Multicenter propensity score-matched analysis to compare perioperative morbidity after laparoscopic or robotic complex hepatectomy for solitary hepatocellular carcinoma.

BACKGROUND: Postoperative complications are vital factors affecting the prognosis of patients with hepatocellular carcinoma (HCC), especially for complex hepatectomy. The present study aimed to compare perioperative complications between laparoscopic and robotic complex hepatectomy (LCH vs. RCH). METHODS: Patients with solitary HCC after complex hepatectomy were collected from a multicenter database. Propensity score-matched (PSM) analysis was adopted to control confounding bias. Multivariable analysis was performed to determine the prognostic factors. RESULTS: 436 patients were included. After PSM, 43 patients were included in both the LCH and RCH groups. The results showed that compared to LCH, RCH had lower rates of blood loss and transfusion, and lower postoperative 30-day and major morbidity, and post-hepatectomy liver failure (PHLF) (all P < 0.05). Additionally, the length of hospital stay was shorter in the RCH group (P = 0.026). Multivariable analysis showed RCH is an independent protective factor for reducing the 30-day morbidity, major morbidity and PHLF. CONCLUSION: RCH has advantages over LCH in the minimally invasive treatment of complex HCC, as it can reduce the incidence of postoperative morbidity. Therefore, RCH should be considered for patients with HCC who require complex hepatectomy.

Prognostic value of the Naples prognostic score in patients with intrahepatic cholangiocarcinoma after hepatectomy.

BACKGROUND: The Naples Prognostic Score (NPS), integrating inflammatory and nutritional biomarkers, has been reported to be associated with the prognosis of various malignancies, but there is no report on intrahepatic cholangiocarcinoma (ICC). This study aimed to explore the prognostic value of NPS in patients with ICC. METHODS: Patients with ICC after hepatectomy were collected, and divided into three groups. The prognosis factors were determined by Cox regression analysis. Predictive efficacy was evaluated by the time-dependent receiver operating characteristic (ROC) curves. RESULTS: A total of 174 patients were included (Group 1: 33 (19.0%) patients; Group 2: 83 (47.7%) patients; and Group 3: 58 (33.3%) patients). The baseline characteristics showed the higher the NPS, the higher the proportion of patients with cirrhosis and Child-Pugh B, and more advanced tumors. The Kaplan-Meier curves reflect higher NPS were associated with poor survival. Multivariable analysis showed NPS was an independent risk factor of overall survival (NPS group 2 vs. 1: HR = 1.671, 95% CI: 1.022-3.027, p = 0.009; NPS group 3 vs. 1: HR = 2.208, 95% CI: 1.259-4.780, p = 0.007) and recurrence-free survival (NPS group 2 vs. 1: HR = 1.506, 95% CI: 1.184-3.498, p = 0.010; NPS group 3 vs. 1: HR = 2.141, 95% CI: 2.519-4.087, P = 0.001). The time ROC indicated NPS was superior to other models in predicting prognosis. CONCLUSIONS: NPS is a simple and effective tool for predicting the long-term survival of patients with ICC after hepatectomy. Patients with high NPS require close follow-up, and improving NPS may prolong the survival time.

Precise planning based on 3D-printed dry-laboratory models can reduce perioperative complications of laparoscopic surgery for complex hepatobiliary diseases: a preoperative cohort study.

BACKGROUND & AIMS: Complications after laparoscopic liver resection (LLR) are important factors affecting the prognosis of patients, especially for complex hepatobiliary diseases. The present study aimed to evaluate the value of a three-dimensional (3D) printed dry-laboratory model in the precise planning of LLR for complex hepatobiliary diseases. METHODS: Patients with complex hepatobiliary diseases who underwent LLR were preoperatively enrolled, and divided into two groups according to whether using a 3D-printed dry-laboratory model (3D vs. control group). Clinical variables were assessed and complications were graded by the Clavien-Dindo classification. The Comprehensive Complication Index (CCI) scores were calculated and compared for each patient. Multivariable analysis was performed to determine the risk factors of postoperative complications. RESULTS: Sixty-two patients with complex hepatobiliary diseases underwent the precise planning of LLR. Among them, thirty-one patients acquired the guidance of a 3D-printed dry-laboratory model, and others were only guided by traditional enhanced CT or MRI. The results showed no significant differences between the two groups in baseline characters. However, compared to the control group, the 3D group had a lower incidence of intraoperative blood loss, as well as postoperative 30-day and major complications, especially bile leakage (all P < 0.05). The median score on the CCI was 20.9 (range 8.7-51.8) in the control group and 8.7 (range 8.7-43.4) in the 3D group (mean difference, -12.2, P = 0.004). Multivariable analysis showed the 3D model was an independent protective factor in decreasing postoperative complications. Subgroup analysis also showed that a 3D model could decrease postoperative complications, especially for bile leakage in patients with intrahepatic cholelithiasis. CONCLUSION: The 3D-printed models can help reduce postoperative complications. The 3D-printed models should be recommended for patients with complex hepatobiliary diseases undergoing precise planning LLR.

