Technical note: Optimization functions for re-irradiation treatment planning.

BACKGROUND: Although re-irradiation is increasingly used in clinical practice, almost no dedicated planning software exists. PURPOSE: Standard dose-based optimization functions were adjusted for re-irradiation planning using accumulated equivalent dose in 2-Gy fractions (EQD2) with rigid or deformable dose mapping, tissue-specific α/ß, treatment-specific recovery coefficients, and voxelwise adjusted EQD2 penalization levels based on the estimated previously delivered EQD2 (EQD2deliv ). METHODS: To demonstrate proof-of-concept, 35 Gy in 5 fractions was planned to a fictitious spherical relapse planning target volume (PTV) in three separate locations following previous prostate treatment on a virtual human phantom. The PTV locations represented one repeated irradiation scenario and two re-irradiation scenarios. For each scenario, three re-planning strategies with identical PTV dose-functions but various organ at risk (OAR) EQD2-functions was used: 1) reRTregular : Regular functions with fixed EQD2 penalization levels larger than EQD2deliv for all OAR voxels. 2) reRTreduce : As reRTregular , but with lower fixed EQD2 penalization levels aiming to reduce OAR EQD2. 3) reRTvoxelwise : As reRTregular and reRTreduce , but with voxelwise adjusted EQD2 penalization levels based on EQD2deliv . PTV near-minimum and near-maximum dose (D98% /D2% ), homogeneity index (HI), conformity index (CI) and accumulated OAR EQD2 (α/ß = 3 Gy) were evaluated. RESULTS: For the repeated irradiation scenario, all strategies resulted in similar dose distributions. For the re-irradiation scenarios, reRTreduce and reRTvoxelwise reduced accumulated average and near-maximum EQD2 by ˜1-10 Gy for all relevant OARs compared to reRTregular . The reduced OAR doses for reRTreduce came at the cost of distorted dose distributions with D98% = 92.3%, HI = 12.0%, CI = 73.7% and normal tissue hot spots ≥150% for the most complex scenario, while reRTregular (D98% = 98.1%, HI = 3.2%, CI = 94.2%) and reRTvoxelwise (D98%  = 96.9%, HI = 6.1%, CI = 93.7%) fulfilled PTV coverage without hot spots. CONCLUSIONS: The proposed re-irradiation-specific EQD2-based optimization functions introduce novel planning possibilities with flexible options to guide the trade-off between target coverage and OAR sparing with voxelwise adapted penalization levels based on EQD2deliv .

Quantifying the Effect of 4-Dimensional Computed Tomography-Based Deformable Dose Accumulation on Representing Radiation Damage for Patients with Locally Advanced Non-Small Cell Lung Cancer Treated with Standard-Fractionated Intensity-Modulated Radiation Therapy.

PURPOSE: The aim of this study was to investigate the dosimetric and clinical effects of 4-dimensional computed tomography (4DCT)-based longitudinal dose accumulation in patients with locally advanced non-small cell lung cancer treated with standard-fractionated intensity-modulated radiation therapy (IMRT). METHODS AND MATERIALS: Sixty-seven patients were retrospectively selected from a randomized clinical trial. Their original IMRT plan, planning and verification 4DCTs, and ∼4-month posttreatment follow-up CTs were imported into a commercial treatment planning system. Two deformable image registration algorithms were implemented for dose accumulation, and their accuracies were assessed. The planned and accumulated doses computed using average-intensity images or phase images were compared. At the organ level, mean lung dose and normal-tissue complication probability (NTCP) for grade ≥2 radiation pneumonitis were compared. At the region level, mean dose in lung subsections and the volumetric overlap between isodose intervals were compared. At the voxel level, the accuracy in estimating the delivered dose was compared by evaluating the fit of a dose versus radiographic image density change (IDC) model. The dose-IDC model fit was also compared for subcohorts based on the magnitude of NTCP difference (|ΔNTCP|) between planned and accumulated doses. RESULTS: Deformable image registration accuracy was quantified, and the uncertainty was considered for the voxel-level analysis. Compared with planned doses, accumulated doses on average resulted in <1-Gy lung dose increase and <2% NTCP increase (up to 8.2 Gy and 18.8% for a patient, respectively). Volumetric overlap of isodose intervals between the planned and accumulated dose distributions ranged from 0.01 to 0.93. Voxel-level dose-IDC models demonstrated a fit improvement from planned dose to accumulated dose (pseudo-R2 increased 0.0023) and a further improvement for patients with ≥2% |ΔNTCP| versus for patients with <2% |ΔNTCP|. CONCLUSIONS: With a relatively large cohort, robust image registrations, multilevel metric comparisons, and radiographic image-based evidence, we demonstrated that dose accumulation more accurately represents the delivered dose and can be especially beneficial for patients with greater longitudinal response.

