Geometric Comparison Research Articles

The first experience of multi-modal in vivo PET/SPECT/CT and MRI imaging of laboratory mice with melanoma B16F10 (short message)

Background. For in vivo determination of size and metabolic characteristics of model tumors in laboratory animals, it is recommended to use imaging methods: positron emission tomography (PET), single-photon emission computed tomography (SPECT), computed tomography (CT) and magnetic resonance imaging (MRI). Due to the limited availability of hybrid scanners, it is necessary to investigate the feasibility of obtaining tomograms on separate devices with subsequent geometric comparison of images.Aim. To investigate the feasibility of multimodal imaging of mice with subcutaneous melanoma B16F10.Materials and methods. PET, SPECT and CT were performed using a preclinical VECTor 6 tomograph (MILabs, the Netherlands), MRI was performed using a preclinical nanoScan® 3T tomograph (Mediso, Hungary). The following modality combinations were studied: MRI and PET / CT with 18F-fluorodeoxyglucose; MRI and PET / CT with 18F-boronphenylalanine; MRI and PET / SPECT / CT with 18F-fluoroethyltyrosine (18F-FET) and 177lutetium – prostate-specific membrane antigen (177Lu-PSMA). Mice were immobilized in a custom-made device during scanning and during movement between tomographs. PET and MRI, SPECT and MRI images were fused in the program InterView FUSION 3.09.008.0000 through an intermediate stage of fusion with CT.Results. Multimodal PET / CT / MRI and PET / SPECT / CT / MRI images of tumor-bearing mice were obtained for the first time in Russia. PET / MRI data confirmed development of the tumor from 6 × 4 × 4 mm in size. PET / SPECT / CT / MRI enabled to image visceral organs with high accumulation of radionuclide tracers: pancreas with 18F-FET (standardized uptake value 2.6) and kidneys with 177Lu-PSMA (standardized uptake value 4.4), while the obtained images of organs did not merge and did not overlap.Conclusion. Multimodal imaging enables the selection of experimental animals with tumors of specific size and metabolic activity for the study of new radiopharmaceuticals, including those with vector delivery.

Geometric and dosimetric evaluation for breast and regional nodal auto-segmentation structures.

The accuracy of artificial intelligence (AI) generated contours for intact-breast and post-mastectomy radiotherapy plans was evaluated. Geometric and dosimetric comparisons were performed between auto-contours (ACs) and manual-contours (MCs) produced by physicians for target structures. Breast and regional nodal structures were manually delineated on 66 breast cancer patients. ACs were retrospectively generated. The characteristics of the breast/post-mastectomy chestwall (CW) and regional nodal structures (axillary [AxN], supraclavicular [SC], internal mammary [IM]) were geometrically evaluated by Dice similarity coefficient (DSC), mean surface distance, and Hausdorff Distance. The structures were also evaluated dosimetrically by superimposing the MC clinically delivered plans onto the ACs to assess the impact of utilizing ACs with target dose (Vx%) evaluation. Positive geometric correlations between volume and DSC for intact-breast, AxN, and CW were observed. Little or anti correlations between volume and DSC for IM and SC were shown. For intact-breast plans, insignificant dosimetric differences between ACs and MCs were observed for AxNV95% (p=0.17) and SCV95% (p=0.16), while IMNV90% ACs and MCs were significantly different. The average V95% for intact-breast MCs (98.4%) and ACs (97.1%) were comparable but statistically different (p=0.02). For post-mastectomy plans, AxNV95% (p=0.35) and SCV95% (p=0.08) were consistent between ACs and MCs, while IMNV90% was significantly different. Additionally, 94.1% of AC-breasts met ΔV95% variation<5% when DSC>0.7. However, only 62.5% AC-CWs achieved the same metrics, despite AC-CWV95% (p=0.43) being statistically insignificant. The AC intact-breast structure was dosimetrically similar to MCs. The AC AxN and SC may require manual adjustments. Careful review should be performed for AC post-mastectomy CW and IMN before treatment planning. The findings of this study may guide the clinical decision-making process for the utilization of AI-driven ACs for intact-breast and post-mastectomy plans. Before clinical implementation of this auto-segmentation software, an in-depth assessment of agreement with each local facilities MCs is needed.