Survival benefit from adjuvant TACE combined with Lenvatinib for patients with hepatocellular carcinoma and microvascular invasion after curative hepatectomy.

BACKGROUND AND AIMS: The prognosis of patients with hepatocellular carcinoma (HCC) undergoing hepatectomy is unsatisfactory, especially for those with microvascular invasion (MVI). This study aimed to determine the impact of adjuvant transcatheter arterial chemoembolization (TACE) and Lenvatinib on the prognosis of patients with HCC and MVI after hepatectomy. METHODS: Patients diagnosed with HCC and MVI were reviewed, and stratified into four groups according to adjuvant TACE and/or Lenvatinib. Multivariate Cox regression analyses are used to determine independent risk factors. RESULTS: 346 patients were included, and divided into four groups (Group I, TACE+ Lenvatinib; Group II, Lenvatinib; Group III, TACE; Group IV, without adjuvant therapy). Multivariable analysis showed that compared to Group IV, Group I had the best effect on improving the overall survival (OS, HR 0.321, 95%CI 0.099-0.406, P = 0.001) and recurrence-free survival (RFS, HR 0.319, 95%CI 0.129-0.372, P = 0.001). Additionally, compared with Group II or Group III, Group I also can significantly improve the OS and RFS. There is no significant difference between Group II and Group III in OS and RFS. CONCLUSION: The combination of TACE and Lenvatinib should be considered for anti-recurrence therapy for patients with HCC and MVI after hepatectomy.

Large valley polarization and the valley-dependent Hall effect in a Janus TiTeBr monolayer.

Ferrovalley materials hold great promise for implementation of logic and memory devices in valleytronics. However, there have so far been limited ferrovalley materials exhibiting significant valley polarization and high Curie temperature (TC). Using first-principles calculations, we predict that the TiTeBr monolayer is a promising ferrovalley candidate. It exhibits intrinsic ferromagnetism with TC as high as 220 K. It is indicated that an out-of-plane alignment of magnetization demonstrates a valley polarization up to 113 meV in the topmost valence band, as further verified by perturbation theory considering both the spin polarization and spin-orbit coupling. Under an in-plane electric field, the valley-dependent Berry curvature results in the anomalous valley Hall effect (AVHE). Moreover, under a suitable in-plane biaxial strain, the TiTeBr monolayer transforms into a Chern insulator with a nonzero Chern number, yet retains its ferrovalley characters and thus the emergent quantum anomalous valley Hall effect (QAVHE). Our study indicates that the TiTeBr monolayer is a promising ferrovalley material, and it provides a platform for investigating the valley-dependent Hall effect.

TyG-GGT is a Reliable Non-Invasive Predictor of Advanced Liver Fibrosis in Overweight or Obese Individuals.

BACKGROUND: Liver fibrosis is a predisposing factor for liver cancer. This study will investigate the predictive role of the Triglyceride-glucose and Gamma-glutamyl transferase index (TyG-GGT) as a non-invasive indicator of advanced liver fibrosis in individuals with obesity or overweight. METHOD: We enrolled patients who underwent metabolic and bariatric surgery as well as intraoperative liver biopsies at Zhejiang provincial people's hospital from August 2020 to March 2023. Clinical characteristics, comorbidities, laboratory data, and pathological variables of patients were collected and analysed. Then, we conducted logistics regression model to compare the performance of the TyG-GGT index with other 4 non-invasive models. RESULTS: A total of 65 patients were included in this study. 43(66.2%) of them were female, with the mean body mass index (BMI) of 39.0 ± 7.3 kg/m2. Meanwhile, 24(36.9%) patients were diagnosed with diabetes. Advanced liver fibrosis were observed in 16.9% of patients, while liver cirrhosis was found in 4.6% of patients. The multivariable logistics regression showed that TyG-GGT was an independent risk factor of advanced liver fibrosis (OR = 6.989, P = 0.049). Additionally, compared to another 4 non-invasive liver fibrosis models (NFS = 0.66, FIB4 = 0.65, METS-IR = 0.68, APRI = 0.65), TyG-GGT exhibits the highest AUC value of 0.75. CONCLUSIONS: More than one-third of patients undergoing metabolic and bariatric surgery are afflicted with nonalcoholic steatohepatitis (NASH), and a significant proportion exhibit advanced fibrosis. TyG-GGT was a potentially reliable predictor for screening individuals with overweight or obesity at high risk of advanced liver fibrosis, thus providing clinical guidance for early intervention in this targeted group.

