An automated technique for global noise level measurement in CT image with a conjunction of image gradient

Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10 × 10 pixel for calculating Hounsfield unit (HU) standard deviation and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson's tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had a similar shape with a close GNI value. In patient CT images, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the entire CT scan range with high heterogeneous tissue compared to the GNI values using Christianson's tissue-type method. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI when using different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom: sharper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT images with either a smooth or grainy appearance. The study's results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control.

Development of automated region of interest selection algorithms for surface-guided radiation therapy of breast cancer

To investigate automation of the preparation of the region of interest (ROI) for surface-guided radiotherapy (SGRT) of the whole breast with two algorithms based on contour anatomies: using the body contour, and using the breast contour. The patient dataset used for modeling consisted of 39 breast cancer patients previously treated with SGRT. The patient's anatomical structures (body and ipsilateral breast) were retrieved from the planning system, and the clinical ROI (cROI) drawn by the planners was retrieved from the SGRT system for comparison. For the body-contour-based algorithm, a convolutional neural network (MobileNet-v2) was utilized to train a synthetic human model dataset to predict body joint locations. With the body joint location knowledge, an automated ROI (aROIbody ) can be created based on: (1) the superior-inferior (S-I) borders defined by the joint locations, (2) the left-right (L-R) borders defined with 3/4 of chest width, and (3) a curation of the ROI to avoid the ipsilateral armpit. For the breast-contour-based algorithm, an aROIbreast was created by first defining the ROI in the S-I direction with the ipsilateral breast boundaries. Other steps are the same as with the body-contour-based algorithm. Among the 39 patients, 24 patients were used to fine-tune the algorithm parameters, and the remaining 15 patients were used to evaluate the quality of the aROIs against the cROIs. A blinded evaluation was performed by three SGRT expert physicists to rate the acceptability and the quality (1-10 scale) of the aROIs and cROIs, and the dice similarity coefficient (DSC) was also calculated to compare the similarity between the aROIs and cROIs. The results showed that the average acceptability was 14/15 (range: 13/15-15/15) for cROIs, 13.3/15 (range: 13/15-14/15) for aROIbody , and 14.6/15 (range: 14/15-15/15) for aROIbreast . The average quality was 7.4 ± 0.8 for cROIs, 8.1 ± 1.2 for aROIbody , and 8.2 ± 0.9 for aROIbreast . The DSC with cROIs was 0.81 ± 0.06 for aROIbody , and 0.83 ± 0.04 for aROIbreast . The ROI creation time was ∼120 s for clinical, 1.3 s for aROIbody , and 1.2 s for aROIbreast . The proposed automated algorithms can improve the ROI compliance with the SGRT protocol, with a shortened preparation time. It is ready to be integrated into the clinical workflow for automated ROI preparation.

Comparison of setup accuracy of optical surface image versus orthogonal x-ray images for VMAT of the left breast using deep-inspiration breath-hold

To compare the setup accuracy of optical surface image (OSI) versus orthogonal x-ray images (2DkV) using cone beam computed tomography (CBCT) as ground truth for radiotherapy of left breast cancer in deep-inspiration breath-hold (DIBH). Ten left breast DIBH patients treated with volumetric modulated arc therapy (VMAT) were studied retrospectively. OSI, 2DkV, and CBCT were acquired weekly at treatment setup. OSI, 2DkV, and CBCT were registered to planning CT or planning DRR based on a breast surface region of interest (ROI), bony anatomy (chestwall and sternum), and both bony anatomy and breast surface, respectively. These registrations provided couch shifts for each imaging system. The setup errors, or the difference in couch shifts between OSI and CBCT were compared to those between 2DkV and CBCT. A second OSI was acquired during last beam delivery to evaluate intrafraction motion. The median absolute setup errors were (0.21, 0.27, 0.23 cm, 0.6°, 1.3°, 1.0°) for OSI, and (0.26, 0.24, 0.18 cm, 0.9°, 1.0°, 0.6°) for 2DkV in vertical, longitudinal and lateral translations, and in rotation, roll and pitch, respectively. None of the setup errors was significantly different between OSI and 2DkV. For both systems, the systematic and random setup errors were ≤0.6 cm and ≤1.5° in all directions. Nevertheless, larger setup errors were observed in some sessions in both systems. There was no correlation between OSI and CBCT whereas there was modest correlation between 2DkV and CBCT. The intrafraction motion in DIBH detected by OSI was small with median absolute translations <0.2 cm, and rotations ≤0.4°. Though OSI showed comparable and small setup errors as 2DkV, it showed no correlation with CBCT. We concluded that to achieve accurate setup for both bony anatomy and breast surface, daily 2DkV can't be omitted following OSI for left breast patients treated with DIBH VMAT.

