What margins should be added to the clinical target volume in radiotherapy treatment planning for lung cancer?

Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks

BACKGROUND

Respiratory-resolved four-dimensional magnetic resonance imaging (4D-MRI) provides essential motion information for accurate radiation treatments of mobile tumors. However, obtaining high-quality 4D-MRI suffers from long acquisition and reconstruction times.

PURPOSE

To develop a deep learning architecture to quickly acquire and reconstruct high-quality 4D-MRI, enabling accurate motion quantification for MRI-guided radiotherapy (MRIgRT).

METHODS

A small convolutional neural network called MODEST is proposed to reconstruct 4D-MRI by performing a spatial and temporal decomposition, omitting the need for 4D convolutions to use all the spatio-temporal information present in 4D-MRI. This network is trained on undersampled 4D-MRI after respiratory binning to reconstruct high-quality 4D-MRI obtained by compressed sensing reconstruction. The network is trained, validated, and tested on 4D-MRI of 28 lung cancer patients acquired with a T1-weighted golden-angle radial stack-of-stars (GA-SOS) sequence. The 4D-MRI of 18, 5, and 5 patients were used for training, validation, and testing. Network performances are evaluated on image quality measured by the structural similarity index (SSIM) and motion consistency by comparing the position of the lung-liver interface on undersampled 4D-MRI before and after respiratory binning. The network is compared to conventional architectures such as a U-Net, which has 30 times more trainable parameters.

RESULTS

MODEST can reconstruct high-quality 4D-MRI with higher image quality than a U-Net, despite a thirty-fold reduction in trainable parameters. High-quality 4D-MRI can be obtained using MODEST in approximately 2.5 min, including acquisition, processing, and reconstruction.

CONCLUSION

High-quality accelerated 4D-MRI can be obtained using MODEST, which is particularly interesting for MRIgRT.

First experimental demonstration of VMAT combined with MLC tracking for single and multi fraction lung SBRT on an MR-linac

BACKGROUND AND PURPOSE

VMAT is not currently available on MR-linacs but could maximize plan conformality. To mitigate respiration without compromising delivery efficiency, MRI-guided MLC tumour tracking was recently developed for the 1.5 T Unity MR-linac (Elekta AB, Stockholm, Sweden) in combination with IMRT. Here, we provide a first experimental demonstration of VMAT+MLC tracking for several lung SBRT indications.

MATERIALS AND METHODS

We created central patient and phantom VMAT plans (8×7.5 Gy, 2 arcs) and we created peripheral phantom plans (3×18 & 1×34 Gy, 4 arcs). A motion phantom mimicked subject-recorded respiratory motion (A‾=11 mm, f‾=0.33 Hz, drift‾=0.3 mm/min). This was monitored using 2D-cine MRI at 4 Hz to continuously realign the beam with the target. VMAT+MLC tracking performance was evaluated using 2D film dosimetry and a novel motion-encoded and time-resolved pseudo-3D dosimetry approach.

RESULTS

We found an MLC leaf and jaw end-to-end latency of 328.05(±3.78) ms and 317.33(±4.64) ms, which was mitigated by a predictor. The VMAT plans required maximum MLC speeds of 12.1 cm/s and MLC tracking superimposes an additional 1.48 cm/s. A local 2%/1 mm gamma analysis with a static measurement as reference, revealed pass-rates of 28-46% without MLC tracking and 88-100% with MLC tracking for the 2D film analysis. Similarly the pseudo-3D gamma passing-rates increased from 22-77% to 92-100%. The dose area histograms show that MLC tracking increased the GTV D_98% by 5-20% and the PTV D_95% by 7-24%, giving similar target coverage as their respective static reference.

CONCLUSION

MRI-guided VMAT+MLC tracking is technically feasible on the MR-linac and results in highly conformal dose distribution.

A hybrid 2D/4D-MRI methodology using simultaneous multislice imaging for radiotherapy guidance

Purpose

Respiratory motion management is important in abdominothoracic radiotherapy. Fast imaging of the tumor can facilitate multileaf collimator (MLC) tracking that allows for smaller treatment margins, while repeatedly imaging the full field‐of‐view is necessary for 4D dose accumulation. This study introduces a hybrid 2D/4D‐MRI methodology that can be used for simultaneous MLC tracking and dose accumulation on a 1.5 T Unity MR‐linac (Elekta AB, Stockholm, Sweden).

