Optimization of treatment planning workflow and tumor coverage during daily adaptive magnetic resonance image guided radiation therapy (MR-IGRT) of pancreatic cancer

Abstract Background To simplify the adaptive treatment planning workflow while achieving the optimal tumor-dose coverage in pancreatic cancer patients undergoing daily adaptive magnetic resonance image guided radiation therapy (MR-IGRT). Methods In daily adaptive MR-IGRT, the plan objective function constructed during simulation is used for plan re-optimization throughout the course of treatment. In this study, we have constructed the initial objective functions using two methods for 16 pancreatic cancer patients treated with the ViewRay™ MR-IGRT system: 1) the conventional method that handles the stomach, duodenum, small bowel, and large bowel as separate organs at risk (OARs) and 2) the OAR grouping method. Using OAR grouping, a combined OAR structure that encompasses the portions of these four primary OARs within 3 cm of the planning target volume (PTV) is created. OAR grouping simulation plans were optimized such that the target coverage was comparable to the clinical simulation plan constructed in the conventional manner. In both cases, the initial objective function was then applied to each successive treatment fraction and the plan was re-optimized based on the patient’s daily anatomy. OAR grouping plans were compared to conventional plans at each fraction in terms of coverage of the PTV and the optimized PTV (PTV OPT), which is the result of the subtraction of overlapping OAR volumes with an additional margin from the PTV. Results Plan performance was enhanced across a majority of fractions using OAR grouping. The percentage of the volume of the PTV covered by 95% of the prescribed dose (D95) was improved by an average of 3.87 ± 4.29% while D95 coverage of the PTV OPT increased by 3.98 ± 4.97%. Finally, D100 coverage of the PTV demonstrated an average increase of 6.47 ± 7.16% and a maximum improvement of 20.19%. Conclusions In this study, our proposed OAR grouping plans generally outperformed conventional plans, especially when the conventional simulation plan favored or disregarded an OAR through the assignment of distinct weighting parameters relative to the other critical structures. OAR grouping simplifies the MR-IGRT adaptive treatment planning workflow at simulation while demonstrating improved coverage compared to delivered pancreatic cancer treatment plans in daily adaptive radiation therapy

A Swedish heterodyne facility instrument for the APEX telescope

In March 2008, the APEX facility instrument was installed on the telescope at the site of Lliano Chajnantor in northern Chile. The main objective of the paper is to introduce the new instrument to the radio astronomical community. It describes the hardware configuration and presents some initial results from the on-sky commissioning. The heterodyne instrument covers frequencies between 211 GHz and 1390 GHz divided into four bands. The first three bands are sideband-separating mixers operating in a single sideband mode and based on superconductor-insulator-superconductor (SIS) tunnel junctions. The fourth band is a hot-electron bolometer, waveguide balanced mixer. All bands are integrated in a closedcycle temperature-stabilized cryostat and are cooled to 4 K. We present results from noise temperature, sideband separation ratios, beam, and stability measurements performed on the telescope as a part of the receiver technical commissioning. Examples of broad extragalactic lines are also included

SEPIA345: A 345 GHz dual polarization heterodyne receiver channel for SEPIA at the APEX telescope

Context. We describe the new SEPIA345 heterodyne receiver channel installed at the Atacama Pathfinder EXperiment (APEX) telescope, including details of its configuration, characteristics, and test results on sky. SEPIA345 is designed and built to be a part of the Swedish ESO PI Instrument for the APEX telescope (SEPIA). This new receiver channel is suitable for very high-resolution spectroscopy and covers the frequency range 272- 376 GHz. It utilizes a dual polarization sideband separating (2SB) receiver architecture, employing superconductor-isolator-superconductor mixers (SIS), and provides an intermediate frequency (IF) band of 4- 12 GHz for each sideband and polarization, thus covering a total instantaneous IF bandwidth of 4 \uc3\uc2 - 8 = 32 GHz. Aims. This paper provides a description of the new receiver in terms of its hardware design, performance, and commissioning results. Methods. The methods of design, construction, and testing of the new receiver are presented. Results. The achieved receiver performance in terms of noise temperature, sideband rejection, stability, and other parameters are described. Conclusions. SEPIA345 is a commissioned APEX facility instrument with state-of-the-art wideband IF performance. It has been available on the APEX telescope for science observations since July 2021

Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR‐only breast radiotherapy

Purpose: The superior soft-tissue contrast achieved using magnetic resonance imaging (MRI) compared to x-ray computed tomography (CT) has led to the popularization of MRI-guided radiation therapy (MR-IGRT), especially in recent years with the advent of first and second generation MRI-based therapy delivery systems for MR-IGRT. The expanding use of these systems is driving interest in MRI-only RT workflows in which MRI is the sole imaging modality used for treatment planning and dose calculations. To enable such a workflow, synthetic CT (sCT) data must be generated based on a patient’s MRI data so that dose calculations may be performed using the electron density information derived from CT images. In this study, we propose a novel deep spatial pyramid convolutional framework for the MRI-to-CT image-to-image translation task and compare its performance to the well established U-Net architecture in a generative adversarial network (GAN) framework. Methods: Our proposed framework utilizes atrous convolution in a method named atrous spatial pyramid pooling (ASPP) to significantly reduce the total number of parameters required to describe the model while effectively capturing rich, multi-scale structural information in a manner that is not possible in the conventional framework. The proposed framework consists of a generative model composed of stacked encoders and decoders separated by the ASPP module, where atrous convolution is applied at increasing rates in parallel to encode large-scale features. The performance of the proposed method is compared to that of the conventional GAN framework in terms of the time required to train the model and the image quality of the generated sCT as measured by the root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) depending on the size of the training data set. Dose calculations based on sCT data generated using the proposed architecture are also compared to clinical plans to evaluate the dosimetric accuracy of the method. Results: Significant reductions in training time and improvements in image quality are observed at every training data set size when the proposed framework is adopted instead of the conventional framework. Over 1042 test images, values of 17.7 ± 4.3 HU, 0.9995 ± 0.0003, and 71.7 ± 2.3 are observed for the RMSE, SSIM, and PSNR metrics, respectively. Dose distributions calculated based on sCT data generated using the proposed framework demonstrate passing rates equal to or greater than 98% using the 3D gamma index with a 2%/2 mm criterion. Conclusions: The deep spatial pyramid convolutional framework proposed here demonstrates improved performance compared to the conventional GAN framework that has been applied to the image-to-image translation task of sCT generation. Adopting the method is a first step toward an MRI-only RT workflow that enables widespread clinical applications for MR-IGRT including online adaptive therapy

Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR-only breast radiotherapy

Purpose: The superior soft-tissue contrast achieved using magnetic resonance imaging (MRI) compared to x-ray computed tomography (CT) has led to the popularization of MRI-guided radiation therapy (MR-IGRT), especially in recent years with the advent of first and second generation MRI-based therapy delivery systems for MR-IGRT. The expanding use of these systems is driving interest in MRI-only RT workflows in which MRI is the sole imaging modality used for treatment planning and dose calculations. To enable such a workflow, synthetic CT (sCT) data must be generated based on a patient’s MRI data so that dose calculations may be performed using the electron density information derived from CT images. In this study, we propose a novel deep spatial pyramid convolutional framework for the MRI-to-CT image-to-image translation task and compare its performance to the well established U-Net architecture in a generative adversarial network (GAN) framework. Methods: Our proposed framework utilizes atrous convolution in a method named atrous spatial pyramid pooling (ASPP) to significantly reduce the total number of parameters required to describe the model while effectively capturing rich, multi-scale structural information in a manner that is not possible in the conventional framework. The proposed framework consists of a generative model composed of stacked encoders and decoders separated by the ASPP module, where atrous convolution is applied at increasing rates in parallel to encode large-scale features. The performance of the proposed method is compared to that of the conventional GAN framework in terms of the time required to train the model and the image quality of the generated sCT as measured by the root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) depending on the size of the training data set. Dose calculations based on sCT data generated using the proposed architecture are also compared to clinical plans to evaluate the dosimetric accuracy of the method. Results: Significant reductions in training time and improvements in image quality are observed at every training data set size when the proposed framework is adopted instead of the conventional framework. Over 1042 test images, values of 17.7 ± 4.3 HU, 0.9995 ± 0.0003, and 71.7 ± 2.3 are observed for the RMSE, SSIM, and PSNR metrics, respectively. Dose distributions calculated based on sCT data generated using the proposed framework demonstrate passing rates equal to or greater than 98% using the 3D gamma index with a 2%/2 mm criterion. Conclusions: The deep spatial pyramid convolutional framework proposed here demonstrates improved performance compared to the conventional GAN framework that has been applied to the image-to-image translation task of sCT generation. Adopting the method is a first step toward an MRI-only RT workflow that enables widespread clinical applications for MR-IGRT including online adaptive therapy

A 0.8 mm heterodyne facility receiver for the APEX telescope

Aims.The new APEX telescope, located on Llano Chajnantor in Northern Chile, will have high resolution spectroscopic instruments covering the wavelength region from 0.20 to 1.30 mm (210-1500 GHz). Methods.In May 2005, the first facility receiver for the band 0.79-1.07 mm (279-381 GHz) was installed together with backends providing down to 60 kHz spectral resolution. This instrument that operates in double sideband mode uses superconducting tunnel junctions (SIS) as mixing elements operating at 4 K to achieve close to quantum-limited noise performances. The receiver is cooled by a closed-cycle cooling machine that allows continuous operation. The receiver design minimizes moving parts and is fully operated by remote to improve its reliability and the ease of use. Results.The double sideband (DSB) receiver temperatures are in the range 50-70 K, which typically results in a DSB system noise temperature of about 100 K in excellent weather conditions and between 100-200 K in good weather conditions