Deep learning-based ultrasound auto-segmentation of the prostate with brachytherapy implanted needles.

BACKGROUND: Accurate segmentation of the clinical target volume (CTV) corresponding to the prostate with or without proximal seminal vesicles is required on transrectal ultrasound (TRUS) images during prostate brachytherapy procedures. Implanted needles cause artifacts that may make this task difficult and time-consuming. Thus, previous studies have focused on the simpler problem of segmentation in the absence of needles at the cost of reduced clinical utility. PURPOSE: To use a convolutional neural network (CNN) algorithm for segmentation of the prostatic CTV in TRUS images post-needle insertion obtained from prostate brachytherapy procedures to better meet the demands of the clinical procedure. METHODS: A dataset consisting of 144 3-dimensional (3D) TRUS images with implanted metal brachytherapy needles and associated manual CTV segmentations was used for training a 2-dimensional (2D) U-Net CNN using a Dice Similarity Coefficient (DSC) loss function. These were split by patient, with 119 used for training and 25 reserved for testing. The 3D TRUS training images were resliced at radial (around the axis normal to the coronal plane) and oblique angles through the center of the 3D image, as well as axial, coronal, and sagittal planes to obtain 3689 2D TRUS images and masks for training. The network generated boundary predictions on 300 2D TRUS images obtained from reslicing each of the 25 3D TRUS images used for testing into 12 radial slices (15° apart), which were then reconstructed into 3D surfaces. Performance metrics included DSC, recall, precision, unsigned and signed volume percentage differences (VPD/sVPD), mean surface distance (MSD), and Hausdorff distance (HD). In addition, we studied whether providing algorithm-predicted boundaries to the physicians and allowing modifications increased the agreement between physicians. This was performed by providing a subset of 3D TRUS images of five patients to five physicians who segmented the CTV using clinical software and repeated this at least 1 week apart. The five physicians were given the algorithm boundary predictions and allowed to modify them, and the resulting inter- and intra-physician variability was evaluated. RESULTS: Median DSC, recall, precision, VPD, sVPD, MSD, and HD of the 3D-reconstructed algorithm segmentations were 87.2 [84.1, 88.8]%, 89.0 [86.3, 92.4]%, 86.6 [78.5, 90.8]%, 10.3 [4.5, 18.4]%, 2.0 [-4.5, 18.4]%, 1.6 [1.2, 2.0] mm, and 6.0 [5.3, 8.0] mm, respectively. Segmentation time for a set of 12 2D radial images was 2.46 [2.44, 2.48] s. With and without U-Net starting points, the intra-physician median DSCs were 97.0 [96.3, 97.8]%, and 94.4 [92.5, 95.4]% (p < 0.0001), respectively, while the inter-physician median DSCs were 94.8 [93.3, 96.8]% and 90.2 [88.7, 92.1]%, respectively (p < 0.0001). The median segmentation time for physicians, with and without U-Net-generated CTV boundaries, were 257.5 [211.8, 300.0] s and 288.0 [232.0, 333.5] s, respectively (p = 0.1034). CONCLUSIONS: Our algorithm performed at a level similar to physicians in a fraction of the time. The use of algorithm-generated boundaries as a starting point and allowing modifications reduced physician variability, although it did not significantly reduce the time compared to manual segmentations.

Is Ultrahypofractionated Whole Pelvis Radiation Therapy (WPRT) as Well Tolerated as Conventionally Fractionated WPRT in Patients With Prostate Cancer? Early Results From the HOPE Trial.

OBJECTIVE: The aim of this work was to evaluate the acute toxicity and quality-of-life (QOL) impact of ultrahypofractionated whole pelvis radiation therapy (WPRT) compared with conventional WPRT fractionation after high-dose-rate prostate brachytherapy (HDR-BT). METHODS AND MATERIALS: The HOPE trial is a phase 2, multi-institutional randomized controlled trial of men with prostate-confined disease and National Comprehensive Cancer Network unfavorable intermediate-, high-, or very-high-risk prostate cancer. Patients were randomly assigned to receive conventionally fractionated WPRT (standard arm) or ultrahypofractionated WPRT (experimental arm) in a 1:1 ratio. All patients underwent radiation therapy with 15 Gy HDR-BT boost in a single fraction followed by WPRT delivered with conventional fractionation (45 Gy in 25 daily fractions or 46 Gy in 23 fractions) or ultrahypofractionation (25 Gy in 5 fractions delivered on alternate days). Acute toxicities measured during radiation therapy and at 6 weeks posttreatment were assessed using the clinician-reported Common Terminology Criteria for Adverse Events version 5.0, and QOL was measured using the Expanded Prostate Cancer Index Composite (EPIC-50) and International Prostate Symptom Score (IPSS). RESULTS: A total of 80 patients were enrolled and treated across 3 Canadian institutions, of whom 39 and 41 patients received external radiation therapy with conventionally fractionated and ultrahypofractionated WPRT, respectively. All patients received androgen deprivation therapy except for 2 patients treated in the ultrahypofractionated arm. The baseline clinical characteristics of the 2 arms were similar, with 51 (63.8%) patients having high or very-high-risk prostate cancer disease. Treatment was well tolerated with no significant differences in the rate of acute adverse events between arms. No grade 4 adverse events or treatment-related deaths were reported. Ultrahypofractionated WPRT had a less detrimental impact on the EPIC-50 bowel total, function, and bother domain scores compared with conventional WPRT in the acute setting. By contrast, more patients treated with ultrahypofractionated WPRT reached the minimum clinical important difference on the EPIC-50 urinary domains. No significant QOL differences between arms were noted in the sexual and hormonal domains. CONCLUSIONS: Ultrahypofractionated WPRT after HDR-BT is a well-tolerated treatment strategy in the acute setting that has less detrimental impact on bowel QOL domains compared with conventional WPRT.

