A generalized simultaneous algebraic reconstruction technique (GSART) for dual-energy X-ray computed tomography.

BACKGROUND: Dual-energy computed tomography (DECT) is a widely used and actively researched imaging modality that can estimate the physical properties of an object more accurately than single-energy CT (SECT). Recently, iterative reconstruction methods called one-step methods have received attention among various approaches since they can resolve the intermingled limitations of the conventional methods. However, the one-step methods typically have expensive computational costs, and their material decomposition performance is largely affected by the accuracy in the spectral coefficients estimation. OBJECTIVE: In this study, we aim to develop an efficient one-step algorithm that can effectively decompose into the basis material maps and is less sensitive to the accuracy of the spectral coefficients. METHODS: By use of a new loss function that employs the non-linear forward model and the weighted squared errors, we propose a one-step reconstruction algorithm named generalized simultaneous algebraic reconstruction technique (GSART). The proposed algorithm was compared with the image-domain material decomposition and other existing one-step reconstruction algorithm. RESULTS: In both simulation and experimental studies, we demonstrated that the proposed algorithm effectively reduced the beam-hardening artifacts thereby increasing the accuracy in the material decomposition. CONCLUSIONS: The proposed one-step reconstruction for material decomposition in dual-energy CT outperformed the image-domain approach and the existing one-step algorithm. We believe that the proposed method is a practically very useful addition to the material-selective image reconstruction field.

Data-specific mask-guided image reconstruction for diffuse optical tomography.

Conventional approaches in diffuse optical tomography (DOT) image reconstruction often address the ill-posed inverse problem via regularization with a constant penalty parameter, which uniformly smooths out the solution. In this study, we present a data-specific mask-guided scheme that incorporates a prior mask constraint into the image reconstruction framework. The prior mask was created from the DOT data itself by exploiting the multi-measurement vector formulation. We accordingly propose two methods to integrate the prior mask into the reconstruction process. First, as a soft prior by exploiting a spatially varying regularization. Second, as a hard prior by imposing a region-of-interest-limited reconstruction. Furthermore, the latter method iterates between discrete and continuous steps to update the mask and optical parameters, respectively. The proposed methods showed enhanced optical contrast accuracy, improved spatial resolution, and reduced noise level in DOT reconstructed images compared with the conventional approaches such as the modified Levenberg-Marquardt approach and the l1-regularization based sparse recovery approach.

Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography.

Deep learning has been actively investigated for various applications such as image classification, computer vision, and regression tasks, and it has shown state-of-the-art performance. In diffuse optical tomography (DOT), the accurate estimation of the bulk optical properties of a medium is paramount because it directly affects the overall image quality. In this work, we exploit deep learning to propose a novel, to the best of our knowledge, convolutional neural network (CNN)-based approach to estimate the bulk optical properties of a highly scattering medium such as biological tissue in DOT. We validated the proposed method by using experimental, as well as, simulated data. For performance assessment, we compared the results of the proposed method with those of existing approaches. The results demonstrate that the proposed CNN-based approach for bulk optical property estimation outperforms existing methods in terms of estimation accuracy, with lower computation time.

An additional tilted-scan-based CT metal-artifact-reduction method for radiation therapy planning.

PURPOSE: As computed tomography (CT) imaging is the most commonly used modality for treatment planning in radiation therapy, metal artifacts in the planning CT images may complicate the target delineation and reduce the dose calculation accuracy. Although current CT scanners do provide certain correction steps, it is a common understanding that there is not a universal solution yet to the metal artifact reduction (MAR) in general. Particularly noting the importance of MAR for radiation treatment planning, we propose a novel MAR method in this work that recruits an additional tilted CT scan and synthesizes nearly metal-artifact-free CT images. METHODS: The proposed method is based on the facts that the most pronounced metal artifacts in CT images show up along the x-ray beam direction traversing multiple metallic objects and that a tilted CT scan can provide complementary information free of such metal artifacts in the earlier scan. Although the tilted CT scan would contain its own metal artifacts in the images, the artifacts may manifest in a different fashion leaving a chance to concatenate the two CT images with the metal artifacts much suppressed. We developed an image processing technique that uses the structural similarity (SSIM) for suppressing the metal artifacts. On top of the additional scan, we proposed to use an existing MAR method for each scan if necessary to further suppress the metal artifacts. RESULTS: The proposed method was validated by a simulation study using the pelvic region of an XCAT numerical phantom and also by an experimental study using the head part of the Rando phantom. The proposed method was found to effectively reduce the metal artifacts. Quantitative analyses revealed that the proposed method reduced the mean absolute percentages of the error by up to 86% and 89% in the simulation and experimental studies, respectively. CONCLUSIONS: It was confirmed that the proposed method, using complementary information acquired from an additional tilted CT scan, can provide nearly metal-artifact-free images for the treatment planning.