Deep learning for melt pool depth contour prediction from surface thermal images via vision transformers

Anomalous melt pools during metal additive manufacturing (AM) can lead to deteriorated mechanical and fatigue performance. In-situ monitoring of the melt pool subsurface morphology requires specialized equipment that may not be readily accessible or scalable. Therefore, we introduce a machine learning framework to correlate in-situ two-color thermal images observed via high-speed color imaging to the two-dimensional profile of the melt pool cross-section. We employ a hybrid CNN-Transformer architecture to establish a correlation between single bead off-axis thermal image sequences and melt pool cross-section contours measured via optical microscopy. Specifically, a ResNet model embeds the spatial information contained within the thermal images to a latent vector, while a Transformer model correlates the sequence of embedded vectors to extract temporal information. The performance of this model is evaluated through dimensional and geometric comparisons to the corresponding experimental no-powder melt pool observations. Our framework is able to model the curvature of the subsurface melt pool structure, with improved performance in high energy density regimes compared to analytical models. Additionally, the use of ratiometric temperature estimates improves the accuracy of the model predictions compared to monochromatic imaging. This work establishes a framework extensible towards powder-based AM builds.

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment

Objective. Head and neck cancer patients experience systematic as well as random day to day anatomical changes during fractionated radiotherapy treatment. Modelling the expected systematic anatomical changes could aid in creating treatment plans which are more robust against such changes. Approach. Inter- patient correspondence aligned all patients to a model space. Intra- patient correspondence between each planning CT scan and on treatment cone beam CT scans was obtained using diffeomorphic deformable image registration. The stationary velocity fields were then used to develop B-Spline based patient specific (SM) and population average (AM) models. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models. Main results. Both SMs and AMs were able to capture systematic changes. The average surface distance between the registration propagated contours and the contours generated by the SM was less than 2 mm, showing that the SM are able to capture the anatomical changes which a patient experiences during the course of radiotherapy. The testing accuracy was lower than the training accuracy of the SM, suggesting that the model overfits to the limited data available and therefore, also captures some of the random day to day changes. For most patients the AMs were a better estimate of the anatomical changes than assuming there were no changes, but the AMs could not capture the variability in the anatomical changes seen in all patients. No difference was seen in the training and testing accuracy of the AMs. These observations were highlighted in both the geometric and dosimetric evaluations and comparisons. Significance. In this work, a SM and AM are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. The AM is able to capture the overall trend of the population, but there is large patient variability which highlights the need for more complex, capable population models.

Zn(II), Mn(III), and Co(III) complexes of Schiff base derived from 2‐hydroxy‐1‐naphthaldehyde: Synthesis, spectral surveys, single crystal structure studies, Hirshfeld surface analysis, density functional theory calculation, molecular electrostatic potential, and molecular docking evaluations

A new series of transition metal complexes of bidentate Schiff base compound derived from 2‐hydroxy‐1‐(2‐methoxyethylimino)‐naphthalene (HL) were synthesized and characterized using elemental analysis, Fourier transform infrared spectroscopy (FT‐IR), and UV–vis spectroscopies. X‐ray single crystal structures of Zn(II), Mn(III), and Co(III) complexes (1–4) have also been determined, and it was indicated that these Zn(II) and Co(III) complexes (1, 4) are in an octahedral geometry. Whereas, it was found that the Zn complex (2) is in quite a regular tetrahedral environment. Also, the Mn center in complex (3) is ligated by two azomethine nitrogen atoms, two ligand oxygen atoms, and one Cl atom of metal salt forming five coordinated tetragonal pyramid geometry around the Mn atoms. The cyclic voltammetry of the complexes specifies an irreversible redox behavior for all complexes (1–4). Next, DFT/B3LYP theoretical method was used to determine the optimal geometrical configurations and comparison between experimental and theoretical data, as well as for calculations of molecular electrostatic potential, highest‐occupied molecular orbital (HOMO)‐lowest‐unoccupied molecular orbital (LUMO) energy values of selected compounds. The study of non‐covalent interactions was done by Hirshfeld surface analysis using CrystalExplorer program. Furthermore, molecular docking inspection was carried out to study the binding affinity of the tested complexes towards protein B‐cell lymphorna (PDB ID: 4LXD). Using the results obtained from the molecular docking and the summarized interaction of the binding processes, these structures show the validity of effective inhibition against liver cancer.

