Phase-Tailoring Wx V1-x O2 Meta-Nanofiber Enables Temperature-Editing Energy Control.

Room-temperature phase change materials (RTPCMs) exhibit promise to address challenges in thermal energy storage and release, greatly aiding in numerous domains of human existence and productivity. The conventional RTPCMs undergo inevitable volume expansion, structural collapse, and diffusion of active ingredients while maintaining desirable phase change enthalpy and ideal phase change temperature. Here, a sol-gel 1D-induced growth approach is presented to fabricate meta nanofibers (Meta-NFs) comprised of vanadium dioxide with monoclinic crystal structure, and further achieve the editable phase change temperature from 68 to 37 °C through W-doping, which allowed for tailored length variation of the zigzag V-V bond. Subsequently, Meta-NFs are assembled into 3D aerogels with self-standing architecture, thereby enabling the independent use of the RTPCMs. The obtained metamaterials demonstrate not only the temperature-editing solid-solid phase transition, but also the stiffness of the ceramic matrix, exhibiting the thermal energy control capability at room temperature (37 °C), thermal insulation properties, temperature resistance, and flame retardancy. The effective creation of these fascinating metamaterials might offer new insights for next-generation and self-standing solid-solid RTPCMs.

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.

The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography-guided adaptive radiation therapy. A deep unsupervised learning model was introduced to map breast's tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord from planning computed tomography to cone-beam CT. To improve the traditional image registration method's performance, we used a regional deformable framework based on the narrow-band mapping, which can mitigate the effect of the image artifacts on the cone-beam CT. We retrospectively selected 373 anonymized cone-beam CT volumes from 111 patients with breast cancer. The cone-beam CTs are divided into three sets. 311 / 20 / 42 cone-beam CT images were used for training, validating, and testing. The manual contour was used as reference for the testing set. We compared the results between the reference and the model prediction for evaluating the performance. The mean Dice between manual reference segmentations and the model predicted segmentations for breast tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord were 0.78 ± 0.09, 0.90 ± 0.03, 0.88 ± 0.04, 0.94 ± 0.03, 0.95 ± 0.02, and 0.77 ± 0.07, respectively. The results demonstrated a good agreement between the reference and the proposed contours. The proposed deep learning-based regional deformable model technique can automatically propagate contours for breast cancer adaptive radiotherapy. Deep learning in contour propagation was promising, but further investigation was warranted.

Human induced pluripotent stem cell-derived cardiomyocytes reveal abnormal TGFß signaling in type 2 diabetes mellitus.

Diabetes mellitus is a serious metabolic condition associated with a multitude of cardiovascular complications. Moreover, the prevalence of diabetes in heart failure populations is higher than that in control populations. However, the role of cardiomyocyte alterations in type 2 diabetes mellitus (T2DM) has not been well characterized and the underlying mechanisms remain elusive. In this study, two patients who were diagnosed as T2DM were recruited and patient-specific induced pluripotent stem cells (iPSCs) were generated from urine epithelial cells using nonintegrated Sendai virus. The iPSC lines derived from five healthy subjects were used as controls. All iPSCs were differentiated into cardiomyocytes (iPSC-CMs) using the monolayer-based differentiation protocol. T2DM iPSC-CMs exhibited various disease phenotypes, including cellular hypertrophy and lipid accumulation. Moreover, T2DM iPSC-CMs exhibited higher susceptibility to high-glucose/high-lipid challenge than control iPSC-CMs, manifesting an increase in apoptosis. RNA-Sequencing analysis revealed a differential transcriptome profile and abnormal activation of TGFß signaling pathway in T2DM iPSC-CMs. We went on to show that inhibition of TGFß significantly rescued the hypertrophic phenotype in T2DM iPSC-CMs. In conclusion, we demonstrate that the iPSC-CM model is able to recapitulate cellular phenotype of T2DM. Our results indicate that iPSC-CMs can therefore serve as a suitable model for investigating molecular mechanisms underlying diabetic cardiomyopathies and for screening therapeutic drugs.

Assessment of liver fibrosis using pharmacokinetic parameters of dynamic contrast-enhanced magnetic resonance imaging.