Visceral and ectopic fat are more predictively associated with primary liver cancer than overall obesity from genetic sights: A Mendelian randomization study.

Several observational studies have reported an association between obesity and primary liver cancer (PLC), while the causality behind this association and the comparison of the risk effects of different obesity indicators on PLC remain unclear. In this study, we performed two-sample Mendelian randomization (MR) analyses to assess the associations of genetically determined liver fat, visceral adipose tissue (VAT), and body mass index (BMI) with the risk of PLC. The summary statistics of exposures were obtained from two genome-wide association studies (GWASs) based on the UK Biobank (UKB) imaging cohort and the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. GWAS summary statistics for PLC were obtained from FinnGen consortium R7 release data, including 304 PLC cases and 218 488 controls. Inverse-variance weighted (IVW) was used as the primary analysis, and a series of sensitivity analyses were performed to further verify the robustness of these findings. IVW analysis highlighted a significant association of genetically determined liver fat (OR per SD increase: 7.14; 95% CI: 5.10-9.99; P = 2.35E-30) and VAT (OR per SD increase: 5.70; 95% CI: 1.32-24.72; P = .020) with PLC but not of BMI with PLC. The findings were further confirmed by a series of MR methods. No evidence of horizontal pleiotropy between these associations existed. Our study suggested that genetically determined liver fat and VAT rather than BMI were associated with an increased risk of PLC, which suggested that visceral fat distribution is more predictive of the clinical risk of PLC than common in vitro measures.

Intrinsic ferromagnetism and the quantum anomalous Hall effect in two-dimensional MnOCl2 monolayers.

Due to their potential application in spintronic devices, two-dimensional (2D) ferromagnetic materials are highly desired. We used first-principles calculations and Monte Carlo simulations to investigate the electronic structure and magnetic characteristics of the MnOCl2 monolayers. We discovered two stable monolayer structures, Pmna-MnOCl2 and Pmmn-MnOCl2. Our findings show that the Pmna-MnOCl2 monolayer is an intrinsic ferromagnetic semiconductor with an indirect band gap of 0.152 eV and a Curie temperature (TC) of 202 K, while the Pmmn-MnOCl2 monolayer is an intrinsic ferromagnetic Dirac semimetal with a high TC (910 K) and triaxial magnetic anisotropy. We also show that a Pmmn-MnOCl2 monolayer with a nontrivial band gap of 6.2 meV can achieve the quantum anomalous Hall effect (QAHE) with Chern number C = 1. Additionally, the existence of a gapless edge state can be flexibly regulated by choosing the terminal edges. Our studies reveal that the Pmmn-MnOCl2 monolayer can serve as a candidate material to achieve high-temperature QAHE.

Regulation of the lysosome by sphingolipids: Potential role in aging.

Sphingolipids are a class of bioactive complex lipids that have been closely associated with aging and aging-related diseases. However, the mechanism through which sphingolipids control aging has long been a mystery. Emerging studies reveal that sphingolipids exert tight control over lysosomal homeostasis and function, as evidenced by sphingolipid-related diseases, including but not limited to lysosomal storage disorders. These diseases are defined by primary lysosomal defects and a few secondary defects such as mitochondrial dysfunction. Intriguingly, recent research indicates that the majority of these defects are also associated with aging, implying that sphingolipid-related diseases and aging may share common mechanisms. We propose that the lysosome is a pivotal hub for sphingolipid-mediated aging regulation. This review discusses the critical roles of sphingolipid metabolism in regulating various lysosomal functions, with an emphasis on how such regulation may contribute to aging and aging-related diseases.