Patient specific prior cross attention for kV decomposition in paraspinal motion tracking

BACKGROUND

X-ray image quality is critical for accurate intrafraction motion tracking in radiation therapy.

PURPOSE

This study aims to develop a deep-learning algorithm to improve kV image contrast by decomposing the image into bony and soft tissue components. In particular, we designed a priori attention mechanism in the neural network framework for optimal decomposition. We show that a patient-specific prior cross-attention (PCAT) mechanism can boost the performance of kV image decomposition. We demonstrate its use in paraspinal SBRT motion tracking with online kV imaging.

METHODS

Online 2D kV projections were acquired during paraspinal SBRT for patient motion monitoring. The patient-specific prior images were generated by randomly shifting and rotating spine-only DRR created from the setup CBCT, simulating potential motions. The latent features of the prior images were incorporated into the PCAT using multi-head cross attention. The neural network aimed to learn to selectively amplify the transmission of the projection image features that correlate with features of the priori. The PCAT network structure consisted of (1) a dual-branch generator that separates the spine and soft tissue component of the kV projection image and (2) a dual-function discriminator (DFD) that provides the realness score of the predicted images. For supervision, we used a loss combining mean absolute error loss, discriminator loss, perceptual loss, total variation, and mean squared error loss for soft tissues. The proposed PCAT approach was benchmarked against previous work using the ResNet generative adversarial network (ResNetGAN) without prior information.

RESULTS

The trained PCAT had improved performance in effectively retaining and preserving the spine structure and texture information while suppressing the soft tissues from the kV projection images. The decomposed spine-only x-ray images had the submillimeter matching accuracy at all beam angles. The decomposed spine-only x-ray significantly reduced the maximum errors to 0.44 mm (<2 pixels) in comparison to 0.92 mm (∼4 pixels) of ResNetGAN. The PCAT decomposed spine images also had higher PSNR and SSIM (p-value < 0.001).

CONCLUSION

The PCAT selectively learned the important latent features by incorporating the patient-specific prior knowledge into the deep learning algorithm, significantly improving the robustness of the kV projection image decomposition, and leading to improved motion tracking accuracy in paraspinal SBRT.

Clinical Experience and Feasibility of Using 2D-kVimage Online Intervention in the Ultrafractionated Stereotactic Radiation Treatment of Prostate Cancer

PURPOSE

This study reports clinical experience and feasibility of using a 2-dimensional (2D)-kV image system with online intervention in the ultrafractionated stereotactic body radiation treatment (UF-SBRT) of prostate cancer.

METHODS AND MATERIALS

Fifteen patients with prostate cancer who had a low- to intermediate-risk marker implanted received UF-SBRT with online 2D-kV image tracking and a manual beam interruption strategy with a 2-mm motion threshold. A total of 180 kV paired setup images and 1272 intrabeam 2D-kV images were analyzed to evaluate the setup deviation and intratreatment target deviation. Correlation of expected treatment interruptions with a set of parameters (eg, image and treatment time; direction of deviation) was performed (Spearman test). A subset of the data from 22 fractions was re-evaluated to check the differences in analysis results between using the planning position and using the pretreatment setup position as a reference. Margins based on the derived system and random errors were calculated to evaluate the feasibility of the workflow in ensuring prostate coverage during treatment.