Custom-Trained Deep Learning-Based Auto-Segmentation for Male Pelvic Iterative CBCT on C-Arm Linear Accelerators

Purpose: To evaluate the clinical applicability of a commercial artificial intelligence (AI)-driven deep learning auto-segmentation (DLAS) tool on enhanced iterative cone-beam CT (iCBCT) acquisitions for intact prostate and prostate bed treatments. Methods and MaterialsDLAS models were trained using 116 iCBCT datasets with manually delineated organs-at-risk (OARs – bladder, femoral heads, and rectum) and target volumes (intact prostate and prostate bed) adhering to institution-specific contouring guidelines. An additional 25 intact prostate and prostate bed iCBCT datasets were utilized for model testing. Segmentation accuracy relative to a reference structure set was quantified using various geometric comparison metrics and qualitatively evaluated by trained physicists and physicians. These results were compared to those obtained for an additional DLAS-based model trained on planning CT (pCT) datasets and for a deformable image registration (DIR)-based automatic contour propagation method. Results: In most instances, statistically significant differences in the Dice similarity coefficient (DSC), 95% directed Hausdorff distance, and mean surface distance metrics were observed between the models, as the iCBCT-trained DLAS model outperformed the pCT-trained DLAS model and DIR-based method for all OARs and the intact prostate target volume. Mean DSC values for the proposed method were ≥0.90 for these volumes-of-interest. The iCBCT-trained DLAS model demonstrated a relatively suboptimal performance for the prostate bed segmentation, as the mean DSC value was <0.75 for this target contour. Overall, 90% of bladder, 93% of femoral head, 67% of rectum, and 92% of intact prostate contours generated by the proposed method were deemed clinically acceptable based on qualitative scoring, while approximately 63% of prostate bed contours required moderate or major manual editing to adhere to institutional contouring guidelines. Conclusion: The proposed method presents the potential for improved segmentation accuracy and efficiency when compared to the DIR-based automatic contour propagation method as commonly applied in CBCT-based dose evaluation and calculation studies.

Structured-light 3D Scanning Performance in Offline and In-process Measurement of 3D Printed Parts

This paper presents a geometric analysis comparison of structured-light 3D scanners against coordinate measuring machines (CMMs) in measuring 3D printed plastic parts. The resulting geometric analysis of a 3D printed part measured with a contact probe on a CMM and measured with a structured light 3D scanner is presented, along with an error analysis that includes a statistical comparison of the measured geometric deviation. This analysis is then used to determine if structured-light 3D scanners are reliable enough to perform a GD&T analysis of specific features. This paper also presents the results of in-process 3D scanning and compares them to offline 3D scanning to determine the suitability of in-process 3D scanning for comprehensive analysis of geometric deviation and GD&T features.

Validation of a deep-learning segmentation model for adult and pediatric head and neck radiotherapy in different patient positions

Background and purposeAutocontouring for radiotherapy has the potential to significantly save time and reduce interobserver variability. We aimed to assess the performance of a commercial autocontouring model for head and neck (H&N) patients in eight orientations relevant to particle therapy with fixed beam lines, focusing on validation and implementation for routine clinical use. Materials and methodsAutocontouring was performed on sixteen organs at risk (OARs) for 98 adult and pediatric patients with 137 H&N CT scans in eight orientations. A geometric comparison of the autocontours and manual segmentations was performed using the Hausdorff Distance 95th percentile, Dice Similarity Coefficient (DSC) and surface DSC and compared to interobserver variability where available. Additional qualitative scoring and dose-volume-histogram (DVH) parameters analyses were performed for twenty patients in two positions, consisting of scoring on a 0–3 scale based on clinical usability and comparing the mean (Dmean) and near-maximum (D2%) dose, respectively. ResultsFor the geometric analysis, the model performance in head-first-supine straight and hyperextended orientations was in the same range as the interobserver variability. HD95, DSC and surface DSC was heterogeneous in other orientations. No significant geometric differences were found between pediatric and adult autocontours. The qualitative scoring yielded a median score of ≥ 2 for 13/16 OARs while 7/32 DVH parameters were significantly different. ConclusionsFor head-first-supine straight and hyperextended scans, we found that 13/16 OAR autocontours were suited for use in daily clinical practice and subsequently implemented. Further development is needed for other patient orientations before implementation.

