6 research outputs found
Image synthesis of interictal SPECT from MRI and PET using machine learning
BackgroundCross-modality image estimation can be performed using generative adversarial networks (GANs). To date, SPECT image estimation from another medical imaging modality using this technique has not been considered. We evaluate the estimation of SPECT from MRI and PET, and additionally assess the necessity for cross-modality image registration for GAN training.MethodsWe estimated interictal SPECT from PET and MRI as a single-channel input, and as a multi-channel input to the GAN. We collected data from 48 individuals with epilepsy and converted them to 3D isotropic images for consistence across the modalities. Training and testing data were prepared in native and template spaces. The Pix2pix framework within the GAN network was adopted. We evaluated the addition of the structural similarity index metric to the loss function in the GAN implementation. Root-mean-square error, structural similarity index, and peak signal-to-noise ratio were used to assess how well SPECT images were able to be synthesised.ResultsHigh quality SPECT images could be synthesised in each case. On average, the use of native space images resulted in a 5.4% percentage improvement in SSIM than the use of images registered to template space. The addition of structural similarity index metric to the GAN loss function did not result in improved synthetic SPECT images. Using PET in either the single channel or dual channel implementation led to the best results, however MRI could produce SPECT images close in quality.ConclusionSynthesis of SPECT from MRI or PET can potentially reduce the number of scans needed for epilepsy patient evaluation and reduce patient exposure to radiation
Constrained CycleGAN for Effective Generation of Ultrasound Sector Images of Improved Spatial Resolution
Objective. A phased or a curvilinear array produces ultrasound (US) images
with a sector field of view (FOV), which inherently exhibits spatially-varying
image resolution with inferior quality in the far zone and towards the two
sides azimuthally. Sector US images with improved spatial resolutions are
favorable for accurate quantitative analysis of large and dynamic organs, such
as the heart. Therefore, this study aims to translate US images with
spatially-varying resolution to ones with less spatially-varying resolution.
CycleGAN has been a prominent choice for unpaired medical image translation;
however, it neither guarantees structural consistency nor preserves
backscattering patterns between input and generated images for unpaired US
images. Approach. To circumvent this limitation, we propose a constrained
CycleGAN (CCycleGAN), which directly performs US image generation with unpaired
images acquired by different ultrasound array probes. In addition to
conventional adversarial and cycle-consistency losses of CycleGAN, CCycleGAN
introduces an identical loss and a correlation coefficient loss based on
intrinsic US backscattered signal properties to constrain structural
consistency and backscattering patterns, respectively. Instead of
post-processed B-mode images, CCycleGAN uses envelope data directly obtained
from beamformed radio-frequency signals without any other non-linear
postprocessing. Main Results. In vitro phantom results demonstrate that
CCycleGAN successfully generates images with improved spatial resolution as
well as higher peak signal-to-noise ratio (PSNR) and structural similarity
(SSIM) compared with benchmarks. Significance. CCycleGAN-generated US images of
the in vivo human beating heart further facilitate higher quality heart wall
motion estimation than benchmarks-generated ones, particularly in deep regions
Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis
Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the
developing brain but is not suitable for second-trimester anomaly screening,
for which ultrasound (US) is employed. Although expert sonographers are adept
at reading US images, MR images which closely resemble anatomical images are
much easier for non-experts to interpret. Thus in this paper we propose to
generate MR-like images directly from clinical US images. In medical image
analysis such a capability is potentially useful as well, for instance for
automatic US-MRI registration and fusion. The proposed model is end-to-end
trainable and self-supervised without any external annotations. Specifically,
based on an assumption that the US and MRI data share a similar anatomical
latent space, we first utilise a network to extract the shared latent features,
which are then used for MRI synthesis. Since paired data is unavailable for our
study (and rare in practice), pixel-level constraints are infeasible to apply.
We instead propose to enforce the distributions to be statistically
indistinguishable, by adversarial learning in both the image domain and feature
space. To regularise the anatomical structures between US and MRI during
synthesis, we further propose an adversarial structural constraint. A new
cross-modal attention technique is proposed to utilise non-local spatial
information, by encouraging multi-modal knowledge fusion and propagation. We
extend the approach to consider the case where 3D auxiliary information (e.g.,
3D neighbours and a 3D location index) from volumetric data is also available,
and show that this improves image synthesis. The proposed approach is evaluated
quantitatively and qualitatively with comparison to real fetal MR images and
other approaches to synthesis, demonstrating its feasibility of synthesising
realistic MR images.Comment: IEEE Transactions on Medical Imaging 202
Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors
Black-blood (BB) imaging is used to complement contrast-enhanced 3D gradient-echo (CE 3D-GRE) imaging for detecting brain metastases, requiring additional scan time. In this study, we proposed deep-learned 3D BB imaging with an auto-labelling technique and 3D convolutional neural networks for brain metastases detection without additional BB scan. Patients were randomly selected for training (29 sets) and testing (36 sets). Two neuroradiologists independently evaluated deep-learned and original BB images, assessing the degree of blood vessel suppression and lesion conspicuity. Vessel signals were effectively suppressed in all patients. The figure of merits, which indicate the diagnostic performance of radiologists, were 0.9708 with deep-learned BB and 0.9437 with original BB imaging, suggesting that the deep-learned BB imaging is highly comparable to the original BB imaging (difference was not significant; pā=ā0.2142). In per patient analysis, sensitivities were 100% for both deep-learned and original BB imaging; however, the original BB imaging indicated false positive results for two patients. In per lesion analysis, sensitivities were 90.3% for deep-learned and 100% for original BB images. There were eight false positive lesions on the original BB imaging but only one on the deep-learned BB imaging. Deep-learned 3D BB imaging can be effective for brain metastases detection.ope
Image-Guided Interventions Using Cone-Beam CT: Improving Image Quality with Motion Compensation and Task-Based Modeling
Cone-beam CT (CBCT) is an increasingly important modality for intraoperative 3D imaging in interventional radiology (IR). However, CBCT exhibits several factors that diminish image quality ā notably, the major challenges of patient motion and detectability of low-contrast structures ā which motivate the work undertaken in this thesis.
A 3Dā2D registration method is presented to compensate for rigid patient motion. The method is fiducial-free, works naturally within standard clinical workflow, and is applicable to image-guided interventions in locally rigid anatomy, such as the head and pelvis. A second method is presented to address the challenge of deformable motion, presenting a 3D autofocus concept that is purely image-based and does not require additional fiducials, tracking hardware, or prior images. The proposed method is intended to improve interventional CBCT in scenarios where patient motion may not be sufficiently managed by immobilization and breath-hold, such as the prostate, liver, and lungs.
Furthermore, the work aims to improve the detectability of low-contrast structures by computing sourceādetector trajectories that are optimal to a particular imaging task. The approach is applicable to CBCT systems with the capability for general sourceādetector positioning, as with a robotic C-arm. A ātask-drivenā analytical framework is introduced, various objective functions and optimization methods are described, and the method is investigated via simulation and phantom experiments and translated to task-driven sourceādetector trajectories on a clinical robotic C-arm to demonstrate the potential for improved image quality in intraoperative CBCT.
Overall, the work demonstrates how novel optimization-based imaging techniques can address major challenges to CBCT image quality