Genetics and Epigenetics in Obesity: What Do We Know so Far?

PURPOSE OF REVIEW: Enormous progress has been made in understanding the genetic architecture of obesity and the correlation of epigenetic marks with obesity and related traits. This review highlights current research and its challenges in genetics and epigenetics of obesity. RECENT FINDINGS: Recent progress in genetics of polygenic traits, particularly represented by genome-wide association studies, led to the discovery of hundreds of genetic variants associated with obesity, which allows constructing polygenic risk scores (PGS). In addition, epigenome-wide association studies helped identifying novel targets and methylation sites being important in the pathophysiology of obesity and which are essential for the generation of methylation risk scores (MRS). Despite their great potential for predicting the individual risk for obesity, the use of PGS and MRS remains challenging. Future research will likely discover more loci being involved in obesity, which will contribute to better understanding of the complex etiology of human obesity. The ultimate goal from a clinical perspective will be generating highly robust and accurate prediction scores allowing clinicians to predict obesity as well as individual responses to body weight loss-specific life-style interventions.

Development and benchmarking diffusion magnetic resonance imaging analysis for integration into radiation treatment planning.

PURPOSE: The rising promise in the utility of advanced multi-parametric magnetic resonance (MR) imaging in radiotherapy (RT) treatment planning creates a necessity for testing and enhancing the accuracy of quantitative imaging analysis. Standardizing the analysis of diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) to generate meaningful and reproducible apparent diffusion coefficient (ADC) and fractional anisotropy (FA) lays the requisite needed for clinical integration. The aim of the demonstrated work is to benchmark the generation of the ADC and FA parametric map analyses using integrated tools in a commercial treatment planning system against the currently used ones. METHODS: Three software packages were used for generating ADC and FA maps in this study; one tool was developed within a commercial treatment planning system, another by the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library FSL (Analysis Group, FMRIB, Oxford, United Kingdom), and an in-house tool developed at the M.D. Anderson Cancer Center. The ADC and FA maps generated by all three packages for 35 subjects were subtracted from one another, and the standard deviation of the images' differences was used to compare the reproducibility. The reproducibility of the ADC maps was compared with the Quantitative Imaging Biomarkers Alliance (QIBA) protocol, while that of the FA maps was compared to data in published literature. RESULTS: Results show that the discrepancies between the ADC maps calculated for each patient using the three different software algorithms are less than 2% which meets the 3.6% recommended QIBA requirement. Except for a small number of isolated points, the majority of differences in FA maps for each patient produced by the three methods did not exceed 0.02 which is 10 times lower than the differences seen in healthy gray and white matter. The results were also compared to the maps generated by existing MR Imaging consoles and showed that the robustness of console generated ADC and FA maps is largely dependent on the correct application of scaling factors, that only if correctly placed; the differences between the three tested methods and the console generated values were within the recommended QIBA guidelines. CONCLUSIONS: Cross-comparison difference maps demonstrated that quantitative reproducibility of ADC and FA metrics generated using our tested commercial treatment planning system were comparable to in-house and established tools as benchmarks. This integrated approach facilitates the clinical utility of diffusion imaging in radiation treatment planning workflow.

Brain Re-Irradiation Robustly Accounting for Previously Delivered Dose.