Computing Topological Descriptors of Prime Ideal Sum Graphs of Commutative Rings

Let n≥1 be a fixed integer. The main objective of this paper is to compute some topological indices and coindices that are related to the graph complement of the prime ideal sum (PIS) graph of Zn, where n=pα,p2q,p2q2,pqr,p3q,p2qr, and pqrs for the different prime integers p,q,r, and s. Moreover, we construct M-polynomials and CoM-polynomials using the PIS-graph structure of Zn to avoid the difficulty of computing the descriptors via formulas directly. Furthermore, we present a geometric comparison for representations of each surface obtained by M-polynomials and CoM-polynomials. Finally, we discuss the applicability of algebraic graphs to chemical graph theory.

Observational constraints on anisotropic cosmological model in Lyra’s manifold

In the present study, we have explored an axially symmetric Bianchi I universe with Lyra’s geometry. We assumed variable displacement vector as component of Lyra’s geometry in the Einstein’s field equations. We developed and presented exact solutions of the field equations of proposed model. The parameters of model have been extracted using latest observational finding of OHD, Pantheon and BAO data sets. The best fit values are estimated using MCMC method. Features like deceleration parameter, particle horizon, age of universe and jerk parameter are explored. Further Om and statefinder diagnosis of the assumed model are also discussed for geometrical comparison with the standard ΛCDM model.

Evaluation Coastal Volume Changes with UAV Photogrammetry: An Example in West Coast Malaysia

Variations of beach volume changes have been put into place at the coastline to measure the changes in volume and analyse beach response using geometric and volumetric comparison of beach profile sets. This study examined the volume change of a beach in an eroded location in Batu Pahat. As Pantai Punggur was identified as an eroding area, it was chosen as the research area where identifying changes to the beach's volume is necessary to evaluate changes to the condition of the beach. There is a rather strong demand for both technologies and research methods since technology tends to advance quickly. Therefore, in order to satisfy demand, the process of identifying the volumetric of the shoreline area was carried out using an unmanned aerial vehicle with the assistance of Pix4D Mapper and Global Mapper as a medium to study the changes in beach volume. Pix4D Mapper was used to process the beach volume changes and to analyze the aerial image, while the Global Mapper software conducted an analysis of beach volume changes. This study demonstrates that there is a noticeable shift in the volume of the beach within a month. Overall, the data provided demonstrates that Zones A and E are more vulnerable to erosion than the other zones.

Mechanical behavior of three‐harness twill woven composite plates under tension loading

AbstractThree‐harness twill woven composite (THT‐WC) is an advanced carbon fiber reinforced composite that compromises the mechanical behaviors in both longitudinal and latitudinal directions. To explore the further practical application, the structural characteristics and static mechanical behavior of the material were investigated in this article. X‐ray computerized tomography was adopted to obtain the mesostructure details and to capture the key characteristics. With the help of mesoscale information, the 2D and 3D geometric models were established in the form of representative volume elements (RVEs). Combined with voxel mesh generation technology, a finite element model was proposed based on a 3D geometric model where macroscopic mechanical properties of materials along different yarn directions were gained. To verify the simulated geometry and predicted mechanical properties, mesoscale geometric comparison and specimen tensile tests were performed. The results show that the geometric model established in this article can reasonably reflect the periodicity fluctuation of binder warp yarns and the randomness fluctuation of weft yarns. Compared with the experimental results, the relative error of stiffness in the weft direction of the RVE is 4.25%, and that in the warp direction is 5.33%.Highlights The mechanical properties of THT‐WC were investigated experimentally. Meso‐scale reconstruction of THT‐WC was presented. A model to predict the mechanical properties of THT‐WC is established.