A power Doppler ultrasound method for improving intraoperative tip localization for visually obstructed needles in interstitial prostate brachytherapy.

PURPOSE: High-dose-rate (HDR) interstitial brachytherapy (BT) is a common treatment technique for localized intermediate to high-risk prostate cancer. Transrectal ultrasound (US) imaging is typically used for guiding needle insertion, including localization of the needle tip which is critical for treatment planning. However, image artifacts can limit needle tip visibility in standard brightness (B)-mode US, potentially leading to dose delivery that deviates from the planned dose. To improve intraoperative tip visualization in visually obstructed needles, we propose a power Doppler (PD) US method which utilizes a novel wireless mechanical oscillator, validated in phantom experiments and clinical HDR-BT cases as part of a feasibility clinical trial. METHODS: Our wireless oscillator contains a DC motor housed in a 3D printed case and is powered by rechargeable battery allowing the device to be operated by one person with no additional equipment required in the operating room. The oscillator end-piece features a cylindrical shape designed for BT applications to fit on top of the commonly used cylindrical needle mandrins. Phantom validation was completed using tissue-equivalent agar phantoms with the clinical US system and both plastic and metal needles. Our PD method was tested using a needle implant pattern matching a standard HDR-BT procedure as well as an implant pattern designed to maximize needle shadowing artifacts. Needle tip localization accuracy was assessed using the clinical method based on ideal reference needles as well as a comparison to computed tomography (CT) as a gold standard. Clinical validation was completed in five patients who underwent standard HDR-BT as part of a feasibility clinical trial. Needle tips positions were identified using B-mode US and PD US with perturbation from our wireless oscillator. RESULTS: Absolute mean ± standard deviation tip error for B-mode alone, PD alone, and B-mode combined with PD was respectively: 0.3 ± 0.3 mm, 0.6 ± 0.5 mm, and 0.4 ± 0.2 mm for the mock HDR-BT needle implant; 0.8 ± 1.7 mm, 0.4 ± 0.6 mm, and 0.3 ± 0.5 mm for the explicit shadowing implant with plastic needles; and 0.5 ± 0.2 mm, 0.5 ± 0.3 mm, and 0.6 ± 0.2 mm for the explicit shadowing implant with metal needles. The total mean absolute tip error for all five patients in the feasibility clinical trial was 0.9 ± 0.7 mm using B-mode US alone and 0.8 ± 0.5 mm when including PD US, with increased benefit observed for needles classified as visually obstructed. CONCLUSIONS: Our proposed PD needle tip localization method is easy to implement and requires no modifications or additions to the standard clinical equipment or workflow. We have demonstrated decreased tip localization error and variation for visually obstructed needles in both phantom and clinical cases, including providing the ability to visualize needles previously not visible using B-mode US alone. This method has the potential to improve needle visualization in challenging cases without burdening the clinical workflow, potentially improving treatment accuracy in HDR-BT and more broadly in any minimally invasive needle-based procedure.

Validation of a surface-based deformable MRI-3D ultrasound image registration algorithm toward clinical implementation for interstitial prostate brachytherapy.

PURPOSE: The purpose of this study was to evaluate and clinically implement a deformable surface-based magnetic resonance imaging (MRI) to three-dimensional ultrasound (US) image registration algorithm for prostate brachytherapy (BT) with the aim to reduce operator dependence and facilitate dose escalation to an MRI-defined target. METHODS AND MATERIALS: Our surface-based deformable image registration (DIR) algorithm first translates and scales to align the US- and MR-defined prostate surfaces, followed by deformation of the MR-defined prostate surface to match the US-defined prostate surface. The algorithm performance was assessed in a phantom using three deformation levels, followed by validation in three retrospective high-dose-rate BT clinical cases. For comparison, manual rigid registration and cognitive fusion by physician were also employed. Registration accuracy was assessed using the Dice similarity coefficient (DSC) and target registration error (TRE) for embedded spherical landmarks. The algorithm was then implemented intraoperatively in a prospective clinical case. RESULTS: In the phantom, our DIR algorithm demonstrated a mean DSC and TRE of 0.74 ± 0.08 and 0.94 ± 0.49 mm, respectively, significantly improving the performance compared to manual rigid registration with 0.64 ± 0.16 and 1.88 ± 1.24 mm, respectively. Clinical results demonstrated reduced variability compared to the current standard of cognitive fusion by physicians. CONCLUSIONS: We successfully validated a DIR algorithm allowing for translation of MR-defined target and organ-at-risk contours into the intraoperative environment. Prospective clinical implementation demonstrated the intraoperative feasibility of our algorithm, facilitating targeted biopsies and dose escalation to the MR-defined lesion. This method provides the potential to standardize the registration procedure between physicians, reducing operator dependence.

