radiation oncologist (senior observer, SO). The resulting contours were then evaluated in terms of interobserver variability, characterized as the agreement among different observers (JO and SO) when contouring OARs in a selected modality (CT or MR), and intermodality variability, characterized as the agreement among different modalities (CT and MR) when OARs were contoured by a selected observer (JO or SO), both by the Dice coefficient (DC) and 95‐percentile Hausdorff distance (HD95).ResultsThe mean (±standard deviation) interobserver variability was 69.0 ± 20.2% and 5.1 ± 4.1 mm, while the mean intermodality variability was 61.6 ± 19.0% and 6.1 ± 4.3 mm in terms of DC and HD95, respectively, across all OARs. Statistically significant differences were only found for specific OARs. The performed MR to CT image registration resulted in a mean target registration error of 1.7 ± 0.5 mm, which was considered as valid for the analysis of intermodality variability.ConclusionsThe contouring variability was, in general, similar for both image modalities, and experience did not considerably affect the contouring performance. However, the results indicate that an OAR is difficult to contour regardless of whether it is contoured in the CT or MR image, and that observer experience may be an important factor for OARs that are deemed difficult to contour. Several of the differences in the resulting variability can be also attributed to adherence to guidelines, especially for OARs with poor visibility or without distinctive boundaries in either CT or MR images. Although considerable contouring differences were observed for specific OARs, it can be concluded that almost all OARs can be contoured with a similar degree of variability in either the CT or MR modality, which works in favor of MR images from the perspective of MR‐only and MR‐guided RT.

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Section: Introductionmentioning

confidence: 99%

vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images

Podobnik,

Ibragimov,

Peterlin

et al. 2024

Medical Physics

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T1‐contrast enhanced MRI generation from multi‐parametric MRI for glioma patients with latent tumor conditioning

Eidex,

Safari,

Qiu

et al. 2024

Medical Physics

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BackgroundGadolinium‐based contrast agents (GBCAs) are commonly used in MRI scans of patients with gliomas to enhance brain tumor characterization using T1‐weighted (T1W) MRI. However, there is growing concern about GBCA toxicity. This study develops a deep‐learning framework to generate T1‐postcontrast (T1C) from pre‐contrast multiparametric MRI.PurposeWe propose the tumor‐aware vision transformer (TA‐ViT) model that predicts high‐quality T1C images. The predicted tumor region is significantly improved (p < 0.001) by conditioning the transformer layers from predicted segmentation maps through the adaptive layer norm zero mechanism. The predicted segmentation maps were generated with the multi‐parametric residual (MPR) ViT model and transformed into a latent space to produce compressed, feature‐rich representations. The TA‐ViT model was applied to T1w and T2‐FLAIR to predict T1C MRI images of 501 glioma cases from an open‐source dataset. Selected patients were split into training (N = 400), validation (N = 50), and test (N = 51) sets. Model performance was evaluated with the peak‐signal‐to‐noise ratio (PSNR), normalized cross‐correlation (NCC), and normalized mean squared error (NMSE).ResultsBoth qualitative and quantitative results demonstrate that the TA‐ViT model performs superior against the benchmark MPR‐ViT model. Our method produces synthetic T1C MRI with high soft tissue contrast and more accurately synthesizes both the tumor and whole brain volumes. The synthesized T1C images achieved remarkable improvements in both tumor and healthy tissue regions compared to the MPR‐ViT model. For healthy tissue and tumor regions, the results were as follows: NMSE: 8.53 ± 4.61E‐4; PSNR: 31.2 ± 2.2; NCC: 0.908 ± 0.041 and NMSE: 1.22 ± 1.27E‐4, PSNR: 41.3 ± 4.7, and NCC: 0.879 ± 0.042, respectively.ConclusionThe proposed method generates synthetic T1C images that closely resemble real T1C images. Future development and application of this approach may enable contrast‐agent‐free MRI for brain tumor patients, eliminating the risk of GBCA toxicity and simplifying the MRI scan protocol.

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A novel loss function to reproduce texture features for deep learning‐based MRI‐to‐CT synthesis

Cited by 2 publications

References 39 publications

vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images

vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images

T1‐contrast enhanced MRI generation from multi‐parametric MRI for glioma patients with latent tumor conditioning

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