Coexistence of intrinsic room-temperature ferromagnetism and piezoelectricity in monolayer BiCrX3 (X = S, Se, and Te).

Two-dimensional (2D) materials with intrinsic ferromagnetism and piezoelectricity have received growing attention due to their potential applications in nanoscale spintronic devices. However, their applications are highly limited by the low Curie temperatures (TC) and small piezoelectric coefficients. Here, using first-principles calculations, we have successfully predicted that BiCrX3 (X = S, Se, and Te) monolayers simultaneously possess ferromagnetism and piezoelectricity by replacing one layer of Bi atoms with Cr atoms in Bi2X3 monolayers. Our results demonstrate that BiCrX3 monolayers are not only intrinsic ferromagnetic semiconductors with indirect band gaps, adequate TC values of higher than 300 K, and significant out-of-plane magnetic anisotropic energies, but also exhibit appreciable in-plane and out-of-plane piezoelectricity. In particular, the in-plane piezoelectric coefficients of BiCrX3 monolayers with ABCAB configuration are up to 15.16 pm V-1, which is higher than those of traditional three-dimensional piezoelectric materials such as α-quartz. The coexistence of ferromagnetism and piezoelectricity in BiCrX3 monolayers gives them promising applications in spintronics and nano-sized sensors.

Optimization of Ferroelectric Ordering and Thermal Stability in Na1/2Bi1/2TiO3-Based Lead-Free Single Crystal through Defect Engineering.

Environmentally friendly lead-free piezoelectric materials have been attracting significant attention in recent years. Na1/2Bi1/2TiO3-based relaxor ferroelectrics have found acceptance for application in promising lead-free transducers in high-power ultrasonic devices. However, their low thermal stability, i.e., their relatively low ferroelectric-relaxor transition temperature (TF-R), hinders their practical application. Herein, a thermal-quenching approach is applied on a Na1/2Bi1/2TiO3 (NBT)-based single crystal, which yields a large increase in TF-R and dramatic enhancement of its ferroelectric ordering, leading to excellent thermal stability of its dielectric, ferroelectric, and piezoelectric properties. This behavior is mainly attributed to quenching-induced domain evolution as well as its octahedral tilt, which is linked to the increased oxygen vacancies. The substitution of long-range ordered ferroelectric domains for short-range polar nanodomains contributes to its increased coherence length and, consequently, enhancement of TF-R. This work provides an approach to the optimization of the ferroelectric ordering and thermal stability of NBT as well as an in-depth understanding of the quenching effect on the local structure, which could be applied to other relaxor-based ferroelectrics for optimization of their macroscopic properties.

Automatic liver tumor localization using deep learning-based liver boundary motion estimation and biomechanical modeling (DL-Bio).

PURPOSE: Recently, two-dimensional-to-three-dimensional (2D-3D) deformable registration has been applied to deform liver tumor contours from prior reference images onto estimated cone-beam computed tomography (CBCT) target images to automate on-board tumor localizations. Biomechanical modeling has also been introduced to fine-tune the intra-liver deformation-vector-fields (DVFs) solved by 2D-3D deformable registration, especially at low-contrast regions, using tissue elasticity information and liver boundary DVFs. However, the caudal liver boundary shows low contrast from surrounding tissues in the cone-beam projections, which degrades the accuracy of the intensity-based 2D-3D deformable registration there and results in less accurate boundary conditions for biomechanical modeling. We developed a deep-learning (DL)-based method to optimize the liver boundary DVFs after 2D-3D deformable registration to further improve the accuracy of subsequent biomechanical modeling and liver tumor localization. METHODS: The DL-based network was built based on the U-Net architecture. The network was trained in a supervised fashion to learn motion correlation between cranial and caudal liver boundaries to optimize the liver boundary DVFs. Inputs of the network had three channels, and each channel featured the 3D DVFs estimated by the 2D-3D deformable registration along one Cartesian direction (x, y, z). To incorporate patient-specific liver boundary information into the DVFs, the DVFs were masked by a liver boundary ring structure generated from the liver contour of the prior reference image. The network outputs were the optimized DVFs along the liver boundary with higher accuracy. From these optimized DVFs, boundary conditions were extracted for biomechanical modeling to further optimize the solution of intra-liver tumor motion. We evaluated the method using 34 liver cancer patient cases, with 24 for training and 10 for testing. We evaluated and compared the performance of three methods: 2D-3D deformable registration, 2D-3D-Bio (2D-3D deformable registration with biomechanical modeling), and DL-Bio (DL model prediction with biomechanical modeling). The tumor localization errors were quantified through calculating the center-of-mass-errors (COMEs), DICE coefficients, and Hausdorff distance between deformed liver tumor contours and manually segmented "gold-standard" contours. RESULTS: The predicted DVFs by the DL model showed improved accuracy at the liver boundary, which translated into more accurate liver tumor localizations through biomechanical modeling. On a total of 90 evaluated images and tumor contours, the average (± sd) liver tumor COMEs of the 2D-3D, 2D-3D-Bio, and DL-Bio techniques were 4.7 ± 1.9 mm, 2.9 ± 1.0 mm, and 1.7 ± 0.4 mm. The corresponding average (± sd) DICE coefficients were 0.60 ± 0.12, 0.71 ± 0.07, and 0.78 ± 0.03; and the average (± sd) Hausdorff distances were 7.0 ± 2.6 mm, 5.4 ± 1.5 mm, and 4.5 ± 1.3 mm, respectively. CONCLUSION: DL-Bio solves a general correlation model to improve the accuracy of the DVFs at the liver boundary. With improved boundary conditions, the accuracy of biomechanical modeling can be further increased for accurate intra-liver low-contrast tumor localization.