(1) Background: The STRIDeR (Support Tool for Re-Irradiation Decisions guided by Radiobiology) planning pathway aims to facilitate anatomically appropriate and radiobiologically meaningful re-irradiation (reRT). This work evaluated the STRIDeR pathway for robustness compared to a more conservative manual pathway. (2) Methods: For ten high-grade glioma reRT patient cases, uncertainties were applied and cumulative doses re-summed. Geometric uncertainties of 3, 6 and 9 mm were applied to the background dose, and LQ model robustness was tested using α/ß variations (values 1, 2 and 5 Gy) and the linear quadratic linear (LQL) model δ variations (values 0.1 and 0.2). STRIDeR robust optimised plans, incorporating the geometric and α/ß uncertainties during optimisation, were also generated. (3) Results: The STRIDeR and manual pathways both achieved clinically acceptable plans in 8/10 cases but with statistically significant improvements in the PTV D98% (p < 0.01) for STRIDeR. Geometric and LQ robustness tests showed comparable robustness within both pathways. STRIDeR plans generated to incorporate uncertainties during optimisation resulted in a superior plan robustness with a minimal impact on PTV dose benefits. (4) Conclusions: Our results indicate that STRIDeR pathway plans achieved a similar robustness to manual pathways with improved PTV doses. Geometric and LQ model uncertainties can be incorporated into the STRIDeR pathway to facilitate robust optimisation.

Treatment plan optimisation for reirradiation.

BACKGROUND: The STRIDeR (Support Tool for Re-Irradiation Decisions guided by Radiobiology) project aims to create a clinically viable re-irradiation planning pathway within a commercial treatment planning system (TPS). Such a pathway should account for previously delivered dose, voxel-by-voxel, taking fractionation effects, tissue recovery and anatomical changes into account. This work presents the workflow and technical solutions in the STRIDeR pathway. METHODS: The pathway was implemented in RayStation (version 9B DTK) to allow an original dose distribution to be used as background dose to guide optimisation of re-irradiation plans. Organ at risk (OAR) planning objectives in equivalent dose in 2 Gy fractions (EQD2) were applied cumulatively across the original and re-irradiation treatments, with optimisation of the re-irradiation plan performed voxel-by-voxel in EQD2. Different approaches to image registration were employed to account for anatomical change. Data from 21 patients who received pelvic Stereotactic Ablative Radiotherapy (SABR) re-irradiation were used to illustrate the use of the STRIDeR workflow. STRIDeR plans were compared to those produced using a standard manual method. RESULTS: The STRIDeR pathway resulted in clinically acceptable plans in 20/21 cases. Compared to plans produced using the laborious manual method, less constraint relaxation was required or higher re-irradiation doses could be prescribed in 3/21. CONCLUSION: The STRIDeR pathway used background dose to guide radiobiologically meaningful, anatomically-appropriate re-irradiation treatment planning within a commercial TPS. This provides a standardised and transparent approach, offering more informed re-irradiation and improved cumulative OAR dose evaluation.

Optimization of mesh generation for geometric accuracy, robustness, and efficiency of biomechanical-model-based deformable image registration.

