Development of a comprehensive cardiac atlas on a 1.5 Tesla Magnetic Resonance Linear Accelerator.

Background and purpose: The 1.5 Tesla (T) Magnetic Resonance Linear Accelerator (MRL) provides an innovative modality for improved cardiac imaging when planning radiation treatment. No MRL based cardiac atlases currently exist, thus, we sought to comprehensively characterize cardiac substructures, including the conduction system, from cardiac images acquired using a 1.5 T MRL and provide contouring guidelines. Materials and methods: Five volunteers were enrolled in a prospective protocol (NCT03500081) and were imaged on the 1.5 T MRL with Half Fourier Single-Shot Turbo Spin-Echo (HASTE) and 3D Balanced Steady-State Free Precession (bSSFP) sequences in axial, short axis, and vertical long axis. Cardiac anatomy was contoured by (AS) and confirmed by a board certified cardiologist (JR) with expertise in cardiac MR imaging. Results: A total of five volunteers had images acquired with the HASTE sequence, with 21 contours created on each image. One of these volunteers had additional images obtained with 3D bSSFP sequences in the axial plane and additional images obtained with HASTE sequences in the key cardiac planes. Contouring guidelines were created and outlined. 15-16 contours were made for the short axis and vertical long axis. The cardiac conduction system was demonstrated with eleven representative contours. There was reasonable variation of contour volume across volunteers, with structures more clearly delineated on the 3D bSSFP sequence. Conclusions: We present a comprehensive cardiac atlas using novel images acquired prospectively on a 1.5 T MRL. This cardiac atlas provides a novel resource for radiation oncologists in delineating cardiac structures for treatment with radiotherapy, with special focus on the cardiac conduction system.

Auto-detection of necessity for MRI-guided online adaptive replanning using a machine learning classifier.

PURPOSE: MRI-guided adaptive radiation therapy (MRgART), particularly daily online adaptive replanning (OLAR) can substantially improve radiation therapy delivery, however, it can be labor-intensive and time-consuming. Currently, the decision to perform OLAR for a treatment fraction is determined subjectively. In this work, we develop a machine learning algorithm based on structural similarity index measure (SSIM) and change in entropy to quickly and objectively determine whether OLAR is necessary for a daily MRI set. METHODS: A total of 109 daily MRI sets acquired on a 1.5T MR-Linac during MRgART for 22 pancreatic cancer patients each treated with five fractions were retrospectively analyzed. For each daily MRI set, OLAR and reposition (No-OLAR) plans were created and the superior plan with the daily fraction determined per clinical dose-volume criteria. SSIM and entropy maps were extracted from each daily MRI set, with respect to its reference (e.g., dry-run) MRI in the region enclosed by 50-100% isodose surfaces. A total of six common features were extracted from SSIM maps. Pearson's rank correlation coefficient was utilized to rule out redundant SSIM features. A t-test was used to determine significant SSIM features which were combined with the change in entropy to develop anensemble machine classifier with fivefold cross validation. The performance of the classifier was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: A machine learning classifier model using two SSIM features (mean and full width at half maximum) and change in entropy was determined to be able to significantly discriminate between No-OLAR and OLAR groups. The obtained machine learning ensemble classifier can predict OLAR necessity with a cross validated AUC of 0.93. Misclassification was found primarily for No-OLAR cases with dosimetric plan quality closely comparable to the corresponding OLAR plans, thus, are not a major practical concern. CONCLUSION: A machine learning classifier based on simple first-order image features, that is, SSIM features and change in entropy, was developed to determine when OLAR is necessary for a daily MRI set with practical acceptable prediction accuracy. This classifier may be implemented in the MRgART process to automatically and objectively determine if OLAR is required following daily MRI.

Multi-parametric magnetic resonance imaging for radiation treatment planning.

In recent years, multi-parametric magnetic resonance imaging (MpMRI) has played a major role in radiation therapy treatment planning. The superior soft tissue contrast, functional or physiological imaging capabilities, and the flexibility of site-specific image sequence development has placed MpMRI at the forefront. In this article, the present status of MpMRI for external beam radiation therapy planning is reviewed. Common MpMRI sequences, preprocessing, and quality assurance strategies are briefly discussed, and various image registration techniques and strategies are addressed. Image segmentation methods including automatic segmentation and deep learning techniques for organs at risk and target delineation are reviewed. Due to the advancement in MRI-guided online adaptive radiotherapy, treatment planning considerations addressing MRI only planning are also discussed.

Preliminary results on the feasibility of using ultrasound to monitor intrafractional motion during radiation therapy for pancreatic cancer.