Kinetic modelling of intraband carrier relaxation in bulk and nanocrystalline lead-halide perovskites.

The relaxation of high-energy "hot" carriers in semiconductors is known to involve the redistribution of energy between hot and cold carriers, as well as the transfer of energy from hot carriers to phonons. Over the past few years, these two processes have been identified in lead-halide perovskites (LHPs) using ultrafast pump-probe experiments, but their interplay is not fully understood. Here we present a practical and intuitive kinetic model that accounts for the effects of both hot and cold carriers on carrier relaxation in LHPs. We apply this model to describe the dynamics of hot carriers in bulk and nanocrystalline CsPbBr3 as observed by multi-pulse "pump-push-probe" spectroscopy. The model captures the slowing of the relaxation dynamics in the materials as the number of hot carriers increases, which has previously been explained by a "hot-phonon bottleneck" mechanism. The model also correctly predicts an acceleration of the relaxation kinetics as the number of cold carriers in the samples is increased. Using a series of natural approximations, we reduce our model to a simple form containing terms for the carrier-carrier and carrier-phonon interactions. The model can be instrumental for evaluating the details of carrier relaxation and carrier-phonon couplings in LHPs and other soft optoelectronic materials.

Statistical image-based material decomposition for triple-energy computed tomography using total variation regularization.

BACKGROUND: Triple-energy computed tomography (TECT) can obtain x-ray attenuation measurements at three energy spectra, thereby allowing identification of different material compositions with same or very similar attenuation coefficients. This ability is known as material decomposition, which can decompose TECT images into different basis material image. However, the basis material image would be severely degraded when material decomposition is directly performed on the noisy TECT measurements using a matrix inversion method. OBJECTIVE: To achieve high quality basis material image, we present a statistical image-based material decomposition method for TECT, which uses the penalized weighted least-squares (PWLS) criteria with total variation (TV) regularization (PWLS-TV). METHODS: The weighted least-squares term involves the noise statistical properties of the material decomposition process, and the TV regularization penalizes differences between local neighboring pixels in a decomposed image, thereby contributing to improving the quality of the basis material image. Subsequently, an alternating optimization method is used to minimize the objective function. RESULTS: The performance of PWLS-TV is quantitatively evaluated using digital and mouse thorax phantoms. The experimental results show that PWLS-TV material decomposition method can greatly improve the quality of decomposed basis material image compared to the quality of images obtained using the competing methods in terms of suppressing noise and preserving edge and fine structure details. CONCLUSIONS: The PWLS-TV method can simultaneously perform noise reduction and material decomposition in one iterative step, and it results in a considerable improvement of basis material image quality.

Hot Carrier Dynamics in Perovskite Nanocrystal Solids: Role of the Cold Carriers, Nanoconfinement, and the Surface.