An Image-Based Reduction of Metal Artifacts in Computed Tomography.

OBJECTIVE: Various strategies have been developed in the past to reduce the excessive effects of metal artifacts in computed tomography images. From straightforward sinogram inpainting-based methods to computationally expensive iterative methods, all have been successful in improving the image quality up to a certain degree. We propose a novel image-based metal artifact subtraction method that achieves a superior image quality and at the same time provides a quantitatively more accurate image. METHODS: Our proposed method consists of prior image-based sinogram inpainting, metal sinogram extraction, and metal artifact image subtraction. Reconstructing the metal images from the extracted metal-contaminated portions in the sinogram yields a streaky image that eventually can be subtracted from the uncorrected image. The prior image is reconstructed from the sinogram that is free from the metal-contaminated portions by use of a total variation (TV) minimization algorithm, and the reconstructed prior image is fed into the forward projector so that the missing portions in the sinogram can be recovered. Image quality of the metal artifact-reduced images on selected areas was assessed by the structure similarity index for the simulated data and SD for the real dental data. RESULTS: Simulation phantom studies showed higher structure similarity index values for the proposed metal artifact reduction (MAR) images than the standard MAR images. Thus, more artifact suppression was observed in proposed MAR images. In real dental phantom data study, lower SD values were calculated from the proposed MAR images. The findings in real human arm study were also consistent with the results in all phantom studies. Thus, compared with standard MAR images, lesser artifact intensity was exhibited by the proposed MAR images. CONCLUSIONS: From the quantitative calculations, our proposed method has shown to be effective and superior to the conventional approach in both simulation and real dental phantom cases.

Sparse-view computed laminography with a spherical sinusoidal scan for nondestructive testing.

X-ray computed laminography is widely used in nondestructive testing of relatively flat objects using an oblique scanning configuration for data acquisition. In this work, a new scanning scheme is proposed in conjunction with the compressive-sensing-based image reconstruction for reducing imaging radiation dose and scanning time. We performed a numerical study comparing image qualities acquired by various scanning configurations that are practically implementable: single-arc, double-arc, oblique, and spherical-sinusoidal trajectories. A compressive-sensing-inspired total-variation (TV) minimization algorithm was used to reconstruct the images from the data acquired at only 40 projection views in those trajectories. It was successfully demonstrated that the proposed scanning scheme outperforms the others in terms of image contrast and spatial resolution, although the oblique scanning scheme showed a comparable resolution property. We believe that the proposed scanning method may provide a solution to fast and low-dose nondestructive testing of radiation-sensitive and highly integrated devices such as multilayer microelectronic circuit boards.

Improving image accuracy of region-of-interest in cone-beam CT using prior image.

In diagnostic follow-ups of diseases, such as calcium scoring in kidney or fat content assessment in liver using repeated CT scans, quantitatively accurate and consistent CT values are desirable at a low cost of radiation dose to the patient. Region of-interest (ROI) imaging technique is considered a reasonable dose reduction method in CT scans for its shielding geometry outside the ROI. However, image artifacts in the reconstructed images caused by missing data outside the ROI may degrade overall image quality and, more importantly, can decrease image accuracy of the ROI substantially. In this study, we propose a method to increase image accuracy of the ROI and to reduce imaging radiation dose via utilizing the outside ROI data from prior scans in the repeated CT applications. We performed both numerical and experimental studies to validate our proposed method. In a numerical study, we used an XCAT phantom with its liver and stomach changing their sizes from one scan to another. Image accuracy of the liver has been improved as the error decreased from 44.4 HU to -0.1 HU by the proposed method, compared to an existing method of data extrapolation to compensate for the missing data outside the ROI. Repeated cone-beam CT (CBCT) images of a patient who went through daily CBCT scans for radiation therapy were also used to demonstrate the performance of the proposed method experimentally. The results showed improved image accuracy inside the ROI. The magnitude of error decreased from -73.2 HU to 18 HU, and effectively reduced image artifacts throughout the entire image.