Methods

We developed a hybrid 2D/4D‐MRI methodology that uses a simultaneous multislice (SMS) accelerated MRI sequence, which acquires two coronal slices simultaneously and repeatedly cycles through slice positions over the image volume. As a result, the fast 2D imaging can be used prospectively for MLC tracking and the SMS slices can be sorted retrospectively into respiratory‐correlated 4D‐MRIs for dose accumulation. Data were acquired in five healthy volunteers with an SMS‐bTFE and SMS‐TSE MRI sequence. For each sequence, a prebeam dataset and a beam‐on dataset were acquired simulating the two phases of MR‐linac treatments. Prebeam data were used to generate a 4D‐based motion model and a reference mid‐position volume, while beam‐on data were used for real‐time motion extraction and reconstruction of beam‐on 4D‐MRIs. In addition, an in‐silico computational phantom was used for validation of the hybrid 2D/4D‐MRI methodology. MLC tracking experiments were performed with the developed methodology, for which real‐time SMS data reconstruction was enabled on the scanner. A 15‐beam 8× 7.5 Gy intensity‐modulated radiotherapy plan for lung stereotactic body radiotherapy with isotropic 3 mm GTV‐to‐PTV margins was created. Dosimetry experiments were performed using a 4D motion phantom. The latency between target motion and updating the radiation beam was determined and compensated. Local gamma analyses were performed to quantify dose differences compared to a static reference delivery, and dose area histograms (DAHs) were used to quantify the GTV and PTV coverage.

Results

In‐vivo data acquisition and MLC tracking experiments were successfully performed with the developed hybrid 2D/4D‐MRI methodology. Real‐time liver–lung interface motion estimation had a Pearson's correlation of 0.996 (in‐vivo) and 0.998 (in‐silico). A median (5th–95th percentile) error of 0.0 (−0.9 to 0.7) mm and 0.0 (−0.2 to 0.2) mm was found for real‐time motion estimation for in‐vivo and in‐silico, respectively. Target motion prediction beyond the liver–lung interface had a median root mean square error of 1.6 mm (in‐vivo) and 0.5 mm (in‐silico). Beam‐on 4D MRI reconstruction required a median amount of data equal to an acquisition time of 2:21–3:17 min, which was 20% less data compared to the prebeam‐derived 4D‐MRI. System latency was reduced from 501 ± 12 ms to −1 ± 3 ms (SMS‐TSE) and from 398 ± 10 ms to −10 ± 4 ms (SMS‐bTFE) by a linear regression prediction filter. The local gamma analysis agreed within −3.8% to 3.3% (SMS‐bTFE) and −5.3% to 10% (SMS‐TSE) with a reference MRI sequence. The DAHs revealed a relative D98% GTV coverage between 97% and 100% (SMS‐bTFE) and 100% and 101% (SMS‐TSE) compared to the static reference.

Conclusions

The presented 2D/4D‐MRI methodology demonstrated the potential for accurately extracting real‐time motion for MLC tracking in abdominothoracic radiotherapy, while simultaneously reconstructing contiguous respiratory‐correlated 4D‐MRIs for dose accumulation.

Fiducial marker position affects target volume in stereotactic lung irradiation

Purpose

Real‐time tracking systems of moving respiratory targets such as CyberKnife, Radixact, or Vero4DRT are an advanced robotic radiotherapy device used to deliver stereotactic body radiotherapy (SBRT). The internal target volume (ITV) of lung tumors is assessed through a fiducial marker fusion using four‐dimensional computed tomography (CT). It is important to minimize the ITV to protect normal lung tissue from exposure to radiation and the associated side effects post SBRT. However, the ITV may alter if there is a change in the position of the fiducial marker with respect to the tumor. This study investigated the relationship between fiducial marker position and the ITV in order to prevent radiation exposure of normal lung tissue, and correct target coverage.

Materials and methods

This study retrospectively reviewed 230 lung cancer patients who received a fiducial marker for SBRT between April 2015 and September 2021. The distance of the fiducial marker to the gross tumor volume (GTV) in the expiratory (d_ex) and inspiratory (d_in) CT, and the ratio of the ITV/V(GTV_ex), were investigated.

Results

Upon comparing each lobe, although there was no significant difference in the d_diff and the ITV/V(GTV_ex) between all lobes for d_ex < 10 mm, there was significant difference in the d_diff and the ITV/V(GTV_ex) between the lower and upper lobes for d_ex ≥ 10 mm (p < 0.05). Moreover, there was significant difference in the d_diff and the ITV/V(GTV_ex) between d_ex ≥10 mm and d_ex < 10 mm in all lung regions (p < 0.05).

Conclusion

The ITV that had no margin from GTVs increased when d_ex was ≥10 mm for all lung regions (p < 0.05). Furthermore, the increase in ITV tended to be greater in the lower lung lobe. These findings can help decrease the possibility of adverse events post SBRT, and correct target coverage.

