Challenges in ensuring the generalizability of image quantitation methods for MRI

Keenan, Kathryn E.; Delfino, Jana G.; Jordanova, Kalina V.; Poorman, Megan E.; Chirra, Prathyush; Chaudhari, Akshay; Baeßler, Bettina; Winfield, Jessica M.; Viswanath, Satish; deSouza, Nandita M.

doi:10.1002/mp.15195

Cited by 22 publications

(10 citation statements)

References 153 publications

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“…Quantitative MRI and deep learning deviations from normal tissue values could be detected prior to the manifestation of visible morphological changes (Keenan et al, 2019(Keenan et al, , 2022. In particular, several studies have shown the potential of MR relaxometry (qMRI) in clinical applications (Cashmore et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…By measuring tissue properties, quantitative MRI has the possibility of complementing the morphological analysis currently performed by clinicians on conventional images with quantitative information from normal and abnormal tissue. In addition, it could also aid early diagnosis since deviations from normal tissue values could be detected prior to the manifestation of visible morphological changes (Keenan et al, 2019, 2022). In particular, several studies have shown the potential of MR relaxometry (qMRI) in clinical applications (Cashmore et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for quantitative MRI brain tumor analysis

Tampu

Haj‐Hosseini

Blystad

et al. 2023

Preprint

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The infiltrative nature of malignant gliomas results in active tumor spreading into the peritumoral edema, which is not visible in conventional magnetic resonance imaging (cMRI) even after contrast injection. MR relaxometry (qMRI) measures relaxation rates dependent on tissue properties, and can offer additional contrast mechanisms to highlight the non-enhancing infiltrative tumor. The aim of this study is to investigate if qMRI data provides additional information compared to cMRI sequences (T1w, T1wGd, T2w, FLAIR), when considering deep learning-based brain tumor (1) detection and (2) segmentation. A total of 23 patients with histologically confirmed malignant glioma were retrospectively included in the study. Quantitative MR imaging was used to obtain R1(1/T1), R2(1/T2) and proton density maps pre- and post-gadolinium contrast injection. Conventional MR imaging was also performed. A 2D CNN detection model and a 2D U-Net were trained on transversal slices (n=528) using either cMRI or a combination of qMRI pre- and post-contrast data for tumor detection and segmentation, respectively. Moreover, trends in quantitative R1and R2rates of regions identified as relevant for tumor detection by model explainability methods were qualitatively analyzed. Tumor detection and segmentation performance for models trained with a combination of qMRI pre- and post-contrast was the highest (detection MCC=0.72, segmentation Dice=0.90), however, improvements were not statistically significant compared to cMRI (detection MCC=0.67, segmentation Dice=0.90). The analysis of the relaxation rates of the relevant regions identified using model explainability methods showed no differences between models trained on cMRI or qMRI. Relevant regions which fell outside the annotation showed changes in relaxation rates after contrast injection similar to those within the annotation, when looking at majority of the individual cases. A similar trend could not be seen when looking at relaxation trends over all the dataset. In conclusion, models trained on qMRI data obtain similar performance to those trained on cMRI data, with the advantage of quantitatively measuring brain tissue properties within the scan time (11.8 minutes for qMRI with and without contrast, and 12.2 minutes for cMRI). Moreover, when considering individual patients, regions identified by model explainability methods as relevant for tumor detection outside the manual annotation of the tumor showed changes in quantitative relaxation rates after contrast injection similar to regions within the annotation, suggestive of infiltrative tumor in the peritumoral edema.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning for quantitative MRI brain tumor analysis

Tampu

Haj‐Hosseini

Blystad

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…By measuring tissue properties, quantitative MRI has the possibility of complementing the morphological analysis currently performed by clinicians on conventional images with quantitative information from normal and abnormal tissue. In addition, it could also aid early diagnosis since deviations from normal tissue values could be detected prior to the manifestation of visible morphological changes (Keenan et al 2019(Keenan et al , 2022. In particular, several studies have shown the potential of MR relaxometry (qMRI) in clinical applications (Cashmore et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based detection and identification of brain tumor biomarkers in quantitative MR-images

Tampu,

Haj-Hosseini,

Blystad

et al. 2023

Mach. Learn.: Sci. Technol.

