Rapid head‐pose detection for automated slice prescription of fetal‐brain <scp>MRI</scp>

Hoffmann, M.; Türk, Esra Abacı; Gagoski, Borjan; Morgan, Leah; Wighton, Paul; Tisdall, M. Dylan; Reuter, Martin; Adalsteinsson, Elfar; Grant, P. Ellen; Wald, Lawrence L.; Kouwe, André van der

doi:10.1002/ima.22563

Cited by 13 publications

(7 citation statements)

References 63 publications

(92 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In line with recent work, 22 the eyes were successfully used as landmarks and were crucial for calculating the orientation of the fetal brain, however, the detection shown here did not include further pose estimation or image processing steps. While recent work employing AI to detect landmarks and organs on the MR acquisitions focused on 1.5T and 3T, 14,18,20,22,23,27 the present work was performed on low field fetal MRI at 0.55T, providing increasing challenges regarding SNR and resolution, but allowing to take key steps toward wider accessibility of fetal MRI.…”

Section: Discussionsupporting

confidence: 61%

“…The network and achieved complete automatic planning can, however, be extended to whole uterus ssTSE localiser scans in the future. Furthermore, while the landmarks were chosen as independently as possible from brain structures involved in common pathologies such as the ventricles and the corpus callosum-and in line with recent work 22 -the automatic planning in fetuses with developmental abnormalities in the location of the lower edge of the cerebellum or the orbits of the eyes might involve the need for additional manual adjustment. Finally, adding a fourth landmark in the back of the skull might be helpful to further stabilize the planning of the acquisition in the axial orientation.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Fully automated planning for anatomical fetal brain MRI on 0.55T

Neves Silva,

McElroy,

Aviles Verdera

et al. 2024

Magnetic Resonance in Med

View full text Add to dashboard Cite

PurposeWidening the availability of fetal MRI with fully automatic real‐time planning of radiological brain planes on 0.55T MRI.MethodsDeep learning‐based detection of key brain landmarks on a whole‐uterus echo planar imaging scan enables the subsequent fully automatic planning of the radiological single‐shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times, and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. A comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality, and the resulting image quality was conducted.ResultsProspective automatic planning was performed in real‐time without latency in all subjects. The landmark detection accuracy was 4.2 2.6 mm for the fetal eyes and 6.5 3.2 for the cerebellum, planning quality was 2.4/3 (compared to 2.6/3 for manual planning) and diagnostic image quality was 2.2 compared to 2.1 for manual planning.ConclusionsReal‐time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers.

show abstract

Section: Discussionsupporting

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Fully automated planning for anatomical fetal brain MRI on 0.55T

Neves Silva,

McElroy,

Aviles Verdera

et al. 2024

Magnetic Resonance in Med

View full text Add to dashboard Cite

show abstract

“…The proposed implementation can reconstruct a high-quality fetal brain volume in about a minute (Fig. 14), and potentially enables online reconstruction of fetal MRI during scans, which can be combined with online image quality assessment [43] and fetal brain tracking [44] to implement a fully automated pipeline for fetal MRI. Also, for an input dataset with 9 stacks (309 slices), the peak GPU memory usage of NeSVoR is only 832MB.…”

Section: Discussionmentioning

confidence: 99%

NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI

Moyer

Gagoski³

et al. 2023

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has shown promise in imaging of moving subjects, e.g., fetal MRI. However, existing slice-to-volume reconstruction methods are time-consuming, especially when a high-resolution volume is desired. Moreover, they are still vulnerable to severe subject motion and when image artifacts are present in acquired slices. In this work, we present NeSVoR, a resolution-agnostic slice-to-volume reconstruction method, which models the underlying volume as a continuous function of spatial coordinates with implicit neural representation. To improve robustness to subject motion and other image artifacts, we adopt a continuous and comprehensive slice acquisition model that takes into account rigid inter-slice motion, point spread function, and bias fields. NeSVoR also estimates pixel-wise and slicewise variances of image noise and enables removal of outliers during reconstruction and visualization of uncertainty. Extensive experiments are performed on both simulated and in vivo data to evaluate the proposed method. Results show that NeSVoR achieves state-of-the-art reconstruction quality while providing two to ten-fold acceleration in reconstruction times over the state-of-the-art algorithms.

show abstract

“…The emergence of physics‐informed DL methods will allow researchers to develop models that are privy to the underlying physical phenomena, potentially resulting in improved interpretability because the outputs can be evaluated using existing task‐specific knowledge 85–89 . Performing automated and intelligent slice planning for localizers is also an active area of research 90,91 …”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Developing and deploying deep learning models in brain magnetic resonance imaging: A review

et al. 2023

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools has resulted in a rapid increase of DL models and subsequent peer‐reviewed publications. However, the rate of deployment in clinical settings is low. Therefore, this review attempts to bring together the ideas from data collection to deployment in the clinic, building on the guidelines and principles that accreditation agencies have espoused. We introduce the need for and the role of DL to deliver accessible MRI. This is followed by a brief review of DL examples in the context of neuropathologies. Based on these studies and others, we collate the prerequisites to develop and deploy DL models for brain MRI. We then delve into the guiding principles to develop good machine learning practices in the context of neuroimaging, with a focus on explainability. A checklist based on the United States Food and Drug Administration's good machine learning practices is provided as a summary of these guidelines. Finally, we review the current challenges and future opportunities in DL for brain MRI.

show abstract

Rapid head‐pose detection for automated slice prescription of fetal‐brain MRI

Cited by 13 publications

References 63 publications

Fully automated planning for anatomical fetal brain MRI on 0.55T

Fully automated planning for anatomical fetal brain MRI on 0.55T

NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI

Developing and deploying deep learning models in brain magnetic resonance imaging: A review

Contact Info

Product

Resources

About