PURPOSE: To evaluate the pharmacokinetic parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in diagnosing and staging liver fibrosis in rabbits. MATERIALS AND METHODS: DCE-MRI with gadodiamide (Gd-DTPA-BMA) was performed on a 3.0 Tesla, 60 cm bore MR scanner for rabbits with CCl4 -induced liver fibrosis, and an untreated control group. Fibrosis was staged according to the METAVIR system: control (F0; n = 13), nonadvanced fibrosis (F1-2; n = 15), and advanced fibrosis (F3-4; n = 12). The DCE-MRI parameters K(trans) , kep , Ve , and vp were measured with a dual-input extended Tofts model. Receiver operating characteristic analyses were performed to assess the diagnostic performance of K(trans) , Ve , and vp in staging liver fibrosis. RESULTS: Both K(trans) and Ve decreased with increasing fibrosis stage. K(trans) of the control group was significantly different from that of the overall fibrosis group, nonadvanced group, and advanced group (P < 0.001 for all). Significant differences were found between Ve of the control group and that of the overall fibrosis and advanced groups (P = 0.019 and P = 0.009, respectively). For K(trans) , the areas under the receiver operating characteristic curve (AUROCs) for discriminating the control group from the overall fibrosis and advanced fibrosis groups were 0.909 (95% confidence interval [CI], 0.809-1.000), and 0.936 (95% CI,0.847-1.000), respectively. For discriminating between the control and nonadvanced fibrosis groups, the AUROC of K(trans) was 0.887 (95% CI, 0.762-1.000). The AUROCs of K(trans) were higher than those of Ve and vp for discriminating between the control and overall fibrosis groups, the control and nonadvanced fibrosis groups, and the control and advanced fibrosis groups. Pharmacokinetic parameters were negatively correlated with fibrosis stage (K(trans) , rho = -0.668, P < 0.001; Ve , rho = -0.438, P = 0.005; vp , rho = -0.360, P = 0.023). CONCLUSION: Among pharmacokinetic parameters of DCE-MRI in our study, K(trans) was an excellent predictor for differentiating fibrotic livers from normal livers, and differentiating normal livers from nonadvanced or advanced fibrosis livers. J. Magn. Reson. Imaging 2016;44:98-104.

Image-domain shading correction for cone-beam CT without prior patient information.

In the era of high-precision radiotherapy, cone-beam CT (CBCT) is frequently utilized for on-board treatment guidance. However, CBCT images usually contain severe shading artifacts due to strong photon scatter from illumination of a large volume and non-optimized patient-specific data measurements, limiting the full clinical applications of CBCT. Many algorithms have been proposed to alleviate this problem by data correction on projections. Sophisticated methods have also been designed when prior patient information is available. Nevertheless, a standard, efficient, and effective approach with large applicability remains elusive for current clinical practice. In this work, we develop a novel algorithm for shading correction directly on CBCT images. Distinct from other image-domain correction methods, our approach does not rely on prior patient information or prior assumption of patient data. In CBCT, projection errors (mostly from scatter and non-ideal usage of bowtie filter) result in dominant low-frequency shading artifacts in image domain. In circular scan geometry, these artifacts often show global or local radial patterns. Hence, the raw CBCT images are first preprocessed into the polar coordinate system. Median filtering and polynomial fitting are applied on the transformed image to estimate the low-frequency shading artifacts (referred to as the bias field) angle-by-angle and slice-by-slice. The low-pass filtering process is done firstly along the angular direction and then the radial direction to preserve image contrast. The estimated bias field is then converted back to the Cartesian coordinate system, followed by 3D low-pass filtering to eliminate possible high-frequency components. The shading-corrected image is finally obtained as the uncorrected volume divided by the bias field. The proposed algorithm was evaluated on CBCT images of a pelvis patient and a head patient. Mean CT number values and spatial non-uniformity on the reconstructed images were used as image quality metrics. Within selected regions of interest, the average CT number error was reduced from around 300 HU to 42 and 38 HU, and the spatial nonuniformity error was reduced from above 17.5% to 2.1% and 1.7% for the pelvis and the head patients, respectively. As our method suppresses only low-frequency shading artifacts, patient anatomy and contrast were retained in the corrected images for both cases. Our shading correction algorithm on CBCT images offers several advantages. It has a high efficiency, since it is deterministic and directly operates on the reconstructed images. It requires no prior information or assumptions, which not only achieves the merits of CBCT-based treatment monitoring by retaining the patient anatomy, but also facilitates its clinical use as an efficient image-correction solution.