RESULTS

Mean target motion in 3D propagated from 1.0 mm (setup at 0 minutes) to 2.0 mm (beam on at 7 minutes) to 2.4 mm (end at 13.5 minutes). Out of 75 fractions, 50 were found to require beam interruption. Interruption had a strong correlation with prostate motion along the longitudinal direction and had moderate correlation with prostate motion along the vertical direction and the prostate's treatment starting position along vertical and longitudinal directions. Using the pretreatment position as a reference for intrabeam monitoring, the magnitude of motion deviation from the reference position was reduced by 0.3 mm at a vertical direction and 0.4 mm at lateral and longitudinal directions. The calculated 3D margin to ensure target coverage was 3.7 mm, 4.6 mm, and 5.0 mm in lateral, vertical, and longitudinal directions, respectively.

CONCLUSIONS

Prostate motion propagated over time. It is feasible to use a 2D-kV online intrabeam monitoring system with a proper intervention scheme to perform UF-SBRT for prostate cancer.

Reproducibility of chestwall and heart position using surface-guided versus RPM-guided DIBH radiotherapy for left breast cancer

This study compared the reproducibility of chestwall and heart position using surface-guided versus RPM (real-time position management)-guided deep inspiration breath hold (DIBH) radiotherapy for left sided breast cancer. Forty DIBH patients under either surface-guided radiotherapy (SGRT) or RPM guidance were studied. For patients treated with tangential fields, reproducibility was measured as the displacements in central lung distance (CLD) and heart shadow to field edge distance (HFD) between pretreatment MV (megavoltage) images and planning DRRs (digitally reconstructed radiographs). For patients treated with volumetric modulated arc therapy (VMAT), sternum to isocenter (ISO) distance (StID), spine to rib edge distance (SpRD), and heart shadow to central axis (CAX) distance (HCD) between pretreatment kV images and planning DRRs were measured. These displacements were compared between SGRT and RPM-guided DIBH. In tangential patients, the mean absolute displacements of SGRT versus RPM guidance were 0.19 versus 0.23 cm in CLD, and 0.33 versus 0.62 cm in HFD. With respect to planning DRR, heart appeared closer to the field edge by 0.04 cm with surface imaging versus 0.62 cm with RPM. In VMAT patients, the displacements of surface imaging versus RPM guidance were 0.21 versus 0.15 cm in StID, 0.24 versus 0.19 cm in SpRD, and 0.72 versus 0.41 cm in HCD. Heart appeared 0.41 cm further away from CAX with surface imaging, whereas 0.10 cm closer to field CAX with RPM. None of the differences between surface imaging and RPM guidance was statistically significant. In conclusion, the displacements of chestwall were small and were comparable with SGRT- or RPM-guided DIBH. The position deviations of heart were larger than those of chestwall with SGRT or RPM. Although none of the differences between SGRT and RPM guidance were statistically significant, there was a trend that the position deviations of heart were smaller and more favorable with SGRT than with RPM guidance in tangential patients.

Automatic stent recognition using perceptual attention U-net for quantitative intrafraction motion monitoring in pancreatic cancer radiotherapy

PURPOSE

Stent has often been used as an internal surrogate to monitor intrafraction tumor motion during pancreatic cancer radiotherapy. Based on the stent contours generated from planning CT images, the current intrafraction motion review (IMR) system on Varian TrueBeam only provides a tool to verify the stent motion visually but lacks quantitative information. The purpose of this study is to develop an automatic stent recognition method for quantitative intrafraction tumor motion monitoring in pancreatic cancer treatment.

METHODS

A total of 535 IMR images from 14 pancreatic cancer patients were retrospectively selected in this study, with the manual contour of the stent on each image serving as the ground truth. We developed a deep learning-based approach that integrates two mechanisms that focus on the features of the segmentation target. The objective attention modeling was integrated into the U-net framework to deal with the optimization difficulties when training a deep network with 2D IMR images and limited training data. A perceptual loss was combined with the binary cross-entropy loss and a Dice loss for supervision. The deep neural network was trained to capture more contextual information to predict binary stent masks. A random-split test was performed, with images of ten patients (71%, 380 images) randomly selected for training, whereas the rest of four patients (29%, 155 images) were used for testing. Sevenfold cross-validation of the proposed PAUnet on the 14 patients was performed for further evaluation.