Dosimetric evaluation of MRI-guided multi-leaf collimator tracking and trailing for lung stereotactic body radiation therapy

PURPOSE

The treatment margins for lung stereotactic body radiotherapy (SBRT) are often large to cover the tumor excursions resulting from respiration, such that underdosage of the tumor can be avoided. Magnetic resonance imaging (MRI)-guided multi-leaf collimator (MLC) tracking can potentially reduce the influence of respiration to allow for smaller treatment margins. However, tracking is accompanied by system latency that may induce residual tracking errors. Alternatively, a simpler mid-position delivery combined with trailing can be used. Trailing reduces influences of respiration by compensating for baseline motion, to potentially improve target coverage. In this study, we aim to show the feasibility of MRI-guided tracking and trailing to reduce influences of respiration during lung SBRT.

METHODS

We implemented MRI-guided tracking on the MR-linac using an Elekta research tracking interface to track tumor motion during intensity modulated radiotherapy (IMRT). A Quasar MRI 4 D phantom was used to generate Lujan motion ( cos 4 , 4 s period, 20 mm peak-to-peak amplitude) with and without 1.0 mm/min cranial drift. Phantom tumor positions were estimated from sagittal 2D cine-MRI (4 or 8 Hz) using cross-correlation-based template matching. To compensate the anticipated system latency, a linear ridge regression predictor was optimized for online MRI by comparing two predictor training approaches: training on multiple traces and training on a single trace. We created 15-beam clinical-grade lung SBRT plans for central targets (8 × 7.5 Gy) and peripheral targets (3 × 18 Gy) with different PTV margins for mid-position based motion management (3-5 mm) and for MLC tracking (3 mm). We used a film insert with a 3 cm spherical target to measure the spatial distribution and quantity of the delivered dose. A 1%/1 mm local gamma-analysis quantified dose differences between motion management strategies and reference cases. Additionally, a dose area histogram (DAH) revealed the target coverage relative to the reference scenario.

RESULTS

The prediction filter gain was on average 25% when trained on multiple traces and 44% when trained on a single trace. The filter reduced system latency from 313 ± 2 ms to 0 ± 5 ms for 4 Hz imaging and from 215 ± 3 ms to 3 ± 3 ms for 8 Hz. The local gamma analysis for the central delivery showed that tracking improved the gamma pass-rate from 23% to 96% for periodic motion and from 14% to 93% when baseline drift was applied. For the peripheral delivery during periodic motion, delivery pass-rates improved from 22% to 93%. Comparing mid-position delivery to trailing for periodic+drift motion increased the local gamma pass rate from 15% to 98% for a central delivery and from 8% to 98% for a peripheral delivery. Furthermore, the DAHs revealed a relative D 98 % GTV coverage of 101% and 97% compared to the reference scenario for, respectively, central and peripheral tracking of periodic+drift motion. For trailing, a relative D 98 % of 99% for central and 98% for peripheral trailing was found.

CONCLUSIONS

We provided a first experimental demonstration of the technical feasibility of MRI-guided MLC tracking and trailing for central and peripheral lung SBRT. Tracking maximizes the sparing of healthy tissue, while trailing is highly effective in mitigating baseline motion.

New method for measurement of chest surface motion in lung cancer patients: Quantification using a technique of deformable image registration

The purpose of this study was to measure the motion of the chest surface during breath-holding treatment for lung cancer using deformable image registration (DIR). Forty non-small-cell lung cancer patients treated with breath-holding stereotactic body radiation therapy were retrospectively examined. First, intensity-based DIR between 2 breath-holding computed tomography (CT) images was performed. Subsequently, deformation vector field (DVF) for all dimensions (left-right, anterior-posterior, and superior-inferior) was calculated from the result. For the analysis of chest surface, the DVF value of the only chest surface area was extracted after the chest surface was divided into 12 regions of interest (ROI) based on anatomy. Additionally, for the analysis of the correlation with the internal tumor motion, the median value of DVF for each surface ROI and the motion of the center of gravity of the tumor volume were used. It was possible to calculate the motion of chest surface without any outliers for all patients. For the average of 12 surface ROIs, the motion of 3D chest surface was within 2 mm (30 cases), 3 mm (8 cases), and 4 mm (2 cases). There was no correlation between the motion of the chest surface and that of the tumor for all 12 surface ROIs. We proposed a technique to evaluate the surface motion using DIR between multiple CT images. It could be a useful tool to calculate the motion of chest surface.

