Increasing the Size of a Piece of Popcorn

Popcorn is an extremely popular snack food in the world today. Thermodynamics can be used to analyze how popcorn is produced. By treating the popping mechanism of the corn as a thermodynamic expansion, a method of increasing the volume or size of a kernel of popcorn can be studied. By lowering the pressure surrounding the unpopped kernel, one can use a thermodynamic argument to show that the expanded volume of the kernel when it pops must increase. In this project, a variety of experiments are run to test the validity of this theory. The results show that there is a significant increase in the average kernel size when the pressure of the surroundings is reduced.Comment: Latex document, 14 pages, 4 figures, 1 page of table

Evaluation of CBCT-based synthetic CTs for clinical adoption in proton therapy of head & neck patients.:E-Poster

PurposeIn adaptive proton therapy, weekly verification CTs (rCTs) are commonly acquired and used to monitor patient anatomy. Cone-Beam CTs (CBCT) on the other hand are used for daily pre-treatment position verification. These CBCT images however suffer from severe imaging artifacts preventing accurate proton dose calculations, meaning that CBCTs are unsuitable for treatment planning purposes. Recent advances in converting CBCT images to high quality synthetic CTs (sCTs) using Deep Convolution Neural Networks (DCNN) show that these sCTs can be suitable for proton dose calculations and therefore assist clinical adaptation decisions.The aim of this study was to compare weekly high definition rCTs to same-day sCT images of head and neck cancer patients in order to verify dosimetric accuracy of DCNN generated CBCT-based sCTs.Materials and MethodsA dataset of 46 previously treated head and neck cancer patients was used to generate synthetic CTs from daily pre-treatment patient alignment CBCTs using a previously developed and trained U-net like DCNN. Proton dose was then recalculated on weekly rCTs and same-day sCTs utilizing clinical treatment plans. To assess the dosimetric accuracy of sCTs, dose to the clinical target volumes (CTV D98) and mean dose in selected organs-at-risk (OAR; Oral cavity, Parotid gland left, Submandibular gland right) was calculated and compared between rCTs and same-day sCTs. Furthermore, Normal Tissue Complication Probability (NTCP) models for xerostomia and dysphagia were used to assess the clinical significance of dose differences.ResultsFor target volumes, the average difference in D98% between rCT and sCT pairs (N=284) was 0.34±3.86 % [-0.18±2.06 Gy] for the low dose CTV (54.25 Gy) and 0.23±3.62 % [-0.16±2.48 Gy] for the high dose CTV (70 Gy). For the OARs the following mean dose differences were observed; Oral Cavity: 4.15±9.78 % [0.75±1.39 Gy], Parotid L: 5.34±11.6 % [0.58±1.40 Gy], Submandibular R: 2.17±8.55 % [0.55±2.57 Gy]. The average NTCP difference was -0.15±0.58 % for grade 3 dysphagia, -0.26±0.54 % for grade 3 xerostomia, -0.53±1.20 % for grade 2 dysphagia and -0.71±1.40 % for grade 2 xerostomia. ConclusionFor target coverage and NTCP difference, the deep learning based sCTs showed high agreement with weekly verification CTs. However, some outliers were observed (also indicated by the increased standard deviation) and warrant further investigation and improvements before clinical implementation. Furthermore, stringent quality control tools for synthetic CTs are required to allow reliable deployment in adaptive proton therapy workflows.<br/

Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy

Background and purpose: Auto-contouring performance has been widely studied in development and commissioning studies in radiotherapy, and its impact on clinical workflow assessed in that context. This study aimed to evaluate the manual adjustment of auto-contouring in routine clinical practice and to identify improvements regarding the auto-contouring model and clinical user interaction, to improve the efficiency of auto-contouring. Materials and methods: A total of 103 clinical head and neck cancer cases, contoured using a commercial deep-learning contouring system and subsequently checked and edited for clinical use were retrospectively taken from clinical data over a twelve-month period (April 2019–April 2020). The amount of adjustment performed was calculated, and all cases were registered to a common reference frame for assessment purposes. The median, 10th and 90th percentile of adjustment were calculated and displayed using 3D renderings of structures to visually assess systematic and random adjustment. Results were also compared to inter-observer variation reported previously. Assessment was performed for both the whole structures and for regional sub-structures, and according to the radiation therapy technologist (RTT) who edited the contour. Results: The median amount of adjustment was low for all structures (<2 mm), although large local adjustment was observed for some structures. The median was systematically greater or equal to zero, indicating that the auto-contouring tends to under-segment the desired contour. Conclusion: Auto-contouring performance assessment in routine clinical practice has identified systematic improvements required technically, but also highlighted the need for continued RTT training to ensure adherence to guidelines

Technical Note: 4D cone-beam CT reconstruction from sparse-view CBCT data for daily motion assessment in pencil beam scanned proton therapy (PBS-PT)

