Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging

A motion assessment method for reference stack selection in fetal brain MRI reconstruction based on tensor rank approximation

Slice-to-volume registration and super-resolution reconstruction are commonly used to generate 3D volumes of the fetal brain from 2D stacks of slices acquired in multiple orientations. A critical initial step in this pipeline is to select one stack with the minimum motion among all input stacks as a reference for registration. An accurate and unbiased motion assessment (MA) is thus crucial for successful selection. Here, we presented an MA method that determines the minimum motion stack based on 3D low-rank approximation using CANDECOMP/PARAFAC (CP) decomposition. Compared to the current 2D singular value decomposition (SVD) based method that requires flattening stacks into matrices to obtain ranks, in which the spatial information is lost, the CP-based method can factorize 3D stack into low-rank and sparse components in a computationally efficient manner. The difference between the original stack and its low-rank approximation was proposed as the motion indicator. Experiments on linearly and randomly simulated motion illustrated that CP demonstrated higher sensitivity in detecting small motion with a lower baseline bias, and achieved a higher assessment accuracy of 95.45% in identifying the minimum motion stack, compared to the SVD-based method with 58.18%. CP also showed superior motion assessment capabilities in real-data evaluations. Additionally, combining CP with the existing SRR-SVR pipeline significantly improved 3D volume reconstruction. The results indicated that our proposed CP showed superior performance compared to SVD-based methods with higher sensitivity to motion, assessment accuracy, and lower baseline bias, and can be used as a prior step to improve fetal brain reconstruction.

Magnetic resonance imaging and tractography of sensorimotor tracts in fetuses with intraventricular hemorrhage: feasibility and added prognostic value

OBJECTIVES

To assess the feasibility, characteristics and prognostic value of prenatal visualization of the corticospinal tracts (CSTs) using diffusion-weighted magnetic resonance imaging (MRI)-based tractography in fetuses with intraventricular hemorrhage (IVH).

METHODS

This was a retrospective single-center cohort study of singleton fetuses diagnosed with IVH on MRI from January 2011 to December 2018. The left and right CSTs were reconstructed according to an in-utero diffusion tensor imaging sequence using a multi-region of interest (ROI) deterministic tractography approach. The CSTs were segmented by two polygonal ROI: at the level of the posterior limb of the internal capsule and the crus cerebri. The morphology and integrity of the CSTs were assessed visually. Internal capsule and crus cerebri apparent diffusion coefficient and fractional anisotropy values were measured. Postnatal motor function data were obtained from the parents using the functional status scale.

RESULTS

A total of 35 fetuses with IVH (mean ± SD gestational age, 29.1 ± 5.1 (range, 19.9-38.9) weeks) were included in the analysis. Parenchymal involvement on T2-weighted sequences was demonstrated in 19 (54%) of the cohort. CST involvement correlated significantly with the presence of parenchymal damage on T2-weighted imaging (P = 0.02). Among liveborn cases, the rate of motor impairment was 14% (1/7) in children with intact CSTs compared with 100% (5/5) in cases in which the CSTs were impaired (P = 0.015).

CONCLUSIONS

Fetal corticospinal tractography is feasible technically and offers valuable prognostic information. It enhances parental counseling by providing insights into potential motor outcome, underscoring its utility in complementing fetal neurosonography in cases of prenatal IVH. © 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Deep learning for accelerated and robust MRI reconstruction

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.

Fetal brain MRI atlases and datasets: A review

Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.

Real-time fetal brain tracking for functional fetal MRI

PURPOSE

To improve motion robustness of functional fetal MRI scans by developing an intrinsic real-time motion correction method. MRI provides an ideal tool to characterize fetal brain development and growth. It is, however, a relatively slow imaging technique and therefore extremely susceptible to subject motion, particularly in functional MRI experiments acquiring multiple Echo-Planar-Imaging-based repetitions, for example, diffusion MRI or blood-oxygen-level-dependency MRI.

METHODS

A 3D UNet was trained on 125 fetal datasets to track the fetal brain position in each repetition of the scan in real time. This tracking, inserted into a Gadgetron pipeline on a clinical scanner, allows updating the position of the field of view in a modified echo-planar imaging sequence. The method was evaluated in real-time in controlled-motion phantom experiments and ten fetal MR studies (17 + 4-34 + 3 gestational weeks) at 3T. The localization network was additionally tested retrospectively on 29 low-field (0.55T) datasets.