Evaluation of radiation dose to organs during kilovoltage cone-beam computed tomography using Monte Carlo simulation.

Image-guided techniques for radiation therapy have improved the precision of radiation delivery by sparing normal tissues. Cone-beam computed tomography (CBCT) has emerged as a key technique for patient positioning and target localization in radiotherapy. Here, we investigated the imaging radiation dose delivered to radiosensitive organs of a patient during CBCT scan. The 4D extended cardiac-torso (XCAT) phantom and Geant4 Application for Tomographic Emission (GATE) Monte Carlo (MC) simulation tool were used for the study. A computed tomography dose index (CTDI) standard polymethyl methacrylate (PMMA) phantom was used to validate the MC-based dosimetric evaluation. We implemented an MC model of a clinical on-board imager integrated with the Trilogy accelerator. The MC model's accuracy was validated by comparing its weighted CTDI (CTDIw) values with those of previous studies, which revealed good agreement. We calculated the absorbed doses of various human organs at different treatment sites such as the head-and-neck, chest, abdomen, and pelvis regions, in both standard CBCT scan mode (125 kVp, 80 mA, and 25 ms) and low-dose scan mode (125 kVp, 40 mA, and 10 ms). In the former mode, the average absorbed doses of the organs in the head and neck and chest regions ranged 4.09-8.28 cGy, whereas those of the organs in the abdomen and pelvis regions were 4.30-7.48 cGy. In the latter mode, the absorbed doses of the organs in the head and neck and chest regions ranged 1.61-1.89 cGy, whereas those of the organs in the abdomen and pelvis region ranged between 0.79-1.85 cGy. The reduction in the radiation dose in the low-dose mode compared to the standard mode was about 20%, which is in good agreement with previous reports. We opine that the findings of this study would significantly facilitate decisions regarding the administration of extra imaging doses to radiosensitive organs.

Optimizing angular range in digital breast tomosynthesis: A phantom study investigating lesion detection across varied breast density and thickness.

PURPOSE: To determine the optimal angular range (AR) for digital breast tomosynthesis (DBT) systems that provides highest lesion visibility across various breast densities and thicknesses. METHOD: A modular DBT phantom, consisting of tissue-equivalent adipose and glandular modules, along with a module embedded with test objects (speckles, masses, fibers), was used to create combinations simulating different breast thicknesses, densities, and lesion locations. A prototype DBT system operated at four ARs (AR±7.5°, AR±12.5°, AR±19°, and AR±25°) to acquire 11 projection images for each combination, with separate fixed doses for thin and thick combinations. Three blinded radiologists independently assessed lesion visibility in reconstructed images; assessments were averaged and compared using linear mixed models. RESULTS: Speckle visibility was highest with AR±7.5° or AR±12.5°, decreasing with wider ARs in all density and thickness combinations. The difference between AR±7.5° and AR±12.5° was not statistically significant, except for the tube-side speckles in thin-fatty combinations (5.83 [AR±7.5°] vs. 5.39 [AR±12.5°], P = 0.019). Mass visibility was not affected by AR in thick combinations, while AR±12.5° exhibited the highest mass visibility for both thin-fatty and thin-dense combinations (P = 0.032 and 0.007, respectively). Different ARs provided highest fiber visibility for different combinations; however, AR±12.5° consistently provided highest or comparable visibility. AR±12.5° showed highest overall lesion visibility for all density and thickness combinations. CONCLUSIONS: AR±12.5° exhibited the highest overall lesion visibility across various phantom thicknesses and densities using a projection number of 11.