Purpose: The number of pencil beam scanned proton therapy (PBS-PT) facilities equipped with cone-beam computed tomography (CBCT) imaging treating thoracic indications is constantly rising. To enable daily internal motion monitoring during PBS-PT treatments of thoracic tumors, we assess the performance of Motion-Aware RecOnstructiOn method using Spatial and Temporal Regularization (MA-ROOSTER) four-dimensional CBCT (4DCBCT) reconstruction for sparse-view CBCT data and a realistic data set of patients treated with proton therapy. Methods: Daily CBCT projection data for nine non-small cell lung cancer (NSCLC) patients and one SCLC patient were acquired at a proton gantry system (IBA Proteus® One). Four-dimensional CBCT images were reconstructed applying the MA-ROOSTER and the conventional phase-correlated Feldkamp-Davis-Kress (PC-FDK) method. Image quality was assessed by visual inspection, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and the structural similarity index measure (SSIM). Furthermore, gross tumor volume (GTV) centroid motion amplitudes were evaluated. Results: Image quality for the 4DCBCT reconstructions using MA-ROOSTER was superior to the PC-FDK reconstructions and close to FDK images (median CNR: 1.23 [PC-FDK], 1.98 [MA-ROOSTER], and 1.98 [FDK]; median SNR: 2.56 [PC-FDK], 4.76 [MA-ROOSTER], and 5.02 [FDK]; median SSIM: 0.18 [PC-FDK vs FDK], 0.31 [MA-ROOSTER vs FDK]). The improved image quality of MA-ROOSTER facilitated GTV contour warping and realistic motion monitoring for most of the reconstructions. Conclusion: MA-ROOSTER based 4DCBCTs performed well in terms of image quality and appear to be promising for daily internal motion monitoring in PBS-PT treatments of (N)SCLC patients

Robustness assessment of clinical adaptive proton and photon radiotherapy for oesophageal cancer in the model-based approach

Purpose In the Netherlands, oesophageal cancer (EC) patients are selected for intensity modulated proton therapy (IMPT) using the expected normal tissue complication probability reduction (ΔNTCP) when treating with IMPT compared to volumetric modulated arc therapy (VMAT). In this study, we evaluate the robustness of the first EC patients treated with IMPT in our clinic in terms of target and organs-at-risk (OAR) dose with corresponding NTCP, as compared to VMAT. Materials and Methods For 20 consecutive EC patients, clinical IMPT and VMAT plans were created on the average planning 4DCT. Both plans were robustly evaluated on weekly repeated 4DCTs and if target coverage degraded, replanning was performed. Target coverage was evaluated for complete treatment trajectories with and without replanning. The planned and accumulated mean lung dose (MLD) and mean heart dose (MHD) were additionally evaluated and translated into NTCP. Results Replanning in the clinic was performed more often for IMPT (15x) than would have been needed for VMAT (8x) (p = 0.11). Both adaptive treatments would have resulted in adequate accumulated target dose coverage. Replanning in the first week of treatment had most clinical impact, as anatomical changes resulting in insufficient accumulated target coverage were already observed at this stage. No differences were found in MLD between the planned dose and the accumulated dose. Accumulated MHD differed from the planned dose (p < 0.001), but since these differences were similar for VMAT and IMPT (1.0 and 1.5 Gy, respectively), the ΔNTCP remained unchanged. Conclusion Following an adaptive clinical workflow, adequate target dose coverage and stable OAR doses with corresponding NTCPs was assured for both IMPT and VMAT

The necessity of 4D-motion monitoring for thoracic tumors treated with pencil beam scanning proton therapy:a comprehensive 4D-imaging study

Purpose/Objective For pencil beam scanning proton therapy (PBS-PT), moving targets remain challenging due to the interplay effect. Even when using motion mitigation strategies, one needs to be aware of motion variations. We investigated weekly and daily motion variations to define the most optimal motion monitoring protocol for PBS-PT treatments of lung cancer patients. Material/Methods For 20 stage II-IV (N)SCLC patients 4DCT imaging was performed during treatment simulation (week 0) and weekly during the treatment course. GTVs were delineated on the maximum inspiration and expiration 4DCT phases and the centroid 3D-vector translations were evaluated. For one patient, daily 3D-vector centroid 4DCBCT motion was evaluated additionally. Results A median initial tumor motion (Figure 1) of 1.3 mm (range: 0.0 – 7.4 mm) was observed. GTV motions varied each week; 7 out of 20 patients showed motion variation >3 mm compared to the motion measured in week 0. Figure 2 shows that motion amplitudes extracted from weekly 4DCTs were not predictive for motion amplitudes extracted from daily 4DCBCTs. Conclusion For a considerable part of the patients, the motion measured in week 0 based on weekly repeat 4DCT imaging was not predictive for motion in the following weeks. Daily motion measured by 4DCBCT imaging for one patient suggests that weekly measured 4DCT motion is not predictive for the daily motion in between the weekly 4DCTs. This indicates that breathing motion differs from day to day and daily 4D-imaging is therefore needed to assure safe PBS-PT treatments for lung cancer patients