21 research outputs found
Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning
The ability to synthesise Computed Tomography images - commonly known as
pseudo CT, or pCT - from MRI input data is commonly assessed using an
intensity-wise similarity, such as an L2-norm between the ground truth CT and
the pCT. However, given that the ultimate purpose is often to use the pCT as an
attenuation map (-map) in Positron Emission Tomography Magnetic Resonance
Imaging (PET/MRI), minimising the error between pCT and CT is not necessarily
optimal. The main objective should be to predict a pCT that, when used as
-map, reconstructs a pseudo PET (pPET) which is as close as possible to
the gold standard PET. To this end, we propose a novel multi-hypothesis deep
learning framework that generates pCTs by minimising a combination of the
pixel-wise error between pCT and CT and a proposed metric-loss that itself is
represented by a convolutional neural network (CNN) and aims to minimise
subsequent PET residuals. The model is trained on a database of 400 paired
MR/CT/PET image slices. Quantitative results show that the network generates
pCTs that seem less accurate when evaluating the Mean Absolute Error on the pCT
(69.68HU) compared to a baseline CNN (66.25HU), but lead to significant
improvement in the PET reconstruction - 115a.u. compared to baseline 140a.u.Comment: Aceppted at SASHIMI201
Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images
Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the
developing brain but is not suitable for anomaly screening. For this ultrasound
(US) is employed. While expert sonographers are adept at reading US images, MR
images are much easier for non-experts to interpret. Hence in this paper we
seek to produce images with MRI-like appearance directly from clinical US
images. Our own clinical motivation is to seek a way to communicate US findings
to patients or clinical professionals unfamiliar with US, but in medical image
analysis such a capability is potentially useful, for instance, for US-MRI
registration or fusion. Our model is self-supervised and end-to-end trainable.
Specifically, based on an assumption that the US and MRI data share a similar
anatomical latent space, we first utilise an extractor to determine shared
latent features, which are then used for data synthesis. Since paired data was
unavailable for our study (and rare in practice), we propose to enforce the
distributions to be similar instead of employing pixel-wise constraints, by
adversarial learning in both the image domain and latent space. Furthermore, we
propose an adversarial structural constraint to regularise the anatomical
structures between the two modalities during the synthesis. A cross-modal
attention scheme is proposed to leverage non-local spatial correlations. The
feasibility of the approach to produce realistic looking MR images is
demonstrated quantitatively and with a qualitative evaluation compared to real
fetal MR images.Comment: MICCAI-MLMI 201
Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging
Hybrid X-ray and magnetic resonance (MR) imaging promises large potential in interventional medical imaging applications due to the broad variety of contrast of MRI combined with fast imaging of X-ray-based modalities. To fully utilize the potential of the vast amount of existing image enhancement techniques, the corresponding information from both modalities must be present in the same domain. For image-guided interventional procedures, X-ray fluoroscopy has proven to be the modality of choice. Synthesizing one modality from another in this case is an ill-posed problem due to ambiguous signal and overlapping structures in projective geometry. To take on these challenges, we present a learning-based solution to MR to X-ray projection-to-projection translation. We propose an image generator network that focuses on high representation capacity in higher resolution layers to allow for accurate synthesis of fine details in the projection images. Additionally, a weighting scheme in the loss computation that favors high-frequency structures is proposed to focus on the important details and contours in projection imaging. The proposed extensions prove valuable in generating X-ray projection images with natural appearance. Our approach achieves a deviation from the ground truth of only 6% and structural similarity measure of 0.913 ± 0.005. In particular the high frequency weighting assists in generating projection images with sharp appearance and reduces erroneously synthesized fine details
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