Super-sparsely view-sampled cone-beam CT by incorporating prior data.

Computed tomography (CT) is widely used in medicine for diagnostics or for image-guided therapies, and is also popular in industrial applications for nondestructive testing. CT conventionally requires a large number of projections to produce volumetric images of a scanned object, because the conventional image reconstruction algorithm is based on filtered-backprojection. This requirement may result in relatively high radiation dose to the patients in medical CT unless the radiation dose at each view angle is reduced, and can cause expensive scanning time and efforts in industrial CT applications. Sparse- view CT may provide a viable option to address both issues including high radiation dose and expensive scanning efforts. However, image reconstruction from sparsely sampled data in CT is in general very challenging, and much efforts have been made to develop algorithms for such an image reconstruction problem. Image total-variation minimization algorithm inspired by compressive sensing theory has recently been developed, which exploits the sparseness of the image derivative magnitude and can reconstruct images from sparse-view data to a similar quality of the images conventionally reconstructed from many views. In successive CT scans, prior CT image of an object and its projection data may be readily available, and the current CT image may have not much difference from the prior image. Considering the sparseness of such a difference image between the successive scans, image reconstruction of the difference image may be achieved from very sparsely sampled data. In this work, we showed that one can further reduce the number of projections, resulting in a super-sparse scan, for a good quality image reconstruction with the aid of a prior data. Both numerical and experimental results are provided.

Image quality improvement in bowtie-filter-equipped cone-beam CT using a dual-domain neural network.

BACKGROUND: The bowtie-filter in cone-beam CT (CBCT) causes spatially nonuniform x-ray beam often leading to eclipse artifacts in the reconstructed image. The artifacts are further confounded by the patient scatter, which is therefore patient-dependent as well as system-specific. PURPOSE: In this study, we propose a dual-domain network for reducing the bowtie-filter-induced artifacts in CBCT images. METHODS: In the projection domain, the network compensates for the filter-induced beam-hardening that are highly related to the eclipse artifacts. The output of the projection-domain network was used for image reconstruction and the reconstructed images were fed into the image-domain network. In the image domain, the network further reduces the remaining cupping artifacts that are associated with the scatter. A single image-domain-only network was also implemented for comparison. RESULTS: The proposed approach successfully enhanced soft-tissue contrast with much-reduced image artifacts. In the numerical study, the proposed method decreased perceptual loss and root-mean-square-error (RMSE) of the images by 84.5% and 84.9%, respectively, and increased the structure similarity index measure (SSIM) by 0.26 compared to the original input images on average. In the experimental study, the proposed method decreased perceptual loss and RMSE of the images by 87.2% and 92.1%, respectively, and increased SSIM by 0.58 compared to the original input images on average. CONCLUSIONS: We have proposed a deep-learning-based dual-domain framework to reduce the bowtie-filter artifacts and to increase the soft-tissue contrast in CBCT images. The performance of the proposed method has been successfully demonstrated in both numerical and experimental studies.

Homogenization of multi-institutional chest x-ray images in various data transformation schemes.

Purpose: Although there are several options for improving the generalizability of learned models, a data instance-based approach is desirable when stable data acquisition conditions cannot be guaranteed. Despite the wide use of data transformation methods to reduce data discrepancies between different data domains, detailed analysis for explaining the performance of data transformation methods is lacking. Approach: This study compares several data transformation methods in the tuberculosis detection task with multi-institutional chest x-ray (CXR) data. Five different data transformations, including normalization, standardization with and without lung masking, and multi-frequency-based (MFB) standardization with and without lung masking were implemented. A tuberculosis detection network was trained using a reference dataset, and the data from six other sites were used for the network performance comparison. To analyze data harmonization performance, we extracted radiomic features and calculated the Mahalanobis distance. We visualized the features with a dimensionality reduction technique. Through similar methods, deep features of the trained networks were also analyzed to examine the models' responses to the data from various sites. Results: From various numerical assessments, the MFB standardization with lung masking provided the highest network performance for the non-reference datasets. From the radiomic and deep feature analyses, the features of the multi-site CXRs after MFB with lung masking were found to be well homogenized to the reference data, whereas the others showed limited performance. Conclusions: Conventional normalization and standardization showed suboptimal performance in minimizing feature differences among various sites. Our study emphasizes the strengths of MFB standardization with lung masking in terms of network performance and feature homogenization.

