114 research outputs found
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Proton Therapy Verification with PET Imaging
Proton therapy is very sensitive to uncertainties introduced during treatment planning and dose delivery. PET imaging of proton induced positron emitter distributions is the only practical approach for in vivo, in situ verification of proton therapy. This article reviews the current status of proton therapy verification with PET imaging. The different data detecting systems (in-beam, in-room and off-line PET), calculation methods for the prediction of proton induced PET activity distributions, and approaches for data evaluation are discussed
Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images
Positron Emission Tomography (PET) is a functional imaging modality widely
used in neuroscience studies. To obtain meaningful quantitative results from
PET images, attenuation correction is necessary during image reconstruction.
For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance
(MR) images do not reflect attenuation coefficients directly. To address this
issue, we present deep neural network methods to derive the continuous
attenuation coefficients for brain PET imaging from MR images. With only Dixon
MR images as the network input, the existing U-net structure was adopted and
analysis using forty patient data sets shows it is superior than other Dixon
based methods. When both Dixon and zero echo time (ZTE) images are available,
we have proposed a modified U-net structure, named GroupU-net, to efficiently
make use of both Dixon and ZTE information through group convolution modules
when the network goes deeper. Quantitative analysis based on fourteen real
patient data sets demonstrates that both network approaches can perform better
than the standard methods, and the proposed network structure can further
reduce the PET quantification error compared to the U-net structure.Comment: 15 pages, 12 figure
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation
Unsupervised domain adaptation (UDA) has been a vital protocol for migrating
information learned from a labeled source domain to facilitate the
implementation in an unlabeled heterogeneous target domain. Although UDA is
typically jointly trained on data from both domains, accessing the labeled
source domain data is often restricted, due to concerns over patient data
privacy or intellectual property. To sidestep this, we propose "off-the-shelf
(OS)" UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor
trained in a source domain to a target domain, in the absence of source domain
data in adaptation. Toward this goal, we aim to develop a novel batch-wise
normalization (BN) statistics adaptation framework. In particular, we gradually
adapt the domain-specific low-order BN statistics, e.g., mean and variance,
through an exponential momentum decay strategy, while explicitly enforcing the
consistency of the domain shareable high-order BN statistics, e.g., scaling and
shifting factors, via our optimization objective. We also adaptively quantify
the channel-wise transferability to gauge the importance of each channel, via
both low-order statistics divergence and a scaling factor.~Furthermore, we
incorporate unsupervised self-entropy minimization into our framework to boost
performance alongside a novel queued, memory-consistent self-training strategy
to utilize the reliable pseudo label for stable and efficient unsupervised
adaptation. We evaluated our OSUDA-based framework on both cross-modality and
cross-subtype brain tumor segmentation and cardiac MR to CT segmentation tasks.
Our experimental results showed that our memory consistent OSUDA performs
better than existing source-relaxed UDA methods and yields similar performance
to UDA methods with source data.Comment: Published in Medical Image Analysis (extension of MICCAI paper
Myocardial Defect Detection Using PET-CT: Phantom Studies
It is expected that both noise and activity distribution can have impact on the detectability of a myocardial defect in a cardiac PET study. In this work, we performed phantom studies to investigate the detectability of a defect in the myocardium for different noise levels and activity distributions. We evaluated the performance of three reconstruction schemes: Filtered Back-Projection (FBP), Ordinary Poisson Ordered Subset Expectation Maximization (OP–OSEM), and Point Spread Function corrected OSEM (PSF–OSEM). We used the Channelized Hotelling Observer (CHO) for the task of myocardial defect detection. We found that the detectability of a myocardial defect is almost entirely dependent on the noise level and the contrast between the defect and its surroundings
Posterior Estimation for Dynamic PET imaging using Conditional Variational Inference
This work aims efficiently estimating the posterior distribution of kinetic
parameters for dynamic positron emission tomography (PET) imaging given a
measurement of time of activity curve. Considering the inherent information
loss from parametric imaging to measurement space with the forward kinetic
model, the inverse mapping is ambiguous. The conventional (but expensive)
solution can be the Markov Chain Monte Carlo (MCMC) sampling, which is known to
produce unbiased asymptotical estimation. We propose a deep-learning-based
framework for efficient posterior estimation. Specifically, we counteract the
information loss in the forward process by introducing latent variables. Then,
we use a conditional variational autoencoder (CVAE) and optimize its evidence
lower bound. The well-trained decoder is able to infer the posterior with a
given measurement and the sampled latent variables following a simple
multivariate Gaussian distribution. We validate our CVAE-based method using
unbiased MCMC as the reference for low-dimensional data (a single brain region)
with the simplified reference tissue model.Comment: Published on IEEE NSS&MI
Synthesizing audio from tongue motion during speech using tagged MRI via transformer
Investigating the relationship between internal tissue point motion of the
tongue and oropharyngeal muscle deformation measured from tagged MRI and
intelligible speech can aid in advancing speech motor control theories and
developing novel treatment methods for speech related-disorders. However,
elucidating the relationship between these two sources of information is
challenging, due in part to the disparity in data structure between
spatiotemporal motion fields (i.e., 4D motion fields) and one-dimensional audio
waveforms. In this work, we present an efficient encoder-decoder translation
network for exploring the predictive information inherent in 4D motion fields
via 2D spectrograms as a surrogate of the audio data. Specifically, our encoder
is based on 3D convolutional spatial modeling and transformer-based temporal
modeling. The extracted features are processed by an asymmetric 2D convolution
decoder to generate spectrograms that correspond to 4D motion fields.
Furthermore, we incorporate a generative adversarial training approach into our
framework to further improve synthesis quality on our generated spectrograms.
We experiment on 63 paired motion field sequences and speech waveforms,
demonstrating that our framework enables the generation of clear audio
waveforms from a sequence of motion fields. Thus, our framework has the
potential to improve our understanding of the relationship between these two
modalities and inform the development of treatments for speech disorders.Comment: SPIE Medical Imaging: Deep Dive Ora
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Time of flight PET reconstruction using nonuniform update for regional recovery uniformity
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147742/1/mp13321.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147742/2/mp13321_am.pd
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