Long Wang1, Suzie Bash2, Sara Dupont1, Sebastian Magda3, Chris Airriess3, Enhao Gong1, Greg Zaharchuk1,4, Ajit Shankaranarayanan1, and Tao Zhang1
1Subtle Medical Inc, Menlo Park, CA, United States, 2RadNet, Encino, CA, United States, 3CorTechs Labs Inc, San Diego, CA, United States, 4Stanford University, Stanford, CA, United States
Synopsis
3D T1-weighted MRI
is valuable for providing high resolution structural information and is
commonly used in brain MRI exams despite long scan times. The recent development of
deep learning (DL) has shown great potential for scan time reduction. However, the generalizability of DL methods is of concern
for actual clinical deployment. In this study, we apply FDA-cleared DL software
to accelerate 3D T1-weighted MRI scans by two folds and evaluate the
quantification accuracy using FDA-cleared image analysis software. This
study provides insight into the generalizability and accuracy of DL in clinical
settings.
Purpose
3D T1-weighted MRI
is valuable for providing high resolution structural information and is
commonly used in neuro MRI exams despite long scan times. The availability of commercially
available image processing software makes it possible to provide fast and accurate
brain image analyses (e.g., image segmentation) and use them as biomarkers for
various clinical indications [1-3]. The recent development of deep learning
(DL) has shown great potential for image acceleration and image analysis [4].
However, the generalizability of DL methods is of concern for actual clinical
deployment. In this study, we apply FDA-cleared DL software to accelerate 3D
T1-weighted MRI scans by two times and evaluate the quantification accuracy using
FDA-cleared image analysis software. This study provides insight into the generalizability
and accuracy of DL in clinical settings.Methods
With IRB approval
and patient consent, 32 subjects (age: 68$$$\pm$$$18 years; 19 male) undergoing
clinical MRI exams were recruited. The study cohort includes cognitively
normal, mild cognitive impairment, and Alzheimer’s disease subjects. Two
T1-weighted volumetric scans were acquired for each subject: one from routine
clinical protocol and the other with half the phase encodes and scan time. The
faster scans were enhanced post acquisition by FDA-cleared DL software SubtleMR
(Subtle Medical Inc, Menlo Park, CA). The paired datasets were collected on one
of five different 3T scanners (3 GE and 2 Siemens). Both the standard images
and DL-processed images were processed by FDA-cleared software NeuroQuant
(CorTechs Labs Inc, San Diego, CA) for quantitative analysis. Results from the
age related atrophy reports were compared. Hippocampal occupancy score (HOC), a
biomarker to predict the progression of neurodegenerative diseases, as well as
the volumes of hippocampi, superior lateral ventricles (SLV), and inferior
lateral ventricles (ILV), were analyzed using linear regression, two-sided
paired $$$t$$$-test and Bland-Altman analysis.Results
Example images from the
standard scan, accelerated scan, and DL-processed scan are shown in Fig. 1.
Excellent image quality (high SNR and image resolution) was obtained
by DL. Example results of brain segmentation of the standard scan and DL-accelerated
scan are shown in Fig. 2. Matching segmentation can be visualized. As shown in
Fig. 3, the average HOCs did not differ: 0.68$$$\pm$$$0.17 and 0.68$$$\pm$$$0.17 for the
standard scan and DL-accelerated scan, respectively. Paired $$$t$$$-tests also
suggested that there was no statistical difference of HOC, hippocampal volume,
and ILV (Fig. 3). The difference of SLV between two methods is approximately
2%. The scatter plots in Fig. 4 demonstrate strong correlation and linearity
between two measurements. The Bland-Altman plots in Fig. 5 further demonstrate
the strong agreement between two measurements.Discussion
This study has validated
the high quantification accuracy of DL-accelerated scans with 2x faster scan
times when compared with the standard longer scan. The range of HOC and strong
agreement between standard scan and DL-processed scan has demonstrated the
acceleration capability of DL in various neurogenerative disease conditions. Consistent
results from scans on diverse scanner types demonstrated the good
generalizability of the DL software. The inherent higher SNR from the DL
processing could potentially improve the robustness of brain segmentation and
will be the subject of future investigation.Conclusion
Deep learning can enable accurate quantitative volumetric brain MRI with 2x faster scan times.Acknowledgements
We would like to acknowledge the grant support of NIH R44EB027560.References
[1] McEvoy L, Brewer
J (2012). Biomarkers for the clinical evaluation of the cognitively impaired
elderly: amyloid is not enough. Imag. Med. 4:343-357.
[2] Heister D, et
al. (2011). Predicting MCI Outcome with Clinically Available MRI and CSF
Biomarkers. Neurology 77:1619-1628.
[3] Villemagne V,
et al. (2013). Longitudinal assessment of neuroimaging and clinical markers in
autosomal dominant Alzheimer’s disease: a prospective cohort study. Lancet
Neurol. 12:357-367.
[4] Zaharchuk G, et al. (2018). Deep learning in
neuroradiology. AJNR AM J Neuroradiol. 39:1776-1784.