Deep Learning Enables Accurate Quantitative Volumetric Brain MRI with 2x
Faster Scan Times
Long Wang ​1 ​, Suzie Bash ​2 ​, Sara Dupont ​1 ​, Sebastian Magda ​3 ​, Chris Airriess ​3 ​, Enhao Gong ​1 ​, Greg Zaharchuk ​1,4 ​, Ajit Shankaranarayanan ​1 ​, and Tao Zhang
1
Medical Inc, Menlo Park, CA, United States, ​2 ​RadNet, Encino, CA, United States, ​3 ​CorTechs Labs Inc, San Diego, CA, United States, ​4
Stanford University, Stanford, CA, United States
1S
​ ubtle
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 ​±
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 ​± 0
​ .17 and 0.68 ​±
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.
Figures
Fig. 1. Examples of images from the standard scan, 2x faster scan, and DL-processed faster scan. Significantly improved image quality was
achieved by DL. Note that the perceived SNR of the DL-processed image is higher than the standard image.
Fig. 2. Segmentation results of standard scan and DL-accelerated scan of the same subject. High quality images were obtained by DL
despite the significantly shortened scan time, which can be visualized in all three orientations.
Fig. 3. Mean, standard deviation, and value of the paired -tests of HOC, hippocampal volume, SLV, and ILV in 32 subjects.
Fig. 4. Scatter plots (horizontal axis – standard scan, vertical axis – DL-processed scan) of (a) hippocampal occupancy scores, (b)
hippocampal volume, (c) superior lateral ventricle volume, and (d) inferior lateral ventricle volume.
Pt
Fig. 5. Bland-Altman plots of (a) hippocampal occupancy scores, (b) hippocampal volume, (c) superior lateral ventricle volume, and (d)
inferior lateral ventricle volume.