PURPOSE: Substantial intrafraction organ motion during radiation therapy (RT) for pancreatic cancer is well recognized as a major limiting factor for accurate delivery of RT. The aim of this work is to determine the feasibility of monitoring the intrafractional motion of the pancreas or surrounding structures using ultrasound for RT delivery. METHODS: Transabdominal ultrasound (TAUS) and 4DCT data were acquired on ten pancreatic cancer patients during radiation therapy process in a prospective study. In addition, TAUS and MRI were collected for five healthy volunteers. The portal vein (PV) and the head of the pancreas (HP) along with other structures were contoured on these images. Volume changes, distance between the HP and PV, and motion difference between the HP and PV were measured to examine whether PV can be used as a motion surrogate for HP. TAUS images were acquired and processed using a research version of the Clarity autoscan ultrasound system (CAUS). Motion monitoring was performed with the ultrasound probe mounted on an arm fixed to the couch. Video segments of the monitoring sessions were captured. RESULTS: On TAUS, PV is better visualized than HP. The measured mean volume deviation for all patients for the HP and PV was 1.4 and 0.6 ml, respectively. The distance between the HP and PV was close to a constant with 0.22 mm mean deviation throughout the ten breathing phases. The mean of the absolute motion difference for all patients was 1.7 ± 0.8 mm in LR, 1.5 ± 0.5 mm in AP, and 2.3 ± 0.7 mm in SI, suggesting that the PV is a good surrogate for HP motion estimation. By using this surrogate, the HP motion tracking using TAUS was demonstrated. CONCLUSIONS: Large intrafractional organ motion due to respiratory and/or bowel motion is a limiting factor in administering curative radiation doses to pancreatic tumors. The authors investigate the use of real-time ultrasound to track pancreas motion. Due to the poor visibility of the pancreas head on an ultrasound image, the portal vein is identified as a surrogate. The authors have demonstrated the feasibility of tracking HP motion through the localization of the PV using TAUS. This will potentially allow real-time tracking of intrafractional motion to justify small PTV-margins and to account for unusual motions, thus, improving normal tissue sparing.

Dynamic and quasi-static mechanical testing for characterization of the viscoelastic properties of human uterine tissue.

Ultrasound elastography is envisioned as an optional modality to augment standard ultrasound B-mode imaging and is a promising technique to aid in detecting uterine masses which cause abnormal uterine bleeding in both pre- and post-menopausal women. In order to determine the effectiveness of strain imaging, mechanical testing to establish the elastic contrast between normal uterine tissue and stiffer masses such as leiomyomas (fibroids) and between softer pathologies such as uterine cancer and adenomyosis has to be performed. In this paper, we evaluate the stiffness of normal uterine tissue, leiomyomas, and endometrial cancers using a EnduraTEC ElectroForce (ELF) system. We quantify the viscoelastic characteristics of uterine tissue and associated pathologies globally by using two mechanical testing approaches, namely a dynamic and a quasi-static (ramp testing) approach. For dynamic testing, 21 samples obtained from 18 patients were tested. The testing frequencies were set to 1, 10, 20, and 30 Hz. We also report on stiffness variations with pre-compression from 1% to 6% for testing at 2%, 3%, and 4% strain amplitude. Our results show that human uterine tissue stiffness is both dependent on percent pre-compression and testing frequencies. For ramp testing, 20 samples obtained from 14 patients were used. A constant strain rate of 0.1% was applied and comparable results to dynamic testing were obtained. The mean modulus contrast at 2% amplitude between normal uterine tissue (the background) and leiomyomas was 2.29 and 2.17, and between the background and cancer was 0.47 and 0.39 for dynamic and ramp testing, respectively.

A novel saline infusion sonohysterography-based strain imaging approach for evaluation of uterine abnormalities in vivo: preliminary results.

In this article, we demonstrate the feasibility of saline infusion sonohysterography-based strain imaging for the determination of stiffness variations in uterine masses in vivo. Strain images are estimated using a 2-dimensional multilevel hybrid algorithm developed for sector array ultrasound transducers. Coarse displacements are initially estimated using envelope echo signals, followed by a guided finer displacement estimation using window lengths on the order of 6 wavelengths and 7 A-lines on radiofrequency data. Strain images are obtained by estimating displacement slopes using least squares estimation. In this prospective study, we show that stiffer masses such as fibroids appear darker or as regions with low strain on strain images and are thus clearly differentiated when compared to normal uterine tissue. A high strain boundary around stiffer masses referred to as a "halo" due to increased slipping or sliding of the mass during the applied deformation is also visualized. Uterine polyps, on the other hand, are visualized as masses that are brighter or regions with high strain when compared to the background myometrium, indicating the presence of a softer mass. Axial strain images provide additional new information that may supplement current clinical B-mode imaging used for the diagnosis of uterine abnormalities. Our results show the feasibility of improving clinical diagnosis based on strain imaging.