Iterative CT reconstruction via minimizing adaptively reweighted total variation.

BACKGROUND: Iterative reconstruction via total variation (TV) minimization has demonstrated great successes in accurate CT imaging from under-sampled projections. When projections are further reduced, over-smoothing artifacts appear in the current reconstruction especially around the structure boundaries. OBJECTIVE: We propose a practical algorithm to improve TV-minimization based CT reconstruction on very few projection data. METHOD: Based on the theory of compressed sensing, the L-0 norm approach is more desirable to further reduce the projection views. To overcome the computational difficulty of the non-convex optimization of the L-0 norm, we implement an adaptive weighting scheme to approximate the solution via a series of TV minimizations for practical use in CT reconstruction. The weight on TV is initialized as uniform ones, and is automatically changed based on the gradient of the reconstructed image from the previous iteration. The iteration stops when a small difference between the weighted TV values is observed on two consecutive reconstructed images. RESULTS: We evaluate the proposed algorithm on both a digital phantom and a physical phantom. Using 20 equiangular projections, our method reduces reconstruction errors in the conventional TV minimization by a factor of more than 5, with improved spatial resolution. CONCLUSIONS: By adaptively reweighting TV in iterative CT reconstruction, we successfully further reduce the projection number for the same or better image quality.

DeCoGAN: MVCT image denoising via coupled generative adversarial network.

Objective.In helical tomotherapy, image-guided radiotherapy employs megavoltage computed tomography (MVCT) for precise targeting. However, the high voltage of megavoltage radiation introduces substantial noise, significantly compromising MVCT image clarity. This study aims to enhance MVCT image quality using a deep learning-based denoising method.Approach.We propose an unpaired MVCT denoising network using a coupled generative adversarial network framework (DeCoGAN). Our approach assumes that a universal latent code within a shared latent space can reconstruct any given pair of images. By employing an encoder, we enforce this shared-latent space constraint, facilitating the conversion of low-quality (noisy) MVCT images into high-quality (denoised) counterparts. The network learns the joint distribution of images from both domains by leveraging samples from their respective marginal distributions, enhanced by adversarial training for effective denoising.Main Results.Compared to an analytical algorithm (BM3D) and three deep learning-based methods (RED-CNN, WGAN-VGG and CycleGAN), the proposed method excels in preserving image details and enhancing human visual perception by removing most noise and retaining structural features. Quantitative analysis demonstrates that our method achieves the highest peak signal-to-noise ratio and Structural Similarity Index Measurement values, indicating superior denoising performance.Significance.The proposed DeCoGAN method shows remarkable MVCT denoising performance, making it a promising tool in the field of radiation therapy.

Prediction of SBRT response in liver cancer by combining original and delta cone-beam CT radiomics: a pilot study.

This study aims to explore the feasibility of utilizing a combination of original and delta cone-beam CT (CBCT) radiomics for predicting treatment response in liver tumors undergoing stereotactic body radiation therapy (SBRT). A total of 49 patients are included in this study, with 36 receiving 5-fraction SBRT, 3 receiving 4-fraction SBRT, and 10 receiving 3-fraction SBRT. The CBCT and planning CT images from liver cancer patients who underwent SBRT are collected to extract overall 547 radiomics features. The CBCT features which are reproducible and interchangeable with pCT are selected for modeling analysis. The delta features between fractions are calculated to depict tumor change. The patients with 4-fraction SBRT are only used for screening robust features. In patients receiving 5-fraction SBRT, the predictive ability of both original and delta CBCT features for two-level treatment response (local efficacy vs. local non-efficacy; complete response (CR) vs. partial response (PR)) is assessed by utilizing multivariable logistic regression with leave-one-out cross-validation. Additionally, univariate analysis is conducted to validate the capability of CBCT features in identifying local efficacy in patients receiving 3-fraction SBRT. In patients receiving 5-fraction SBRT, the combined models incorporating original and delta CBCT radiomics features demonstrate higher area under the curve (AUC) values compared to models using either original or delta features alone for both classification tasks. The AUC values for predicting local efficacy vs. local non-efficacy are 0.58 for original features, 0.82 for delta features, and 0.90 for combined features. For distinguishing PR from CR, the respective AUC values for original, delta and combined features are 0.79, 0.80, and 0.89. In patients receiving 3-fraction SBRT, eight valuable CBCT radiomics features are identified for predicting local efficacy. The combination of original and delta radiomics derived from fractionated CBCT images in liver cancer patients undergoing SBRT shows promise in providing comprehensive information for predicting treatment response.