Improving the accuracy of computational fluid dynamics simulations of coiled cerebral aneurysms using finite element modeling

Cerebral aneurysms are a serious clinical challenge, with ∼half resulting in death or disability. Treatment via endovascular coiling significantly reduces the chances of rupture, but the techniquehas failure rates of ∼20 %. This presents a pressing need to develop a method fordetermining optimal coildeploymentstrategies. Quantification of the hemodynamics of coiled aneurysms using computational fluid dynamics (CFD) has the potential to predict post-treatment outcomes, but representing the coil mass in CFD simulations remains a challenge. We use the Finite Element Method (FEM) for simulating patient-specific coil deployment for n = 4 ICA aneurysms for which 3D printed in vitro models were also generated, coiled, and scanned using ultra-high resolution synchrotron micro-CT. The physical and virtual coil geometries were voxelized onto a binary structured grid and porosity maps were generated for geometric comparison. The average binary accuracy score is 0.8623 and the average error in porosity map is 4.94 %. We then conduct patient-specific CFD simulations of the aneurysm hemodynamics using virtual coils geometries, micro-CT generated oil geometries, and using the porous medium method to represent the coil mass. Hemodynamic parameters including Neck Inflow Rate (Qneck) and Wall Shear Stress (WSS) were calculated for each of the CFD simulations. The average relative error in Qneck and WSS from CFD using FEM geometry were 6.6 % and 21.8 % respectively, while the error from CFD using a porous media approximation resulted in errors of 55.1 % and 36.3 % respectively; demonstrating a marked improvement in the accuracy of CFD simulations using FEM generated coil geometries.

Deep-learning magnetic resonance imaging-based automatic segmentation for organs-at-risk in the brain: Accuracy and impact on dose distribution.

Normal tissue sparing in radiotherapy relies on proper delineation. While manual contouring is time consuming and subject to inter-observer variability, auto-contouring could optimize workflows and harmonize practice. We assessed the accuracy of a commercial, deep-learning, MRI-based tool for brain organs-at-risk delineation. Thirty adult brain tumor patients were retrospectively manually recontoured. Two additional structure sets were obtained: AI (artificial intelligence) and AIedit (manually corrected auto-contours). For 15 selected cases, identical plans were optimized for each structure set. We used Dice Similarity Coefficient (DSC) and mean surface-distance (MSD) for geometric comparison and gamma analysis and dose-volume-histogram comparison for dose metrics evaluation. Wilcoxon signed-ranks test was used for paired data, Spearman coefficient(ρ) for correlations and Bland-Altman plots to assess level of agreement. Auto-contouring was significantly faster than manual (1.1/20min, p<0.01). Median DSC and MSD were 0.7/0.9mm for AI and 0.8/0.5mm for AIedit. DSC was significantly correlated with structure size (ρ=0.76, p<0.01), with higher DSC for large structures. Median gamma pass rate was 74% (71-81%) for Plan_AI and 82% (75-86%) for Plan_AIedit, with no correlation with DSC or MSD. Differences between Dmean_AI and Dmean_Ref were≤0.2Gy (p<0.05). The dose difference was moderately correlated with DSC. Bland Altman plot showed minimal discrepancy (0.1/0) between AI and reference Dmean/Dmax. The AI-model showed good accuracy for large structures, but developments are required for smaller ones. Auto-segmentation was significantly faster, with minor differences in dose distribution caused by geometric variations.

Global and local defect detection for 3D printout surface based on geometric shape comparison

A 3D printer produces parts from CAD models. The printout must be checked for defects. We present a surface defect detection algorithm that constructs a 3D mesh of the printout by multi-view scanning and compares it to the part CAD model. It computes and displays the distance from each vertex of the printout mesh to the closest point on the part model. These distances comprise the global deviation of the printout from the model. When the error is within a threshold, the algorithm compares the curvature at each printout vertex to that at the closest part point. Qualitative differences in curvature indicate local defects, such as blobs, cracks, surface roughness, and faded embossing and engraving. The results are grouped into local defect regions that are displayed. We demonstrate the algorithm on metal printing and on PLA 3D printing.