RESULTS

Our method achieved real-time fetal head tracking and prospective correction of the acquisition geometry. Localization performance achieved Dice scores of 84.4% and 82.3%, respectively for both the unseen 1.5T/3T and 0.55T fetal data, with values higher for cephalic fetuses and increasing with gestational age.

CONCLUSIONS

Our technique was able to follow the fetal brain even for fetuses under 18 weeks GA in real-time at 3T and was successfully applied "offline" to new cohorts on 0.55T. Next, it will be deployed to other modalities such as fetal diffusion MRI and to cohorts of pregnant participants diagnosed with pregnancy complications, for example, pre-eclampsia and congenital heart disease.

Fetal MRI: what's new? A short review

Fetal magnetic resonance imaging (fetal MRI) is usually performed as a second-level examination following routine ultrasound examination, generally exploiting morphological and diffusion MRI sequences. The objective of this review is to describe the novelties and new applications of fetal MRI, focusing on three main aspects: the new sequences with their applications, the transition from 1.5-T to 3-T magnetic field, and the new applications of artificial intelligence software. This review was carried out by consulting the MEDLINE references (PubMed) and including only peer-reviewed articles written in English. Among the most important novelties in fetal MRI, we find the intravoxel incoherent motion model which allow to discriminate the diffusion from the perfusion component in fetal and placenta tissues. The transition from 1.5-T to 3-T magnetic field allowed for higher quality images, thanks to the higher signal-to-noise ratio with a trade-off of more frequent artifacts. The application of motion-correction software makes it possible to overcome movement artifacts by obtaining higher quality images and to generate three-dimensional images useful in preoperative planning.Relevance statementThis review shows the latest developments offered by fetal MRI focusing on new sequences, transition from 1.5-T to 3-T magnetic field and the emerging role of AI software that are paving the way for new diagnostic strategies.Key points• Fetal magnetic resonance imaging (MRI) is a second-line imaging after ultrasound.• Diffusion-weighted imaging and intravoxel incoherent motion sequences provide quantitative biomarkers on fetal microstructure and perfusion.• 3-T MRI improves the detection of cerebral malformations.• 3-T MRI is useful for both body and nervous system indications.• Automatic MRI motion tracking overcomes fetal movement artifacts and improve fetal imaging.

Automatic Ventriculomegaly Detection in Fetal Brain MRI: A Step-by-Step Deep Learning Model for Novel 2D-3D Linear Measurements

In this study, we developed an automated workflow using a deep learning model (DL) to measure the lateral ventricle linearly in fetal brain MRI, which are subsequently classified into normal or ventriculomegaly, defined as a diameter wider than 10 mm at the level of the thalamus and choroid plexus. To accomplish this, we first trained a UNet-based deep learning model to segment the brain of a fetus into seven different tissue categories using a public dataset (FeTA 2022) consisting of fetal T2-weighted images. Then, an automatic workflow was developed to perform lateral ventricle measurement at the level of the thalamus and choroid plexus. The test dataset included 22 cases of normal and abnormal T2-weighted fetal brain MRIs. Measurements performed by our AI model were compared with manual measurements performed by a general radiologist and a neuroradiologist. The AI model correctly classified 95% of fetal brain MRI cases into normal or ventriculomegaly. It could measure the lateral ventricle diameter in 95% of cases with less than a 1.7 mm error. The average difference between measurements was 0.90 mm in AI vs. general radiologists and 0.82 mm in AI vs. neuroradiologists, which are comparable to the difference between the two radiologists, 0.51 mm. In addition, the AI model also enabled the researchers to create 3D-reconstructed images, which better represent real anatomy than 2D images. When a manual measurement is performed, it could also provide both the right and left ventricles in just one cut, instead of two. The measurement difference between the general radiologist and the algorithm (p = 0.9827), and between the neuroradiologist and the algorithm (p = 0.2378), was not statistically significant. In contrast, the difference between general radiologists vs. neuroradiologists was statistically significant (p = 0.0043). To the best of our knowledge, this is the first study that performs 2D linear measurement of ventriculomegaly with a 3D model based on an artificial intelligence approach. The paper presents a step-by-step approach for designing an AI model based on several radiological criteria. Overall, this study showed that AI can automatically calculate the lateral ventricle in fetal brain MRIs and accurately classify them as abnormal or normal.

Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging

Central nervous system abnormalities in fetuses are fairly common, happening in 0.1% to 0.2% of live births and in 3% to 6% of stillbirths. So initial detection and categorization of fetal Brain abnormalities are critical. Manually detecting and segmenting fetal brain magnetic resonance imaging (MRI) could be time-consuming, and susceptible to interpreter experience. Artificial intelligence (AI) algorithms and machine learning approaches have a high potential for assisting in the early detection of these problems, improving the diagnosis process and follow-up procedures. The use of AI and machine learning techniques in fetal brain MRI was the subject of this narrative review paper. Using AI, anatomic fetal brain MRI processing has investigated models to predict specific landmarks and segmentation automatically. All gestation age weeks (17-38 wk) and different AI models (mainly Convolutional Neural Network and U-Net) have been used. Some models' accuracy achieved 95% and more. AI could help preprocess and post-process fetal images and reconstruct images. Also, AI can be used for gestational age prediction (with one-week accuracy), fetal brain extraction, fetal brain segmentation, and placenta detection. Some fetal brain linear measurements, such as Cerebral and Bone Biparietal Diameter, have been suggested. Classification of brain pathology was studied using diagonal quadratic discriminates analysis, K-nearest neighbor, random forest, naive Bayes, and radial basis function neural network classifiers. Deep learning methods will become more powerful as more large-scale, labeled datasets become available. Having shared fetal brain MRI datasets is crucial because there aren not many fetal brain pictures available. Also, physicians should be aware of AI's function in fetal brain MRI, particularly neuroradiologists, general radiologists, and perinatologists.

Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Registration

Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb. Usual scanning techniques employ single-shot multi-slice sequences where anatomical information in different slices may be subject to different deformations, contrast variations or artifacts. Volumetric reconstruction formulations have been proposed to correct for these factors, but they must accommodate a non-homogeneous and non-isotropic sampling, so regularization becomes necessary. Thus, in this paper we propose a deep generative prior for robust volumetric reconstructions integrated with a diffeomorphic volume to slice registration method. Experiments are performed to validate our contributions and compare with ifdefined tmiformat R2.5a state of the art method methods in the literature in a cohort of 72 fetal datasets in the range of 20-36 weeks gestational age. Results suggest improved image resolution Quantitative as well as radiological assessment suggest improved image quality and more accurate prediction of gestational age at scan is obtained when comparing to a state of the art reconstruction method methods. In addition, gestational age prediction results from our volumetric reconstructions compare favourably are competitive with existing brain-based approaches, with boosted accuracy when integrating information of organs other than the brain. Namely, a mean absolute error of 0.618 weeks ( R²=0.958 ) is achieved when combining fetal brain and trunk information.

AFFIRM: Affinity Fusion-Based Framework for Iteratively Random Motion Correction of Multi-Slice Fetal Brain MRI

Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequential motion from multiple stacks of slices and integrates the features between 2D slices and reconstructed 3D volume using affinity fusion, which resembles the iterations between slice-to-volume registration and volumetric reconstruction in the regular pipeline. The method accurately estimates the motion regardless of brain orientations and outperforms other state-of-the-art learning-based methods on the simulated motion-corrupted data, with a 48.4% reduction of mean absolute error for rotation and 61.3% for displacement. We then incorporated AFFIRM into the multi-resolution slice-to-volume registration and tested it on the real-world fetal MRI scans at different gestation stages. The results indicated that adding AFFIRM to the conventional pipeline improved the success rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%.

SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI

Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task that is highly sensitive to the initialization of slice-to-volume transformations. We propose a novel slice-to-volume registration method using Transformers trained on synthetically transformed data, which model multiple stacks of MR slices as a sequence. With the attention mechanism, our model automatically detects the relevance between slices and predicts the transformation of one slice using information from other slices. We also estimate the underlying 3D volume to assist slice-to-volume registration and update the volume and transformations alternately to improve accuracy. Results on synthetic data show that our method achieves lower registration error and better reconstruction quality compared with existing state-of-the-art methods. Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.

Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T

PURPOSE

Fetal brain Magnetic Resonance Imaging suffers from unpredictable and unconstrained fetal motion that causes severe image artifacts even with half-Fourier single-shot fast spin echo (HASTE) readouts. This work presents the implementation of a closed-loop pipeline that automatically detects and reacquires HASTE images that were degraded by fetal motion without any human interaction.

METHODS

A convolutional neural network that performs automatic image quality assessment (IQA) was run on an external GPU-equipped computer that was connected to the internal network of the MRI scanner. The modified HASTE pulse sequence sent each image to the external computer, where the IQA convolutional neural network evaluated it, and then the IQA score was sent back to the sequence. At the end of the HASTE stack, the IQA scores from all the slices were sorted, and only slices with the lowest scores (corresponding to the slices with worst image quality) were reacquired.