RESULTS

Our stent segmentation results were compared with the manually segmented contours. For the random-split test, the trained model achieved a mean (±standard deviation) stent Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), the center-of-mass distance (CMD), and volume difference V o l d i f f $Vo{l_{diff}}$ were 0.96 (±0.01), 1.01 (±0.55) mm, 0.66 (±0.46) mm, and 3.07% (±2.37%), respectively. The sevenfold cross-validation of the proposed PAUnet had the mean (±standard deviation) of 0.96 (±0.02), 0.72 (±0.49) mm, 0.85 (±0.96) mm, and 3.47% (±3.27%) for the DSC, HD95, CMD, and V o l d i f f $Vo{l_{diff}}$ .

CONCLUSION

We developed a novel deep learning-based approach to automatically segment the stent from IMR images, demonstrated its clinical feasibility, and validated its accuracy compared to manual segmentation. The proposed technique could be a useful tool for quantitative intrafraction motion monitoring in pancreatic cancer radiotherapy.

Decompose kV projection using neural network for improved motion tracking in paraspinal SBRT

PURPOSE

 On-treatment kV images have been used in tracking patient motion. One challenge of markerless motion tracking in paraspinal SBRT is the reduced contrast when the X-ray beam needs to pass through a large portion of the patient's body, for example, from the lateral direction. Besides, due to the spine's overlapping with the surrounding moving organs in the X-ray images, auto-registration could lead to potential errors. This work aims to automatically extract the spine component from the conventional 2D X-ray images, to achieve more robust and more accurate motion management.

METHODS

 A ResNet generative adversarial network (ResNetGAN) consisting of one generator and one discriminator was developed to learn the mapping between 2D kV image and the reference spine digitally reconstructed radiograph (DRR). A tailored multi-channel multi-domain loss function was used to improve the quality of the decomposed spine image. The trained model took a 2D kV image as input and learned to generate the spine component of the X-ray image. The training dataset included 1347 2D kV thoracic and lumbar region X-ray images from 20 randomly selected patients, and the corresponding matched reference spine DRR. Another 226 2D kV images from the remaining four patients were used for evaluation. The resulted decomposed spine images and the original X-ray images were registered to the reference spine DRRs, to compare the spine tracking accuracy.

RESULTS

 The decomposed spine image had the mean peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of 60.08 and 0.99, respectively, indicating the model retained and enhanced the spine structure information in the original 2D X-ray image. The decomposed spine image matching with the reference spine DRR had submillimeter accuracy (in mm) with a mean error of 0.13, 0.12, and a maximum of 0.58, 0.49 in the x - and y -directions (in the imager coordinates), respectively. The accuracy improvement is robust in all lateral and anteroposterior X-ray beam angles.

CONCLUSION

 We developed a deep learning-based approach to remove soft tissues in the kV image, leading to more accurate spine tracking in paraspinal SBRT.

Coronary Artery Disease in Young Women After Radiation Therapy for Breast Cancer: The WECARE Study

Background

Radiation therapy (RT) for breast cancer increases risk of coronary artery disease (CAD). Women treated for left- vs right-sided breast cancer receive greater heart radiation exposure, which may further increase this risk. The risk of radiation-associated CAD specifically among younger breast cancer survivors is not well defined.

Objectives

The purpose of this study was to report CAD risk among participants in the Women's Environmental Cancer and Radiation Epidemiology Study.

Methods

A total of 1,583 women who were <55 years of age when diagnosed with breast cancer between 1985 and 2008 completed a cardiovascular health questionnaire. Risk of radiation-associated CAD was evaluated by comparing women treated with left-sided RT with women treated with right-sided RT using multivariable Cox proportional hazards models. Effect modification by treatment and cardiovascular risk factors was examined.