Carrier cooling is of widespread interest in the field of semiconductor science. It is linked to carrier-carrier and carrier-phonon coupling and has profound implications for the photovoltaic performance of materials. Recent transient optical studies have shown that a high carrier density in lead-halide perovskites (LHPs) can reduce the cooling rate through a "phonon bottleneck". However, the role of carrier-carrier interactions, and the material properties that control cooling in LHPs, is still disputed. To address these factors, we utilize ultrafast "pump-push-probe" spectroscopy on LHP nanocrystal (NC) films. We find that the addition of cold carriers to LHP NCs increases the cooling rate, competing with the phonon bottleneck. By comparing different NCs and bulk samples, we deduce that the cooling behavior is intrinsic to the LHP composition and independent of the NC size or surface. This can be contrasted with other colloidal nanomaterials, where confinement and trapping considerably influence the cooling dynamics.

U-net-based deformation vector field estimation for motion-compensated 4D-CBCT reconstruction.

PURPOSE: For four-dimensional cone-beam computed tomography (4D-CBCT), its image quality is usually degraded by insufficient projections at each respiratory phase after phase-sorting. Recently, we developed a simultaneous motion estimation and image reconstruction (SMEIR) technique, which can improve lung 4D-CBCT reconstruction quality by incorporating an interphase motion model generated as deformation vector fields (DVFs). Simultaneous motion estimation and image reconstruction uses an intensity-driven two-dimensional (2D)-three-dimensional (3D) deformation technique to estimate these DVFs by intensity-matching 2D projections. However, 2D-3D deformation may fail to generate accurate intra-lung DVFs, since the motion of intricate, small lung structures only leads to subtle intensity variations on 2D projections that are insufficient to drive accurate DVF optimization. This study is to develop convolutional neural network (CNN)-based methods to fine-tune the 2D-3D deformation DVFs to improve the efficiency and accuracy of 4D-CBCT reconstruction. METHODS: We built two U-net-based architectures for this study. The first architecture (U-net-3C) uses 2D-3D deformation-estimated DVFs (in three cardinal directions) as the input with three channels (3C), and outputs are fine-tuned DVFs. For the second architecture (U-net-4C), the reference phase CBCT image reconstructed by SMEIR was added as an additional input channel (4C) to represent patient-specific heterogeneous properties of the lung. The output fine-tuned high-quality DVFs of both models were input again into the SMEIR workflow, as an optimized motion model, to generate the final 4D-CBCT. Both methods were evaluated on 11 lung patient cases, using fivefold cross-validation. We also reconstructed 4D-CBCTs by the original SMEIR and the SMEIR-Bio (SMEIR with biomechanical modeling) algorithms for comparison. The 4D-CBCT accuracy was quantitatively assessed through metrics including root-mean-square-error (RMSE), universal quality index (UQI), and normalized cross-correlation (NCC). The DVF accuracy was evaluated by manually tracked lung landmarks. We also evaluated our proposed methods on the SPARE challenge dataset based on reconstructed 4D-CBCT quality using the above metrics. RESULTS: The average (±standard deviation) residual DVF errors of SMEIR-U-net-3C, SMEIR-U-net-4C, SMEIR-Bio, and SMEIR were 3.88 ± 3.12 mm, 3.71 ± 2.90 mm, 3.75 ± 3.40 mm, and 5.73 ± 4.61 mm, respectively. The SMEIR-U-net-3C and SMEIR-U-net-4C generated images of generally improved RMSE, UQI, and NCC as compared to the other methods. Compared with SMERI-U-net-3C, SMEIR-U-net-4C has slightly higher 4D-CBCT reconstruction and DVF estimation accuracy. For the SPARE dataset, the UQI for SMEIR-U-net-3C, SMEIR-U-net-4C, SMEIR-Bio, and SMEIR were 0.96, 0.97, 0.96, and 0.94. CONCLUSION: The CNN-based models can achieve fast (~10 s) and accurate DVF fine-tuning to improve the efficiency and accuracy of 4D-CBCT reconstruction.

Enhancing liver tumor localization accuracy by prior-knowledge-guided motion modeling and a biomechanical model.

