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/

Dissociation of carbon monoxide on rhodium surfaces

The dissocn. of CO on the (111), stepped (111), (100), and (110) surfaces of Rh was studied with the atom superposition and electron delocalization (ASED) MO method. For each surface several possible reaction paths are proposed, which the activation energy is calcd. Reaction paths with the lowest activation energy have in common that in the 1st stage the O atom bends to the metal surface, while in the 2nd stage the O atom is moving away from the C over a surface Rh atom. This results in a transition state where both C and O atoms are bonded to this surface Rh atom. Calcns. of the local d. of states of CO show considerable occupation of the 2p* orbital of CO in the transition state. Large metal ensembles are necessary for CO dissocn., since after dissocn. C and O are adsorbed on sites sharing a min. no. of surface metal atom

Quantum chemistry of CO chemisorption and activation

A review with .apprx.70 refs. on quantum chem. studies of the chemisorption and activation of CO on catalyst

Een instrument om een leernetwerk te typeren

Het leren van ervaren leraren, aanstaande leraren, lerarenopleiders en beginnende leraren krijgt steeds vaker vorm door middel van leergemeenschappen of leernetwerken. Uit de literatuur over professionele ontwikkeling van (aanstaande) leraren blijkt dat het samen leren een betekenisvolle en productieve aanpak is. In de praktijk merken we dat voor deze vorm van leren steeds weer een andere benaming wordt gekozen. We komen termen tegen als vakdidactische netwerken, professionele leergemeenschappen of docent-ontwikkelteams. Hoewel een poging om deze verschillende vormen van leren in netwerken te definiëren zeker zinvol is, hebben wij ervoor gekozen te zoeken naar dimensies waarop vormen van netwerkleren gekarakteriseerd kunnen worden. We zijn gekomen tot tien mogelijke dimensies. Deze dimensies kunnen door groepen (aanstaande) leraren gebruikt worden om het netwerk waarin zij deelnemen te karakteriseren. Dit instrument willen we in de sessie presenteren als nieuw idee om in de opleidingspraktijk te gebruiken. De deelnemers gaan hiermee aan het werk om ten slotte in discussie te gaan over zowel de dimensies als de bruikbaarheid van het instrument

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

Purpose In 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 Methods A 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. Results For 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. Conclusion For 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