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The infiltrative nature of malignant gliomas results in active tumor spreading into the peritumoral edema, which is not visible in conventional magnetic resonance imaging (cMRI) even after contrast injection. MR relaxometry (qMRI) measures relaxation rates dependent on tissue properties, and can offer additional contrast mechanisms to highlight the non-enhancing infiltrative tumor. To investigate if qMRI data provides additional information compared to cMRI sequences when considering deep learning-based brain tumor detection and segmentation, preoperative conventional (T1-w per- and post-contrast, T2-w and FLAIR) and quantitative (pre- and post-contrast R1, R2 and proton density) MR data was obtained from 23 patients with typical radiological findings suggestive of a high-grade malignant glioma. 2D deep learning models were trained on transversal slices (n=528) for tumor detection and segmentation using either conventional or quantitative data. Moreover, trends in quantitative R1 and R2 rates of regions identified as relevant for tumor detection by model explainability methods were qualitatively analyzed. Tumor detection and segmentation performance for models trained with a combination of qMRI pre- and post-contrast was the highest (detection MCC=0.72, segmentation DSC=0.90), however, the difference compared to cMRI was not statistically significant. Overall analysis of the relevant regions identified using model explainability showed no differences between models trained on cMRI or qMRI. When looking at the individual cases, relaxation rates of brain regions outside the annotation and identified as relevant for tumor detection exhibited changes after contrast injection similar to region inside the annotation in the majority of cases. In conclusion, models trained on qMRI data obtained similar detection and segmentation performance to those trained on cMRI data, with the advantage of quantitatively measuring brain tissue properties within similar scan time. When considering individual patients, the analysis of relaxation rates of regions identified by model explainability suggests the presence of infiltrative tumor outside the tumor cMRI-based annotation.

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“…MR thermometry as an alternative approach suffers from a coarse temperature resolution [22]. The advent of artificial intelligence (AI) and machine learning in MRI [23][24][25][26][27][28][29][30][31][32][33][34][35][36] has opened up new avenues for the prediction of various imaging characteristics, among them the recent prediction of local SAR in prostate imaging [37][38][39][40], as well as the prediction of temperature rise in the brain for 33 different tissue types [41].…”

Section: Introductionmentioning

confidence: 99%

MRSaiFE: An AI-Based Approach Towards the Real-Time Prediction of Specific Absorption Rate

Robb¹,

Kainz

Chaudhari³

et al. 2021

IEEE Access

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The purpose of this study is to investigate feasibility of estimating the specific absorption rate (SAR) in MRI in real time. To this goal, SAR maps are predicted from 3T-and 7T-simulated magnetic resonance (MR) images in 10 realistic human body models via a convolutional neural network. Twodimensional (2-D) U-Net architectures with varying contraction layers and different convolutional filters were designed to estimate the SAR distribution in realistic body models. Sim4Life (ZMT, Switzerland) was used to create simulated anatomical images and SAR maps at 3T and 7T imaging frequencies for Duke, Ella, Charlie, and Pregnant Women (at 3, 7, and 9 month gestational stages) body models. Mean squared error (MSE) was used as the cost function and the structural similarity index (SSIM) was reported. A 2-D U-Net with 4 contracting (and 4 expanding) layers and 64 convolutional filters at the initial stage showed the best compromise to estimate SAR distributions. Adam optimizer outperformed stochastic gradient descent (SGD) for all cases with an average SSIM of 90.5∓3.6 % and an average MSE of 0.7∓0.6% for head images at 7T, and an SSIM of >85.1∓6.2 % and an MSE of 0.4∓0.4% for 3T body imaging. Algorithms estimated the SAR maps for 224x224 slices under 30 ms. The proposed methodology shows promise to predict real-time SAR in clinical imaging settings without using extra mapping techniques or patient-specific calibrations.

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Challenges in ensuring the generalizability of image quantitation methods for MRI

Cited by 22 publications

References 153 publications

Deep learning for quantitative MRI brain tumor analysis

Deep learning for quantitative MRI brain tumor analysis

Deep learning-based detection and identification of brain tumor biomarkers in quantitative MR-images

MRSaiFE: An AI-Based Approach Towards the Real-Time Prediction of Specific Absorption Rate

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