2 research outputs found
Deep Residual Network for Off-Resonance Artifact Correction with Application to Pediatric Body Magnetic Resonance Angiography with 3D Cones
Purpose: Off-resonance artifact correction by deep-learning, to facilitate
rapid pediatric body imaging with a scan time efficient 3D cones trajectory.
Methods: A residual convolutional neural network to correct off-resonance
artifacts (Off-ResNet) was trained with a prospective study of 30 pediatric
magnetic resonance angiography exams. Each exam acquired a short-readout scan
(1.18 ms +- 0.38) and a long-readout scan (3.35 ms +- 0.74) at 3T.
Short-readout scans, with longer scan times but negligible off-resonance
blurring, were used as reference images and augmented with additional
off-resonance for supervised training examples. Long-readout scans, with
greater off-resonance artifacts but shorter scan time, were corrected by
autofocus and Off-ResNet and compared to short-readout scans by normalized
root-mean-square error (NRMSE), structural similarity index (SSIM), and peak
signal-to-noise ratio (PSNR). Scans were also compared by scoring on eight
anatomical features by two radiologists, using analysis of variance with
post-hoc Tukey's test. Reader agreement was determined with intraclass
correlation. Results: Long-readout scans were on average 59.3% shorter than
short-readout scans. Images from Off-ResNet had superior NRMSE, SSIM, and PSNR
compared to uncorrected images across +-1kHz off-resonance (P<0.01). The
proposed method had superior NRMSE over -677Hz to +1kHz and superior SSIM and
PSNR over +-1kHz compared to autofocus (P<0.01). Radiologic scoring
demonstrated that long-readout scans corrected with Off-ResNet were
non-inferior to short-readout scans (P<0.01). Conclusion: The proposed method
can correct off-resonance artifacts from rapid long-readout 3D cones scans to a
non-inferior image quality compared to diagnostically standard short-readout
scans
Prostate Segmentation from Ultrasound Images using Residual Fully Convolutional Network
Medical imaging based prostate cancer diagnosis procedure uses
intra-operative transrectal ultrasound (TRUS) imaging to visualize the prostate
shape and location to collect tissue samples. Correct tissue sampling from
prostate requires accurate prostate segmentation in TRUS images. To achieve
this, this study uses a novel residual connection based fully convolutional
network. The advantage of this segmentation technique is that it requires no
pre-processing of TRUS images to perform the segmentation. Thus, it offers a
faster and straightforward prostate segmentation from TRUS images. Results show
that the proposed technique can achieve around 86% Dice Similarity accuracy
using only few TRUS datasets.Comment: 6 pages, 4 figure