3,285 research outputs found
Current and Future Trends in Magnetic Resonance Imaging Assessments of the Response of Breast Tumors to Neoadjuvant Chemotherapy
The current state-of-the-art assessment of treatment response in breast cancer is based on the response evaluation criteria in solid tumors (RECIST). RECIST reports on changes in gross morphology and divides response into one of four categories. In this paper we highlight how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) may be able to offer earlier, and more precise, information on treatment response in the neoadjuvant setting than RECIST. We then describe how longitudinal registration of breast images and the incorporation of intelligent bioinformatics approaches with imaging data have the potential to increase the sensitivity of assessing treatment response. We conclude with a discussion of the potential benefits of breast MRI at the higher field strength of 3T. For each of these areas, we provide a review, illustrative examples from clinical trials, and offer insights into future research directions
Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks
Quantitative assessment of the abdominal region from clinically acquired CT
scans requires the simultaneous segmentation of abdominal organs. Thanks to the
availability of high-performance computational resources, deep learning-based
methods have resulted in state-of-the-art performance for the segmentation of
3D abdominal CT scans. However, the complex characterization of organs with
fuzzy boundaries prevents the deep learning methods from accurately segmenting
these anatomical organs. Specifically, the voxels on the boundary of organs are
more vulnerable to misprediction due to the highly-varying intensity of
inter-organ boundaries. This paper investigates the possibility of improving
the abdominal image segmentation performance of the existing 3D encoder-decoder
networks by leveraging organ-boundary prediction as a complementary task. To
address the problem of abdominal multi-organ segmentation, we train the 3D
encoder-decoder network to simultaneously segment the abdominal organs and
their corresponding boundaries in CT scans via multi-task learning. The network
is trained end-to-end using a loss function that combines two task-specific
losses, i.e., complete organ segmentation loss and boundary prediction loss. We
explore two different network topologies based on the extent of weights shared
between the two tasks within a unified multi-task framework. To evaluate the
utilization of complementary boundary prediction task in improving the
abdominal multi-organ segmentation, we use three state-of-the-art
encoder-decoder networks: 3D UNet, 3D UNet++, and 3D Attention-UNet. The
effectiveness of utilizing the organs' boundary information for abdominal
multi-organ segmentation is evaluated on two publically available abdominal CT
datasets. A maximum relative improvement of 3.5% and 3.6% is observed in Mean
Dice Score for Pancreas-CT and BTCV datasets, respectively.Comment: 15 pages, 16 figures, journal pape
3-D lung deformation and function from respiratory-gated 4-D x-ray CT images : application to radiation treatment planning.
Many lung diseases or injuries can cause biomechanical or material property changes that can alter lung function. While the mechanical changes associated with the change of the material properties originate at a regional level, they remain largely asymptomatic and are invisible to global measures of lung function until they have advanced significantly and have aggregated. In the realm of external beam radiation therapy of patients suffering from lung cancer, determination of patterns of pre- and post-treatment motion, and measures of regional and global lung elasticity and function are clinically relevant. In this dissertation, we demonstrate that 4-D CT derived ventilation images, including mechanical strain, provide an accurate and physiologically relevant assessment of regional pulmonary function which may be incorporated into the treatment planning process. Our contributions are as follows: (i) A new volumetric deformable image registration technique based on 3-D optical flow (MOFID) has been designed and implemented which permits the possibility of enforcing physical constraints on the numerical solutions for computing motion field from respiratory-gated 4-D CT thoracic images. The proposed optical flow framework is an accurate motion model for the thoracic CT registration problem. (ii) A large displacement landmark-base elastic registration method has been devised for thoracic CT volumetric image sets containing large deformations or changes, as encountered for example in registration of pre-treatment and post-treatment images or multi-modality registration. (iii) Based on deformation maps from MOFIO, a novel framework for regional quantification of mechanical strain as an index of lung functionality has been formulated for measurement of regional pulmonary function. (iv) In a cohort consisting of seven patients with non-small cell lung cancer, validation of physiologic accuracy of the 4-0 CT derived quantitative images including Jacobian metric of ventilation, Vjac, and principal strains, (V?1, V?2, V?3, has been performed through correlation of the derived measures with SPECT ventilation and perfusion scans. The statistical correlations with SPECT have shown that the maximum principal strain pulmonary function map derived from MOFIO, outperforms all previously established ventilation metrics from 40-CT. It is hypothesized that use of CT -derived ventilation images in the treatment planning process will help predict and prevent pulmonary toxicity due to radiation treatment. It is also hypothesized that measures of regional and global lung elasticity and function obtained during the course of treatment may be used to adapt radiation treatment. Having objective methods with which to assess pre-treatment global and regional lung function and biomechanical properties, the radiation treatment dose can potentially be escalated to improve tumor response and local control
BeyondPixels: A Comprehensive Review of the Evolution of Neural Radiance Fields
Neural rendering combines ideas from classical computer graphics and machine
learning to synthesize images from real-world observations. NeRF, short for
Neural Radiance Fields, is a recent innovation that uses AI algorithms to
create 3D objects from 2D images. By leveraging an interpolation approach, NeRF
can produce new 3D reconstructed views of complicated scenes. Rather than
directly restoring the whole 3D scene geometry, NeRF generates a volumetric
representation called a ``radiance field,'' which is capable of creating color
and density for every point within the relevant 3D space. The broad appeal and
notoriety of NeRF make it imperative to examine the existing research on the
topic comprehensively. While previous surveys on 3D rendering have primarily
focused on traditional computer vision-based or deep learning-based approaches,
only a handful of them discuss the potential of NeRF. However, such surveys
have predominantly focused on NeRF's early contributions and have not explored
its full potential. NeRF is a relatively new technique continuously being
investigated for its capabilities and limitations. This survey reviews recent
advances in NeRF and categorizes them according to their architectural designs,
especially in the field of novel view synthesis.Comment: 22 page, 1 figure, 5 tabl
Neural Radiance Fields: Past, Present, and Future
The various aspects like modeling and interpreting 3D environments and
surroundings have enticed humans to progress their research in 3D Computer
Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall
et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in
Computer Graphics, Robotics, Computer Vision, and the possible scope of
High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D
models have gained traction from res with more than 1000 preprints related to
NeRFs published. This paper serves as a bridge for people starting to study
these fields by building on the basics of Mathematics, Geometry, Computer
Vision, and Computer Graphics to the difficulties encountered in Implicit
Representations at the intersection of all these disciplines. This survey
provides the history of rendering, Implicit Learning, and NeRFs, the
progression of research on NeRFs, and the potential applications and
implications of NeRFs in today's world. In doing so, this survey categorizes
all the NeRF-related research in terms of the datasets used, objective
functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation
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