BACKGROUND: Successful generation of biomechanical-model-based deformable image registration (BM-DIR) relies on user-defined parameters that dictate surface mesh quality. The trial-and-error process to determine the optimal parameters can be labor-intensive and hinder DIR efficiency and clinical workflow. PURPOSE: To identify optimal parameters in surface mesh generation as boundary conditions for a BM-DIR in longitudinal liver and lung CT images to facilitate streamlined image registration processes. METHODS: Contrast-enhanced CT images of 29 colorectal liver cancer patients and end-exhale four-dimensional CT images of 26 locally advanced non-small cell lung cancer patients were collected. Different combinations of parameters that determine the triangle mesh quality (voxel side length and triangle edge length) were investigated. The quality of DIRs generated using these parameters was evaluated with metrics for geometric accuracy, robustness, and efficiency. Metrics for geometric accuracy included target registration error (TRE) of internal vessel bifurcations, dice similar coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD) for organ contours, and number of vertices in the triangle mesh. American Association of Physicists in Medicine Task Group 132 was used to ensure parameters met TRE, DSC, MDA recommendations before the comparison among the parameters. Robustness was evaluated as the success rate of DIR generation, and efficiency was evaluated as the total time to generate boundary conditions and compute finite element analysis. RESULTS: Voxel side length of 0.2 cm and triangle edge length of 3 were found to be the optimal parameters for both liver and lung, with success rate of 1.00 and 0.98 and average DIR computation time of 100 and 143 s, respectively. For this combination, the average TRE, DSC, MDA, and HD were 0.38-0.40, 0.96-0.97, 0.09-0.12, and 0.87-1.17 mm, respectively. CONCLUSION: The optimal parameters were found for the analyzed patients. The decision-making process described in this study serves as a recommendation for BM-DIR algorithms to be used for liver and lung. These parameters can facilitate consistence in the evaluation of published studies and more widespread utilization of BM-DIR in clinical practice.

Leveraging deep learning-based segmentation and contours-driven deformable registration for dose accumulation in abdominal structures.

Purpose: Discrepancies between planned and delivered dose to GI structures during radiation therapy (RT) of liver cancer may hamper the prediction of treatment outcomes. The purpose of this study is to develop a streamlined workflow for dose accumulation in a treatment planning system (TPS) during liver image-guided RT and to assess its accuracy when using different deformable image registration (DIR) algorithms. Materials and Methods: Fifty-six patients with primary and metastatic liver cancer treated with external beam radiotherapy guided by daily CT-on-rails (CTOR) were retrospectively analyzed. The liver, stomach and duodenum contours were auto-segmented on all planning CTs and daily CTORs using deep-learning methods. Dose accumulation was performed for each patient using scripting functionalities of the TPS and considering three available DIR algorithms based on: (i) image intensities only; (ii) intensities + contours; (iii) a biomechanical model (contours only). Planned and accumulated doses were converted to equivalent dose in 2Gy (EQD2) and normal tissue complication probabilities (NTCP) were calculated for the stomach and duodenum. Dosimetric indexes for the normal liver, GTV, stomach and duodenum and the NTCP values were exported from the TPS for analysis of the discrepancies between planned and the different accumulated doses. Results: Deep learning segmentation of the stomach and duodenum enabled considerable acceleration of the dose accumulation process for the 56 patients. Differences between accumulated and planned doses were analyzed considering the 3 DIR methods. For the normal liver, stomach and duodenum, the distribution of the 56 differences in maximum doses (D2%) presented a significantly higher variance when a contour-driven DIR method was used instead of the intensity only-based method. Comparing the two contour-driven DIR methods, differences in accumulated minimum doses (D98%) in the GTV were >2Gy for 15 (27%) of the patients. Considering accumulated dose instead of planned dose in standard NTCP models of the duodenum demonstrated a high sensitivity of the duodenum toxicity risk to these dose discrepancies, whereas smaller variations were observed for the stomach. Conclusion: This study demonstrated a successful implementation of an automatic workflow for dose accumulation during liver cancer RT in a commercial TPS. The use of contour-driven DIR methods led to larger discrepancies between planned and accumulated doses in comparison to using an intensity only based DIR method, suggesting a better capability of these approaches in estimating complex deformations of the GI organs.

Preclinical model for phenotypic correction of dystrophic epidermolysis bullosa by in vivo CRISPR-Cas9 delivery using adenoviral vectors.

