41 research outputs found
Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization
We present a reconstruction method involving maximum-likelihood expectation
maximization (MLEM) to model Poisson noise as applied to fluorescence molecular
tomography (FMT). MLEM is initialized with the output from a sparse
reconstruction-based approach, which performs truncated singular value
decomposition-based preconditioning followed by fast iterative
shrinkage-thresholding algorithm (FISTA) to enforce sparsity. The motivation
for this approach is that sparsity information could be accounted for within
the initialization, while MLEM would accurately model Poisson noise in the FMT
system. Simulation experiments show the proposed method significantly improves
images qualitatively and quantitatively. The method results in over 20 times
faster convergence compared to uniformly initialized MLEM and improves
robustness to noise compared to pure sparse reconstruction. We also
theoretically justify the ability of the proposed approach to reduce noise in
the background region compared to pure sparse reconstruction. Overall, these
results provide strong evidence to model Poisson noise in FMT reconstruction
and for application of the proposed reconstruction framework to FMT imaging
Incorporating reflection boundary conditions in the Neumann series radiative transport equation: Application to photon propagation and reconstruction in diffuse optical imaging
We propose a formalism to incorporate boundary conditions in a Neumann-series-based radiative transport equation. The formalism accurately models the reflection of photons at the tissue-external medium interface using Fresnel’s equations. The formalism was used to develop a gradient descent-based image reconstruction technique. The proposed methods were implemented for 3D diffuse optical imaging. In computational studies, it was observed that the average root-mean-square error (RMSE) for the output images and the estimated absorption coefficients reduced by 38% and 84%, respectively, when the reflection boundary conditions were incorporated. These results demonstrate the importance of incorporating boundary conditions that model the reflection of photons at the tissue-external medium interface
Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET
Artificial intelligence (AI)-based methods are showing substantial promise in
segmenting oncologic positron emission tomography (PET) images. For clinical
translation of these methods, assessing their performance on clinically
relevant tasks is important. However, these methods are typically evaluated
using metrics that may not correlate with the task performance. One such widely
used metric is the Dice score, a figure of merit that measures the spatial
overlap between the estimated segmentation and a reference standard (e.g.,
manual segmentation). In this work, we investigated whether evaluating AI-based
segmentation methods using Dice scores yields a similar interpretation as
evaluation on the clinical tasks of quantifying metabolic tumor volume (MTV)
and total lesion glycolysis (TLG) of primary tumor from PET images of patients
with non-small cell lung cancer. The investigation was conducted via a
retrospective analysis with the ECOG-ACRIN 6668/RTOG 0235 multi-center clinical
trial data. Specifically, we evaluated different structures of a commonly used
AI-based segmentation method using both Dice scores and the accuracy in
quantifying MTV/TLG. Our results show that evaluation using Dice scores can
lead to findings that are inconsistent with evaluation using the task-based
figure of merit. Thus, our study motivates the need for objective task-based
evaluation of AI-based segmentation methods for quantitative PET
DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT
There is an important need for methods to process myocardial perfusion
imaging (MPI) SPECT images acquired at lower radiation dose and/or acquisition
time such that the processed images improve observer performance on the
clinical task of detecting perfusion defects. To address this need, we build
upon concepts from model-observer theory and our understanding of the human
visual system to propose a Detection task-specific deep-learning-based approach
for denoising MPI SPECT images (DEMIST). The approach, while performing
denoising, is designed to preserve features that influence observer performance
on detection tasks. We objectively evaluated DEMIST on the task of detecting
perfusion defects using a retrospective study with anonymized clinical data in
patients who underwent MPI studies across two scanners (N = 338). The
evaluation was performed at low-dose levels of 6.25%, 12.5% and 25% and using
an anthropomorphic channelized Hotelling observer. Performance was quantified
using area under the receiver operating characteristics curve (AUC). Images
denoised with DEMIST yielded significantly higher AUC compared to corresponding
low-dose images and images denoised with a commonly used task-agnostic DL-based
denoising method. Similar results were observed with stratified analysis based
on patient sex and defect type. Additionally, DEMIST improved visual fidelity
of the low-dose images as quantified using root mean squared error and
structural similarity index metric. A mathematical analysis revealed that
DEMIST preserved features that assist in detection tasks while improving the
noise properties, resulting in improved observer performance. The results
provide strong evidence for further clinical evaluation of DEMIST to denoise
low-count images in MPI SPECT
Fully automated 3D segmentation of dopamine transporter SPECT images using an estimation-based approach
Quantitative measures of uptake in caudate, putamen, and globus pallidus in
dopamine transporter (DaT) brain SPECT have potential as biomarkers for the
severity of Parkinson disease. Reliable quantification of uptake requires
accurate segmentation of these regions. However, segmentation is challenging in
DaT SPECT due to partial-volume effects, system noise, physiological
variability, and the small size of these regions. To address these challenges,
we propose an estimation-based approach to segmentation. This approach
estimates the posterior mean of the fractional volume occupied by caudate,
putamen, and globus pallidus within each voxel of a 3D SPECT image. The
estimate is obtained by minimizing a cost function based on the binary
cross-entropy loss between the true and estimated fractional volumes over a
population of SPECT images, where the distribution of the true fractional
volumes is obtained from magnetic resonance images from clinical populations.
The proposed method accounts for both the sources of partial-volume effects in
SPECT, namely the limited system resolution and tissue-fraction effects. The
method was implemented using an encoder-decoder network and evaluated using
realistic clinically guided SPECT simulation studies, where the ground-truth
fractional volumes were known. The method significantly outperformed all other
considered segmentation methods and yielded accurate segmentation with dice
similarity coefficients of ~ 0.80 for all regions. The method was relatively
insensitive to changes in voxel size. Further, the method was relatively robust
up to +/- 10 degrees of patient head tilt along transaxial, sagittal, and
coronal planes. Overall, the results demonstrate the efficacy of the proposed
method to yield accurate fully automated segmentation of caudate, putamen, and
globus pallidus in 3D DaT-SPECT images
Classification of Targets Using Statistical Features from Range FFT of mmWave FMCW Radars
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