Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction

Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine. However, acquiring certain modalities, such as T2-weighted images (T2WIs), is time-consuming and prone to be with motion artifacts. It negatively impacts subsequent multi-modal image analysis. To address this issue, we propose an end-to-end deep learning framework that utilizes T1-weighted images (T1WIs) as auxiliary modalities to expedite T2WIs' acquisitions. While image pre-processing is capable of mitigating misalignment, improper parameter selection leads to adverse pre-processing effects, requiring iterative experimentation and adjustment. To overcome this shortage, we employ Optimal Transport (OT) to synthesize T2WIs by aligning T1WIs and performing cross-modal synthesis, effectively mitigating spatial misalignment effects. Furthermore, we adopt an alternating iteration framework between the reconstruction task and the cross-modal synthesis task to optimize the final results. Then, we prove that the reconstructed T2WIs and the synthetic T2WIs become closer on the T2 image manifold with iterations increasing, and further illustrate that the improved reconstruction result enhances the synthesis process, whereas the enhanced synthesis result improves the reconstruction process. Finally, experimental results from FastMRI and internal datasets confirm the effectiveness of our method, demonstrating significant improvements in image reconstruction quality even at low sampling rates

Enhancing the specificity and efficiency of polymerase chain reaction using polyethyleneimine-based derivatives and hybrid nanocomposites

There is a general necessity to improve the specificity and efficiency of the polymerase chain reaction (PCR), and exploring the PCR-enhancing mechanism still remains a great challenge. In this paper we report the use of branched polyethyleneimine (PEI)-based derivatives and hybrid nanocomposites as a novel class of enhancers to improve the specificity and efficiency of a nonspecific PCR system. We show that the surface-charge polarity of PEI and PEI derivatives plays a major role in their effectiveness to enhance the PCR. Positively charged amine-terminated pristine PEI, partially (50%) acetylated PEI (PEI-Ac50), and completely acetylated PEI (PEI-Ac) are able to improve PCR efficiency and specificity with an optimum concentration order of PEI < PEI-Ac50 < PEI-Ac, whereas negatively charged carboxyl-terminated PEI (PEI-SAH; SAH denotes succinamic acid groups) and neutralized PEI modified with both polyethylene glycol (PEG) and acetyl (Ac) groups (PEI-PEG-Ac) are unable to improve PCR specificity and efficiency even at concentrations three orders of magnitude higher than that of PEI. Our data clearly suggests that the PCR-enhancing effect is primarily based on the interaction between the PCR components and the PEI derivatives, where electrostatic interaction plays a major role in concentrating the PCR components locally on the backbones of the branched PEI. In addition, multiwalled carbon nanotubes modified with PEI and PEI-stabilized gold nanoparticles are also able to improve the PCR specificity and efficiency with an optimum PEI concentration less than that of the PEI alone, indicating that the inorganic component of the nanocomposites may help improve the interaction between PEI and the PCR components. The developed PEI-based derivatives or nanocomposites may be used as efficient additives to enhance other PCR systems for different biomedical applications

Hierarchical unbiased graph shrinkage (HUGS): A novel groupwise registration for large data set

Normalizing all images in a large data set into a common space is a key step in many clinical and research studies, e.g., for brain development, maturation, and aging. Recently, groupwise registration has been developed for simultaneous alignment of all images without selecting a particular image as template, thus potentially avoiding bias in the registration. However, most conventional groupwise registration methods do not explore the data distribution during the image registration. Thus, their performance could be affected by large inter-subject variations in the data set under registration. To solve this potential issue, we propose to use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images). Then, the procedure of warping all images to their population center turns to the dynamic shrinking of the graph nodes along their graph edges until all graph nodes become close to each other. Thus, the topology of image distribution on the image manifold is always preserved during the groupwise registration. More importantly, by modeling the distribution of all images via a graph, we can potentially reduce registration error since every time each image is warped only according to its nearby images with similar structures in the graph. We have evaluated our proposed groupwise registration method on both infant and adult data sets, by also comparing with the conventional group-mean based registration and the ABSORB methods. All experimental results show that our proposed method can achieve better performance in terms of registration accuracy and robustness

Semantic modeling of indoor scenes with support inference from a single photograph