Recessive dystrophic epidermolysis bullosa, a devastating skin fragility disease characterized by recurrent skin blistering, scarring, and a high risk of developing squamous cell carcinoma is caused by mutations in COL7A1, the gene encoding type VII collagen, which is the major component of the anchoring fibrils that bind the dermis and epidermis. Ex vivo correction of COL7A1 by gene editing in patients' cells has been achieved before. However, in vivo editing approaches are necessary to address the direct treatment of the blistering lesions characteristic of this disease. We have now generated adenoviral vectors for CRISPR-Cas9 delivery to remove exon 80 of COL7A1, which contains a highly prevalent frameshift mutation in Spanish patients. For in vivo testing, a humanized skin mouse model was used. Efficient viral transduction of skin was observed after excisional wounds generated with a surgical punch on regenerated patient skin grafts were filled with the adenoviral vectors embedded in a fibrin gel. Type VII collagen deposition in the basement membrane zone of the wounded areas treated with the vectors correlated with restoration of dermal-epidermal adhesion, demonstrating that recessive dystrophic epidermolysis bullosa (RDEB) patient skin lesions can be directly treated by CRISPR-Cas9 delivery in vivo.

Dose summation and image registration strategies for radiobiologically and anatomically corrected dose accumulation in pelvic re-irradiation.

BACKGROUND: Re-irradiation (reRT) is a promising technique for patients with localized recurrence in a previously irradiated area but presents major challenges. These include how to deal with anatomical change between two courses of radiotherapy and integration of radiobiology when summating original and re-irradiation doses. The Support Tool for Re-Irradiation Decisions guided by Radiobiology (STRIDeR) project aims to develop a software tool for use in a commercial treatment planning system to facilitate more informed reRT by accounting for anatomical changes and incorporating radiobiology. We evaluated three approaches to dose summation, incorporating anatomical change and radiobiology to differing extents. METHODS: In a cohort of 21 patients who previously received pelvic re-irradiation the following dose summation strategies were compared: (1) Rigid registration (RIR) and physical dose summation, to reflect the current clinical approach, (2) RIR and radiobiological dose summation in equivalent dose in 2 Gy fractions (EQD2), and (3) Patient-specific deformable image registration (DIR) with EQD2 dose summation. RESULTS: RIR and physical dose summation (Strategy 1) resulted in high cumulative organ at risk (OAR) doses being 'missed' in 14% of cases, which were highlighted by EQD2 dose summation (Strategy 2). DIR (with EQD2 dose summation; Strategy 3) resulted in improved OAR overlap and distance to agreement metrics compared to RIR (with EQD2 dose summation; Strategy 2) and was consistently preferred in terms of clinical utility. DIR was considered to have a clinically important impact on dose summation in 38% of cases. CONCLUSION: Re-irradiation cases require individualized assessment when considering dose summation with the previous treatment plan. Fractionation correction is necessary to meaningfully assess cumulative doses and reduce the risk of unintentional OAR overdose. DIR can add clinically relevant information in selected cases, especially for significant anatomical change. Robust solutions for cumulative dose assessment offer the potential for future improved understanding of cumulative OAR tolerances.

Automatic Segmentation Using Deep Learning to Enable Online Dose Optimization During Adaptive Radiation Therapy of Cervical Cancer.