A novel analysis of the formation and resorption changes in dental hard tissue using longitudinal in vivo micro computed tomography.

This study was to investigate the new analysis manner of dental hard tissue change using in vivo micro-computed tomography (CT) in rat. Scanning, registration, analyzing, and presenting method to track longitudinal in vivo micro-CT data on dental hard tissues were validated in murine models: formative, dentin thickness after direct pulp capping with mineral trioxide aggregate; resorptive, development of apical bone rarefaction in apical periodontitis model. Serial in vivo micro-CT scans were analyzed through rigid-registration, active-contouring, deformable-registration, and motion vector-based quantitative analyses. The rate and direction of hard tissue formation after direct pulp capping was datafied by tracing coordinate shift of fiducial points on pulp chamber outline in formative model. The development of apical periodontitis could be monitored with voxel counts, and quantitatively analyzed in terms of lesion size, bone loss, and mineral density in resorptive model. This study supports the application of longitudinal in vivo micro-CT for resorptive- and formative-phase specific monitoring of dental hard tissues.

Panoramic dental tomosynthesis imaging by use of CBCT projection data.

Dental CBCT and panoramic images are important imaging modalities used in dental diagnosis and treatment planning. In order to acquire a panoramic image without an additional panoramic scan, in this study, we proposed a method of reconstructing a panoramic image by extracting panoramic projection data from dental CBCT projection data. After specifying the patient's dental arch from the patient's CBCT image, panoramic projection data are extracted from the CBCT projection data along the appropriate panoramic scan trajectory that fits the dental arch. A total of 40 clinical human datasets and one head phantom dataset were used to test the proposed method. The clinical human dataset used in this study includes cases in which it is difficult to reconstruct panoramic images from CBCT images, such as data with severe metal artifacts or data without teeth. As a result of applying the panoramic image reconstruction method proposed in this study, we were able to successfully acquire panoramic images from the CBCT projection data of various patients. The proposed method acquires a universally applicable panoramic image that is less affected by CBCT image quality and metal artifacts by extracting panoramic projection data from dental CBCT data and reconstructing a panoramic image.

A feasibility study on deep-neural-network-based dose-neutral dual-energy digital breast tomosynthesis.

BACKGROUND: Diagnostic performance based on x-ray breast imaging is subject to breast density. Although digital breast tomosynthesis (DBT) is reported to outperform conventional mammography in denser breasts, mass detection and malignancy characterization are often considered challenging yet. PURPOSE: As an improved diagnostic solution to the dense breast cases, we propose a dual-energy DBT imaging technique that enables breast compositional imaging at comparable scanning time and patient dose compared to the conventional single-energy DBT. METHODS: The proposed dual-energy DBT acquires projection data by alternating two different energy spectra. Then, we synthesize unmeasured projection data using a deep neural network that exploits the measured projection data and adjacent projection data obtained under the other x-ray energy spectrum. For material decomposition, we estimate partial path lengths of an x-ray through water, lipid, and protein from the measured and the synthesized projection data with the object thickness information. After material decomposition in the projection domain, we reconstruct material-selective DBT images. The deep neural network is trained with the numerical breast phantoms. A pork meat phantom is scanned with a prototype dual-energy DBT system to demonstrate the feasibility of the proposed imaging method. RESULTS: The developed deep neural network successfully synthesized missing projections. Material-selective images reconstructed from the synthesized data present comparable compositional contrast of the cancerous masses compared with those from the fully measured data. CONCLUSIONS: The proposed dual-energy DBT scheme is expected to substantially contribute to enhancing mass malignancy detection accuracy particularly in dense breasts.

Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions.

