TrimLLaMA: Progressive Layer Dropping for Domain-SpecificLLMs
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Computer science↗
Engineering topics
Hu, L
Publications and source records attributed to Hu, L.
SU-C-BRD-07: Three-Dimensional Dose Reconstruction in the Presence of Inhomogeneities Using Fast EPID-Based Back-Projection Method
Purpose: Three-dimensional dose verification can detect errors introduced by the treatment planning system (TPS) or differences between planned and delivered dose distribution during the treatment. The aim of the study is to extend a previous in-house developed three-dimensional dose reconstructed model in homogeneous phantom to situtions in which tissue inhomogeneities are present. Methods: The method was based on the portal grey images from an electronic portal imaging device (EPID) and the relationship between beamlets and grey-scoring voxels at the position of the EPID. The relationship was expressed in the form of grey response matrix that was quantified using thickness-dependence scatter kernels determined by series of experiments. From the portal grey-value distribution information measured by the EPID the two-dimensional incident fluence distribution was reconstructed based on the grey response matrix using a fast iterative algorithm. The accuracy of this approach was verified using a four-field intensity-modulated radiotherapy (IMRT) plan for the treatment of lung cancer in anthopomorphic phantom. Each field had between twenty and twenty-eight segments and was evaluated by comparing the reconstructed dose distribution with the measured dose. Results: The gamma-evaluation method was used with various evaluation criteria of dose difference and distance-to-agreement: 3%/3mm and 2%/2 mm. The dose comparison for all irradiated fields showed a pass rate of 100% with the criterion of 3%/3mm, and a pass rate of higher than 92% with the criterion of 2%/2mm. Conclusion: Our experimental results demonstrate that our method is capable of accurately reconstructing three-dimensional dose distribution in the presence of inhomogeneities. Using the method, the combined planning and treatment delivery process is verified, offing an easy-to-use tool for the verification of complex treatments.
60 APPLIED LIFE SCIENCES↗
SU-E-J-191: Motion Prediction Using Extreme Learning Machine in Image Guided Radiotherapy
Purpose: Real-time motion tracking is a critical issue in image guided radiotherapy due to the time latency caused by image processing and system response. It is of great necessity to fast and accurately predict the future position of the respiratory motion and the tumor location. Methods: The prediction of respiratory position was done based on the positioning and tracking module in ARTS-IGRT system which was developed by FDS Team (www.fds.org.cn). An approach involving with the extreme learning machine (ELM) was adopted to predict the future respiratory position as well as the tumor’s location by training the past trajectories. For the training process, a feed-forward neural network with one single hidden layer was used for the learning. First, the number of hidden nodes was figured out for the single layered feed forward network (SLFN). Then the input weights and hidden layer biases of the SLFN were randomly assigned to calculate the hidden neuron output matrix. Finally, the predicted movement were obtained by applying the output weights and compared with the actual movement. Breathing movement acquired from the external infrared markers was used to test the prediction accuracy. And the implanted marker movement for the prostate cancer was used to test the implementation of the tumor motion prediction. Results: The accuracy of the predicted motion and the actual motion was tested. Five volunteers with different breathing patterns were tested. The average prediction time was 0.281s. And the standard deviation of prediction accuracy was 0.002 for the respiratory motion and 0.001 for the tumor motion. Conclusion: The extreme learning machine method can provide an accurate and fast prediction of the respiratory motion and the tumor location and therefore can meet the requirements of real-time tumor-tracking in image guided radiotherapy.