PURPOSE: This study investigated deep learning models for automatic segmentation to support the development of daily online dose optimization strategies, eliminating the need for internal target volume expansions and thereby reducing toxicity events of intensity modulated radiation therapy for cervical cancer. METHODS AND MATERIALS: The cervix-uterus, vagina, parametrium, bladder, rectum, sigmoid, femoral heads, kidneys, spinal cord, and bowel bag were delineated on 408 computed tomography (CT) scans from patients treated at MD Anderson Cancer Center (n = 214), Polyclinique Bordeaux Nord Aquitaine (n = 30), and enrolled in a Medical Image Computing & Computer Assisted Intervention challenge (n = 3). The data were divided into 255 training, 61 validation, 62 internal test, and 30 external test CT scans. Two models were investigated: the 2-dimensional (2D) DeepLabV3+ (Google) and 3-dimensional (3D) Unet in RayStation (RaySearch Laboratories). Three intensity modulated radiation therapy plans were generated on each CT of the internal and external test sets using either the manual, 2D model, or 3D model segmentations. The dose constraints followed the External beam radiochemotherapy and MRI based adaptive BRAchytherapy in locally advanced CErvical cancer (EMBRACE) II protocol, with reduced margins of 5 and 3 mm for the target and nodal planning target volume. Geometric discrepancies between the manual and predicted contours were assessed using the Dice similarity coefficient (DSC), distance-to-agreement, and Hausdorff distance. Dosimetric discrepancies between the manual and model doses were assessed using clinical indices on the manual contours and the gamma index. Interobserver variability was assessed for the cervix-uterus, parametrium, and vagina for the definition of the primary clinical target volume (CTVT) on the external test set. RESULTS: Average DSCs across all organs were 0.67 to 0.96, 0.71 to 0.97, and 0.42 to 0.92 for the 2D model and 0.66 to 0.96, 0.70 to 0.97, and 0.37 to 0.93 for the 3D model on the validation, internal, and external test sets. Average DSCs of the CTVT were 0.88 and 0.81 for the 2D model and 0.87 and 0.82 for the 3D model on the internal and external test sets. Interobserver variability of the CTVT corresponded to a mean (range) DSC of 0.85 (0.77-0.90) on the external test set. On the internal test set, the doses from the 2D and 3D model contours provided a CTVT V42.75 Gy >98% for 98% and 91% of the CT scans, respectively. On the external test set, these percentages were increased to 100% and 93% for the 2D and 3D models, respectively. CONCLUSIONS: The investigated models provided auto-segmentation of the cervix anatomy with similar performances on 2 institutional data sets and reasonable dosimetric accuracies using small planning target volume margins, paving the way to automatic online dose optimization for advanced adaptive radiation therapy strategies.

Technical Note: Efficient and accurate MRI-only based treatment planning of the prostate using bulk density assignment through atlas-based segmentation.

PURPOSE: This study investigates the dosimetric accuracy as well as the robustness of a bulk density assignment approach to magnetic resonance imaging (MRI)-only based treatment planning of the prostate, with bulk density regions automatically identified using atlas-based segmentation (ABS). METHODS: Twenty prostate radiotherapy patients received planning computed tomography (CT) and MRI scans and were treated with volumetric modulated arc therapy (VMAT). Two bulk densities were set, one for bone and one for soft tissue. The bone contours were created by using ABS followed by manual modification if considered necessary. A range of soft tissue and bone density pairs, between 0.95 and 1.03 g/cm3 with increments of 0.01 for soft tissue, and between 1.15 and 1.65 g/cm3 with increments of 0.05 for bone, were evaluated. Using the density pair giving the lowest dose difference compared to the CT-based dose, dose differences were calculated using both the manually modified bone contours and the bone contours from ABS. Contour overlap measurements between the ABS contours and the manually modified contours were calculated. RESULTS: The dose comparison shows a very good agreement with the CT when using 0.98 g/cm3 for soft tissue and 1.20 g/cm3 for bone, with a dose difference less than 1 % in average dose in all regions of interest. The mean Dice similarity coefficient for bone was 0.94 and the Mean Distance to Agreement was <1 mm in most cases. CONCLUSIONS: Using bulk density assignment on MR images with suitable densities for bone and soft tissue results in clinically acceptable dose differences compared to dose calculated on the CT, for both atlas-based and manual bone contours. This demonstrates that an integrated MRI-only pathway utilizing a bulk density assignment for two tissue types is a feasible and robust approach for patients with prostate cancer treated with VMAT.

Accurate estimation of daily delivered radiotherapy dose with an external treatment planning system.

Accurate estimation of the daily radiotherapy dose is challenging in a multi-institutional collaboration when the institution specific treatment planning system (TPS) is not available. We developed and evaluated a method to tackle this problem. Residual errors in daily estimations were minimized with single correction based on the planned dose. For nine patients, medians of the absolute estimation errors for targets and OARs were less than 0.2 Gy ( D mean ), 0.3 Gy ( D 1 ), and 0.1 Gy ( D 99 ). In general, mimicking errors were significantly smaller than dose differences caused by anatomical changes. The demonstrated accuracy may facilitate dose accumulation in a multi-institutional/multi-vendor setting.