The use of adjuvant Immune Checkpoint Inhibitors (ICI) after concurrent chemo-radiation therapy (CCRT) has become the standard of care for locally advanced non-small cell lung cancer (LA-NSCLC). However, prolonged radiotherapy regimens are known to cause radiation-induced lymphopenia (RIL), a long-neglected toxicity that has been shown to correlate with response to ICIs and survival of patients treated with adjuvant ICI after CCRT. In this study, we aim to develop a novel neural network (NN) approach that integrates patient characteristics, treatment related variables, and differential dose volume histograms (dDVH) of lung and heart to predict the incidence of RIL at the end of treatment. Multi-institutional data of 139 LA-NSCLC patients from two hospitals were collected for training and validation of our suggested model. Ensemble learning was combined with a bootstrap strategy to stabilize the model, which was evaluated internally using repeated cross validation. The performance of our proposed model was compared to conventional models using the same input features, such as Logistic Regression (LR) and Random Forests (RF), using the Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) curves. Our suggested model (AUC=0.77) outperformed the comparison models (AUC=0.72, 0.74) in terms of absolute performance, indicating that the convolutional structure of the network successfully abstracts additional information from the differential DVHs, which we studied using Gradient-weighted Class Activation Map. This study shows that clinical factors combined with dDVHs can be used to predict the risk of RIL for an individual patient and shows a path toward preventing lymphopenia using patient-specific modifications of the radiotherapy plan.

Deep-learning-based projection-domain breast thickness estimation for shape-prior iterative image reconstruction in digital breast tomosynthesis.

BACKGROUND: Digital breast tomosynthesis (DBT) is a technique that can overcome the shortcomings of conventional X-ray mammography and can be effective for the early screening of breast cancer. The compression of the breast is essential during the DBT imaging. However, since the periphery of the breast cannot be compressed to a constant value, nonuniformity of thickness and in-plane shape variation happen. These cause inconvenience in diagnosis, scatter correction, and breast density estimation. PURPOSE: In this study, we propose a deep-learning-based methodology for projection-domain breast thickness estimation and demonstrate a shape-prior iterative DBT image reconstruction. METHODS: We prepared the Euclidean distance map, the thickness map, and the thickness corrected image of the simulated breast projections for thickness and shape estimation. Each pixel of the Euclidean distance map denotes a distance to the closest skin-line. The thickness map is defined as a conceptual projection of ideal breast support that differentiates the inner and outer regions of the breast phantom. The thickness projection map thus represents the X-ray path lengths of a homogeneous breast phantom. We generated the thickness corrected image by dividing the projection image by the thickness map in a pixel-wise manner. We developed a convolutional neural network for thickness estimation and correction. The network utilizes a projection image and a Euclidean distance image together as a dual input. An estimated breast thickness map is then used for constructing the breast shape mask by use of the discrete algebraic reconstruction technique. RESULTS: The proposed network effectively corrected the breast thickness in various simulation situations. Low normalized root-mean-squared error (1.976%) and high structural similarity (99.997%) indicated a good agreement between the network-generated thickness corrected image and the ground truth image. Compared to the existing methods and simple single-input network, the proposed method showed outperformance in breast thickness estimation and accordingly in breast shape recovery for various numerical phantoms without provoking any significant artifact. We have demonstrated that the uniformity of voxel value has improved by the inclusion of a shape prior for the iterative DBT reconstruction. CONCLUSIONS: We presented a novel deep-learning-based breast thickness correction and a shape reconstruction method. This approach to estimating the true thickness map and the shape of the breast undergoing compression can benefit various fields such as improvement of diagnostic breast images, scatter correction, material decomposition, and breast density estimation.

Lymphocyte dynamics during and after chemo-radiation correlate to dose and outcome in stage III NSCLC patients undergoing maintenance immunotherapy.

