Simulation-Based Parameter Optimization for Fetal Brain MRI Super-Resolution Reconstruction

de Dumast, Priscille; Sanchez, Thomas; Lajous, Hélène; Bach Cuadra, Meritxell

doi:10.1007/978-3-031-43990-2_32

Cited by 2 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thanks to their ability to provide a flexible and controlled environment that facilitates accurate, robust, and reproducible research, computer simulations are widely used for MR developments to mitigate data scarcity and post-processing complexity [20][21][22][23][24][25][26] . In this context, we demonstrated that synthetic, yet realistic data can efficiently complement scarce clinical datasets, providing valuable support fot data-demanding deep learning (DL) models for fetal brain MRI tissue segmentation [26][27][28] , as well as the optimization of advanced reconstruction techniques 26,[29][30][31] . These exploratory studies were based on the first Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN) that simulates as closely as possible the FSE sequences used in clinical routine for fetal brain examination to generate realistic T2w images of the fetal brain throughout maturation from a variety of segmented HR anatomical images of healthy and pathological subjects 26 .…”

Section: Background and Summarymentioning

confidence: 99%

Maturation-informed synthetic Magnetic Resonance Images of the Developing Human Fetal Brain

Lajous,

Boeuf Fló,

Gordaliza

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Magnetic resonance imaging is a powerful modality to investigate abnormal developmental patterns in utero. However, since it is not the first-line diagnostic tool in this sensitive population, data remain scarce and heterogeneous between scanners and centers. In addressing the data scarcity issue while generating data representative of real fetal brain MRI, we proposed FaBiAN, a `Fetal Brain magnetic resonance Acquisition Numerical phantom. Here, we present a novel synthetic dataset of 594 two-dimensional, low-resolution series of T2-weighted images corresponding to 78 developing human fetal brains between 20.0 and 34.8 weeks of gestational age. Data are generated with substantive improvements from the original FaBiAN to account for local heterogeneities within white matter tissues throughout maturation. These synthetic-yet-highly-realistic images cover both healthy and pathological development trajectories simulated with standard clinical settings and anatomically informed by the Fetal Tissue Annotations (FeTA) dataset. Two independent radiologists qualitatively assessed the realism of the simulated images. We also quantitatively demonstrate the simulated data's increased fidelity to real data compared to the previous FaBiAN version. The reuse potential of the proposed dataset was also evaluated in the context of automated fetal brain tissue segmentation. Besides, our dataset that combines images generated from various clinical scenarios has been made publicly available to support the continuous endeavor of the community to develop advanced post-processing methods aswell as cutting-edge artificial intelligence models.

show abstract

Section: Background and Summarymentioning

confidence: 99%

Maturation-informed synthetic Magnetic Resonance Images of the Developing Human Fetal Brain

Lajous,

Boeuf Fló,

Gordaliza

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Assessing data quality on fetal brain MRI reconstruction: a multi-site and multi-rater study

Sanchez,

Mihailov,

Gomez

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Quality assessment (QA) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where unpredictable fetal motion can lead to substantial artifacts in the acquired images. Multiple images are then combined into a single volume through super-resolution reconstruction (SRR) pipelines, a step that can also introduce additional artifacts. While multiple studies designed automated quality control pipelines, no work evaluated the reproducibility of the manual quality ratings used to train these pipelines. In this work, our objective is twofold. First, we assess the inter- and intra-rater variability of the quality scoring performed by three experts on over 100 SRR images reconstructed using three different SRR pipelines. The raters were asked to assess the quality of images following 8 specific criteria like blurring or tissue contrast, providing a multi-dimensional view on image quality. We show that, using a protocol and training sessions, artifacts like bias field and blur level still have a low agreement (ICC below 0.5), while global quality scores show very high agreement (ICC = 0.9) across raters. We also observe that the SRR methods are influenced differently by factors like gestational age, input data quality and number of stacks used by reconstruction. Finally, our quality scores allow us to unveil systematic weaknesses of the different pipelines, indicating how further development could lead to more robust, well rounded SRR methods.

show abstract

Simulation-Based Parameter Optimization for Fetal Brain MRI Super-Resolution Reconstruction

Cited by 2 publications

References 21 publications

Maturation-informed synthetic Magnetic Resonance Images of the Developing Human Fetal Brain

Maturation-informed synthetic Magnetic Resonance Images of the Developing Human Fetal Brain

Assessing data quality on fetal brain MRI reconstruction: a multi-site and multi-rater study

Contact Info

Product

Resources

About