An indirect estimation of x-ray spectrum via convolutional neural network and transmission measurement.

Objective.In this work, we aim to propose an accurate and robust spectrum estimation method by synergistically combining x-ray imaging physics with a convolutional neural network (CNN).Approach.The approach relies on transmission measurements, and the estimated spectrum is formulated as a convolutional summation of a few model spectra generated using Monte Carlo simulation. The difference between the actual and estimated projections is utilized as the loss function to train the network. We contrasted this approach with the weighted sums of model spectra approach previously proposed. Comprehensive studies were performed to demonstrate the robustness and accuracy of the proposed approach in various scenarios.Main results.The results show the desirable accuracy of the CNN-based method for spectrum estimation. The ME and NRMSE were -0.021 keV and 3.04% for 80 kVp, and 0.006 keV and 4.44% for 100 kVp, superior to the previous approach. The robustness test and experimental study also demonstrated superior performances. The CNN-based approach yielded remarkably consistent results in phantoms with various material combinations, and the CNN-based approach was robust concerning spectrum generators and calibration phantoms.Significance. We proposed a method for estimating the real spectrum by integrating a deep learning model with real imaging physics. The results demonstrated that this method was accurate and robust in estimating the spectrum, and it is potentially helpful for broad x-ray imaging tasks.

Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network.

Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and critical to study AD through multi-modal data. Existing learning methods, however, usually ignore the influence of feature heterogeneity and directly fuse features in the last stages. Furthermore, most of these methods only focus on local fusion features or global fusion features, neglecting the complementariness of features at different levels and thus not sufficiently leveraging information embedded in multi-modal data. To overcome these shortcomings, we propose a novel framework for AD diagnosis that fuses gene, imaging, protein, and clinical data. Our framework learns feature representations under the same feature space for different modalities through a feature induction learning (FIL) module, thereby alleviating the impact of feature heterogeneity. Furthermore, in our framework, local and global salient multi-modal feature interaction information at different levels is extracted through a novel dual multilevel graph neural network (DMGNN). We extensively validate the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and experimental results demonstrate our method consistently outperforms other state-of-the-art multi-modal fusion methods. The code is publicly available on the GitHub website. (https://github.com/xiankantingqianxue/MIA-code.git).

Exploring the biological basis of CT imaging features in pancreatic neuroendocrine tumors: a two-center study.

Objective.Medical imaging offered a non-invasive window to visualize tumors, with radiomics transforming these images into quantitative data for tumor phenotyping. However, the intricate web linking imaging features, clinical endpoints, and tumor biology was mostly uncharted. This study aimed to unravel the connections between CT imaging features and clinical characteristics, including tumor histopathological grading, clinical stage, and endocrine symptoms, alongside immunohistochemical markers of tumor cell growth, such as the Ki-67 index and nuclear mitosis rate.Approach.We conducted a retrospective analysis of data from 137 patients with pancreatic neuroendocrine tumors who had undergone contrast-enhanced CT scans across two institutions. Our study focused on three clinical factors: pathological grade, clinical stage, and endocrine symptom status, in addition to two immunohistochemical markers: the Ki-67 index and the rate of nuclear mitosis. We computed both predefined (2D and 3D) and learning-based features (via sparse autoencoder, or SAE) from the scans. To unearth the relationships between imaging features, clinical factors, and immunohistochemical markers, we employed the Spearman rank correlation along with the Benjamini-Hochberg method. Furthermore, we developed and validated radiomics signatures to foresee these clinical factors.Main results.The 3D imaging features showed the strongest relationships with clinical factors and immunohistochemical markers. For the association with pathological grade, the mean absolute value of the correlation coefficient (CC) of 2D, SAE, and 3D features was 0.3318 ± 0.1196, 0.2149 ± 0.0361, and 0.4189 ± 0.0882, respectively. While for the association with Ki-67 index and rate of nuclear mitosis, the 3D features also showed higher correlations, with CC as 0.4053 ± 0.0786 and 0.4061 ± 0.0806. In addition, the 3D feature-based signatures showed optimal performance in clinical factor prediction.Significance.We found relationships between imaging features, clinical factors, and immunohistochemical markers. The 3D features showed higher relationships with clinical factors and immunohistochemical markers.