Parametrically upscaled crack nucleation model (PUCNM) for fatigue nucleation in titanium alloys containing micro-texture regions (MTR)

Micro-texture regions (MTRs), delineated as the clusters of grains with similar crystallographic orientations in the polycrystalline microstructure, play a significant role in fatigue crack nucleation and life of structures of Ti alloys. This paper develops a parametrically upscaled constitutive and crack nucleation modeling (PUCM/PUCNM) platform for predicting structural-scale fatigue crack nucleation in α/β Ti–6Al–4V alloys, whose polycrystalline microstructures contain MTRs. The PUCM/PUCNM platform bridges micro and macro scales through thermodynamically consistent incorporation of representative aggregated microstructural parameters (RAMPs) in macroscopic constitutive relations. A novel RAMP kMTRθc that captures both the MTR size and contrast in the overall texture is proposed to quantitatively represent the MTR intensity in the microstructure. Geometric analysis and comparison with experimental data establish the effectiveness of kMTRθc in characterizing the MTR distributions. The impact of MTR characteristics on fatigue crack nucleation is evaluated through the support vector regression (SVR)-aided Sobol analysis, based on data from crystal plasticity FE simulations of polycrystalline microstructure volumes with a grain-scale crack nucleation model. The PUCNM is uniquely suitable for the incorporation of the MTR RAMP kMTRθc in its probabilistic framework. A novel functional form is derived using the genetic programming-based symbolic regression (GPSR). The PUCM/PUCNM tool is used to simulate an engine blade under dwell loading conditions. Results exhibit the reduction of nucleation life with a higher level of MTR intensity, despite the same overall textures.

The role of adaptive planning in margin-reduced, MRI-guided stereotactic body radiotherapy to the prostate bed following radical prostatectomy: Post-hoc analysis of a phase II clinical trial

Background and purposeWe examined the interfractional variations of clinical target volumes (CTVs), planning target volumes (PTVs), and organs-at-risk (OARs) in patients receiving MRI-guided stereotactic body radiotherapy (SBRT) to the prostate bed and evaluated the potential role of adaptive planning. Materials and Methods31 patients received 30–34 Gy in five fractions to the prostate bed on a phase II clinical trial. OARs, CTVs, and PTVs were retrospectively contoured on daily pretreatment MRIs (n = 155). Geometric comparisons were made between initial planning contours and daily pretreatment contours. Predicted treatment plans for each fraction were evaluated using the following constraints: CTV V95%>93%, PTV V95%>90%, bladder Dmax < 36.7 Gy, bladder V32.5 Gy < 35%, rectum Dmax < 36.7 Gy, rectum V27.5 Gy < 45%, rectum 32.5 Gy < 30%, and rectal wall V24Gy < 50%. Adaptive planning was simulated for all fractions that failed to meet these criteria. Plans were then re-evaluated. ResultsMedian change in volume was 0.48% for CTV, −24.5% for bladder, and 6.95% for rectum. Median DSC was 0.89 for CTV, 0.79 for bladder, and 0.76 for rectum. 145/155 fractions (93.5%) met CTV V95%>93%. 75/155 fractions (48.4%) failed at least one OAR dose constraint. Overall, 83/155 fractions (53.5%) met criteria for adapting planning. This affected 24/31 patients (77.4%). Following adaptive planning, all fractions met CTV V95%>93% and PTV V95%>90% and 120/155 fractions (77.4%) met all OAR constraints. ConclusionDue to significant interfractional variations in anatomy, a majority of fractions failed to meet both target volume and OAR constraints. However, adaptive planning was effective in overcoming these anatomic changes. Adaptive planning should be routinely considered in prostate bed SBRT.

A preliminary study on rectal dose reduction associated with hyaluronic acid implantation in brachytherapy for prostate cancer

ObjectivesHydrogel spacer (HS) was developed to reduce rectal toxicities caused by radiotherapy, but has been reported to cause major adverse events. Our institute has attempted to introduce a hyaluronic acid (HA) as an alternative spacer. This study aimed to compare rectal doses and geometric distributions between the HS and HA implantation in prostate cancer. MethodsHS and HA were inserted in 20 and 18 patients undergoing high-dose brachytherapy, respectively. The rectum spacer volumes injected were 10 mL and 22 mL, respectively. In the treatment planning system, 13.5 Gy was administered with common catheter positions. The rectal dose indices were assessed between the spacer groups for dosimetry evaluation. Distances between the prostate and rectum and configurations of the spacers were compared. ResultsThe mean doses irradiated to 0.1 and 2 mL of the rectum were 10.45 Gy and 6.71 Gy for HS, and 6.73 Gy and 4.90 Gy for HA (p<0.001). The mean minimum distances between the prostate and rectum were 1.23 cm and 1.79 cm for HS and HA, respectively (p<0.05). Geometrical configuration comparisons revealed that HA has a higher ability to expand the space than HS. ConclusionThe rectal dose reduction ability of HA is significantly greater than that of HS, suggesting its potential as a new spacer.