Results

In total, 517 women who did not receive RT and 94 women who had a pre-existing cardiovascular disease diagnosis were excluded, leaving 972 women eligible for analysis. Their median follow-up time was 14 years (range 1-29 years). The 27.5-year cumulative incidences of CAD for women receiving left- vs right-sided RT were 10.5% and 5.8%, respectively (P = 0.010). The corresponding HR of CAD for left- vs right-sided RT in the multivariable Cox model was 2.5 (95% CI: 1.3-4.7). There was no statistically significant effect modification by any factor evaluated.

Conclusions

Young women treated with RT for left-sided breast cancer had over twice the risk of CAD compared with women treated with RT for right-sided breast cancer. Laterality of RT is independently associated with an increased risk of CAD and should be considered in survivorship care of younger breast cancer patients.

AAPM Task Group 264: The safe clinical implementation of MLC tracking in radiotherapy

The era of real-time radiotherapy is upon us. Robotic and gimbaled linac tracking are clinically established technologies with the clinical realization of couch tracking in development. Multileaf collimators (MLCs) are a standard equipment for most cancer radiotherapy systems, and therefore MLC tracking is a potentially widely available technology. MLC tracking has been the subject of theoretical and experimental research for decades and was first implemented for patient treatments in 2013. The AAPM Task Group 264 Safe Clinical Implementation of MLC Tracking in Radiotherapy Report was charged to proactively provide the broader radiation oncology community with (a) clinical implementation guidelines including hardware, software, and clinical indications for use, (b) commissioning and quality assurance recommendations based on early user experience, as well as guidelines on Failure Mode and Effects Analysis, and (c) a discussion of potential future developments. The deliverables from this report include: an explanation of MLC tracking and its historical development; terms and definitions relevant to MLC tracking; the clinical benefit of, clinical experience with and clinical implementation guidelines for MLC tracking; quality assurance guidelines, including example quality assurance worksheets; a clinical decision pathway, future outlook and overall recommendations.

Intrafractional relationship changes between an external breathing signal and fiducial marker positions in pancreatic cancer patients

Background and purpose

The purpose of this study of pancreatic cancer patients treated with respiratory‐guided stereotactic body radiotherapy (SBRT) on a standard linac was to investigate (a) the intrafractional relationship change (IRC) between a breathing signal and the tumor position, (b) the impact of IRC on the delivered dose, and (c) potential IRC predictors.

Materials and methods

We retrospectively investigated 10 pancreatic cancer patients with 2–4 implanted fiducial markers in the tumor treated with SBRT. Fluoroscopic images were acquired before and after treatment delivery simultaneously with the abdominal breathing motion. We quantified the IRC as the change in fiducial location for a given breathing amplitude in the left–right (LR), anterior–posterior (AP), and superior–inferior (SI) directions from before to after treatment delivery. The treatment plans were re‐calculated after changing the isocenter coordinates according to the IRCs. Four treatment‐ or patient‐related factors were investigated as potential predictors for IRC using linear models.

Results

The average (±1 SD) absolute IRCs in the LR, AP, and SI directions were 1.2 ± 1.2 mm, 0.7 ± 0.7 mm, and 1.1 ± 0.8 mm, respectively. The average 3D IRC was 2.0 ± 1.3 mm (range: 0.4–5.3 mm) for a median treatment delivery time of 8.5 min (range: 5.7–19.9 min; n = 31 fractions). The dose coverage of the internal target volume (ITV) decreased by more than 3% points in three of 31 fractions. In those cases, the 3D IRC had been larger than 4.3 mm. The 3D IRC was found to correlate with changes in the minimum breathing amplitude during treatment delivery.

Conclusion

On average, 2 mm of treatment delivery accuracy was lost due to IRC. Periodical intrafractional imaging is needed to safely deliver respiratory‐guided SBRT.