MR and CT data with multiobserver delineations of organs in the pelvic area-Part of the Gold Atlas project.

PURPOSE: We describe a public dataset with MR and CT images of patients performed in the same position with both multiobserver and expert consensus delineations of relevant organs in the male pelvic region. The purpose was to provide means for training and validation of segmentation algorithms and methods to convert MR to CT like data, i.e., so called synthetic CT (sCT). ACQUISITION AND VALIDATION METHODS: T1- and T2-weighted MR images as well as CT data were collected for 19 patients at three different departments. Five experts delineated nine organs for each patient based on the T2-weighted MR images. An automatic method was used to fuse the delineations. Starting from each fused delineation, a consensus delineation was agreed upon by the five experts for each organ and patient. Segmentation overlap between user delineations with respect to the consensus delineations was measured to describe the spread of the collected data. Finally, an open-source software was used to create deformation vector fields describing the relation between MR and CT images to further increase the usability of the dataset. DATA FORMAT AND USAGE NOTES: The dataset has been made publically available to be used for academic purposes, and can be accessed from https://zenodo.org/record/583096. POTENTIAL APPLICATIONS: The dataset provides a useful source for training and validation of segmentation algorithms as well as methods to convert MR to CT-like data (sCT). To give some examples: The T2-weighted MR images with their consensus delineations can directly be used as a template in an existing atlas-based segmentation engine; the expert delineations are useful to validate the performance of a segmentation algorithm as they provide a way to measure variability among users which can be compared with the result of an automatic segmentation; and the pairwise deformably registered MR and CT images can be a source for an atlas-based sCT algorithm or for validation of sCT algorithm.

Validation of biomechanical deformable image registration in the abdomen, thorax, and pelvis in a commercial radiotherapy treatment planning system.

PURPOSE: The accuracy of deformable image registration tools can vary widely between imaging modalities and specific implementations of the same algorithms. A biomechanical model-based algorithm initially developed in-house at an academic institution was translated into a commercial radiotherapy treatment planning system and validated for multiple imaging modalities and anatomic sites. METHODS: Biomechanical deformable registration (Morfeus) is a geometry-driven algorithm based on the finite element method. Boundary conditions are derived from the model-based segmentation of controlling structures in each image which establishes a point-to-point surface correspondence. For each controlling structure, material properties and fixed or sliding interfaces are assigned. The displacements of internal volumes for controlling structures and other structures implicitly deformed are solved with finite element analysis. Registration was performed for 74 patients with images (mean vector resolution) of thoracic and abdominal 4DCT (2.8 mm) and MR (5.3 mm), liver CT-MR (4.5 mm), and prostate MR (2.6 mm). Accuracy was quantified between deformed and actual target images using distance-to-agreement (DTA) for structure surfaces and the target registration error (TRE) for internal point landmarks. RESULTS: The results of the commercial implementation were as follows. The mean DTA was ≤ 1.0 mm for controlling structures and 1.0-3.5 mm for implicitly deformed structures on average. TRE ranged from 2.0 mm on prostate MR to 5.1 mm on lung MR on average, within 0.1 mm or lower than the image voxel sizes. Accuracy was not overly sensitive to changes in the material properties or variability in structure segmentations, as changing these inputs affected DTA and TRE by ≤ 0.8 mm. Maximum DTA > 5 mm occurred for 88% of the structures evaluated although these were within the inherent segmentation uncertainty for 82% of structures. Differences in accuracy between the commercial and in-house research implementations were ≤ 0.5 mm for mean DTA and ≤ 0.7 mm for mean TRE. CONCLUSIONS: Accuracy of biomechanical deformable registration evaluated on a large cohort of images in the thorax, abdomen and prostate was similar to the image voxel resolution on average across multiple modalities. Validation of this treatment planning system implementation supports biomechanical deformable registration as a versatile clinical tool to enable accurate target delineation at planning and treatment adaptation.

The ANACONDA algorithm for deformable image registration in radiotherapy.

