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.

Signal to noise ratio comparisons for ultrasound attenuation slope estimation algorithms.

PURPOSE: Attenuation imaging has a promising role in the detection of tissue abnormalities. The authors have previously compared three different frequency domain ultrasound attenuation estimation methods, for accuracy and bias. The mean estimated attenuation value in a region of interest has been the determining factor of how well a method performs; however, the noise level has not been quantified for attenuation estimated using different methods. METHODS: The authors compare three different frequency domain ultrasound attenuation estimation methods [the reference phantom method (RPM), the centroid downshift method (CEN), and the hybrid method (HYB)] using the signal to noise ratio (SNR) metric. Both simulated and experimental tissue-mimicking phantoms are used in the performance comparison study, evaluating the impact of the variation in acoustical properties. RESULTS: For attenuation estimation in a tissue-mimicking phantom with a known attenuation coefficient of 0.5 dB/cm/MHz, all the three methods estimated the attenuation coefficient to be ≈ 0.49 dB/cm/MHz for a transmit center frequency of 6 MHz, however, the signal to noise ratio obtained was found to be 8.5, 5.7, and 2.2 for the HYB, RPM, and CEN methods, respectively. These results demonstrate the need for the SNR metric in the comparison of different algorithms and to evaluate the impact of varying different ultrasound system and tissue parameters. CONCLUSIONS: In this paper, the authors demonstrate that although the estimated mean attenuation value with a region of interest may be closely estimated using different methods, the signal to noise ratio obtained of the estimates can vary significantly. The centroid downshift method presented with the lowest signal-to-noise ratio of the methods compared. The hybrid method was the least susceptible to changes in the acoustical properties and provided unbiased attenuation coefficient estimates with the highest signal-to-noise ratios.

Evaluation of the impact of backscatter intensity variations on ultrasound attenuation estimation.

PURPOSE: Quantitative ultrasound based approaches such as attenuation slope estimation can be used to determine underlying tissue properties and eventually used as a supplemental diagnostic technique to B-mode imaging. The authors investigate the impact of backscatter intensity and frequency dependence variations on the attenuation slope estimation accuracy. METHODS: The authors compare three frequency domain based attenuation slope estimation algorithms, namely, a spectral difference method, the reference phantom method, and two spectral shift methods: a hybrid method and centroid downshift method. Both the reference phantom and hybrid method use a tissue-mimicking phantom with well-defined acoustic properties to reduce system dependencies and diffraction effects. The normalized power spectral ratio obtained is then filtered by a Gaussian filter centered at the transmit center frequency in the hybrid method. A spectral shift method is then used to estimate the attenuation coefficient from the normalized and filtered spectrum. The centroid downshift method utilizes the shift in power spectrum toward lower frequencies with depth. Numerical phantoms that incorporate variations in the backscatter intensity from -3 to 3 dB, by varying the scatterer number density and variations in the scatterer diameters ranging from 10 to 100 µm are simulated. Experimental tissue mimicking phantoms with three different scatterer diameter ranges (5-40, 75-90, and 125-150 µm) are also used to evaluate the accuracy of the estimation methods. RESULTS: The reference phantom method provided accurate results when the acoustical properties of the reference and the sample are well matched. Underestimation occurs when the reference phantom possessed a higher sound speed than the sample, and overestimation occurs when the reference phantom had a lower sound speed than the sample. The centroid downshift method depends significantly on the bandwidth of the power spectrum, which in turn depends on the frequency dependence of the backscattering. The hybrid method was the least susceptible to changes in the sample's acoustic properties and provided the lowest standard deviation in the numerical simulations and experimental evaluations. CONCLUSIONS: No significant variations in the estimation accuracy of the attenuation coefficient were observed with an increase in the scatterer number density in the simulated numerical phantoms for the three methods. Changes in the scatterer diameters, which result in different frequency dependence of backscatter, do not significantly affect attenuation slope estimation with the reference phantom and hybrid approaches. The centroid method is sensitive to variations in the scatterer diameter due to the frequency shift introduced in the power spectrum.

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.

Polymer semiconductors as a biosensing platform: peroxidase activity of enzyme bound to organic semiconducting films.

Organic polymer semiconductors have unique electronic properties which make them attractive for use in microelectronic and optoelectronic devices fabricated using inexpensive manufacturing processes. In addition, novel chemical and biological sensors have been proposed which make use of the photophysical and electrical properties of conjugated polymer semiconducting films. The work described herein illustrates one such biosensing application by demonstrating successful immobilization of horseradish peroxidase enzyme onto a thin film of the semiconducting polymer MDMO-PPV. Validation of bound peroxidase activity is accomplished through the use of a substrate solution of 3,3'-diaminobenzidine and hydrogen peroxide, which yields a dark brown precipitate in the presence of peroxidase. Photometric measurements are used to derive a quantitative assay of bound peroxidase concentration. This work supports the feasibility of organic semiconducting polymer films as a biosensing platform in microelectronic sensor devices.