Assessment of the use of different imaging and delivery techniques for cranial treatments on the Halcyon linac

Purpose

In this work, we investigated the effect on the workflow and setup accuracy of using surface guided radiation therapy (SGRT) for patient setup, megavoltage cone beam CT (MVCBCT) or kilovoltage cone beam CT (kVCBCT) for imaging and fixed IMRT or volumetric‐modulated arc therapy (VMAT) for treatment delivery with the Halcyon linac.

Methods

We performed a retrospective investigation of 272 treatment fractions, using three different workflows. The first and second workflows used MVCBCT and fixed IMRT for imaging and treatment delivery, and the second one also used SGRT for patient setup. The third workflow used SGRT for setup, kVCBCT for imaging and VMAT for delivery. Workflows were evaluated by comparing the number of fractions requiring repeated imaging acquisitions and the time required for setup, imaging and treatment delivery. Setup position accuracy was assessed by comparing the daily kV‐ or MV‐ CBCT with the planning CT and measuring the residual rotational errors for pitch, yaw and roll angles.

Results

Without the use of SGRT, the imaging fields were delivered more than once on 11.1% of the fractions, while re‐imaging was necessary in 5.5% of the fractions using SGRT. The total treatment time, including setup, imaging, and delivery, for the three workflows was 531 ± 157 s, 503 ± 130 s and 457 ± 91 s, respectively. A statistically significant difference was observed when comparing the third workflow with the first two. The total residual rotational errors were 1.96 ± 1.29°, 1.28 ± 0.67° and 1.22 ± 0.76° and statistically significant differences were observed when comparing workflows with and without SGRT.

Conclusions

The use of SGRT allowed for a reduction of re‐imaging during patient setup and improved patient position accuracy by reducing residual rotational errors. A reduction in treatment time using kVCBCT with SGRT was observed. The most efficient workflow was the one including kVCBCT and SGRT for setup and VMAT for delivery.

An in vitro study for the dosimetric and radiobiological validation of respiratory gating in conventional and hypofractionated radiotherapy of the lung: effect of dose, dose rate, and breathing pattern

Stereotactic body radiotherapy (SBRT) of the lung has become a standard of care for early-stage inoperable non-small cell lung cancer (NSCLC). A common strategy to manage respiratory motion is gating, which inevitably results in an increase in treatment time, especially in irregularly-breathing patients. Flattening-filter free (FFF) beams allow for delivery of the treatment at a higher dose rate, therefore counteracting the lengthened treatment time due to frequent interruption of the beam during gated radiotherapy. In this study, we perform our in vitro evaluation of the dosimetric and radiobiological effect of gated lung SBRT with simultaneous integrated boost (SIB) using both flattened and FFF beams. A moving thorax-shaped phantom with inserts and applicators was used for simulation, planning, gated treatment delivery measurements and in vitro tests. The effects of gating window, dose rate, and breathing pattern were evaluated. Planned doses represented a typical conventional fractionation, 200 cGy per fraction with SIB to 240 cGy, flattened beam only, and SBRT, 800 cGy with SIB to 900 cGy, flattened and FFF beams. Ideal, as well as regular and irregular patient-specific breathing patterns with and without gating were used. A survival assay for lung adenocarcinoma A549 cell line was performed. Delivered dose was within 6% for locations planned to receive 200 and 800 cGy and within 4% for SIB locations. Time between first beam-on and last beam-off varied from approximately 1.5 min for conventional fractionation, 200/240 cGy, to 10.5 min for gated SBRT, 800/900 cGy doses, flattened beam and irregular breathing motion pattern. With FFF beams dose delivery time was shorter by a factor of 2-3, depending on the gating window and breathing pattern. We have found that, for the most part, survival depended on dose and not on dose rate, gating window, or breathing regularity.

Dosimetric study of the plan quality and dose to organs at risk on tangential breast treatments using the Halcyon linac

Purpose

To investigate the plan quality and doses to the heart, contralateral breast (CB), ipsilateral lung (IL), and contralateral lung (CL) in tangential breast treatments using the Halcyon linac with megavoltage setup fields.