PURPOSE: The purpose of this work was to describe a versatile algorithm for deformable image registration with applications in radiotherapy and to validate it on thoracic 4DCT data as well as CT/cone beam CT (CBCT) data. METHODS: ANAtomically CONstrained Deformation Algorithm (ANACONDA) combines image information (i.e., intensities) with anatomical information as provided by contoured image sets. The registration problem is formulated as a nonlinear optimization problem and solved with an in-house developed solver, tailored to this problem. The objective function, which is minimized during optimization, is a linear combination of four nonlinear terms: 1. image similarity term; 2. grid regularization term, which aims at keeping the deformed image grid smooth and invertible; 3. a shape based regularization term which works to keep the deformation anatomically reasonable when regions of interest are present in the reference image; and 4. a penalty term which is added to the optimization problem when controlling structures are used, aimed at deforming the selected structure in the reference image to the corresponding structure in the target image. RESULTS: To validate ANACONDA, the authors have used 16 publically available thoracic 4DCT data sets for which target registration errors from several algorithms have been reported in the literature. On average for the 16 data sets, the target registration error is 1.17 ± 0.87 mm, Dice similarity coefficient is 0.98 for the two lungs, and image similarity, measured by the correlation coefficient, is 0.95. The authors have also validated ANACONDA using two pelvic cases and one head and neck case with planning CT and daily acquired CBCT. Each image has been contoured by a physician (radiation oncologist) or experienced radiation therapist. The results are an improvement with respect to rigid registration. However, for the head and neck case, the sample set is too small to show statistical significance. CONCLUSIONS: ANACONDA performs well in comparison with other algorithms. By including CT/CBCT data in the validation, the various aspects of the algorithm such as its ability to handle different modalities, large deformations, and air pockets are shown.

The human skin barrier is organized as stacked bilayers of fully extended ceramides with cholesterol molecules associated with the ceramide sphingoid moiety.

The skin barrier is fundamental to terrestrial life and its evolution; it upholds homeostasis and protects against the environment. Skin barrier capacity is controlled by lipids that fill the extracellular space of the skin's surface layer--the stratum corneum. Here we report on the determination of the molecular organization of the skin's lipid matrix in situ, in its near-native state, using a methodological approach combining very high magnification cryo-electron microscopy (EM) of vitreous skin section defocus series, molecular modeling, and EM simulation. The lipids are organized in an arrangement not previously described in a biological system-stacked bilayers of fully extended ceramides (CERs) with cholesterol molecules associated with the CER sphingoid moiety. This arrangement rationalizes the skin's low permeability toward water and toward hydrophilic and lipophilic substances, as well as the skin barrier's robustness toward hydration and dehydration, environmental temperature and pressure changes, stretching, compression, bending, and shearing.

The CEACAM1 N-terminal Ig domain mediates cis- and trans-binding and is essential for allosteric rearrangements of CEACAM1 microclusters.

Cell adhesion molecules (CAMs) sense the extracellular microenvironment and transmit signals to the intracellular compartment. In this investigation, we addressed the mechanism of signal generation by ectodomains of single-pass transmembrane homophilic CAMs. We analyzed the structure and homophilic interactions of carcinoembryonic antigen (CEA)-related CAM 1 (CEACAM1), which regulates cell proliferation, apoptosis, motility, morphogenesis, and microbial responses. Soluble and membrane-attached CEACAM1 ectodomains were investigated by surface plasmon resonance-based biosensor analysis, molecular electron tomography, and chemical cross-linking. The CEACAM1 ectodomain, which is composed of four glycosylated immunoglobulin-like (Ig) domains, is highly flexible and participates in both antiparallel (trans) and parallel (cis) homophilic binding. Membrane-attached CEACAM1 ectodomains form microclusters in which all four Ig domains participate. Trans-binding between the N-terminal Ig domains increases formation of CEACAM1 cis-dimers and changes CEACAM1 interactions within the microclusters. These data suggest that CEACAM1 transmembrane signaling is initiated by adhesion-regulated changes of cis-interactions that are transmitted to the inner phase of the plasma membrane.