BACKGROUND: Pre-treatment liver tumor localization remains a challenging task for radiation therapy, mostly due to the limited tumor contrast against normal liver tissues, and the respiration-induced liver tumor motion. Recently, we developed a biomechanical modeling-based, deformation-driven cone-beam CT estimation technique (Bio-CBCT), which achieved substantially improved accuracy on low-contrast liver tumor localization. However, the accuracy of Bio-CBCT is still affected by the limited tissue contrast around the caudal liver boundary, which reduces the accuracy of the boundary condition that is fed into the biomechanical modeling process. In this study, we developed a motion modeling and biomechanical modeling-guided CBCT estimation technique (MM-Bio-CBCT), to further improve the liver tumor localization accuracy by incorporating a motion model into the CBCT estimation process. METHODS: MM-Bio-CBCT estimates new CBCT images through deforming a prior high-quality CT or CBCT volume. The deformation vector field (DVF) is solved by iteratively matching the digitally-reconstructed-radiographs (DRRs) of the deformed prior image to the acquired 2D cone-beam projections. Using the same solved DVF, the liver tumor volume contoured on the prior image can be transferred onto the new CBCT image for automatic tumor localization. To maximize the accuracy of the solved DVF, MM-Bio-CBCT employs two strategies for additional DVF optimization: (I) prior-knowledge-guided liver boundary motion modeling with motion patterns extracted from a prior 4D imaging set like 4D-CTs/4D-CBCTs, to improve the liver boundary DVF accuracy; and (II) finite-element-analysis-based biomechanical modeling of the liver volume to improve the intra-liver DVF accuracy. We evaluated the accuracy of MM-Bio-CBCT on both the digital extended-cardiac-torso (XCAT) phantom images and real liver patient images. The liver tumor localization accuracy of MM-Bio-CBCT was evaluated and compared with that of the purely intensity-driven 2D-3D deformation technique, the 2D-3D deformation technique with motion modeling, and the Bio-CBCT technique. Metrics including the DICE coefficient and the center-of-mass-error (COME) were assessed for quantitative evaluation. RESULTS: Using limited-view 20 projections for CBCT estimation, the average (± SD) DICE coefficients between the estimated and the 'gold-standard' liver tumors of the XCAT study were 0.57±0.31, 0.78±0.26, 0.83±0.21, and 0.89±0.11 for 2D-3D deformation, 2D-3D deformation with motion modeling, Bio-CBCT and MM-Bio-CBCT techniques, respectively. Using 20 projections for estimation, the patient study yielded average DICE results of 0.63±0.21, 0.73±0.13 and 0.78±0.12, and 0.83±0.09, correspondingly. The MM-Bio-CBCT localized the liver tumor to an average COME of ~2 mm for both the XCAT and the liver patient studies. CONCLUSIONS: Compared to Bio-CBCT, MM-Bio-CBCT further improves the accuracy of liver tumor localization. MM-Bio-CBCT can potentially be used towards pre-treatment liver tumor localization and intra-treatment liver tumor location verification to achieve substantial radiotherapy margin reduction.

Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motion-compensated reconstruction, biomechanical modeling and deep learning.

4-Dimensional cone-beam computed tomography (4D-CBCT) offers several key advantages over conventional 3D-CBCT in moving target localization/delineation, structure de-blurring, target motion tracking, treatment dose accumulation and adaptive radiation therapy. However, the use of the 4D-CBCT in current radiation therapy practices has been limited, mostly due to its sub-optimal image quality from limited angular sampling of cone-beam projections. In this study, we summarized the recent developments of 4D-CBCT reconstruction techniques for image quality improvement, and introduced our developments of a new 4D-CBCT reconstruction technique which features simultaneous motion estimation and image reconstruction (SMEIR). Based on the original SMEIR scheme, biomechanical modeling-guided SMEIR (SMEIR-Bio) was introduced to further improve the reconstruction accuracy of fine details in lung 4D-CBCTs. To improve the efficiency of reconstruction, we recently developed a U-net-based deformation-vector-field (DVF) optimization technique to leverage a population-based deep learning scheme to improve the accuracy of intra-lung DVFs (SMEIR-Unet), without explicit biomechanical modeling. Details of each of the SMEIR, SMEIR-Bio and SMEIR-Unet techniques were included in this study, along with the corresponding results comparing the reconstruction accuracy in terms of CBCT images and the DVFs. We also discussed the application prospects of the SMEIR-type techniques in image-guided radiation therapy and adaptive radiation therapy, and presented potential schemes on future developments to achieve faster and more accurate 4D-CBCT imaging.

4D liver tumor localization using cone-beam projections and a biomechanical model.