60 APPLIED LIFE SCIENCES↗
WE-AB-BRA-11: Development of Physiological-Based Virtual Thorax Phantoms for Evaluation of Deformable Image Registration
Purpose: To develop a methodology of constructing physiological-based virtual thorax phantom based on hyperpolarized (HP) gas tagging MRI for evaluating deformable image registration (DIR). Methods: Three healthy subjects were imaged at both the end-of-inhalation (EOI) and the end-of-exhalation (EOE) phases using a high-resolution (2.5mm isovoxel) 3D proton MRI, as well as a hybrid MRI which combines HP gas tagging MRI and a low-resolution (4.5mm isovoxel) proton MRI. A sparse tagging displacement vector field (tDVF) was derived from the HP gas tagging MRI by tracking the displacement of tagging grids between EOI and EOE. Using the tDVF and the high-resolution MR images, we determined the motion model of the entire thorax in the following two steps: 1) the DVF inside of lungs was estimated based on the sparse tDVF using a novel multi-step natural neighbor interpolation method; 2) the DVF outside of lungs was estimated from the DIR between the EOI and EOE images (Velocity AI). The derived motion model was then applied to the high-resolution EOI image to create a deformed EOE image, forming the virtual phantom where the motion model provides the ground truth of deformation. Five DIR methods were evaluated using the developed virtual phantom. Errors in DVF magnitude (Em) and angle (Ea) were determined and compared for each DIR method. Results: Among the five DIR methods, free form deformation produced DVF results that are most closely resembling the ground truth (Em=1.04mm, Ea=6.63°). The two DIR methods based on B-spline produced comparable results (Em=2.04mm, Ea=13.66°; and Em =2.62mm, Ea=17.67°), and the two optical-flow methods produced least accurate results (Em=7.8mm; Ea=53.04°; Em=4.45mm, Ea=31.02°). Conclusion: A methodology for constructing physiological-based virtual thorax phantom based on HP gas tagging MRI has been developed. Initial evaluation demonstrated its potential as an effective tool for robust evaluation of DIR in the lung.
60 APPLIED LIFE SCIENCES↗
WE-AB-204-04: Feature Selection and Clustering Optimization for Pseudo-CT Generation in MR-Based Attenuation Correction and Radiation Therapy Planning
Purpose: Accurate and robust photon attenuation derived from MR is essential for PET/MR and MR-based radiation treatment planning applications. Although the fuzzy C-means (FCM) algorithm has been applied for pseudo-CT generation, the input feature combination and the number of clusters have not been optimized. This study aims to optimize both for clinically practical pseudo-CT generation. Methods: Nine volunteers were recruited. A 190-second, single-acquisition UTE-mDixon with 25% (angular) sampling and 3D radial readout was performed to acquire three primitive MR features at TEs of 0.1, 1.5, and 2.8 ms: the free-induction-decay (FID), the first and the second echo images. Three derived images, Dixon-fat and Dixon-water generated by two-point Dixon water/fat separation, and R2* (1/T2*) map, were also created. To identify informative inputs for generating a pseudo-CT image volume, all 63 combinations, choosing one to six of the feature images, were used as inputs to FCM for pseudo-CT generation. Further, the number of clusters was varied from four to seven to find the optimal approach. Mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R) of different combinations were compared for feature selection. Results: Among the 63 feature combinations, the four that resulted in the best MAPD and R were further compared along with the set containing all six features. The results suggested that R2* and Dixon-water are the most informative features. Further, including FID also improved the performance of pseudo-CT generation. Consequently, the set containing FID, Dixon-water, and R2* resulted in the most accurate, robust pseudo-CT when the number of cluster equals to five (5C). The clusters were interpreted as air, fat, bone, brain, and fluid. The six-cluster Result additionally included bone marrow. Conclusion: The results suggested that FID, Dixon-water, R2* are the most important features. The findings can be used to facilitate pseudo-CT generation for unsupervised clustering. Please note that the project was completed with partial funding from the Ohio Department of Development grant TECH 11-063 and a sponsored research agreement with Philips Healthcare that is managed by Case Western Reserve University. As noted in the affiliations, some of the authors are Philips employees.
60 APPLIED LIFE SCIENCES↗
WE-AB-204-06: Pseudo-CT Generation Using Undersampled, Single-Acquisition UTE-MDixon and Direct-Mapping Artificial Neural Networks for MR-Based Attenuation Correction and Radiation Therapy Planning
Purpose: Emerging technologies such as dedicated PET/MRI and MR-therapy systems require robust and clinically practical methods for determining photon attenuation. Herein, we propose using novel MR acquisition methods and processing for the generation of pseudo-CTs. Methods: A single acquisition, 190-second UTE-mDixon sequence with 25% (angular) sampling density and 3D radial readout was performed on nine volunteers. Three water-filled tubes were placed in the FOV for trajectory-delay correction. The MR data were reconstructed to generate three primitive images acquired at TEs of 0.1, 1.5 and 2.8 ms. In addition, three derived MR images were generated, i.e. two-point Dixon water/fat separation and R2* (1/T2*) map. Furthermore, two spatial features, i.e. local binary pattern (S-1) and relative spatial coordinates (S-2), were incorporated. A direct-mapping operator was generated using Artificial Neural Networks (ANNs) for transforming the MR features to a pseudo-CT. CT images served as the training data and, using a leave-one-out method, for performance evaluation using mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R). Results: The errors between measured CT and pseudo-CT declined dramatically when the spatial features, i.e. S-1 and S-2, were included. The MPD, MAPD, and R were, respectively, 5±57 HU, 141±41 HU, and 0.815±0.066 for results generated by the ANN trained without the spatial features and were 32±26 HU, 115±18 HU, and 0.869±0.035 with the spatial features. The estimation errors of the pseudo-CT were smaller when both the S-1 and S-2 were used together than when either the S-1 or the S-2 was used. Pseudo-CT generation (256×256×256 voxels) by ANN took < 0.5 s using a computer having an Intel i7 3.4GHz CPU and 16 GB RAM. Conclusion: The proposed direct-mapping ANN approach is a technically accurate, clinically practical method for pseudo-CT generation and can potentially help improve the accuracy of MR-AC and MR-RTP applications. Please note that the project was completed with partial funding from the Ohio Department of Development grant TECH 11-063 and a sponsored research agreement with Philips Healthcare that is managed by Case Western Reserve University. As noted in the affiliations, some of the authors are Philips employees.
60 APPLIED LIFE SCIENCES↗
MO-G-18C-03: Evaluation of Deformable Image Registration for Lung Motion Estimation Using Hyperpolarized Gas Tagging MRI
Purpose: Hyperpolarized gas (HP) tagging MRI is a novel imaging technique for direct measurement of lung motion during breathing. This study aims to quantitatively evaluate the accuracy of deformable image registration (DIR) in lung motion estimation using HP tagging MRI as references. Methods: Three healthy subjects were imaged using the HP MR tagging, as well as a high-resolution 3D proton MR sequence (TrueFISP) at the end-of-inhalation (EOI) and the end-of-exhalation (EOE). Ground truth of lung motion and corresponding displacement vector field (tDVF) was derived from HP tagging MRI by manually tracking the displacement of tagging grids between EOI and EOE. Seven different DIR methods were applied to the high-resolution TrueFISP MR images (EOI and EOE) to generate the DIR-based DVFs (dDVF). The DIR methods include Velocity (VEL), MIM, Mirada, multi-grid B-spline from Elastix (MGB) and 3 other algorithms from DIRART toolbox (Double Force Demons (DFD), Improved Lucas-Kanade (ILK), and Iterative Optical Flow (IOF)). All registrations were performed by independent experts. Target registration error (TRE) was calculated as tDVF – dDVF. Analysis was performed for the entire lungs, and separately for the upper and lower lungs. Results: Significant differences between tDVF and dDVF were observed. Besides the DFD and IOF algorithms, all other dDVFs showed similarity in deformation magnitude distribution but away from the ground truth. The average TRE for entire lung ranged 2.5−23.7mm (mean=8.8mm), depending on the DIR method and subject's breathing amplitude. Larger TRE (13.3–23.7mm) was found in subject with larger breathing amplitude of 45.6mm. TRE was greater in lower lung (2.5−33.9 mm, mean=12.4mm) than that in upper lung (2.5−11.9 mm, mean=5.8mm). Conclusion: Significant differences were observed in lung motion estimation between the HP gas tagging MRI method and the DIR methods, especially when lung motion is large. Large variation among different DIR methods was also observed.
60 APPLIED LIFE SCIENCES↗
SU-E-J-08: A Hybrid Three Dimensional Registration Framework for Image-Guided Accurate Radiotherapy System ARTS-IGRT
Purpose: The purpose of this work was to develop a registration framework and method based on the software platform of ARTS-IGRT and implement in C++ based on ITK libraries to register CT images and CBCT images. ARTS-IGRT was a part of our self-developed accurate radiation planning system ARTS. Methods: Mutual information (MI) registration treated each voxel equally. Actually, different voxels even having same intensity should be treated differently in the registration procedure. According to their importance values calculated from self-information, a similarity measure was proposed which combined the spatial importance of a voxel with MI (S-MI). For lung registration, Firstly, a global alignment method was adopted to minimize the margin error and achieve the alignment of these two images on the whole. The result obtained at the low resolution level was then interpolated to become the initial conditions for the higher resolution computation. Secondly, a new similarity measurement S-MI was established to quantify how close the two input image volumes were to each other. Finally, Demons model was applied to compute the deformable map. Results: Registration tools were tested for head-neck and lung images and the average region was 128*128*49. The rigid registration took approximately 2 min and converged 10% faster than traditional MI algorithm, the accuracy reached 1mm for head-neck images. For lung images, the improved symmetric Demons registration process was completed in an average of 5 min using a 2.4GHz dual core CPU. Conclusion: A registration framework was developed to correct patient's setup according to register the planning CT volume data and the daily reconstructed 3D CBCT data. The experiments showed that the spatial MI algorithm can be adopted for head-neck images. The improved Demons deformable registration was more suitable to lung images, and rigid alignment should be applied before deformable registration to get more accurate result. Supported by National Natural Science Foundation of China (NO.81101132) and Natural Science Foundation of Anhui Province (NO.11040606Q55)
60 APPLIED LIFE SCIENCES↗
SU-E-J-251: Fast MR-Based DRR Generation Using Highly Undersampled 3D Radial Trajectories
Purpose: The construction of a digitally reconstructed radiograph (DRR) from a magnetic resonance image (MRI) is possible if the cortical bone signal can be acquired and separated from air and soft tissue. This may be accomplished by subtracting a long echo-time, in-phase, gradient echo (GRE) image volume from an ultra-short echo time free induction decay (FID) image to produce a bone-enhanced (BE) image that reveals cortical bone. One limitation of this approach is the length of time required for data acquisition, which can limit the quality of the DRRs due to patient and organ motion. This study aimed to significantly reduce the acquisition time without compromising DRR quality. Methods: Brain data were acquired from two volunteers using a 3T MR scanner (Ingenia, Philips Healthcare). The FID and GRE images were acquired in a single acquisition using a 3D radial readout sequence with the following parameters: TE1=0.142ms (ultra-short), TE2=2.197ms (nearly in-phase), 2*2*2mm3 isotropic voxels, 250*250*250mm3 FOV. To reduce the acquisition time, k-space was sampled at 75, 50 and 25% of a full 3D sphere . The TE2 image was subtracted from the TE1 image to generate the BE images. The BE images were used to generate DRRs using the Pinnacle treatment planning system (Philips-version 9.2). The quality of the DRRs was evaluated qualitatively by 5 board certified medical physicists for clinical usefulness. Results: The acquisition time for 75, 50 and 25% sampling schemes were 219s, 146s, and 73s, respectively, the latter of which was a four-fold reduction in scan time compared to a 300s fully-sampled acquisition. All DRRs obtained were of acceptable quality and were shown to have sufficient information for clinical 2D image matching. Conclusion: Undersampling k-space while maintaining the same range of frequency information results in significantly reduced scan time and clinically acceptable DRR image quality. Drs. B Traughber and R Muzic have research support from Philips Healthcare. Drs. M Traughber and L Hu are employees of Philips Healthcare.
60 APPLIED LIFE SCIENCES↗
Upper critical field and vortex phase diagram of polycrystalline delta-Mo1-xZrxN thin films by sol-gel
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75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Transient Oxidation of a γ-Ni-28Cr-11Al Alloy
γ-NiCrAl alloys with relatively low Al contents tend to form a layered oxide scale during the early stages of oxidation, rather than an exclusive α-A l2 O 3 scale, the so-called 'thermally grown oxide' (TGO). A layered oxide scale was established on a model γ-Ni-28Cr-11Al (at.%) alloy after isothermal oxidation for several minutes at 1100 °C. The layered scale consisted of an NiO layer at the oxide/gas interface, an inner Cr 2 O 3 layer, and an α-Al 2 O 3 layer at the oxide/alloy interface. The evolution of such an NiO/Cr 2 O 3 /Al 2 O 3 layered structure on this alloy differs from that proposed in earlier work. During heating, a Cr 2 O 3 outer layer and a discontinuous inner layer of Al 2 O 3 initially formed, with metallic Ni particles dispersed between the two layers. A rapid transformation occurred in the scale shortly after the sample reached maximum temperature (1100°C), when two (possibly coupled) phenomena occurred: (i) the inner transition alumina transformed to α-Al 2 O 3 , and (ii) Ni particles oxidized to form the outer NiO layer. Subsequently, NiO reacted with Cr 2 O 3 and Al 2 O 3 to form spinel. Continued growth of the oxide scale and development of the TGO was dominated by growth of the inner α-Al 2 O 3 layer.
36 MATERIALS SCIENCE↗
PHD Finger Recognition of Unmodified Histone H3R2 Links UHRF1 to Regulation of Euchromatic Gene Expression
Histone methylation occurs on both lysine and arginine residues, and its dynamic regulation plays a critical role in chromatin biology. Here we identify the UHRF1 PHD finger (PHD{sub UHRF1}), an important regulator of DNA CpG methylation, as a histone H3 unmodified arginine 2 (H3R2) recognition modality. This conclusion is based on binding studies and cocrystal structures of PHD{sub UHRF1} bound to histone H3 peptides, where the guanidinium group of unmodified R2 forms an extensive intermolecular hydrogen bond network, with methylation of H3R2, but not H3K4 or H3K9, disrupting complex formation. We have identified direct target genes of UHRF1 from microarray and ChIP studies. Importantly, we show that UHRF1's ability to repress its direct target gene expression is dependent on PHD{sub UHRF1} binding to unmodified H3R2, thereby demonstrating the functional importance of this recognition event and supporting the potential for crosstalk between histone arginine methylation and UHRF1 function.
08 HYDROGEN↗
Homologue Structure of the SLAC1 Anion Channel for Closing Stomata in Leaves
The plant SLAC1 anion channel controls turgor pressure in the aperture-defining guard cells of plant stomata, thereby regulating the exchange of water vapour and photosynthetic gases in response to environmental signals such as drought or high levels of carbon dioxide. Here we determine the crystal structure of a bacterial homologue (Haemophilus influenzae) of SLAC1 at 1.20 {angstrom} resolution, and use structure-inspired mutagenesis to analyse the conductance properties of SLAC1 channels. SLAC1 is a symmetrical trimer composed from quasi-symmetrical subunits, each having ten transmembrane helices arranged from helical hairpin pairs to form a central five-helix transmembrane pore that is gated by an extremely conserved phenylalanine residue. Conformational features indicate a mechanism for control of gating by kinase activation, and electrostatic features of the pore coupled with electrophysiological characteristics indicate that selectivity among different anions is largely a function of the energetic cost of ion dehydration.
59 BASIC BIOLOGICAL SCIENCES↗