Novel methods to diagnose rotator cuff tear and predict post-operative Re-tear: Radiomics models.

Objective: To validated a classifier to distinguish the status of rotator cuff tear and predict post-operative re-tear by utilizing magnetic resonance imaging (MRI) markers. Methods: This retrospective study included patients with healthy rotator cuff and patients diagnosed as rotator cuff tear (RCT) by MRI. Radiomics features were identified from the pre-operative shoulder MRI and selected by using maximum relevance minimum redundancy (MRMR) methods. A radiomics model for diagnosis of RCT was constructed, based on the 3D volume of interest (VOI) of supraspinatus. Another model for the prediction of rotator re-tear after rotator cuff repair (Re-RCT) was constructed based on VOI of humerus, supraspinatus, infraspinatus and other clinical parameters. Results: The model for diagnosing the status of RCT produced an area under the receiver operating characteristic curve (AUC) of 0.989 in the training cohort and 0.979 for the validation cohort. The radiomics model for predicting Re-RCT produced an AUC of 0.923 ± 0.017 for the training dataset and 0.790 ± 0.082 for the validation dataset. The nomogram combining radiomics features and clinical factors yielded an AUC of 0.961 ± 0.020 for the training dataset and 0.808 ± 0.081 for the validation dataset, which displayed the best performance among all models. Conclusion: Radiomics models for the diagnosis of rotator cuff tear and prediction of post-operative Re-RCT yielded a decent prediction accuracy.

UBES: Unified scatter correction using ultrafast Boltzmann equation solver for conebeam CT.

A semi-analytical solution to the unified Boltzmann equation is constructed to exactly describe the scatter distribution on a flat-panel detector for high-quality conebeam CT (CBCT) imaging. The solver consists of three parts, including the phase space distribution estimator, the effective source constructor and the detector signal extractor. Instead of the tedious Monte Carlo solution, the derived Boltzmann equation solver achieves ultrafast computational capability for scatter signal estimation by combining direct analytical derivation and time-efficient one-dimensional numerical integration over the trajectory along each momentum of the photon phase space distribution. The execution of scatter estimation using the proposed ultrafast Boltzmann equation solver (UBES) for a single projection is finalized in around 0.4 seconds. We compare the performance of the proposed method with the state-of-the-art schemes, including a time-expensive Monte Carlo (MC) method and a conventional kernel-based algorithm using the same dataset, which is acquired from the CBCT scans of a head phantom and an abdominal patient. The evaluation results demonstrate that the proposed UBES method achieves comparable correction accuracy compared with the MC method, while exhibits significant improvements in image quality over learning and kernel-based methods. With the advantages of MC equivalent quality and superfast computational efficiency, the UBES method has the potential to become a standard solution to scatter correction in high-quality CBCT reconstruction.

PNMC: Four-dimensional conebeam CT reconstruction combining prior network and motion compensation.

Four-dimensional conebeam computed tomography (4D CBCT) is an efficient technique to overcome motion artifacts caused by organ motion during breathing. 4D CBCT reconstruction in a single scan usually divides projections into different groups of sparsely sampled data based on the respiratory phases. The reconstructed images within each group present poor image quality due to the limited number of projections. To improve the image quality of 4D CBCT in a single scan, we propose a novel reconstruction scheme that combines prior knowledge with motion compensation. We apply the reconstructed images of the full projections within a single routine as prior knowledge, providing structural information for the network to enhance the restoration structure. The prior network (PN-Net) is proposed to extract features of prior knowledge and fuse them with the sparsely sampled data using an attention mechanism. The prior knowledge guides the reconstruction process to restore the approximate organ structure and alleviates severe streaking artifacts. The deformation vector field (DVF) extracted using deformable image registration among different phases is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction algorithm to generate 4D CBCT images. Proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Compared with previous methods, our approach exhibits significant improvements across various evaluation metrics.

Evaluation of low-dose computed tomography reconstruction using spatial-radon domain total generalized variation regularization.