We present an automatic approach for the semantic modeling of indoor scenes based on a single photograph, instead of relying on depth sensors. Without using handcrafted features, we guide indoor scene modeling with feature maps extracted by fully convolutional networks. Three parallel fully convolutional networks are adopted to generate object instance masks, a depth map, and an edge map of the room layout. Based on these high-level features, support relationships between indoor objects can be efficiently inferred in a data-driven manner. Constrained by the support context, a global-to-local model matching strategy is followed to retrieve the whole indoor scene. We demonstrate that the proposed method can efficiently retrieve indoor objects including situations where the objects are badly occluded. This approach enables efficient semantic-based scene editing

Smart Compaction for Infrastructure Materials

69A3551847103Compaction is a process of rearranging material particles by various mechanical loadings to densify the materials and form a stable pavement structure. Current methods to assess the compaction quality rely heavily on engineers' experiences or post-compaction methods at selected spots. The experience-based method is prone to cause compaction problems and pavement distresses, particularly when new materials are implemented. Due to the complicated interactions between the compactors and materials, the compaction mechanism of the particulate materials is still unclear. This gap hinders the improvement of compaction quality and the development of intelligent construction. This project was undertaken to investigate the compaction mechanism of the infrastructure material from the mesoscale (particle scale) and develop an innovative compaction monitoring method that determines the compaction condition based on particle kinematics. With the development of sensing technologies, wireless particle-size sensors have become available in research and industry for monitoring particle behaviors during compaction. A wireless sensor, SmartRock, was applied in the project and collected the mesoscale behaviors during compaction. Several lab and field compaction projects were carried out using asphalt mixtures and granular materials, various compaction machines, and pavement structures. It was found that internal particle kinematic behavior is closely correlated to material densification during compaction. The lab and field compaction can be reasonably connected by the particle rotation, and similar three-stage compaction patterns were identified. Three machine learning models were built to predict the compaction condition and the density of the asphalt pavement both in the lab and in the field. The reasonable predictions confirm that the machine learning algorithm is appropriate for compaction prediction. The density results from the pavement cores further verify the applicability and robustness of the intelligent model for compaction prediction. Future studies are still needed to evaluate the model's robustness based on more mixture varieties and field applications

Development of lower limb rehabilitation evaluation system based on virtual reality technology

Nowadays, with the development of the proportion of the elderly population in the world, several problems caused by the population aging gradually into people's horizons. One of the biggest problems plagued the vast majority of the elderly is hemiplegia, which leads to the vigorous development of the physical therapists. However, these traditional methods of physical therapy mainly rely on the skill of the physical therapists. In order to make up the defects of traditional methods, many research groups have developed different kinds of robots for lower limb rehabilitation training but most of them can only realize passive training which cannot adopt rehabilitation training based on the patients' individual condition effectively and they do not have a rehabilitation evaluation system to assess the real time training condition of the hemiplegic patients effectively. In order to solve the problems above, this paper proposed a lower limb rehabilitation evaluation system which is based on the virtual reality technology. This system has an easy observation of the human-computer interaction interface and the doctor is able to adjust the rehabilitation training direct at different patients in different rehabilitation stage based on this lower limb rehabilitation evaluation system. Compared with current techniques, this novel lower limb rehabilitation evaluation system is expected to have significant impacts in medical rehabilitation robot field

Dissipated Energy Concepts for HMA Performance: Fatigue and Healing

227 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.The uniqueness of the RDEC relationship allows it to be implemented into a pavement structural (thickness) design procedure. This procedure has the ability to utilize existing pavement response values with mixture properties to generate the PV-Nf relationship validated in this thesis. It also has the future capability of directly utilizing viscoelastic pavement responses when these structural models are integrated into a pavement analysis procedure.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Dissipated Energy Concepts for HMA Performance: Fatigue and Healing

227 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.The uniqueness of the RDEC relationship allows it to be implemented into a pavement structural (thickness) design procedure. This procedure has the ability to utilize existing pavement response values with mixture properties to generate the PV-Nf relationship validated in this thesis. It also has the future capability of directly utilizing viscoelastic pavement responses when these structural models are integrated into a pavement analysis procedure.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD