Boosting Bulk Oxygen Transport with Accessible Electrode Nanostructure in Low Pt Loading PEMFCs.

Despite the high potential for reducing carbon emissions and contributing to the future of energy utilization, polymer electrolyte membrane fuel cells (PEMFCs) face challenges such as high costs and sluggish oxygen transport in cathode catalyst layers (CCLs). In this study, the impact of pore size distribution on bulk oxygen transport behavior is explored by introducing nano calcium carbonate of varying particle sizes for pore-forming. Physicochemical characterizations for are employed to examine the electrode structure, while in situ electrochemical measurements are used to scrutinize bulk oxygen transport resistance, effective oxygen diffusivity ( D O 2 eff $D_{{{\mathrm{O}}}_2}^{{\mathrm{eff}}}$ ) and fuel cell performance. Additionally, the CCLs are constructed with aid of Lattice Boltzmann method (LBM) simulations and D O 2 eff $D_{{{\mathrm{O}}}_2}^{{\mathrm{eff}}}$ for CCLs with different pore size distribution are calculated. The findings reveal that D O 2 eff $D_{{{\mathrm{O}}}_2}^{{\mathrm{eff}}}$ initially increases and then decreases as the most probable pore size increases. A "sphere-pipe" model is proposed to describe practical bulk oxygen transport in CCLs, highlighting the significant role of not only the pore size of secondary pores but also the number of primary pores in bulk oxygen transport.

Inetetamab combined with tegafur as second-line treatment for human epidermal growth factor receptor-2-positive gastric cancer: A case report.

BACKGROUND: Human epidermal growth factor receptor-2 (HER-2) plays a vital role in tumor cell proliferation and metastasis. However, the prognosis of HER2-positive gastric cancer is poor. Inetetamab, a novel anti-HER2 targeting drug independently developed in China, exhibits more potent antibody-dependent cell-mediated cytotoxicity than trastuzumab, which is administered as the first-line treatment for HER2-positive gastric cancer in combination with chemotherapy. In this case, the efficacy and safety of inetetamab combined with tegafur was investigated as a second-line treatment for HER2-positive gastric cancer. CASE SUMMARY: A 52-year-old male patient with HER2-positive gastric cancer presented with abdominal distension, poor appetite, and fatigue two years after receiving six cycles of oxaliplatin combined with tegafur as first-line treatment after surgery, followed by tegafur monotherapy for six months. The patient was diagnosed with postoperative recurrence of gastric adenocarcinoma. He received 17 cycles of a combination of inetetamab, an innovative domestically developed anti-HER2 monoclonal antibody, and tegafur chemotherapy as the second-line treatment (inetetamab 200 mg on day 1, every 3 wk combined with tegafur twice daily on days 1-14, every 3 wk). Evaluation of the efficacy of the second-line treatment revealed that the patient achieved a stable condition and progression-free survival of 17 months. He tolerated the treatment well without exhibiting any grade 3-4 adverse events. CONCLUSION: Inetetamab combined with chemotherapy for the treatment of metastatic HER2-positive gastric cancer demonstrates significant survival benefits and acceptable safety.

Integrative analysis and risk model construction for super­enhancer­related immune genes in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer associated with poor prognosis, and accounts for the majority of RCC-related deaths. The lack of comprehensive diagnostic and prognostic biomarkers has limited further understanding of the pathophysiology of ccRCC. Super-enhancers (SEs) are congregated enhancer clusters that have a key role in tumor processes such as epithelial-mesenchymal transition, metabolic reprogramming, immune escape and resistance to apoptosis. RCC may also be immunogenic and sensitive to immunotherapy. In the present study, an Arraystar human SE-long non-coding RNA (lncRNA) microarray was first employed to profile the differentially expressed SE-lncRNAs and mRNAs in 5 paired ccRCC and peritumoral tissues and to identify SE-related genes. The overlap of these genes with immune genes was then determined to identify SE-related immune genes. A model for predicting clinical prognosis and response to immunotherapy was built following the comprehensive analysis of a ccRCC gene expression dataset from The Cancer Genome Atlas (TCGA) database. The patients from TCGA were divided into high- and low-risk groups based on the median score derived from the risk model, and the Kaplan-Meier survival analysis showed that the low-risk group had a higher survival probability. In addition, according to the receiver operating characteristic curve analysis, the risk model had more advantages than other clinical factors in predicting the overall survival (OS) rate of patients with ccRCC. Using this model, it was demonstrated that the high-risk group had a more robust immune response. Furthermore, 61 potential drugs with half-maximal inhibitory concentration values that differed significantly between the two patient groups were screened to investigate potential drug treatment of ccRCC. In summary, the present study provided a novel index for predicting the survival probability of patients with ccRCC and may provide some insights into the mechanisms through which SE-related immune genes influence the diagnosis, prognosis and potential treatment drugs of ccRCC.

An improved 3D KiU-Net for segmentation of liver tumor.

It is a challenging task to accurately segment liver tumors from Computed Tomography (CT) images. The widely used U-Net and its variants generally suffer from the issue to accurately segment the detailed edges of small tumors, because the progressive down sampling operations in the encoder module will gradually increase the receptive fields. These enlarged receptive filed have limited ability to learn the information about tiny structures. KiU-Net is a newly proposed dual-branch model that can effectively perform image segmentation for small targets. However, the 3D version of KiU-Net has high computational complexity, which limits its application. In this work, an improved 3D KiU-Net (named TKiU-NeXt) is proposed for liver tumor segmentation from CT images. In TKiU-NeXt, a Transformer-based Kite-Net (TK-Net) branch is proposed to build the over-complete architecture to learn more detailed features for small structures, and an extended 3D version of UNeXt is developed to replace the original U-Net branch, which can effectively reduce computational complexity but still with superior segmentation performance. Moreover, a Mutual Guided Fusion Block (MGFB) is designed to effectively learn more features from two branches and then fuse the complementary features for image segmentation. The experimental results on two public CT datasets and a private dataset demonstrate that the proposed TKiU-NeXt outperforms all the compared algorithms, and it also has less computational complexity. It suggests the effectiveness and efficiency of TKiU-NeXt.

Determination of optimal fiducial marker across image-guided radiation therapy (IGRT) modalities: visibility and artifact analysis of gold, carbon, and polymer fiducial markers.

The purpose of this study was to evaluate the visibility and artifact created by gold, carbon, and polymer fiducial markers in a simple phantom across computed tomography (CT), kilovoltage (kV), and megavoltage (MV) linear accelerator imaging and MV tomotherapy imaging. Three types of fiducial markers (gold, carbon, and polymer) were investigated for their visibility and artifacts in images acquired with various modalities and with different imaging parameters (kV, mAs, slice thickness). The imaging modalities include kV CT, 2D linac-based kilovoltage and megavoltage X-ray imaging systems, kV cone-beam CT, and normal and fine tomotherapy imaging. The images were acquired on a phantom constructed using Superflab bolus in which markers of each type were inserted into the center layer. The visibility and artifacts produced by each marker were assessed qualitatively and quantitatively. All tested markers could be identified clearly on the acquired CT and linac-based kV images; gold markers demonstrated the highest contrast. On the CT images, gold markers produced a significant artifact, while no artifacts were observed with polymer markers. Only gold markers were visible when using linac-based MV and tomotherapy imaging. For linac-based kV images, the contrast increased with kV and mAs values for all the markers, with the gold being the most pronounced. On CT images, the contrast increased with kV for the gold markers, while decreasing for the polymer and carbon marker. With the bolus phantom used, we found that when kV imaging-based treatment verification equipment is available, polymer and carbon markers may be the preferred choice for target localization and patient treatment positioning verification due to less image artifacts. If MV imaging will be the sole modality for positioning verification, it may be necessary to use gold markers despite the artifacts they create on the simulation CT images.

Morphological and heat transfer characteristics of biomass briquette during steam gasification process.

The morphological evolution and heat transfer characteristics of biomass briquette greatly affect the directional regulation of target products during steam gasification process. In this work, a visual gasifier with an on-line temperature monitoring system was developed to investigate the coupling relationship between the morphological change and temperature distribution of biomass briquette. The gasification behaviors of biomass briquette at different temperatures and steam concentrations were comprehensively examined and compared. The shrinkage rate and heating rate of biomass briquette both reached the maximum at 1-2 min. The morphological evolution of biomass briquette in the heating process was shrinking particle mode, then changed to the shrinking core mode when the biomass temperature kept relatively stable. The high-quality syngas with a high H2/CO ratio of 3.07 at 50 vol% steam concentration and 700 °C was obtained, which were idealized to synthesize other fuels/chemicals.

MiR-579 Inhibits Lung Adenocarcinoma Cell Proliferation and Metastasis via Binding to CRABP2.

Background: Lung cancer is the cancer with the highest morbidity and mortality. Lung adenocarcinoma (LUAD) is a subtype of lung cancer. The aim of this study is to explore the functions of miR-579 and CRABP2 in lung adenocarcinoma. Methods: Cell counting kit-8 (CCK-8) and colony formation assays were applied to calculate cell proliferative abilities. Transwell assay was utilized to measure cell invasive ability. Results: MiR-579 is low expressed in LUAD tissues and cell lines. MiR-579 inhibits cell viability and invasion of lung adenocarcinoma. Knockdown of CRABP2 inhibits cell proliferation and invasion of Calu-3 cells. MiR-579 suppresses cell proliferation and invasion by regulating CRABP2 in Calu-3 cells. Conclusion: Our study reveals that miR-579 acts as a tumor suppressor in LUAD and miR-579 can target and regulate the expression of CRABP2 to mediate cell proliferation and invasion. This study indicates that miR-579 has a potential to be a candidate biomarker for the treatment of LUAD.

Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis From Lung CT Scans With Multi-Scale Guided Dense Attention.

Computed Tomography (CT) plays an important role in monitoring radiation-induced Pulmonary Fibrosis (PF), where accurate segmentation of the PF lesions is highly desired for diagnosis and treatment follow-up. However, the task is challenged by ambiguous boundary, irregular shape, various position and size of the lesions, as well as the difficulty in acquiring a large set of annotated volumetric images for training. To overcome these problems, we propose a novel convolutional neural network called PF-Net and incorporate it into a semi-supervised learning framework based on Iterative Confidence-based Refinement And Weighting of pseudo Labels (I-CRAWL). Our PF-Net combines 2D and 3D convolutions to deal with CT volumes with large inter-slice spacing, and uses multi-scale guided dense attention to segment complex PF lesions. For semi-supervised learning, our I-CRAWL employs pixel-level uncertainty-based confidence-aware refinement to improve the accuracy of pseudo labels of unannotated images, and uses image-level uncertainty for confidence-based image weighting to suppress low-quality pseudo labels in an iterative training process. Extensive experiments with CT scans of Rhesus Macaques with radiation-induced PF showed that: 1) PF-Net achieved higher segmentation accuracy than existing 2D, 3D and 2.5D neural networks, and 2) I-CRAWL outperformed state-of-the-art semi-supervised learning methods for the PF lesion segmentation task. Our method has a potential to improve the diagnosis of PF and clinical assessment of side effects of radiotherapy for lung cancers.

The impact of phantom design and material-dependence on repeatability and reproducibility of CT-based radiomics features.

PURPOSE: To understand the design of radiomics phantom and material-dependence on repeatability and reproducibility of computed tomography (CT) radiomics features. METHODS: A radiomics phantom consisting of various materials with uniformity, textural, and biological components, was constructed. The phantom was scanned with different manufacturer CT scanners and the scans were repeated multiple times on the same scanner with different acquisition settings as kVp, mAs, orientation, field of view (FOV), slice thickness, pitch, reconstruction kernels, and acquisition mode. A total of 72 phantom scans were included. For each scan, 18 different regions of interest (ROI) were contoured and 708 radiomics features were extracted from each ROI via an open source radiomics tool, IBEX. To relate the phantom data to patient data, the radiomics features from different phantom materials were compared with those extracted from 50 patients' images of five disease sites as brain, head-and-neck, breast, liver, and lung cases using box-plots comparison and principal component analysis (PCA). The temporal stability of imaging features was then evaluated with respect to a controlled scenario (test-retest) via the intra-class correlation coefficient (ICC). The reproducibility of radiomics features with respect to different scanners or acquisition settings were further evaluated with concordance correlation coefficients (CCC). RESULTS: Among all phantom materials, the biological component had feature values closest to human tissues, especially for tumors in brain and liver. The textural component showed similar ranges of variation to lung lesions, particularly for cartridges of rice, cereal, and the 3D-printed textural phantom with fine and rough grid. It also showed that certain materials, such as polystyrene foam, plaster, and peanuts, did not have comparable values to human tissue and could be excluded for future phantom design. High repeatability was observed in the test-retest study as indicated by an ICC value of 0.998 ± 0.020. All materials were used for feature stability analysis. For the inter-scanner study, shape-related features were the most-reliable category with 94% of features having CCC ≥ 0.9, while gradient orientation histogram (GOH) were the least-reliable with only 14.6% meeting the criteria. For the intra-scanner study, the reproducibility of CT-based radiomics features showed material-dependence. In general, the instability of radiomics features introduced by kVp, mAs, pitch, acquisition mode, and orientation were relatively mild. However, the homogeneous materials were more vulnerable to those changes compared to materials with textural patterns. Regardless of material compositions, resolution parameters like FOV and slice thickness, could have large impact on feature stability. Switching between standard and bone reconstruction kernels could also result significant changes to feature reproducibility. CONCLUSION: We have built a radiomics phantom using materials that cover a wide span of tumor textures seen in oncological CT images. The designed phantom presents a preliminary opportunity for investigating reproducibility of radiomics features and the reproducibility can be material dependent. Thus, in the radiomics quality assurance design, it is important to choose appropriate materials that can provide a close range of radiomics features to patients with specific disease sites dependency taken into consideration.

Object-constrained meshless deformable algorithm for high speed 3D nonrigid registration between CT and CBCT.

PURPOSE: High-speed nonrigid registration between the planning CT and the treatment CBCT data is critical for real time image guided radiotherapy (IGRT) to improve the dose distribution and to reduce the toxicity to adjacent organs. The authors propose a new fully automatic 3D registration framework that integrates object-based global and seed constraints with the grayscale-based "demons" algorithm. METHODS: Clinical objects were segmented on the planning CT images and were utilized as meshless deformable models during the nonrigid registration process. The meshless models reinforced a global constraint in addition to the grayscale difference between CT and CBCT in order to maintain the shape and the volume of geometrically complex 3D objects during the registration. To expedite the registration process, the framework was stratified into hierarchies, and the authors used a frequency domain formulation to diffuse the displacement between the reference and the target in each hierarchy. Also during the registration of pelvis images, they replaced the air region inside the rectum with estimated pixel values from the surrounding rectal wall and introduced an additional seed constraint to robustly track and match the seeds implanted into the prostate. The proposed registration framework and algorithm were evaluated on 15 real prostate cancer patients. For each patient, prostate gland, seminal vesicle, bladder, and rectum were first segmented by a radiation oncologist on planning CT images for radiotherapy planning purpose. The same radiation oncologist also manually delineated the tumor volumes and critical anatomical structures in the corresponding CBCT images acquired at treatment. These delineated structures on the CBCT were only used as the ground truth for the quantitative validation, while structures on the planning CT were used both as the input to the registration method and the ground truth in validation. By registering the planning CT to the CBCT, a displacement map was generated. Segmented volumes in the CT images deformed using the displacement field were compared against the manual segmentations in the CBCT images to quantitatively measure the convergence of the shape and the volume. Other image features were also used to evaluate the overall performance of the registration. RESULTS: The algorithm was able to complete the segmentation and registration process within 1 min, and the superimposed clinical objects achieved a volumetric similarity measure of over 90% between the reference and the registered data. Validation results also showed that the proposed registration could accurately trace the deformation inside the target volume with average errors of less than 1 mm. The method had a solid performance in registering the simulated images with up to 20 Hounsfield unit white noise added. Also, the side by side comparison with the original demons algorithm demonstrated its improved registration performance over the local pixel-based registration approaches. CONCLUSIONS: Given the strength and efficiency of the algorithm, the proposed method has significant clinical potential to accelerate and to improve the CBCT delineation and targets tracking in online IGRT applications.

A 3D global-to-local deformable mesh model based registration and anatomy-constrained segmentation method for image guided prostate radiotherapy.

PURPOSE: In the external beam radiation treatment of prostate cancers, successful implementation of adaptive radiotherapy and conformal radiation dose delivery is highly dependent on precise and expeditious segmentation-and registration of the prostate volume between the simulation and the treatment images. The purpose of this study is to develop a novel, fast, and accurate segmentation and registration method to increase the computational efficiency to meet the restricted clinical treatment time requirement in image guided radiotherapy. METHODS: The method developed in this study used soft tissues to capture the transformation between the 3D planning CT (pCT) images and 3D cone-beam CT (CBCT) treatment images. The method incorporated a global-to-local deformable mesh model based registration framework as well as an automatic anatomy-constrained robust active shape model (ACRASM) based segmentation algorithm in the 3D CBCT images. The global registration was based on the mutual information method, and the local registration was to minimize the Euclidian distance of the corresponding nodal points from the global transformation of deformable mesh models, which implicitly used the information of the segmented target volume. The method was applied on six data sets of prostate cancer patients. Target volumes delineated by the same radiation oncologist on the pCT and CBCT were chosen as the benchmarks and were compared to the segmented and registered results. The distance-based and the volume-based estimators were used to quantitatively evaluate the results of segmentation and registration. RESULTS: The ACRASM segmentation algorithm was compared to the original active shape mode (ASM) algorithm by evaluating the values of the distance-based estimators. With respect to the corresponding benchmarks, the mean distance ranged from -0.85 to 0.84 mm for ACRASM and from -1.44 to 1.17 mm for ASM. The mean absolute distance ranged from 1.77 to 3.07 mm for ACRASM and from 2.45 to 6.54 mm for ASM. The volume overlap ratio ranged from 79% to 91% for ACRASM and from 44% to 80% for ASM. These data demonstrated that the segmentation results of ACRASM were in better agreement with the corresponding benchmarks than those of ASM. The developed registration algorithm was quantitatively evaluated by comparing the registered target volumes from the pCT to the benchmarks on the CBCT. The mean distance and the root mean square error ranged from 0.38 to 2.2 mm and from 0.45 to 2.36 mm, respectively, between the CBCT images and the registered pCT. The mean overlap ratio of the prostate volumes ranged from 85.2% to 95% after registration. The average time of the ACRASM-based segmentation was under 1 min. The average time of the global transformation was from 2 to 4 min on two 3D volumes and the average time of the local transformation was from 20 to 34 s on two deformable superquadrics mesh models. CONCLUSIONS: A novel and fast segmentation and deformable registration method was developed to capture the transformation between the planning and treatment images for external beam radiotherapy of prostate cancers. This method increases the computational efficiency and may provide foundation to achieve real time adaptive radiotherapy.

Incidental Coronary Artery Calcium on Breast Radiation Therapy Planning Scans Identifies Patients for Cardiac Preventive Therapy.

PURPOSE/OBJECTIVE(S): The presence of coronary artery calcium (CAC>0) is associated with increased cardiac-related mortality and is a common indication to initiate statin therapy to prevent future long-term cardiac-related adverse events. CAC is also well visualized on noncontrast chest computed tomography simulation (CT sim) scans used for breast radiation planning. We hypothesize that by screening for incidental CAC on CT sims, radiation oncologists could help identify patients who may benefit from additional preventive medical interventions with their primary care physician or cardiologist. METHODS: A retrospective analysis of 126 consecutive patients with breast cancer treated with external beam radiation therapy at a single institution was performed. Noncontrast CT sim scans were reviewed for the presence of CAC and the 10-year risk of atherosclerotic cardiovascular disease (ASCVD) was calculated to identify patients who may benefit from initiating statin therapy. Patients with CAC>0 and/or ASCVD risk >20% were identified as those who may benefit from statin therapy. RESULTS: Out of 72 patients with CAC>0, only 12(16%) had reported pre-existing coronary artery disease and 32(44%) were not already on recommended statin therapy. CAC>0 visualized on CT sim was able to identify 29 additional patients who would benefit from statin beyond what the ASCVD risk calculator could identify. CONCLUSION: Observation of incidental CAC on breast radiation-planning CT scans identified patients who could benefit from cardiac-related preventive strategies. By increasing attention, awareness, and reporting of incidental CAC visible on CT sims, radiation oncologists may fulfill a unique role to bridge a potential gap in cardiovascular preventive medicine.

Action Levels on Dose and Anatomic Variation for Adaptive Radiation Therapy Using Daily Offline Plan Evaluation: Preliminary Results.

PURPOSE: This study aimed to develop action levels for replanning to accommodate dosimetric variations resulting from anatomic changes during the course of treatments, using daily cone beam computed tomography (CBCT). METHODS AND MATERIALS: Daily or weekly CBCT images of 20 patients (10 head and neck, 5 lung, and 5 prostate cancers) who underwent resimulation per physicians' clinical decisions, mainly from the comparison of CBCT scans, were used to determine action levels. The first CBCT image acquired before the first treatment was used as the reference image to rule out effects of dose inaccuracy from the CBCT. The Pearson correlation of clinical target volume (CTV) was used as a parameter of anatomic variation. Parameters for action levels on dose and anatomic variation were deduced by comparing the parameters and clinical decisions made for replanning. A software tool was developed to automatically perform all procedures, including dose calculations, using the CBCT and plan evaluations. RESULTS: Replans were clinically decided based on either significant dose or anatomic changes in 13 cases. The 7 cases that did not require replanning showed dose differences <5%, and the Pearson correlation of the CTV was >75% for all fractions. A difference in planning target volume dose >5% or a difference in the image correlation coefficient of the CTV <0.75 proved to be indicators for replanning. Once the results of the CBCT plan met the replanning criteria, the software tool automatically alerted the attending physician and physicist by both e-mail and pager so that the case could be examined closely. CONCLUSIONS: Our study shows that a dose difference of 5% and/or anatomy variation at 0.75 Pearson correlations are practical action levels on dose and anatomic variation for replanning for the given data sets.

Should regional ventilation function be considered during radiation treatment planning to prevent radiation-induced complications?

PURPOSE: To investigate the incorporation of pretherapy regional ventilation function in predicting radiation fibrosis (RF) in stage III nonsmall cell lung cancer (NSCLC) patients treated with concurrent thoracic chemoradiotherapy. METHODS: Thirty-seven patients with stage III NSCLC were retrospectively studied. Patients received one cycle of cisplatin-gemcitabine, followed by two to three cycles of cisplatin-etoposide concurrently with involved-field thoracic radiotherapy (46-66 Gy; 2 Gy/fraction). Pretherapy regional ventilation images of the lung were derived from 4D computed tomography via a density change-based algorithm with mass correction. In addition to the conventional dose-volume metrics (V20, V30, V40, and mean lung dose), dose-function metrics (fV20, fV30, fV40, and functional mean lung dose) were generated by combining regional ventilation and radiation dose. A new class of metrics was derived and referred to as dose-subvolume metrics (sV20, sV30, sV40, and subvolume mean lung dose); these were defined as the conventional dose-volume metrics computed on the functional lung. Area under the receiver operating characteristic curve (AUC) values and logistic regression analyses were used to evaluate these metrics in predicting hallmark characteristics of RF (lung consolidation, volume loss, and airway dilation). RESULTS: AUC values for the dose-volume metrics in predicting lung consolidation, volume loss, and airway dilation were 0.65-0.69, 0.57-0.70, and 0.69-0.76, respectively. The respective ranges for dose-function metrics were 0.63-0.66, 0.61-0.71, and 0.72-0.80 and for dose-subvolume metrics were 0.50-0.65, 0.65-0.75, and 0.73-0.85. Using an AUC value = 0.70 as cutoff value suggested that at least one of each type of metrics (dose-volume, dose-function, dose-subvolume) was predictive for volume loss and airway dilation, whereas lung consolidation cannot be accurately predicted by any of the metrics. Logistic regression analyses showed that dose-function and dose-subvolume metrics were significant (P values ≤ 0.02) in predicting volume airway dilation. Likelihood ratio test showed that when combining dose-function and/or dose-subvolume metrics with dose-volume metrics, the achieved improvements of prediction accuracy on volume loss and airway dilation were significant (P values ≤ 0.04). CONCLUSIONS: The authors' results demonstrated that the inclusion of regional ventilation function improved accuracy in predicting RF. In particular, dose-subvolume metrics provided a promising method for preventing radiation-induced pulmonary complications.

Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT.

To resolve challenges in image segmentation in oncologic patients with severely compromised lung, we propose an automated right lung segmentation framework that uses a robust, atlas-based active volume model with a sparse shape composition prior. The robust atlas is achieved by combining the atlas with the output of sparse shape composition. Thoracic computed tomography images (n=38) from patients with lung tumors were collected. The right lung in each scan was manually segmented to build a reference training dataset against which the performance of the automated segmentation method was assessed. The quantitative results of this proposed segmentation method with sparse shape composition achieved mean Dice similarity coefficient (DSC) of (0.72, 0.81) with 95% CI, mean accuracy (ACC) of (0.97, 0.98) with 95% CI, and mean relative error (RE) of (0.46, 0.74) with 95% CI. Both qualitative and quantitative comparisons suggest that this proposed method can achieve better segmentation accuracy with less variance than other atlas-based segmentation methods in the compromised lung segmentation.

Intrafractional target motions and uncertainties of treatment setup reference systems in accelerated partial breast irradiation.

PURPOSE: This study investigated the magnitude of intrafractional motion and level of accuracy of various setup strategies in accelerated partial breast irradiation (APBI) using three-dimensional conformal external beam radiotherapy. METHODS AND MATERIALS: At lumpectomy, gold fiducial markers were strategically sutured to the surrounding walls of the cavity. Weekly fluoroscopy imaging was conducted at treatment to investigate the respiration-induced target motions. Daily pre- and post-RT kV imaging was performed, and images were matched to digitally reconstructed radiographs based on bony anatomy and fiducial markers, respectively, to determine the intrafractional motion magnitudes over the course of treatment. The positioning differences of the laser tattoo- and the bony anatomy-based setups compared with those of the marker-based setup (benchmark) were also determined. The study included 21 patients. RESULTS: Although lung exhibited significant motion, the average marker motion amplitude on the fluoroscopic image was about 1 mm. Over a typical treatment time period, average intrafractional motion magnitude was 4.2 mm and 2.6 mm based on the marker and bony anatomy matching, respectively. The bony anatomy- and laser tattoo-based interfractional setup errors, with respect to the fiducial marker-based setup, were 7.1 and 9.0 mm, respectively. CONCLUSIONS: Respiration has limited effects on the target motion during APBI. Bony anatomy-based treatment setup improves the accuracy relative to that of the laser tattoo-based setup approach. Since fiducial markers are sutured directly to the surgical cavity, the marker-based approach can further improve the interfractional setup accuracy. On average, a seroma cavity exhibits intrafractional motion of more than 4 mm, a magnitude that is larger than that which is otherwise derived based on bony anatomy matching. A seroma-specific marker-based approach has the potential to improve treatment accuracy by taking the true inter- and intrafractional motions into consideration.

A comparison of helical intensity-modulated radiotherapy, intensity-modulated radiotherapy, and 3D-conformal radiation therapy for pancreatic cancer.

We assessed dosimetric differences in pancreatic cancer radiotherapy via helical intensity-modulated radiotherapy (HIMRT), linac-based IMRT, and 3D-conformal radiation therapy (3D-CRT) with regard to successful plan acceptance and dose to critical organs. Dosimetric analysis was performed in 16 pancreatic cases that were planned to 54 Gy; both post-pancreaticoduodenectomy (n = 8) and unresected (n = 8) cases were compared. Without volume modification, plans met constraints 75% of the time with HIMRT and IMRT and 13% with 3D-CRT. There was no statistically significantly improvement with HIMRT over conventional IMRT in reducing liver V35, stomach V45, or bowel V45. HIMRT offers improved planning target volume (PTV) dose homogeneity compared with IMRT, averaging a lower maximum dose and higher volume receiving the prescription dose (D100). HIMRT showed an increased mean dose over IMRT to bowel and liver. Both HIMRT and IMRT offer a statistically significant improvement over 3D-CRT in lowering dose to liver, stomach, and bowel. The results were similar for both unresected and resected patients. In pancreatic cancer, HIMRT offers improved dose homogeneity over conventional IMRT and several significant benefits to 3D-CRT. Factors to consider before incorporating IMRT into pancreatic cancer therapy are respiratory motion, dose inhomogeneity, and mean dose.

AN ADAPTIVE TRACKING ALGORITHM OF LUNG TUMORS IN FLUOROSCOPY USING ONLINE LEARNED COLLABORATIVE TRACKERS.

Accurate tracking of tumor movement in fluoroscopic video sequences is a clinically significant and challenging problem. This is due to blurred appearance, unclear deforming shape, complicate intra- and inter- fractional motion, and other facts. Current offline tracking approaches are not adequate because they lack adaptivity and often require a large amount of manual labeling. In this paper, we present a collaborative tracking algorithm using asymmetric online boosting and adaptive appearance model. The method was applied to track the motion of lung tumors in fluoroscopic sequences provided by radiation oncologists. Our experimental results demonstrate the advantages of the method.

3D meshless prostate segmentation and registration in image guided radiotherapy.

Image Guided Radiation Therapy (IGRT) improves radiation therapy for prostate cancer by facilitating precise radiation dose coverage of the object of interest, and minimizing dose to adjacent normal organs. In an effort to optimize IGRT, we developed a fast segmentation-registration-segmentation framework to accurately and efficiently delineate the clinically critical objects in Cone Beam CT images obtained during radiation treatment. The proposed framework started with deformable models automatically segmenting the prostate, bladder, and rectum in planning CT images. All models were built around seed points and involved in the CT image under the influence of image features using the level set formulation. The deformable models were then converted into meshless point sets and underwent a 3D non rigid registration from the planning CT to the treatment CBCT. The motion of deformable models during the registration was constrained by the global shape prior on the target surface during the deformation. The meshless formulation provided a convenient interface between deformable models and the image feature based registration method. The final registered deformable models in the CBCT domain were further refined using the interaction between objects and other available image features. The segmentation results for 15 data sets has been included in the validation study, compared with manual segmentations by a radiation oncologist. The automatic segmentation results achieved a satisfactory convergence with manual segmentations and met the speed requirement for on line IGRT.

3D-3D tubular organs registration based on bifurcations for the CT images.

The registration of tubular organs (pulmonary tracheobronchial tree or vasculature) of 3D medical images is critical in various clinical applications such as surgical planning and radiotherapy. In this paper, we present a novel method for tubular organs registration based on the automatically detected bifurcation points of the tubular organs. We first perform a 3D tubular organ segmentation method to extract the centerlines of tubular organs and radius estimation in both planning and respiration-correlated CT (RCCT) images. This segmentation method automatically detects the bifurcation points by applying Adaboost algorithm with specially designed filters. We then apply a rigid registration method which minimizes the least square error of the corresponding bifurcation points between the planning CT images and the respiration-correlated CT images. Our method has over 96% success rate for detecting bifurcation points.We present very promising results of our method applied to the registration of the planning and respiration-correlated CT images. On average, the mean distance and the root-mean-square error (RMSE) of the corresponding bifurcation points between the respiration-correlated images and the registered planning images are less than 2.7 mm.