PURPOSE: We investigated the dynamics of lymphocyte depletion and recovery during and after definitive concurrent chemoradiotherapy (CCRT), dose to which structures is correlated to them, and how they affect the prognosis of stage III non-small cell lung cancer (NSCLC) patients undergoing maintenance immunotherapy. METHODS AND MATERIALS: In this retrospective study, absolute lymphocyte counts (ALC) of 66 patients were obtained before, during, and after CCRT. Persistent lymphopenia was defined as ALC < 500/µL at 3 months after CCRT. The impact of regional dose on lymphocyte depletion and recovery was investigated using voxel-based analysis (VBA). RESULTS: Most patients (n = 65) experienced lymphopenia during CCRT: 39 patients (59.0%) had grade (G) 3+ lymphopenia. Fifty-nine patients (89.3%) recovered from treatment-related lymphopenia at 3 months after CCRT, whereas 7 (10.6%) showed persistent lymphopenia. Patient characteristics associated with persistent lymphopenia were older age and ALC before and during treatment. In multivariable Cox regression analysis, recovery from lymphopenia was identified as a significant prognostic factor for Progression Free Survival (HR 0.35, 95% CI 0.13-0.93, p = 0.034) and Overall Survival (HR 0.24, 95% CI 0.08-0.68, p = 0.007). Voxel-based analysis showed strong correlation of dose to the upper mediastinum with lymphopenia at the end of CCRT, but not at 3 months after CCRT. CONCLUSION: Recovery from lymphopenia is strongly correlated to improved survival of patients undergoing CCRT and adjuvant immunotherapy, and is correlated to lymphocyte counts pre- and post-CCRT. VBA reveals high correlation of dose to large vessels to lymphopenia at the end of CCRT. Therefore, efforts should be made not only for preventing lymphocyte depletion during CCRT but also for helping lymphocyte recovery after CCRT.

Three-compartment-breast (3CB) prior-guided diffuse optical tomography based on dual-energy digital breast tomosynthesis (DBT).

Diffuse optical tomography (DOT) is a non-invasive functional imaging modality that uses near-infrared (NIR) light to measure the oxygenation state and the concentration of hemoglobin. By complementarily using DOT with other anatomical imaging modalities, physicians can diagnose more accurately through additional functional image information. In breast imaging, diagnosis of dense breasts is often challenging because the bulky fibrous tissues may hinder the correct tumor characterization. In this work, we proposed a three-compartment-breast (3CB) decomposition-based prior-guided optical tomography for enhancing DOT image quality. We conjectured that the 3CB prior would lead to improvement of the spatial resolution and also of the contrast of the reconstructed tumor image, particularly for the dense breasts. We conducted a Monte-Carlo simulation to acquire dual-energy X-ray projections of a realistic 3D numerical breast phantom and performed digital breast tomosynthesis (DBT) for setting up a 3CB model. The 3CB prior was then used as a structural guide in DOT image reconstruction. The proposed method resulted in the higher spatial resolution of the recovered tumor even when the tumor is surrounded by the fibroglandular tissues compared with the typical two-composition-prior method or the standard Tikhonov regularization method.

A Novel Low-Dose Dual-Energy Imaging Method for a Fast-Rotating Gantry-Type CT Scanner.

CT scan by use of a beam-filter placed between the x-ray source and the patient allows a single-scan low-dose dual-energy imaging with a minimal hardware modification to the existing CT systems. We have earlier demonstrated the feasibility of such imaging method with a multi-slit beam-filter reciprocating along the direction perpendicular to the CT rotation axis in a cone-beam CT system. However, such method would face mechanical challenges when the beam-filter is supposed to cooperate with a fast-rotating gantry in a diagnostic CT system. In this work, we propose a new scanning method and associated image reconstruction algorithm that can overcome these challenges. We propose to slide a beam-filter that has multi-slit structure with its slits being at a slanted angle with the CT gantry rotation axis during a scan. A streaky pattern would show up in the sinogram domain as a result. Using a notch filter in the Fourier domain of the sinogram, we removed the streaks and reconstructed an image by use of the filtered-backprojection algorithm. The remaining image artifacts were suppressed by applying l0 norm based smoothing. Using this image as a prior, we have reconstructed low- and high-energy CT images in the iterative reconstruction framework. An image-based material decomposition then followed. We conducted a simulation study to test its feasibility using the XCAT phantom and also an experimental study using the Catphan phantom, a head phantom, an iodine-solution phantom, and a monkey in anesthesia, and showed its successful performance in image reconstruction and in material decomposition.