Methods

Radiotherapy treatment plans with tangential beams from 25 breast cancer patients previously treated on a C‐arm linac were replanned for Halcyon. Thirteen corresponded to right‐sided breasts and 12 to left‐sided breasts, all with a dose prescription of 50 Gy in 25 fractions. Plans were created with the following setup imaging techniques: low‐dose (LD) MVCBCT, high‐quality (HQ) MVCBCT, LD‐MV and HQ‐MV pairs and the imaging dose was included in the plans. Plan quality metric values for the lumpectomy cavity, whole‐breast and doses to the organs at risk (OARs) were measured and compared with those from the original plans.

Results

No significant differences in plan quality were observed between the original and Halcyon plans. An increase in the mean dose (Mean) for all the organs was observed for the Halcyon plans. For right‐sided plans, the accumulated Mean over the 25 fractions in the C‐arm plans was 0.4 ± 0.3, 0.2 ± 0.2, 5.4 ± 1.3, and 0.1 ± 0.1 Gy for the heart, CB, IL, and CL, respectively, while values in the MVCBCT‐LD Halcyon plans were 1.2 ± 0.2, 0.6 ± 0.1, 6.5 ± 1.4, and 0.4 ± 0.1 Gy, respectively. For left‐sided treatments, Mean in the original plans was 0.9 ± 0.2, 0.1 ± 0.0, 4.2 ± 1.2, and 0.0 ± 0.0 Gy, while for the MVCBCT‐LD Halcyon plans values were 1.9 ± 0.2, 0.6 ± 0.2, 5.1 ± 1.2, and 0.5 ± 0.2 Gy, respectively.

Conclusions

Plan quality for breast treatments using Halcyon is similar to the quality for a 6 MV, C‐arm plan. For treatments using megavoltage setup fields, the dose contribution to OARs from the imaging fields can be equal or higher than the dose from treatment fields.

Clinical efficacy and safety of surface imaging guided radiosurgery (SIG-RS) in the treatment of benign skull base tumors

Frameless, surface imaging guided radiosurgery (SIG-RS) is a novel platform for stereotactic radiosurgery (SRS) wherein patient positioning is monitored in real-time through infra-red camera tracking of facial topography. Here we describe our initial clinical experience with SIG-RS for the treatment of benign neoplasms of the skull base. We identified 48 patients with benign skull base tumors consecutively treated with SIG-RS at a single institution between 2009 and 2011. Patients were diagnosed with meningioma (n = 22), vestibular schwannoma (n = 20), or nonfunctional pituitary adenoma (n = 6). Local control and treatment-related toxicity were retrospectively assessed. Median follow-up was 65 months (range 61-72 months). Prescription doses were 12-13 Gy in a single fraction (n = 18), 8 Gy × 3 fractions (n = 6), and 5 Gy × 5 fractions (n = 24). Actuarial tumor control rate at 5 years was 98%. No grade ≥3 treatment-related toxicity was observed. Grade ≤2 toxicity was associated with symptomatic lesions (p = 0.049) and single fraction treatment (p = 0.005). SIG-RS for benign skull base tumors produces clinical outcomes comparable to conventional frame-based SRS techniques while enhancing patient comfort.

Frameless, Real-Time, Surface Imaging-Guided Radiosurgery

BACKGROUND:

Frameless stereotactic radiosurgery is commonly used to treat intracranial metastases, but mask-based immobilization can be uncomfortable for patients.

OBJECTIVE:

To describe the clinical outcomes using a novel real-time, frameless, surface imaging--guided radiosurgery (SIG-RS) technique to treat brain metastases.

METHODS:

Data were prospectively gathered for 44 consecutive patients totaling 115 intracranial metastases treated with SIG-RS in a median of 1 fraction (range, 1–5) to a median dose of 20 Gy (range, 15-30 Gy). Local control, regional control, and overall survival were estimated by the Kaplan-Meier method.