Functional Lung Avoidance for Individualized Radiation Therapy: Results of a Double-Masked, Randomized Controlled Trial.

PURPOSE: To determine whether functional lung avoidance based on 3He magnetic resonance imaging (MRI) improves quality of life (QOL) for patients undergoing concurrent chemoradiotherapy (CCRT) for advanced non-small cell lung cancer. METHODS AND MATERIALS: Patients with stage III non-small cell lung cancer (or oligometastatic disease treated with curative intent) undergoing CCRT with at least a 10 pack-year smoking history were eligible. Patients underwent pretreatment 3He MRI to measure lung ventilation and had 2 radiation therapy (RT) plans created before randomization: a standard plan, which did not make use of the 3He MRI, and an avoidance plan, preferentially sparing well-ventilated lung. All participants were masked to assignment except the physicist responsible for exporting the selected plan. The primary end point was patient-reported QOL measured at 3-months post-RT by the FACT-L lung cancer subscale (LCS); secondary end points included other QOL metrics, toxicity, and survival outcomes. Target accrual was 64. RESULTS: Twenty-seven patients were randomized before the trial was closed due to slower-than-expected accrual, with 11 randomized to the standard arm and 16 to the avoidance arm. Baseline patient characteristics were well-balanced. At 3 months post-RT, the mean ± SD LCS scores were 17.4 ± 2.8 versus 17.3 ± 6.1 for the standard and avoidance arms, respectively (P = .485). A clinically meaningful, prespecified decline of ≥3 points in the LCS score was observed in 50% (4/8) in the standard arm and 33% (4/12) in the avoidance arm (P = .648). Two patients in each arm developed grade ≥2 radiation pneumonitis, with no grade ≥4 toxicities. CONCLUSIONS: Although this trial did not reach full accrual, QOL scores were very similar between arms. Due to the scarcity of 3He MRI, other, more commonly available methods to measure functional lung, such as 4-dimensional computed tomography ventilation mapping, may be considered in the assessment of functional lung avoidance RT, and a larger, multicenter approach would be needed to accrue sufficient patients to test such approaches.

Effect of dataset size, image quality, and image type on deep learning-based automatic prostate segmentation in 3D ultrasound.

Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image quality grading method. The complete training dataset of 6761 2D images, resliced from 206 3D TRUS volumes acquired using end-fire and side-fire acquisition methods, was split to train two separate networks using either end-fire or side-fire images. Split datasets were reduced to 1000, 500, 250, and 100 2D images. For deep learning prediction, modified U-Net and U-Net++ architectures were implemented and compared using an unseen test dataset of 40 3D TRUS volumes. A 3D TRUS image quality grading scale with three factors (acquisition quality, artifact severity, and boundary visibility) was developed to assess the impact on segmentation performance. For the complete training dataset, U-Net and U-Net++ networks demonstrated equivalent performance, but when trained using split end-fire/side-fire datasets, U-Net++ significantly outperformed the U-Net. Compared to the complete training datasets, U-Net++ trained using reduced-size end-fire and side-fire datasets demonstrated equivalent performance down to 500 training images. For this dataset, image quality had no impact on segmentation performance for end-fire images but did have a significant effect for side-fire images, with boundary visibility having the largest impact. Our algorithm provided fast (<1.5 s) and accurate 3D segmentations across clinically diverse images, demonstrating generalizability and efficiency when employed on smaller datasets, supporting the potential for widespread use, even when data is scarce. The development of an image quality grading scale provides a quantitative tool for assessing segmentation performance.

Feasibility of fusing three-dimensional transabdominal and transrectal ultrasound images for comprehensive intraoperative visualization of gynecologic brachytherapy applicators.

PURPOSE: In this study, we propose combining three-dimensional (3D) transrectal ultrasound (TRUS) and 3D transabdominal ultrasound (TAUS) images of gynecologic brachytherapy applicators to leverage the advantages of each imaging perspective, providing a broader field-of-view and allowing previously obscured features to be recovered. The aim of this study was to evaluate the feasibility of fusing these 3D ultrasound (US) perspectives based on the applicator geometry in a phantom prior to clinical implementation. METHODS: In proof-of-concept experiments, 3D US images of application-specific multimodality pelvic phantoms were acquired with tandem-and-ring and tandem-and-ovoids applicators using previously validated imaging systems. Two TRUS images were acquired at different insertion depths and manually fused based on the position of the ring/ovoids to broaden the TRUS field-of-view. The phantom design allowed "abdominal thickness" to be modified to represent different body habitus and TAUS images were acquired at three thicknesses for each applicator. The merged TRUS images were then combined with TAUS images by rigidly aligning applicator components and manually refining the registration using the positions of source channels and known tandem length, as well as the ring diameter for the tandem-and-ring applicator. Combined 3D US images were manually, rigidly registered to images from a second modality (magnetic resonance (MR) imaging for the tandem-and-ring applicator and X-ray computed tomography (CT) for the tandem-and-ovoids applicator (based on applicator compatibility)) to assess alignment. Four spherical fiducials were used to calculate target registration errors (TREs), providing a metric for validating registrations, where TREs were computed using root-mean-square distances to describe the alignment of manually identified corresponding fiducials. An analysis of variance (ANOVA) was used to identify statistically significant differences (p < 0.05) between the TREs for the three abdominal thicknesses for each applicator type. As an additional indicator of geometric accuracy, the bladder was segmented in the 3D US and corresponding MR/CT images, and volumetric differences and Dice similarity coefficients (DSCs) were calculated. RESULTS: For both applicator types, the combination of 3D TRUS with 3D TAUS images allowed image information obscured by the shadowing artifacts under single imaging perspectives to be recovered. For the tandem-and-ring applicator, the mean ± one standard deviation (SD) TREs from the images with increasing thicknesses were 1.37 ± 1.35 mm, 1.84 ± 1.22 mm, and 1.60 ± 1.00 mm. Similarly, for the tandem-and-ovoids applicator, the mean ± SD TREs from the images with increasing thicknesses were 1.37 ± 0.35 mm, 1.95 ± 0.90 mm, and 1.61 ± 0.76 mm. No statistically significant difference was detected in the TREs for the three thicknesses for either applicator type. The mean volume differences for the bladder segmentations were 3.14% and 2.33% and mean DSCs were 87.8% and 87.7% for the tandem-and-ring and tandem-and-ovoids applicators, respectively. CONCLUSIONS: In this proof-of-concept study, we demonstrated the feasibility of fusing 3D TRUS and 3D TAUS images based on the geometry of tandem-and-ring and tandem-and-ovoids applicators. This represents a step toward an accessible and low-cost 3D imaging method for gynecologic brachytherapy, with the potential to extend this approach to other intracavitary configurations and hybrid applicators.

In-vivo lung biomechanical modeling for effective tumor motion tracking in external beam radiation therapy.

Lung cancer is the most common cause of cancer-related death in both men and women. Radiation therapy is widely used for lung cancer treatment; however, respiratory motion presents challenges that can compromise the accuracy and/or effectiveness of radiation treatment. Respiratory motion compensation using biomechanical modeling is a common approach used to address this challenge. This study focuses on the development and validation of a lung biomechanical model that can accurately estimate the motion and deformation of lung tumor. Towards this goal, treatment planning 4D-CT images of lung cancer patients were processed to develop patient-specific finite element (FE) models of the lung to predict the patients' tumor motion/deformation. The tumor motion/deformation was modeled for a full respiration cycle, as captured by the 4D-CT scans. Parameters driving the lung and tumor deformation model were found through an inverse problem formulation. The CT datasets pertaining to the inhalation phases of respiration were used for validating the model's accuracy. The volumetric Dice similarity coefficient between the actual and simulated gross tumor volumes (GTVs) of the patients calculated across respiration phases was found to range between 0.80 ± 0.03 and 0.92 ± 0.01. The average error in estimating tumor's center of mass calculated across respiration phases ranged between 0.50 ± 0.10 (mm) and 1.04 ± 0.57 (mm), indicating a reasonably good accuracy of the proposed model. The proposed model demonstrates favorable accuracy for estimating the lung tumor motion/deformation, and therefore can potentially be used in radiation therapy applications for respiratory motion compensation.

Optimization of multi-electrode implant configurations and programming for the delivery of non-ablative electric fields in intratumoral modulation therapy.

PURPOSE: Application of low intensity electric fields to interfere with tumor growth is being increasingly recognized as a promising new cancer treatment modality. Intratumoral modulation therapy (IMT) is a developing technology that uses multiple electrodes implanted within or adjacent tumor regions to deliver electric fields to treat cancer. In this study, the determination of optimal IMT parameters was cast as a mathematical optimization problem, and electrode configurations, programming, optimization, and maximum treatable tumor size were evaluated in the simplest and easiest to understand spherical tumor model. The establishment of electrode placement and programming rules to maximize electric field tumor coverage designed specifically for IMT is the first step in developing an effective IMT treatment planning system. METHODS: Finite element method electric field computer simulations for tumor models with 2 to 7 implanted electrodes were performed to quantify the electric field over time with various parameters, including number of electrodes (2 to 7), number of contacts per electrode (1 to 3), location within tumor volume, and input waveform with relative phase shift between 0 and 2π radians. Homogeneous tissue specific conductivity and dielectric values were assigned to the spherical tumor and surrounding tissue volume. In order to achieve the goal of covering the tumor volume with a uniform threshold of 1 V/cm electric field, a custom least square objective function was used to maximize the tumor volume covered by 1 V/cm time averaged field, while maximizing the electric field in voxels receiving less than this threshold. An additional term in the objective function was investigated with a weighted tissue sparing term, to minimize the field to surrounding tissues. The positions of the electrodes were also optimized to maximize target coverage with the fewest number of electrodes. The complexity of this optimization problem including its non-convexity, the presence of many local minima, and the computational load associated with these stochastic based optimizations led to the use of a custom pattern search algorithm. Optimization parameters were bounded between 0 and 2π radians for phase shift, and anywhere within the tumor volume for location. The robustness of the pattern search method was then evaluated with 50 random initial parameter values. RESULTS: The optimization algorithm was successfully implemented, and for 2 to 4 electrodes, equally spaced relative phase shifts and electrodes placed equidistant from each other was optimal. For 5 electrodes, up to 2.5 cm diameter tumors with 2.0 V, and 4.1 cm with 4.0 V could be treated with the optimal configuration of a centrally placed electrode and 4 surrounding electrodes. The use of 7 electrodes allow for 3.4 cm diameter coverage at 2.0 V and 5.5 cm at 4.0 V. The evaluation of the optimization method using 50 random initial parameter values found the method to be robust in finding the optimal solution. CONCLUSIONS: This study has established a robust optimization method for temporally optimizing electric field tumor coverage for IMT, with the adaptability to optimize a variety of parameters including geometrical and relative phase shift configurations.

Technical Note: A fast inverse direct aperture optimization algorithm for volumetric-modulated arc therapy.

PURPOSE: In a recent article, our group proposed a fast direct aperture optimization (DAO) algorithm for fixed-gantry intensity-modulated radiation therapy (IMRT) called fast inverse direct aperture optimization (FIDAO). When tested on fixed-gantry IMRT plans, we observed up to a 200-fold increase in the optimization speed. Compared to IMRT, rotational volumetric-modulated arc therapy (VMAT) is a much larger optimization problem and has many more delivery constraints. The purpose of this work is to extend and evaluate FIDAO for inverse planning of VMAT plans. METHODS: A prototype FIDAO algorithm for VMAT treatment planning was developed in MATLAB using the open-source treatment planning toolkit matRad (v2.2 dev_VMAT build). VMAT treatment plans using one 3600 arc were generated on the AAPM TG-119 phantom, as well as sample clinical liver and prostate cases. The plans were created by first performing fluence map optimization on 28° equispaced beams, followed by aperture sequencing and arc sequencing with a gantry angular sampling rate of 4°. After arc sequencing, a copy of the plan underwent DAO using the prototype FIDAO algorithm, while another copy of the plan underwent DAO using matRad's DAO method, which served as the conventional algorithm. RESULTS: Both algorithms achieved similar plan quality, although the FIDAO plans had considerably fewer hot spots in the unspecified normal tissue. The optimization time (number of iterations) for FIDAO and the conventional DAO algorithm, respectively, were: 65 s (245) vs 602 s (275) in the TG-119 phantom case; 25 s (85) vs 803 s (159) in the liver case; and 99 s (174) vs 754 s (149) in the prostate case. CONCLUSIONS: This study demonstrated promising speed enhancements in using FIDAO for the direct aperture optimization of VMAT plans.

4DCT Ventilation Map Construction Using Biomechanics-base Image Registration and Enhanced Air Segmentation.

Current lung radiation therapy (RT) treatment planning algorithms used in most centers assume homogeneous lung function. However, co-existing pulmonary dysfunctions present in many non-small cell lung cancer (NSCLC) patients, particularly smokers, cause regional variations in both perfusion and ventilation, leading to inhomogeneous lung function. An adaptive RT treatment planning that deliberately avoids highly functional lung regions can potentially reduce pulmonary toxicity and morbidity. The ventilation component of lung function can be measured using a variety of techniques. Recently, 4DCT ventilation imaging has emerged as a cost-effective and accessible method. Current 4DCT ventilation calculation methods, including the intensity-based and Jacobian models, suffer from inaccurate estimations of air volume distribution and unreliability of intensity-based image registration algorithms. In this study, we propose a novel method that utilizes a biomechanical model-based registration along with an accurate air segmentation algorithm to calculate 4DCT ventilation maps. The results show a successful development of ventilation maps using the proposed method.

Incorporating Pathology-Induced Heterogeneities in a Patient-Specific Biomechanical Model of the Lung for Accurate Tumor Motion Estimation.

Radiation therapy (RT) is an important component of treatment for lung cancer. However, the accuracy of this method can be affected by the complex respiratory motion/deformation of the target tumor during treatment. To improve the accuracy of RT, patient-specific biomechanical models of the lung have been proposed for estimating the tumor's respiratory motion/deformation. Chronic obstructive pulmonary disease (COPD) has a high incidence among lung cancer patients and is associated with heterogeneous destruction of lung parenchyma. This key heterogeneity element, however, has not been incorporated in lung biomechanical models developed in previous studies. In this work, we have developed a physiologically and patho-physiologically realistic lung biomechanical model that accounts for lung tissue heterogeneity. Four-dimensional computed tomography (4DCT) images were used to build a patient-specific finite element (FE) model of the lung. Image information was used to identify and incorporate inhomogeneities within the model. Mechanical properties of normal and diseased regions in the lung and the transpulmonary pressure driving the respiratory motion were estimated using an optimization algorithm that maximizes the similarity between the actual and simulated tumor and lung image data. Results from this proof of concept study on a lung cancer patient indicated improved accuracy of tumor motion estimation when COPD-induced lung tissue heterogeneities were incorporated in the model.

A fast inverse direct aperture optimization algorithm for intensity-modulated radiation therapy.

PURPOSE: The goal of this work was to develop and evaluate a fast inverse direct aperture optimization (FIDAO) algorithm for IMRT treatment planning and plan adaptation. METHODS: A previously proposed fluence map optimization algorithm called fast inverse dose optimization (FIDO) was extended to optimize the aperture shapes and weights of IMRT beams. FIDO is a very fast fluence map optimization algorithm for IMRT that finds the global minimum using direct matrix inversion without unphysical negative beam weights. In this study, an equivalent second-order Taylor series expansion of the FIDO objective function was used, which allowed for the objective function value and gradient vector to be computed very efficiently during direct aperture optimization, resulting in faster optimization. To evaluate the speed gained with FIDAO, a proof-of-concept algorithm was developed in MATLAB using an interior-point optimization method to solve the reformulated aperture-based FIDO problem. The FIDAO algorithm was used to optimize four step-and-shoot IMRT cases: on the AAPM TG-119 phantom as well as a liver, prostate, and head-and-neck clinical cases. Results were compared with a conventional DAO algorithm that uses the same interior-point method but using the standard formulation of the objective function and its gradient vector. RESULTS: A substantial gain in optimization speed was obtained with the prototype FIDAO algorithm compared to the conventional DAO algorithm while producing plans of similar quality. The optimization time (number of iterations) for the prototype FIDAO algorithm vs the conventional DAO algorithm was 0.3 s (17) vs 56.7 s (50); 2.0 s (28) vs 134.1 s (57); 2.5 s (26) vs 180.6 s (107); and 6.7 s (20) vs 469.4 s (482) in the TG-119 phantom, liver, prostate, and head-and-neck examples, respectively. CONCLUSIONS: A new direct aperture optimization algorithm based on FIDO was developed. For the four IMRT test cases examined, this algorithm executed approximately 70-200 times faster without compromising the IMRT plan quality.

Patient-specific calibration of cone-beam computed tomography data sets for radiotherapy dose calculations and treatment plan assessment.

PURPOSE: In this work, we propose a new method of calibrating cone beam computed tomography (CBCT) data sets for radiotherapy dose calculation and plan assessment. The motivation for this patient-specific calibration (PSC) method is to develop an efficient, robust, and accurate CBCT calibration process that is less susceptible to deformable image registration (DIR) errors. METHODS: Instead of mapping the CT numbers voxel-by-voxel with traditional DIR calibration methods, the PSC methods generates correlation plots between deformably registered planning CT and CBCT voxel values, for each image slice. A linear calibration curve specific to each slice is then obtained by least-squares fitting, and applied to the CBCT slice's voxel values. This allows each CBCT slice to be corrected using DIR without altering the patient geometry through regional DIR errors. A retrospective study was performed on 15 head-and-neck cancer patients, each having routine CBCTs and a middle-of-treatment re-planning CT (reCT). The original treatment plan was re-calculated on the patient's reCT image set (serving as the gold standard) as well as the image sets produced by voxel-to-voxel DIR, density-overriding, and the new PSC calibration methods. Dose accuracy of each calibration method was compared to the reference reCT data set using common dose-volume metrics and 3D gamma analysis. A phantom study was also performed to assess the accuracy of the DIR and PSC CBCT calibration methods compared with planning CT. RESULTS: Compared with the gold standard using reCT, the average dose metric differences were ≤ 1.1% for all three methods (PSC: -0.3%; DIR: -0.7%; density-override: -1.1%). The average gamma pass rates with thresholds 3%, 3 mm were also similar among the three techniques (PSC: 95.0%; DIR: 96.1%; density-override: 94.4%). CONCLUSIONS: An automated patient-specific calibration method was developed which yielded strong dosimetric agreement with the results obtained using a re-planning CT for head-and-neck patients.

Accuracy and variability of high-dose-rate prostate brachytherapy needle tip localization using live two-dimensional and sagittally reconstructed three-dimensional ultrasound.

PURPOSE: To measure the accuracy and variability of manual high-dose-rate (HDR) prostate brachytherapy (BT) needle tip localization using sagittally reconstructed three-dimensional (3D) transrectal ultrasound (TRUS) augmented with live two-dimensional (2D) sagittal TRUS. METHODS AND MATERIALS: Ten prostate cancer patients underwent HDR-BT during which the sagittally assisted sagittally reconstructed (SASR) segmentation technique was completed in parallel with commercially available sagittally assisted axially reconstructed (SAAR) TRUS for comparison. The SASR technique makes use of live 2D ultrasound intraoperatively and allows needle tip updates using the final 3D image in the absence of image artifacts. These updates were repeated offline twice by two separate users. Needle end-length measurements were used to calculate insertion depth errors (IDEs) for each technique. RESULTS: Images of 147 needles were analyzed. For the SASR technique, both users were confident in tip positions on the final 3D image within 3 mm for 52% of needles, so these tip positions were updated. For the remaining 48% of needles, the tip positions from the live 2D images were used. This SASR technique enabled the localization of all needles with IDEs within ±3 mm for 84% of needles and IDE range of [-6.2 mm, 5.9 mm], compared with 57% and [-8.1 mm, 7.7 mm] when using the commercially available SAAR technique. CONCLUSIONS: The SASR technique mitigates the impact of 3D TRUS image artifacts on HDR-BT needle tip localization by incorporating live 2D sagittal TRUS intraoperatively and provides a statistically significant reduction in IDE variance compared with the routine SAAR technique.

Simultaneous automatic segmentation of multiple needles using 3D ultrasound for high-dose-rate prostate brachytherapy.

PURPOSE: Sagittally reconstructed 3D (SR3D) ultrasound imaging shows promise for improved needle localization for high-dose-rate prostate brachytherapy (HDR-BT); however, needles must be manually segmented intraoperatively while the patient is anesthetized to create a treatment plan. The purpose of this article was to describe and validate an automatic needle segmentation algorithm designed for HDR-BT, specifically capable of simultaneously segmenting all needles in an HDR-BT implant using a single SR3D image with ~5 mm interneedle spacing. MATERIALS AND METHODS: The segmentation algorithm involves regularized feature point classification and line trajectory identification based on the randomized 3D Hough transform modified to handle multiple straight needles in a single image simultaneously. Needle tips are identified based on peaks in the derivative of the signal intensity profile along the needle trajectory. For algorithm validation, 12 prostate cancer patients underwent HDR-BT during which SR3D images were acquired with all needles in place. Needles present in each of the 12 images were segmented manually, providing a gold standard for comparison, and using the algorithm. Tip errors were assessed in terms of the 3D Euclidean distance between needle tips, and trajectory error was assessed in terms of 2D distance in the axial plane and angular deviation between trajectories. RESULTS: In total, 190 needles were investigated. Mean execution time of the algorithm was 11.0 s per patient, or 0.7 s per needle. The algorithm identified 82% and 85% of needle tips with 3D errors ≤3 mm and ≤5 mm, respectively, 91% of needle trajectories with 2D errors in the axial plane ≤3 mm, and 83% of needle trajectories with angular errors ≤3°. The largest tip error component was in the needle insertion direction. CONCLUSIONS: Previous work has indicated HDR-BT needles may be manually segmented using SR3D images with insertion depth errors ≤3 mm and ≤5 mm for 83% and 92% of needles, respectively. The algorithm shows promise for reducing the time required for the segmentation of straight HDR-BT needles, and future work involves improving needle tip localization performance through improved image quality and modeling curvilinear trajectories.

Evaluation of unified intensity-modulated arc therapy for the radiotherapy of head-and-neck cancer.

PURPOSE: Recently our group developed a unified intensity-modulated arc therapy (UIMAT) technique which allows for the simultaneous inverse-optimization and the combined delivery of volume-modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT). The aim of this study was to evaluate the dosimetric benefits of UIMAT plans for radiation treatment of complex head-and-neck cancer cases. METHODS AND MATERIALS: A retrospective treatment planning study was performed on 30 head-and-neck cases, 15 of which were treated clinically with VMAT while the other 15 were treated with step-and-shoot IMRT. These cases were re-planned using our UIMAT technique and the results were compared with the clinically delivered plans. Plans were assessed in terms of clinically relevant metrics describing target volume coverage, dose conformity, and the sparing of organs at risk. RESULTS: When compared to stand-alone VMAT or IMRT, UIMAT plans offered slightly better tumor volume coverage (Median D95: 98.1% vs. 97.5%, p=0.01) and similar dose conformity (Median CI: 0.69 vs. 0.69, p=0.09). More significantly, UIMAT plans had substantially lower doses to all organs at risk, including the spinal cord (Median D2%: 29.9Gy vs. 35.6Gy, p<0.01), brainstem (Median D2%: 21.2Gy vs. 25.6Gy, p<0.01), left parotid (Median DMean: 26.1Gy vs. 28.0Gy, p<0.01), and right parotid (Median DMean: 23.6Gy vs. 27.2Gy, p<0.01). The reduction in OAR doses did not result from the redistribution of dose to unspecified tissue. Furthermore, UIMAT plans can be delivered with comparable delivery times to VMAT (Median time: 135s vs. 168s, p=0.394) but with fewer monitor units (Median MU: 486 vs. 635, p<0.01). CONCLUSIONS: Compared to stand-alone IMRT or VMAT, UIMAT was demonstrated to have a dosimetric advantage for the radiation treatment of head-and-neck cancer.

Three-dimensional transrectal ultrasound guided high-dose-rate prostate brachytherapy: A comparison of needle segmentation accuracy with two-dimensional image guidance.