RESULTS

The closed-loop HASTE acquisition framework was tested on 10 pregnant mothers, for a total of 73 acquisitions of our modified HASTE sequence. The IQA convolutional neural network, which was successfully employed by our modified sequence in real time, achieved an accuracy of 85.2% and area under the receiver operator characteristic of 0.899.

CONCLUSION

The proposed acquisition/reconstruction pipeline was shown to successfully identify and automatically reacquire only the motion degraded fetal brain HASTE slices in the prescribed stack. This minimizes the overall time spent on HASTE acquisitions by avoiding the need to repeat the entire stack if only few slices in the stack are motion-degraded.

Artificial intelligence applied to fetal MRI: A scoping review of current research

Artificial intelligence (AI) is defined as the development of computer systems to perform tasks normally requiring human intelligence. A subset of AI, known as machine learning (ML), takes this further by drawing inferences from patterns in data to 'learn' and 'adapt' without explicit instructions meaning that computer systems can 'evolve' and hopefully improve without necessarily requiring external human influences. The potential for this novel technology has resulted in great interest from the medical community regarding how it can be applied in healthcare. Within radiology, the focus has mostly been for applications in oncological imaging, although new roles in other subspecialty fields are slowly emerging.In this scoping review, we performed a literature search of the current state-of-the-art and emerging trends for the use of artificial intelligence as applied to fetal magnetic resonance imaging (MRI). Our search yielded several publications covering AI tools for anatomical organ segmentation, improved imaging sequences and aiding in diagnostic applications such as automated biometric fetal measurements and the detection of congenital and acquired abnormalities. We highlight our own perceived gaps in this literature and suggest future avenues for further research. It is our hope that the information presented highlights the varied ways and potential that novel digital technology could make an impact to future clinical practice with regards to fetal MRI.

Fetal Neuroimaging Updates

MR imaging is used in conjunction with ultrasound screening for fetal brain abnormalities because it offers better contrast, higher resolution, and has multiplanar capabilities that increase the accuracy and confidence of diagnosis. Fetal motion still severely limits the MR imaging sequences that can be acquired. We outline the current acquisition strategies for fetal brain MR imaging and discuss the near term advances that will improve its reliability. Prospective and retrospective motion correction aim to make the complement of MR neuroimaging modalities available for fetal diagnosis, improve the performance of existing modalities, and open new horizons to understanding in utero brain development.

Diffusion MRI Based Myometrium Tractography for Detection of Placenta Accreta Spectrum Disorder

BACKGROUND

Prenatal diagnosis of placenta accreta spectrum (PAS) disorders is difficult. Magnetic resonance imaging (MRI) has been shown to be a useful supplementary method to ultrasound.

PURPOSE

To investigate diffusion MRI (dMRI) based tractography as a tool for detecting PAS disorders, and to evaluate its performance compared with anatomical MRI.

STUDY TYPE

Prospective.

POPULATION

Forty-seven pregnant women in the third trimester with risk factors for PAS.

FIELD STRENGTH/SEQUENCE

Using fast imaging employing steady-state acquisition and high-angular resolution dMRI at 1.5 Tesla.

ASSESSMENT

Diagnosis of PAS was performed by three radiologists based on the dMRI-based feature of myometrial fiber discontinuity and on commonly used anatomical features including presence of dark band, discontinuous myometrium and bladder wall interruption. We evaluated the sensitivity, specificity, accuracy, and area-under-the-curve (AUC) of the individual features and established an integrated model with random forest analysis.

STATISTICAL TESTS

Maternal age and gestational age at scan were compared between PAS and control group using a t-test, and childbearing history was compared using a chi-squared test. The random forest model was employed to combine the anatomical and dMRI features with 5-fold cross-validation, and the weight of each feature was normalized to evaluate its importance in predicting PAS.

RESULTS

Based on surgical pathology reports, 16 out of 47 patients had confirmed PAS. The anatomical feature of dark bands and tractography marker achieved the highest AUC of 0.842 for predicting PAS, and the integrated anatomical and tractography features further improved the AUC of 0.880 with an accuracy of 87.2%. The tractography feature contributed most (30.1%) to the integrated model.

DATA CONCLUSION

Myometrial tractography demonstrated superior performance in detecting PAS. Moreover, the combination of dMRI-based tractography and anatomical MRI could potentially improve the diagnosis of PAS disorders in clinical practice.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 2.