Dosimetric Implications of Margin-Reduced MRI-Guided Stereotactic Body Radiotherapy to the Prostate Bed Following Radical Prostatectomy: Post-Hoc Analysis of a Phase II Study

<h3>Purpose/Objective(s)</h3> Due to the highly deformable and mobile nature of the target volume and adjacent organs at risk (OARs), generous planning target volume (PTV) margins have historically been used in the context of radiotherapy after radical prostatectomy. MRI-guided radiotherapy systems have improved on-board imaging capabilities and the ability to perform automatic beam holds based on pre-specified structures. Herein, we evaluate the dosimetric implications of interfraction variations in the clinical target volumes (CTV) and OARs in patients receiving margin-reduced MRI-guided stereotactic body radiotherapy (SBRT) to the prostate bed. <h3>Materials/Methods</h3> 31 consecutive patients received MRI-guided SBRT to the prostate bed (30 – 34 Gy in 5 fractions) without adaptive planning on a phase II study. The OAR volumes, CTV, and PTV (created using a 3 mm isotropic expansion of the CTV) were retrospectively contoured on on-board 0.35T MRIs obtained prior to each fraction (n=155). Geometric comparisons were made between the initial planning contours and the 5 daily contours for each patient using change in volume and Dice similarity coefficient (DSC) calculations. Coverage of the CTV and PTV was assessed (CTV V95% >93% and PTV V95% >90%), as were bladder and rectal dose constraints (Bladder Dmax <36.7 Gy, Bladder V32.5 Gy <35%, Rectum Dmax <36.7 Gy, Rectum V27.5 Gy <45%, Rectum 32.5 Gy <30%, and Rectal Wall V24 <50%). <h3>Results</h3> The median change in volume was -24.5% (interquartile range [IQR], -35.5 – -2.2%) for the bladder, 6.95% (IQR, -6.94 – 23.0%) for the rectum, -0.2% (IQR, -8.3 – 8.7%) for the rectal wall, 0.48% (IQR, -3.87 – 7.6%) for the CTV, and -0.48% (IQR, -5.12 – 6.53%) for the PTV. The median DSC was 0.79 (IQR, 0.73 – 0.84) for the bladder, 0.76 (IQR, 0.69 – 0.81) for the rectum, 0.35 (IQR, 0.28 – 0.44) for the rectal wall, 0.89 (IQR, 0.83 – 0.93) for the CTV, and 0.90 (IQR, 0.87 – 0.95) for the PTV. 145/155 fractions (93.5%) met the CTV V95% >93% target and 134/155 fractions (86.5%) met both the CTV V95% >93% and PTV V95% >90% targets. When calculated over 5 cumulative fractions, 29/31 patients (93.5%) met the CTV goal of V95% >93%. 62/155 fractions (40%) failed at least one OAR constraint. The mean number of fractions per patient that failed at least one OAR constraint was 2/5 (median, 1; range, 0 – 5). <h3>Conclusion</h3> With MRI-guidance, a reduction of the PTV margin to 3 mm isotropically allows for adequate CTV coverage in over 90% of patients without the need for adaptive planning. However, OAR constraints are not met in 40% of delivered fractions despite meeting all dose constraints at the time of treatment planning. Online dose prediction and, if indicated, adaptive radiotherapy can optimize this to allow for improved OAR protection and potentially allow for further reductions in the PTV expansion while maintaining adequate CTV coverage.

Left ventricular outflow tract calcification: Does the geometry matter?

Key Points The CoreValve Evolut PRO/PRO + adapt to left ventricular outflow tract (LVOT) calcification by eccentric deformation at the level of the inflow while preserving circularity at the waist, leaflet tips, and outflow. The CoreValve Evolut PRO/PRO + maintain their favorable hemodynamics and sealing in the short term despite the presence of LVOT calcification. The effect of eccentric inflow deformation in the long term and geometric comparison of self‐expanding valves and balloon‐expandable valves require further investigation.