PURPOSE: Conventional transrectal ultrasound guided high-dose-rate prostate brachytherapy (HDR-BT) uses an axially acquired image set for organ segmentation and 2D sagittal images for needle segmentation. Sagittally reconstructed 3D (SR3D) transrectal ultrasound enables both organ and needle segmentation and has the potential to reduce organ-needle alignment uncertainty. This study compares the accuracy of needle tip localization between the conventional 2D sagittally assisted axially reconstructed (SAAR) and SR3D approaches. METHODS AND MATERIALS: Twelve patients underwent SAAR-guided HDR-BT, during which SR3D images were acquired for subsequent segmentation and analysis. A total of 183 needles were investigated. Needle end-length measurements were taken, providing a gold standard for insertion depths. Dosimetric impact of insertion depth errors (IDEs) on clinical treatment plans was assessed. RESULTS: SR3D guidance provided statistically significantly smaller IDEs than SAAR guidance with a mean ± SD of -0.6 ± 3.2 mm and 2.8 ± 3.2 mm, respectively (p < 0.001). Shadow artifacts were found to obstruct the view of some needle tips in SR3D images either partially (12%) or fully (10%); however, SR3D IDEs had a statistically significantly smaller impact on prostate V100% than SAAR IDEs with mean ± SD decreases of -1.2 ± 1.3% and -6.5 ± 6.7%, respectively (p < 0.05). CONCLUSIONS: SR3D-guided HDR-BT eliminates a source of systematic uncertainty from the SAAR-guided approach, providing decreased IDEs for most needles, leading to a significant decrease in dosimetric uncertainty. Although imaging artifacts can limit the accuracy of tip localization in a subset of needles, we identified a method to mitigate these artifacts for clinical implementation.

Feasibility of a unified approach to intensity-modulated radiation therapy and volume-modulated arc therapy optimization and delivery.

PURPOSE: To study the feasibility of unified intensity-modulated arc therapy (UIMAT) which combines intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) optimization and delivery to produce superior radiation treatment plans, both in terms of dose distribution and efficiency of beam delivery when compared with either VMAT or IMRT alone. METHODS: An inverse planning algorithm for UIMAT was prototyped within the pinnacle treatment planning system (Philips Healthcare). The IMRT and VMAT deliveries are unified within the same arc, with IMRT being delivered at specific gantry angles within the arc. Optimized gantry angles for the IMRT and VMAT phases are assigned automatically by the inverse optimization algorithm. Optimization of the IMRT and VMAT phases is done simultaneously using a direct aperture optimization algorithm. Five treatment plans each for prostate, head and neck, and lung were generated using a unified optimization technique and compared with clinical IMRT or VMAT plans. Delivery verification was performed with an ArcCheck phantom (Sun Nuclear) on a Varian TrueBeam linear accelerator (Varian Medical Systems). RESULTS: In this prototype implementation, the UIMAT plans offered the same target dose coverage while reducing mean doses to organs at risk by 8.4% for head-and-neck cases, 5.7% for lung cases, and 3.5% for prostate cases, compared with the VMAT or IMRT plans. In addition, UIMAT can be delivered with similar efficiency as VMAT. CONCLUSIONS: In this proof-of-concept work, a novel radiation therapy optimization and delivery technique that interlaces VMAT or IMRT delivery within the same arc has been demonstrated. Initial results show that unified VMAT/IMRT has the potential to be superior to either standard IMRT or VMAT.

Magnetic resonance imaging biomarkers of chronic obstructive pulmonary disease prior to radiation therapy for non-small cell lung cancer.

OBJECTIVE: In this prospectively planned interim-analysis, the prevalence of chronic obstructive lung disease (COPD) phenotypes was determined using magnetic resonance imaging (MRI) and X-ray computed tomography (CT) in non-small-cell-lung-cancer (NSCLC) patients. MATERIALS AND METHODS: Stage-III-NSCLC patients provided written informed consent for pulmonary function tests, imaging and the 6-min-walk-test. Ventilation defect percent (VDP) and CT lung density (relative-of-CT-density-histogram <-950, RA950) were measured. Patients were classified into three subgroups based on qualitative and quantitative COPD and tumour-specific imaging phenotypes: (1) tumour-specific ventilation defects (TSD), (2) tumour-specific and other ventilation defects without emphysema (TSDV), and, (3) tumour-specific and other ventilation defects with emphysema (TSDVE). RESULTS: Seventeen stage-III NSCLC patients were evaluated (68 ± 7 years, 7 M/10 F, mean FEV1 = 77%pred) including seven current and 10 ex-smokers and eight patients with a prior lung disease diagnosis. There was a significant difference for smoking history (p = .02) and FEV1/FVC (p = .04) for subgroups classified using quantitative imaging. Patient subgroups classified using qualitative imaging findings were significantly different for emphysema (RA950, p < .001). There were significant relationships for whole-lung VDP (p < .05), but not RECIST or tumour-lobe VDP measurements with pulmonary function and exercise measurements. Preliminary analysis for non-tumour burden ventilation abnormalities using Reader-operator-characteristic (ROC) curves reflected a 94% classification rate for smoking pack-years, 93% for FEV1/FVC and 82% for RA950. ROC sensitivity/specificity/positive/negative likelihood ratios were also generated for pack-years, (0.92/0.80/4.6/0.3), FEV1/FVC (0.92/0.80/4.6/0.3), RA950 (0.92/0.80/4.6/0.3) and RECIST (0.58/0.80/2.9/1.1). CONCLUSIONS: In this prospectively planned interim-analysis of a larger clinical trial, NSCLC patients were classified based on COPD imaging phenotypes. A proof-of-concept evaluation showed that FEV1/FVC and smoking history identified NSCLC patients with ventilation abnormalities appropriate for functional lung avoidance radiotherapy.