Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma

PurposeWe have developed a new method to track tumor position using fluoroscopic images, and evaluated it using hepatocellular carcinoma case data.MethodsOur method consists of a training stage and a tracking stage. In the training stage, the model data for the positional relationship between the diaphragm and the tumor are calculated using four-dimensional computed tomography (4DCT) data. The diaphragm is detected along a straight line, which was chosen to avoid 4DCT artifact. In the tracking stage, the tumor position on the fluoroscopic images is calculated by applying the model to the diaphragm. Using data from seven liver cases, we evaluated four metrics: diaphragm edge detection error, modeling error, patient setup error, and tumor tracking error. We measured tumor tracking error for the 15 fluoroscopic sequences from the cases and recorded the computation time.ResultsThe mean positional error in diaphragm tracking was 0.57 ± 0.62 mm. The mean positional error in tumor tracking in three-dimensional (3D) space was 0.63 ± 0.30 mm by modeling error, and 0.81–2.37 mm with 1–2 mm setup error. The mean positional error in tumor tracking in the fluoroscopy sequences was 1.30 ± 0.54 mm and the mean computation time was 69.0 ± 4.6 ms and 23.2 ± 1.3 ms per frame for the training and tracking stages, respectively.ConclusionsOur markerless tracking method successfully estimated tumor positions. We believe our results will be useful in increasing treatment accuracy for liver cases.     

Dosimetric impact of 4DCT artifact in carbon-ion scanning beam treatment: Worst case analysis in lung and liver treatments

IntroductionWe evaluated the impact of 4DCT artifacts on carbon-ion pencil beam scanning dose distributions in lung and liver treatment.Methods & materials4DCT was performed in 20 liver and lung patients using area-detector CT (original 4DCT). 4DCT acquisition by multi-detector row CT was simulated using original 4DCT by selecting other phases randomly (plus/minus 20% phases). Since tumor position can move over the respiratory range in original 4DCT, mid-exhalation was set as reference phase. Total prescribed dose of 60 Gy (RBE) was delivered to the clinical target volume (CTV). Reference dose distribution was calculated with the original CT, and actual dose distributions were calculated with treatment planning parameters optimized using the simulated CT (simulated dose). Dose distribution was calculated by substituting these parameters into the original CT.ResultsFor liver cases, CTV-D95 and CTV-Dmin values for the reference dose were 97.6 ± 0.5% and 89.8 ± 0.6% of prescribed dose, respectively. Values for the simulated dose were significantly degraded, to 88.6 ± 14.0% and 46.3 ± 26.7%, respectively. Dose assessment results for lung cases were 84.8 ± 12.8% and 58.0 ± 24.5% for the simulated dose, showing significant degradation over the reference dose of 95.1 ± 1.5% and 87.0 ± 2.2%, respectively.Conclusions4DCT image quality should be closely checked to minimize degradation of dose conformation due to 4DCT artifacts. Medical staff should pay particular attention to checking the quality of 4DCT images as a function of respiratory phase, because it is difficult to recognize 4DCT artifact on a single phase in some cases     

Organ motion quantification and margins evaluation in carbon ion therapy of abdominal lesions

PurposeIn image-guided particle radiotherapy of abdominal lesions, respiratory motion hinders treatment accuracy. In this study, 2D cineMRI data were used to quantify the tumor (GTV) motion and to evaluate the clinical approach based on deriving an internal target volume (ITV) from a planning 4DCT for gating treatments.MethodsSeven patients with abdominal lesions were treated with carbon-ion therapy at the National Centre of Oncological Hadron-therapy (Italy). The MR scan was performed on the same day of the 4DCT acquisition. For four patients, an additional MR was acquired approximately after 1 week. The cineMRI combined with deformable image registration algorithm was used to quantify tumor motion. Afterwards, two ITVs were defined considering (1) all phases (ITV_FB) and (2) only phases within the gating window (ITV_G), and then compared with the clinical (4DCT-derived) ITVs (ITV_CG and ITV_CFB).ResultsTumor residual motion estimated by cineMRI data in the two MRI sessions resulted not significantly different from 4DCT, although cineMRI accounted for cycle-to-cycle variations. The ITV normalized for the GTV median values were higher for ITV_FB with respect to ITV_G, ITV_CFB and ITV_CG. The Hausdorff distances with respect to the GTV were up to 10.55 mm, 3.13 mm, 5.56 mm and 2.51 mm, for ITV_FB, ITV_G, ITV_CFB and ITV_CG, respectively. According to both metrics, ITV_CG and ITV_G were not found significantly different.ConclusionsCineMRI acquisitions allowed to quantify organ motion without delivering additional dose to the patient and to verify treatment margins in gated carbon-ion therapy of abdominal lesions.     

Irregular breathing during 4DCT scanning of lung cancer patients: Is the midventilation approach robust?

BackgroundWith 4DCT the risk of introducing positional systematic errors in lung cancer radiotherapy can be minimised. A common approach is to plan on the phase bin of the 4DCT best representing the tumour's time-weighted mean position also called the midventilation scan. However breathing irregularities can introduce uncertainties and potentially misrepresent both the tumour trajectory and the determination of the midventilation phase. In this study we evaluated the robustness of the midventilation approach in the presence of irregular breathing patterns.MethodsA LEGO Mindstorms^® phantom with compact balls simulating lung tumours was constructed. The breathing curves loaded in the phantom were either acquired from a human volunteer or constructed with various magnitudes (ranging from 12 to 29 mm) as well as various irregularities of motion pattern. Repeated 4DCT scans were performed while tumour trajectories were recorded with two motion tracking systems.ResultsThe time-weighted mean tumour position is accurately represented in 4DCT scans, even for irregular breathing patterns: the position presentation in the midventilation scan was always within in one standard deviation of the global position presentation (3 mm and 2 mm for regular and irregular breathing patterns, respectively). The displacement representation tended to be underestimated in 4DCT scans.ConclusionThe midventilation approach is robust even in the presence of breathing irregularity. The representation of the tumour trajectory in 4DCT scans is affected by breathing irregularity and the extent of tumour motion can be underestimated, which will affect the calculation of patient-individualised margins based on the 4DCT scan.     

Resolution enhancement for lung 4D-CT based on transversal structures by using multiple Gaussian process regression learning

PurposeFour-dimensional computed tomography (4D-CT) plays a useful role in many clinical situations. However, due to the hardware limitation of system, dense sampling along superior–inferior direction is often not practical. In this paper, we develop a novel multiple Gaussian process regression model to enhance the superior-inferior resolution for lung 4D-CT based on transversal structures.MethodsThe proposed strategy is based on the observation that high resolution transversal images can recover missing pixels in the superior-inferior direction. Based on this observation and motived by random forest algorithm, we employ multiple Gaussian process regression model learned from transversal images to improve superior–inferior resolution. Specifically, we first randomly sample 3 × 3 patches from original transversal images. The central pixel of these patches and the eight-neighbour pixels of their corresponding degraded versions form the label and input of training data, respectively. Multiple Gaussian process regression model is then built on the basis of multiple training subsets obtained by random sampling. Finally, the central pixel of the patch is estimated based on the proposed model, with the eight-neighbour pixels of each 3 × 3 patch from interpolated superior-inferior direction images as inputs.ResultsThe performance of our method is extensively evaluated using simulated and publicly available datasets. Our experiments show the remarkable performance of the proposed method.ConclusionsIn this paper, we propose a new approach to improve the 4D-CT resolution, which does not require any external data and hardware support, and can produce clear coronal/sagittal images for easy viewing.     

Perturbation analysis of 4D dose distribution for scanned carbon-ion beam radiotherapy

PurposeTo evaluate the patients’ set-up error-induced perturbation effects on 4D dose distributions (4DDD) of range-adapted internal target volume-based (raITV) treatment plan using lung and liver 4DCT data sets.MethodsWe enrolled 20 patients with lung and liver cancer treated with respiratory-gated carbon-ion beam scanning therapy. PTVs were generated by adding a 2 mm range-adapted set-up margin on the raITVs. Set-up errors were simulated by shifting the beam isocenter in three translational directions of ±2 mm, ±4 mm, and ±6 mm. 4DDDs were calculated for both nominal and isocenter-shifted situations. Dose metrics of CTV dose coverage (D95) and normal tissue sparing were evaluated. Statistical significance with p < 0.01 was considered by Wilcoxon signed rank test.ResultsThe CTV dose coverage was more sensitive to set-up errors for lung cases than for liver cases, and more serious in superior-inferior direction. The sufficient CTV-D95 > 98% could be achieved with set-up errors less than ±2 mm in all shift directions both for lung and liver cases. With the increase of set-up error, the CTV dose coverage decreased gradually. The clinical criterial of CTV-D95 > 95% could not be fulfilled with set-up error reached to ±4 mm for lung cases, and ±6 mm for liver cases. OAR doses did not have a significant difference with each set-up error for both lung and liver cases.ConclusionsThe range-adapted set-up margin successfully prevented dose degradation of 4DDDs in the presence of the same magnitude of set-up error for raITV-based carbon-ion beam scanning therapy.     

Investigation of the XCAT phantom as a validation tool in cardiac MRI tracking algorithms

PurposeTo describe our magnetic resonance imaging (MRI) simulated implementation of the 4D digital extended cardio torso (XCAT) phantom to validate our previously developed cardiac tracking techniques. Real-time tracking will play an important role in the non-invasive treatment of atrial fibrillation with MRI-guided radiosurgery. In addition, to show how quantifiable measures of tracking accuracy and patient-specific physiology could influence MRI tracking algorithm design.MethodsTwenty virtual patients were subjected to simulated MRI scans that closely model the proposed real-world scenario to allow verification of the tracking technique’s algorithm. The generated phantoms provide ground-truth motions which were compared to the target motions output from our tracking algorithm. The patient-specific tracking error, e_p, was the 3D difference (vector length) between the ground-truth and algorithm trajectories. The tracking errors of two combinations of new tracking algorithm functions that were anticipated to improve tracking accuracy were studied. Additionally, the correlation of key physiological parameters with tracking accuracy was investigated.ResultsOur original cardiac tracking algorithm resulted in a mean tracking error of 3.7 ± 0.6 mm over all virtual patients. The two combinations of tracking functions demonstrated comparable mean tracking errors however indicating that the optimal tracking algorithm may be patient-specific.ConclusionsCurrent and future MRI tracking strategies are likely to benefit from this virtual validation method since no time-resolved 4D ground-truth signal can currently be derived from purely image-based studies.     

Wheat leaf traits monitoring based on machine learning algorithms and high-resolution satellite imagery

The leaf, which is a crucial indicator for evaluating crop status, plays an important role in plants' functions. Determining and monitoring leaf parameters can facilitate the detection and estimation of crop yield, which is essential for food security. Crop monitoring by remote sensing technology is critical to support crop production, especially over large scales. In this study, we developed a methodology to estimate leaf parameters based entirely on vegetation indices (VIs) from remotely sensed imagery in wheat under different management practices. Therefore, the current study aimed to examine the utility of VIs calculated from the sentinel-2 data in estimating the Leaf area index (LAI) and leaf parameters at wheat farms using machine learning algorithms. Leaf parameters included leaf dry weight (LDW), specific leaf area (SLA) and leaf specific weight (SLW), and machine learning algorithms were SVM (support vector machine), ANN (artificial neural network) and DNN (deep neural network). Leaf parameters were measured at several developmental stages of wheat in two contrasting environments in the southern Iran. The results demonstrated that the DNN algorithm could efficiently predict leaf parameters in the southern Iran with an overall precision of >72%, which assessed the potential of employing DNN to achieve the temporal and spatial distribution data of wheat based on the Sentinel-2 imagery. The validation of the DNN model generally showed high accuracy (R = 0.80, RMSE = 1.19, and MAE = 0.98) between observed and estimated LAI values when this model was used. NDVI was also highly sensitive to wheat LDW and SLA parameters, with a good correlation between field measurements and those predicted by the DNN model from sentinel-2 imagery, with the R values of 0.66 and 0.85, respectively. Further, NDVI and PVI (Perpendicular Vegetation Index) were linearly correlated with SLW across both temporal and spatial scales (R = 0.79). Among VIs considered from sentinel-2 imagery to predict wheat leaf parameters, NDVI was more sensitive than other VIs. This research, thus, indicated that using sentinel-2 data within a DNN model could provide a comparatively precise and robust prediction of leaf parameters and yield valuable insights into crop management with high temporal and spatial accuracy.     

Predictive value of vascular calcification identified in 4D planning CT of lung cancer patients treated with stereotactic body radiation therapy

PurposeThe aim was to identify vascular calcification in 4DCT scan of lung cancer patients and establish the association between overall survival (OS) and vascular calcification, as surrogate for vascular health.MethodsVascular calcification within the thoracic cavity were segmented in 334 lung cancer patients treated with stereotactic body radiation therapy (SBRT). This has been done automatically on 4D planning CT and average reconstruction scans. Correlation between cardiac comorbidity and calcification volumes was evaluated for patients with recorded Adult Co-Morbidity Evaluation (n = 303). Associations between the identified calcifications and OS were further investigated.ResultsThe volume of calcification from the average scan was significantly lower than from each phase (p < 0.001). The highest level of correlations between cardiac comorbidity and volume of the calcifications were found for one phase representing inhale and two phases representing exhale with the least motion blurring due to respiration (p < 0.005). The volume of the calcifications was subsequently averaged over these three phases. The average of calcification volumes over the three phases (denoted by inhale-exhale) showed the highest likelihood in univariate analysis and was chosen as vascular calcification measure. Cox-model suggested that tumor volume (Hazard Ratio [HR] = 1.46, p < 0.01) and inhale-exhale volume (HR = 1.05, p < 0.05) are independent factors predicting OS after adjusting for age, sex, and performance status.ConclusionIt was feasible to use. It 4DCT scan for identifying thoracic calcifications in lung cancer patients treated with SBRT. Calcification volumes from inhale-exhale phases had the highest correlation with overall cardiac comorbidity and the average of the calcification volume obtained from these phases was an independent predictive factor for OS.     

A deep learning classifier for digital breast tomosynthesis

PurposeTo develop a computerized detection system for the automatic classification of the presence/absence of mass lesions in digital breast tomosynthesis (DBT) annotated exams, based on a deep convolutional neural network (DCNN).Materials and MethodsThree DCNN architectures working at image-level (DBT slice) were compared: two state-of-the-art pre-trained DCNN architectures (AlexNet and VGG19) customized through transfer learning, and one developed from scratch (DBT-DCNN). To evaluate these DCNN-based architectures we analysed their classification performance on two different datasets provided by two hospital radiology departments. DBT slice images were processed following normalization, background correction and data augmentation procedures. The accuracy, sensitivity, and area-under-the-curve (AUC) values were evaluated on both datasets, using receiver operating characteristic curves. A Grad-CAM technique was also implemented providing an indication of the lesion position in the DBT slice.Results Accuracy, sensitivity and AUC for the investigated DCNN are in-line with the best performance reported in the field. The DBT-DCNN network developed in this work showed an accuracy and a sensitivity of (90% ± 4%) and (96% ± 3%), respectively, with an AUC as good as 0.89 ± 0.04. A k-fold cross validation test (with k = 4) showed an accuracy of 94.0% ± 0.2%, and a F1-score test provided a value as good as 0.93 ± 0.03. Grad-CAM maps show high activation in correspondence of pixels within the tumour regions.Conclusions We developed a deep learning-based framework (DBT-DCNN) to classify DBT images from clinical exams. We investigated also a possible application of the Grad-CAM technique to identify the lesion position.     

Is abdominal compression useful in lung stereotactic body radiation therapy? A 4DCT and dosimetric lobe-dependent study

PurposeTo determine the usefulness of abdominal compression in lung stereotactic body radiation therapy (SBRT) depending on lobe tumor location.Materials and methodsTwenty-seven non-small cell lung cancer patients were immobilized in the Stereotactic Body Frame? (Elekta). Eighteen tumors were located in an upper lobe, one in the middle lobe and nine in a lower lobe (one patient had two lesions). All patients underwent two four-dimensional computed tomography (4DCT) scans, with and without abdominal compression. Three-dimensional tumor motion amplitude was determined using manual landmark annotation. We also determined the internal target volume (ITV) and the influence of abdominal compression on lung dose-volume histograms.ResultsThe mean reduction of tumor motion amplitude was 3.5 mm (p = 0.009) for lower lobe tumors and 0.8 mm (p = 0.026) for upper/middle lobe locations. Compression increased tumor motion in 5 cases. Mean ITV reduction was 3.6 cm³ (p = 0.039) for lower lobe and 0.2 cm³ (p = 0.048) for upper/middle lobe lesions. Dosimetric gain of the compression for lung sparing was not clinically relevant.ConclusionsThe most significant impact of abdominal compression was obtained in patients with lower lobe tumors. However, minor or negative effects of compression were reported for other patients and lung sparing was not substantially improved. At our institute, patients with upper or middle lobe lesions are now systematically treated without compression and the usefulness of compression for lower lobe tumors is evaluated on an individual basis.     

Real-time tumor tracking with an artificial neural networks-based method: A feasibility study

The purpose of this study was to develop and assess the performance of a tumor tracking method designed for application in radiation therapy. This motion compensation strategy is currently applied clinically only in conventional photon radiotherapy but not in particle therapy, as greater accuracy in dose delivery is required.We proposed a tracking method that exploits artificial neural networks to estimate the internal tumor trajectory as a function of external surrogate signals. The developed algorithm was tested by means of a retrospective clinical data analysis in 20 patients, who were treated with state of the art infra-red motion tracking for photon radiotherapy, which is used as a benchmark. Integration into a hardware platform for motion tracking in particle therapy was performed and then tested on a moving phantom, specifically developed for this purpose.Clinical data show that a median tracking error reduction up to 0.7 mm can be achieved with respect to state of the art technologies. The phantom study demonstrates that a real-time tumor position estimation is feasible when the external signals are acquired at 60 Hz.The results of this work show that neural networks can be considered a valuable tool for the implementation of high accuracy real-time tumor tracking methodologies.     

Respiratory motion of adrenal gland metastases: Analyses using four-dimensional computed tomography images

PurposeTo evaluate the respiratory motion of adrenal gland metastases in three-dimensional directions using four-dimensional computed tomography (4DCT) images.MethodsFrom January 2013 to May 2016, 12 patients with adrenal gland metastases were included in this study. They all underwent 4DCT scans to assess respiratory motion of adrenal gland metastases in free breathing state. The 4DCT images were sorted into 10 image series according to the respiratory phase from the end inspiration to the end expiration, and then transferred to FocalSim workstation. All gross tumor volumes (GTVs) of adrenal gland metastases were drawn by a single physician and confirmed by a second. Relative coordinates of adrenal gland metastases were automatically generated to calculate adrenal gland metastases motion in different axial directions.ResultsThe average respiratory motion of adrenal gland metastases in left-right (LR), cranial-caudal (CC), anterior-posterior (AP), 3-dimensional (3D) vector directions was 3.4 ± 2.2 mm, 9.5 ± 5.5 mm, 3.8 ± 2.0 mm and 11.3 ± 5.3 mm, respectively. The ratios were 58.6% ± 11.4% and 63.2% ± 12.5% when the volumes of GTV_In0% and GTV _In100% were compared with volume of IGTV_10phase. The volume ratio of IGTV_10phase to GTV_3D was 1.73 ± 0.48.ConclusionsAdrenal gland metastasis is a respiration-induced moving target, and an internal target volume boundary should be provided when designing the treatment plan. The CC motion of adrenal gland metastasis is predominant and >5 mm, thus motion management strategies are recommended for patients undergoing external radiotherapy for adrenal gland metastasis.     

Static and dynamic error of a biplanar videoradiography system using marker-based and markerless tracking techniques

The use of biplanar videoradiography technology has become increasingly popular for evaluating joint function in vivo. Two fundamentally different methods are currently employed to reconstruct 3D bone motions captured using this technology. Marker-based tracking requires at least three radio-opaque markers to be implanted in the bone of interest. Markerless tracking makes use of algorithms designed to match 3D bone shapes to biplanar videoradiography data. In order to reliably quantify in vivo bone motion, the systematic error of these tracking techniques should be evaluated. Herein, we present new markerless tracking software that makes use of modern GPU technology, describe a versatile method for quantifying the systematic error of a biplanar videoradiography motion capture system using independent gold standard instrumentation, and evaluate the systematic error of the W.M. Keck XROMM Facility's biplanar videoradiography system using both marker-based and markerless tracking algorithms under static and dynamic motion conditions. A polycarbonate flag embedded with 12 radio-opaque markers was used to evaluate the systematic error of the marker-based tracking algorithm. Three human cadaveric bones (distal femur, distal radius, and distal ulna) were used to evaluate the systematic error of the markerless tracking algorithm. The systematic error was evaluated by comparing motions to independent gold standard instrumentation. Static motions were compared to high accuracy linear and rotary stages while dynamic motions were compared to a high accuracy angular displacement transducer. Marker-based tracking was shown to effectively track motion to within 0.1?mm and 0.1 deg under static and dynamic conditions. Furthermore, the presented results indicate that markerless tracking can be used to effectively track rapid bone motions to within 0.15 deg for the distal aspects of the femur, radius, and ulna. Both marker-based and markerless tracking techniques were in excellent agreement with the gold standard instrumentation for both static and dynamic testing protocols. Future research will employ these techniques to quantify in vivo joint motion for high-speed upper and lower extremity impacts such as jumping, landing, and hammering.     

Measuring breathing induced oesophageal motion and its dosimetric impact

Purpose: Stereotactic body radiation therapy allows for a precise dose delivery. Organ motion bears the risk of undetected high dose healthy tissue exposure. An organ very susceptible to high dose is the oesophagus. Its low contrast on CT and the oblong shape render motion estimation difficult. We tackle this issue by modern algorithms to measure oesophageal motion voxel-wise and estimate motion related dosimetric impacts.Methods: Oesophageal motion was measured using deformable image registration and 4DCT of 11 internal and 5 public datasets. Current clinical practice of contouring the organ on 3DCT was compared to timely resolved 4DCT contours. Dosimetric impacts of the motion were estimated by analysing the trajectory of each voxel in the 4D dose distribution. Finally an organ motion model for patient-wise comparisons was built.Results: Motion analysis showed mean absolute maximal motion amplitudes of 4.55 $\pm$ 1.81 mm left-right, 5.29 $\pm$ 2.67 mm anterior-posterior and 10.78 $\pm$ 5.30 mm superior-inferior. Motion between cohorts differed significantly. In around 50% of the cases the dosimetric passing criteria was violated. Contours created on 3DCT did not cover 14% of the organ for 50% of the respiratory cycle and were around 38% smaller than the union of all 4D contours. The motion model revealed that the maximal motion is not limited to the lower part of the organ. Our results showed motion amplitudes higher than most reported values in the literature and that motion is very heterogeneous across patients.Conclusions: Individual motion information should be considered in contouring and planning.     

Integration of the M6 Cyberknife in the Moderato Monte Carlo platform and prediction of beam parameters using machine learning

PurposeThis work describes the integration of the M6 Cyberknife in the Moderato Monte Carlo platform, and introduces a machine learning method to accelerate the modelling of a linac.MethodsThe MLC-equipped M6 Cyberknife was modelled and integrated in Moderato, our in-house platform offering independent verification of radiotherapy dose distributions. The model was validated by comparing TPS dose distributions with Moderato and by film measurements. Using this model, a machine learning algorithm was trained to find electron beam parameters for other M6 devices, by simulating dose curves with varying spot size and energy. The algorithm was optimized using cross-validation and tested with measurements from other institutions equipped with a M6 Cyberknife.ResultsOptimal agreement in the Monte Carlo model was reached for a monoenergetic electron beam of 6.75 MeV with Gaussian spatial distribution of 2.4 mm FWHM. Clinical plan dose distributions from Moderato agreed within 2% with the TPS, and film measurements confirmed the accuracy of the model. Cross-validation of the prediction algorithm produced mean absolute errors of 0.1 MeV and 0.3 mm for beam energy and spot size respectively. Prediction-based simulated dose curves for other centres agreed within 3% with measurements, except for one device where differences up to 6% were detected.ConclusionsThe M6 Cyberknife was integrated in Moderato and validated through dose re-calculations and film measurements. The prediction algorithm was successfully applied to obtain electron beam parameters for other M6 devices. This method would prove useful to speed up modelling of new machines in Monte Carlo systems.     

First-in-human imaging using a MR-compatible e4D ultrasound probe for motion management of radiotherapy

PurposeRespiration-induced tumor or organ positional changes can impact the accuracy of external beam radiotherapy. Motion management strategies are used to account for these changes during treatment. The authors report on the development, testing, and first-in-human evaluation of an electronic 4D (e4D) MR-compatible ultrasound probe that was designed for hands-free operation in a MR and linear accelerator (LINAC) environment.MethodsUltrasound components were evaluated for MR compatibility. Electromagnetic interference (EMI) shielding was used to enclose the entire probe and a factory-fabricated cable shielded with copper braids was integrated into the probe. A series of simultaneous ultrasound and MR scans were acquired and analyzed in five healthy volunteers.ResultsThe ultrasound probe led to minor susceptibility artifacts in the MR images immediately proximal to the ultrasound probe at a depth of <10 mm. Ultrasound and MR-based motion traces that were derived by tracking the salient motion of endogenous target structures in the superior-inferior (SI) direction demonstrated good concordance (Pearson correlation coefficients of 0.95–0.98) between the ultrasound and MRI datasets.ConclusionWe have demonstrated that our hands-free, e4D probe can acquire ultrasound images during a MR acquisition at frame rates of approximately 4 frames per second (fps) without impacting either the MR or ultrasound image quality. This use of this technology for interventional procedures (e.g. biopsies and drug delivery) and motion compensation during imaging are also being explored.     

Motion management with variable cycle-based respiratory guidance method for carbon-ion pencil beam scanning treatment

PurposeA novel variable cycle-based respiratory guidance method was proposed to synchronize the patterns between patients’ breathing and the magnetic excitation of synchrotron under the mode of full-energy depth scanning beam delivery, in order to improve the treatment precision and efficiency for carbon ion therapy.MethodsAudio-visual biofeedback system with variable cycle-based respiratory guidance method was developed. We enrolled 6 healthy volunteers and a simulation study of the fixed cycle-based and variable cycle-based respiratory guidance with three treatment fractions was performed. A total of 72 breathing curves were collected for 4D dose calculations with three 4DCT datasets of lung tumor cases. Target dose coverage (D95: the percent dose covering 95% of the target), dose homogeneity (D5-D95), and treatment time were analyzed. The Wilcoxon signed-rank test was used for statistical difference analysis, and p < 0.05 was considered significant.ResultsWith the variable cycle-based respiratory guidance method, the breath hold phase of breathing curve could be synchronized with the synchrotron flat-top phase over time. The dose homogeneity was improved by factors of 1.94–2.92 compared to the fixed cycle-based respiratory guidance maneuvers alone or in combination with gating technique. Moreover, the treatment efficiency increased by 11–23%, depending on the duty cycle settings of the gating window.ConclusionsThe proposed variable cycle-based respiratory guidance method could improve both the treatment efficiency and precision under the mode of the full-energy depth scanning beam delivery for synchrotron-based carbon ion therapy.     

Evaluation of the target dose coverage of stereotactic body radiotherapy for lung cancer using helical tomotherapy: A dynamic phantom study

AimTo evaluate the target dose coverage for lung stereotactic body radiotherapy (SBRT) using helical tomotherapy (HT) with the internal tumor volume (ITV) margin settings adjusted according to the degree of tumor motion.BackgroundLung SBRT with HT may cause a dosimetric error when the target motion is large.Materials and methodsTwo lung SBRT plans were created using a tomotherapy planning station. Using these original plans, five plans with different ITV margins (4.0–20.0 mm for superior-inferior [SI] dimension) were generated. To evaluate the effects of respiratory motion on HT, an original dynamic motion phantom was developed. The respiratory wave of a healthy volunteer was used for dynamic motion as the typical tumor respiratory motion. Five patterns of motion amplitude that corresponded to five ITV margin sizes and three breathing cycles of 7, 14, and 28 breaths per minute were used. We evaluated the target dose change between a static delivery and a dynamic delivery with each motion pattern.ResultsThe target dose difference increased as the tumor size decreased and as the tumor motion increased. Although a target dose difference of <5 % was observed at ≤10 mm of tumor motion for each condition, a maximum difference of -9.94 % ± 7.10 % was observed in cases of small tumors with 20 mm of tumor motion under slow respiration.ConclusionsMinimizing respiratory movement is recommended as much as possible for lung SBRT with HT, especially for cases involving small tumors.