The x-ray radiation dose in computed tomography (CT) examination has been a major concern for patients. Lowing the tube current and exposure time in data acquisition is a straightforward and cost-effective strategy to reduce the x-ray radiation dose. However, this will inevitably increase the noise fluctuations in measured projection data, and the corresponding CT image quality will be severely degraded if noise suppression is not performed during image reconstruction. To reconstruct high-quality low-dose CT image, we present a spatial-radon domain total generalized variation (SRDTGV) regularization for statistical iterative reconstruction (SIR) based on penalized weighted least-squares (PWLS) principle, which is called PWLS-SRDTGV for simplicity. The presented PWLS-SRDTGV model can simultaneously reconstruct high-quality CT image in space domain and its corresponding projection in radon domain. An efficient split Bregman algorithm was applied to minimize the cost function of the proposed reconstruction model. Qualitative and quantitative studies were performed to evaluate the effectiveness of the PWLS-SRDTGV image reconstruction algorithm using a digital 3D XCAT phantom and an anthropomorphic torso phantom. The experimental results demonstrate that PWLS-SRDTGV algorithm achieves notable gains in noise reduction, streak artifact suppression, and edge preservation compared with competing reconstruction approaches.

Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network.

BACKGROUND: Cone beam computed tomography (CBCT) plays an increasingly important role in image-guided radiation therapy. However, the image quality of CBCT is severely degraded by excessive scatter contamination, especially in the abdominal region, hindering its further applications in radiation therapy. PURPOSE: To restore low-quality CBCT images contaminated by scatter signals, a scatter correction algorithm combining the advantages of convolutional neural networks (CNN) and Swin Transformer is proposed. METHODS: In this paper a scatter correction model for CBCT image, the Flip Swin Transformer U-shape network (FSTUNet) model, is proposed. In this model, the advantages of CNN in texture detail and Swin Transformer in global correlation are used to accurately extract shallow and deep features, respectively. Instead of using the original Swin Transformer tandem structure, we build the Flip Swin Transformer Block to achieve a more powerful inter-window association extraction. The validity and clinical relevance of the method is demonstrated through extensive experiments on a Monte Carlo (MC) simulation dataset and frequency split dataset generated by a validated method, respectively. RESULT: Experimental results on the MC simulated dataset show that the root mean square error of images corrected by the method is reduced from over 100 HU to about 7 HU. Both the structural similarity index measure (SSIM) and the universal quality index (UQI) are close to 1. Experimental results on the frequency split dataset demonstrate that the method not only corrects shading artifacts but also exhibits a high degree of structural consistency. In addition, comparison experiments show that FSTUNet outperforms UNet, Deep Residual Convolutional Neural Network (DRCNN), DSENet, Pix2pixGAN, and 3DUnet methods in both qualitative and quantitative metrics. CONCLUSIONS: Accurately capturing the features at different levels is greatly beneficial for reconstructing high-quality scatter-free images. The proposed FSTUNet method is an effective solution to CBCT scatter correction and has the potential to improve the accuracy of CBCT image-guided radiation therapy.

CT-based radiomics for the identification of colorectal cancer liver metastases sensitive to first-line irinotecan-based chemotherapy.

BACKGROUND: Chemosensitivity prediction in colorectal cancer patients with liver metastases has remained a research hotspot. Radiomics can extract features from patient imaging, and deep learning or machine learning can be used to build models to predict patient outcomes prior to chemotherapy. PURPOSE: In this study, the radiomics features and clinical data of colorectal cancer patients with liver metastases were used to predict their sensitivity to irinotecan-based chemotherapy. METHODS: A total of 116 patients with unresectable colorectal cancer liver metastases who received first-line irinotecan-based chemotherapy from January 2015 to January 2020 in our institution were retrospectively collected. Overall, 116 liver metastases were randomly divided into training (n = 81) and validation (n = 35) cohorts in a 7:3 ratio. The effect of chemotherapy was determined based on Response Evaluation Criteria in Solid Tumors. The lesions were divided into response and nonresponse groups. Regions of interest (ROIs) were manually segmented, and sample sizes of 1×1×1, 3×3×3, 5×5×5 mm3 were used to extract radiomics features. The relevant features were identified through Pearson correlation analysis and the MRMR algorithm, and the clinical data were merged into the artificial neural network. Finally, the p-model was obtained after repeated learning and testing. RESULTS: The p-model could distinguish responders in the training (area under the curve [AUC] 0.754, 95% CI 0.650-0.858) and validation cohorts (AUC 0.752 95% CI 0.581-0.904). AUC values of the pure image group model are 0.720 (95% CI 0.609-0.827) and 0.684 (95% CI 0.529-0.890) for the training and validation cohorts respectively. As for the clinical data model, AUC values of the training and validation cohorts are 0.638 (95% CI 0.500-0.757) and 0.545 (95% CI 0.360-0.785), respectively. The performances of the latter two are less than that of the former. CONCLUSION: The p-model has the potential to discriminate colorectal cancer patients sensitive to chemotherapy. This model holds promise as a noninvasive tool to predict the response of colorectal liver metastases to chemotherapy, allowing for personalized treatment planning.