RESULTS:

Median follow-up for all patients was 6.0 months (range, 0.3-21.6 months), with 31 of 44 (70%) deceased at the time of analysis. The 35 patients (80%) with follow-up imaging totaled 88 lesions evaluable for local control. Actuarial 6- and 12-month local control was 90% (95% confidence interval, 82–98) and 76% (95% confidence interval, 60–91), respectively. Regional failure was observed in 16 patients (46%). The median actuarial overall survival was 7.7 months (95% confidence interval, 5.7-9.7). Analysis of the subset of 22 patients (55 lesions) who received SIG-RS alone (no prior treatment) in a single fraction yielded comparable clinical outcomes. Grade 3 or greater toxicity occurred in 4 patients (9%). The median treatment time from beam on to beam off was 15 minutes (range, 3-36 minutes).

CONCLUSION:

SIG-RS for treating intracranial metastases can produce clinical outcomes comparable to those with conventional frame-based and frameless stereotactic radiosurgery techniques while providing greater patient comfort with an open-faced mask and fast treatment times.

Frame-less and mask-less cranial stereotactic radiosurgery: a feasibility study

Currently, high-precision delivery in stereotactic radiosurgery (SRS) is achieved via high-precision target localization and rigid patient immobilization. Rigid patient immobilization can result in, however, patient discomfort, which is exacerbated by the long duration of SRS treatments and may induce patient movement. To address this issue, we developed a new SRS technique that is aimed to minimize patient discomfort while maintaining high-precision treatment, based on a less-rigid patient immobilization combined with continuous patient motion monitoring. In this paper, we examine the feasibility of this new technique. An anthropomorphic head phantom is used to check the accuracy of a 3D surface imaging system that provides the monitoring. Volunteers are used to study patient motion inside a new type of head mold that is used for minimal immobilization. Results show that for different couch angles, the difference between the phantom positions recorded by the surface imaging system and by an infrared optical tracking system was within 1 mm in displacements and 1 degrees in rotation. The motion detected by both systems during couch shifts is within 1 mm agreement. The average maximum volunteer head motion in the head mold during the 20 min interval in any direction was 0.7 mm (range: 0.4-1.1 mm). Patient motion due to couch motion was always less than 0.2 mm. We conclude that motion inside the minimally immobilizing head mold is small and can be accurately detected by real-time surface imaging.

Classification of breast computed tomography data

Differences in breast tissue composition are important determinants in assessing risk, identifying disease in images and following changes over time. This paper presents an algorithm for tissue classification that separates breast tissue into its three primary constituents of skin, fat and glandular tissue. We have designed and built a dedicated breast CT scanner. Fifty-five normal volunteers and patients with mammographically identified breast lesions were scanned. Breast CT voxel data were filtered using a 5 pt median filter and the image histogram was computed. A two compartment Gaussian fit of histogram data was used to provide an initial estimate of tissue compartments. After histogram analysis, data were input to region-growing algorithms and classified as to belonging to skin, fat or gland based on their value and architectural features. Once tissues were classified, a more detailed analysis of glandular tissue patterns and a more quantitative analysis of breast composition was made. Algorithm performance assessment demonstrated very good or excellent agreement between algorithm and radiologist observers in 97.7% of the segmented data. We observed that even in dense breasts the fraction of glandular tissue seldom exceeded 50%. For most individuals the composition is better characterized as being a 70% (fat)-30% (gland) composition than a 50% (fat)-50% (gland) composition.

Simulation of mechanical compression of breast tissue

Comparison of uncompressed volumetric breast data to compressed projection mammographic data poses a variety of challenges to accurately localize anatomy in both data sets. This work presents a method for simulating the mechanical compression of volumetric breast data. We use an approach based on a rectilinear-grid finite-element mesh and apply the method to known objects including volumetric breast data. Overall results show good agreement with theory and reasonable qualitative agreement with clinical results. Analysis times are sufficiently short to be used in the clinical setting for data comparison. The methods presented here provide a high quality method for simulating mechanical compression of breast data.