PURPOSE: To improve the accuracy of liver tumor localization, this study tests a biomechanical modeling-guided liver cone-beam CT (CBCT) estimation (Bio-CBCT-est) technique, which generates new CBCTs by deforming a prior high-quality CT or CBCT image using deformation vector fields (DVFs). The DVFs can be used to propagate tumor contours from the prior image to new CBCTs for automatic 4D tumor localization. METHODS/MATERIALS: To solve the DVFs, the Bio-CBCT-est technique employs an iterative scheme that alternates between intensity-driven 2D-3D deformation and biomechanical modeling-guided DVF regularization and optimization. The 2D-3D deformation step solves DVFs by matching digitally reconstructed radiographs of the 3D deformed prior image to 2D phase-sorted on-board projections according to imaging intensities. This step's accuracy is limited at low-contrast intra-liver regions without sufficient intensity variations. To boost the DVF accuracy in these regions, we use the intensity-driven DVFs solved at higher-contrast liver boundaries to fine-tune the intra-liver DVFs by finite element analysis-based biomechanical modeling. We evaluated Bio-CBCT-est's accuracy with seven liver cancer patient cases. For each patient, we simulated 4D cone-beam projections from 4D-CT images, and used these projections for Bio-CBCT-est based image estimations. After Bio-CBCT-est, the DVF-propagated liver tumor/cyst contours were quantitatively compared with the manual contours on the original 4D-CT 'reference' images, using the DICE similarity index, the center-of-mass-error (COME), the Hausdorff distance (HD) and the voxel-wise cross-correlation (CC) metrics. In addition to simulation, we also performed a preliminary study to qualitatively evaluate the Bio-CBCT-est technique via clinically acquired cone beam projections. A quantitative study using an in-house deformable liver phantom was also performed. RESULTS: Using 20 projections for image estimation, the average (±s.d.) DICE index increased from 0.48 ±â€¯0.13 (by 2D-3D deformation) to 0.77 ±â€¯0.08 (by Bio-CBCT-est), the average COME decreased from 7.7 ±â€¯1.5 mm to 2.2 ±â€¯1.2 mm, the average HD decreased from 10.6 ±â€¯2.2 mm to 5.9 ±â€¯2.0 mm, and the average CC increased from -0.004 ±â€¯0.216 to 0.422 ±â€¯0.206. The tumor/cyst trajectory solved by Bio-CBCT-est matched well with that manually obtained from 4D-CT reference images. CONCLUSIONS: Bio-CBCT-est substantially improves the accuracy of 4D liver tumor localization via cone-beam projections and a biomechanical model.

4D cone-beam computed tomography (CBCT) using a moving blocker for simultaneous radiation dose reduction and scatter correction.

Four-dimensional (4D) x-ray cone-beam computed tomography (CBCT) is important for a precise radiation therapy for lung cancer. Due to the repeated use and 4D acquisition over a course of radiotherapy, the radiation dose becomes a concern. Meanwhile, the scatter contamination in CBCT deteriorates image quality for treatment tasks. In this work, we propose the use of a moving blocker (MB) during the 4D CBCT acquisition ('4D MB') and to combine motion-compensated reconstruction to address these two issues simultaneously. In 4D MB CBCT, the moving blocker reduces the x-ray flux passing through the patient and collects the scatter information in the blocked region at the same time. The scatter signal is estimated from the blocked region for correction. Even though the number of projection views and projection data in each view are not complete for conventional reconstruction, 4D reconstruction with a total-variation (TV) constraint and a motion-compensated temporal constraint can utilize both spatial gradient sparsity and temporal correlations among different phases to overcome the missing data problem. The feasibility simulation studies using the 4D NCAT phantom showed that 4D MB with motion-compensated reconstruction with 1/3 imaging dose reduction could produce satisfactory images and achieve 37% improvement on structural similarity (SSIM) index and 55% improvement on root mean square error (RMSE), compared to 4D reconstruction at the regular imaging dose without scatter correction. For the same 4D MB data, 4D reconstruction outperformed 3D TV reconstruction by 28% on SSIM and 34% on RMSE. A study of synthetic patient data also demonstrated the potential of 4D MB to reduce the radiation dose by 1/3 without compromising the image quality. This work paves the way for more comprehensive studies to investigate the dose reduction limit offered by this novel 4D MB method using physical phantom experiments and real patient data based on clinical relevant metrics.