Four-Dimensional Cone Beam CT Imaging Using a Single Routine Scan via Deep Learning.

A novel method is proposed to obtain four-dimensional (4D) cone-beam computed tomography (CBCT) images from a routine scan in patients with upper abdominal cancer. The projections are sorted according to the location of the lung diaphragm before being reconstructed to phase-sorted data. A multiscale-discriminator generative adversarial network (MSD-GAN) is proposed to alleviate the severe streaking artifacts in the original images. The MSD-GAN is trained using simulated CBCT datasets from patient planning CT images. The enhanced images are further used to estimate the deformable vector field (DVF) among breathing phases using a deformable image registration method. The estimated DVF is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction approach to generate 4D CBCT images. The proposed MSD-GAN is compared with U-Net on the performance of image enhancement. Results show that the proposed method significantly outperforms the total variation regularization-based iterative reconstruction approach and the method using only MSD-GAN to enhance original phase-sorted images in simulation and patient studies on 4D reconstruction quality. The MSD-GAN also shows higher accuracy than the U-Net. The proposed method enables a practical way for 4D-CBCT imaging from a single routine scan in upper abdominal cancer treatment including liver and pancreatic tumors.

4D-CT deformable image registration using unsupervised recursive cascaded full-resolution residual networks.

A novel recursive cascaded full-resolution residual network (RCFRR-Net) for abdominal four-dimensional computed tomography (4D-CT) image registration was proposed. The entire network was end-to-end and trained in the unsupervised approach, which meant that the deformation vector field, which presented the ground truth, was not needed during training. The network was designed by cascading three full-resolution residual subnetworks with different architectures. The training loss consisted of the image similarity loss and the deformation vector field regularization loss, which were calculated based on the final warped image and the fixed image, allowing all cascades to be trained jointly and perform the progressive registration cooperatively. Extensive network testing was conducted using diverse datasets, including an internal 4D-CT dataset, a public DIRLAB 4D-CT dataset, and a 4D cone-beam CT (4D-CBCT) dataset. Compared with the iteration-based demon method and two deep learning-based methods (VoxelMorph and recursive cascaded network), the RCFRR-Net achieved consistent and significant gains, which demonstrated that the proposed method had superior performance and generalization capability in medical image registration. The proposed RCFRR-Net was a promising tool for various clinical applications.

A generalizable new figure of merit for dose optimization in dual energy cone beam CT scanning protocols.

Objective. This study proposes and evaluates a new figure of merit (FOMn) for dose optimization of Dual-energy cone-beam CT (DE-CBCT) scanning protocols based on size-dependent modeling of radiation dose and multi-scale image quality.Approach. FOMn was defined using Z-score normalization and was proportional to the dose efficiency providing better multi-scale image quality, including comprehensive contrast-to-noise ratio (CCNR) and electron density (CED) for CatPhan604 inserts of various materials. Acrylic annuluses were combined with CatPhan604 to create four phantom sizes (diameters of the long axis are 200 mm, 270 mm, 350 mm, and 380 mm, respectively). DE-CBCT was decomposed using image-domain iterative methods based on Varian kV-CBCT images acquired using 25 protocols (100 kVp and 140 kVp combined with 5 tube currents).Main results. The accuracy of CED was approximately 1% for all protocols, but degraded monotonically with the increased phantom sizes. Combinations of lower voltage + higher current and higher voltage + lower current were optimal protocols balancing CCNR and dose. The most dose-efficient protocols for CED and CCNR were inconsistent, underlining the necessity of including multi-scale image quality in the evaluation and optimization of DE-CBCT. Pediatric and adult anthropomorphic phantom tests confirmed dose-efficiency of FOMn-recommended protocols.Significance. FOMn is a comprehensive metric that collectively evaluates radiation dose and multi-scale image quality for DE-CBCT. The models and data can also serve as lookup tables, suggesting personalized dose-efficient protocols for specific clinical imaging purposes.