Automated MV markerless tumor tracking for VMAT

Tumor tracking during radiotherapy treatment can improve dose accuracy, conformity and sparing of healthy tissue. Many methods have been introduced to tackle this challenge utilizing multiple imaging modalities, including a template matching based approach using the megavoltage (MV) on-board portal imager demonstrated on 3D conformal treatments. However, the complexity of treatments is evolving with the introduction of VMAT and IMRT, and successful motion management is becoming more important due to a trend towards hypofractionation. We have developed a markerless lung tumor tracking algorithm, utilizing the electronic portal imager (EPID) of the treatment machine. The algorithm has been specifically adapted to track during complex treatment deliveries with gantry and MLC motion. The core of the algorithm is an adaptive template matching method that relies on template stability metrics and local relative orientations to perform multiple feature tracking simultaneously. Only a single image is required to initialize the algorithm and features are automatically added, modified or removed in response to the input images. This algorithm was evaluated against images collected during VMAT arcs of a dynamic thorax phantom. Dynamic phantom images were collected during radiation delivery for multiple lung SBRT breathing traces and an example patient data set. The tracking error was 1.34 mm for the phantom data and 0.68 mm for the patient data. A multi-region, markerless tracking algorithm has been developed, capable of tracking multiple features simultaneously without requiring any other a priori information. This novel approach delivers robust target localization during complex treatment delivery. The reported tracking error is similar to previous reports for 3D conformal treatments.

A Fully Convolutional Deep Neural Network for Lung Tumor Boundary Tracking in MRI

Delineation of lung tumor from adjacent tissue from a series of magnetic resonance images (MRI) poses many difficulties due to the image similarities of the region of interest and surrounding area as well as the influence of respiration. However, accurate segmentation of the tumor region is essential in planning a radiation therapy to prevent healthy tissues from receiving excessive radiation. The manual delineation of the entire MRI sequence is tedious, time-consuming and costly. This study investigates how one can perform automatic tracking of tumor boundaries during radiation therapy using convolutional neural networks. We proposed to use a convolutional neural network architecture with modified Dice metric as the cost function. The proposed approach was evaluated over 600 images in comparison to expert manual contours. The proposed method yielded an average Dice score of $0.91 \pm 0.03$ and Hausdorff distance of $2.88 \pm 0.86$ mm. The proposed approach outperformed recent state-of-the-art methods in terms of accuracy in the delineation of the mobile tumors.

Quantifying the dosimetric impact of organ-at-risk delineation variability in head and neck radiation therapy in the context of patient setup uncertainty

The purpose of this study was to quantify the potential dosimetric impact of delineation variability (DV) in head and neck radiation therapy (RT) when inherent patient setup variability (SV) is also considered. The impact of DV was assessed by generating plans with multiple structure sets, cross-evaluating them, including SV, across sets, and determining P _PQM: the probability of achieving organ-specific plan quality metrics (PQM). DV was incorporated by: (1) using multiple organ at risk (OAR) structure sets delineated by independent manual observers; and (2) randomly perturbing manually generated OARs to generate alternatives with varying levels of uncertainty (low, medium, and high DV). For each structure set, independent VMAT plans were auto-generated to meet clinical PQMs. Each plan was cross-evaluated using OARs from multiple structure sets with simulated SV including per-fraction random (σ _s) and per-treatment-course systematic (Σ_s) setup errors. The dosimetric impact of DV was assessed by examining P _PQM with and without SV/DV. Clinically significant differences were defined by those that exceeded differences caused by a  +2% output variation. Without including SV, simulated DV at the medium level reduced P _PQM by an average of 5.5% for all OARs with D _max PQMs. This reduction decreased to 2.8% for SV  =  2 mm and 2.4% for SV  =  4 mm (the average P _PQM reduction due to 2% output errors was 2.7%). For OARs with D _mean PQMs, the average P _PQM reduction was 0.9% for SV  =  0 and  ⩽0.1% for SV  ⩾  2 mm. The effect of DV was larger for OARs that directly abutted a target volume than for those that did not. These trends were also observed with real DV from multi-observer delineations. The dosimetric impact of DV appeared to decrease when random and systematic SV was considered. Sensitivity to DV was affected by OAR objective type (i.e. D _mean versus D _max objectives) as well as distance from the target volume.

Automatic diaphragm segmentation for real-time lung tumor tracking on cone-beam CT projections: a convolutional neural network approach

PURPOSE

To automatically segment the diaphragm on individual lung cone-beam CT projection images, to enable real-time tracking of lung tumors using kilovoltage imaging.

METHODS

The deep neural network Mask R-CNN was trained on 3500 raw cone-beam CT projection images from 10 lung cancer patients, with the diaphragm manually segmented on each image used as a ground truth label. Ground-truth breathing traces were extracted from each patient for both diaphragm hemispheres, and apex positions were compared against the predicted output of the neural network. Ten-fold cross-validation was used to evaluate the segmentation accuracy.

RESULTS

The mean diaphragm apex prediction error was 4.4 mm. The mean percentage of projection images for which a successful prediction could me made was 87.3%. Prediction accuracy at some lateral gantry angles was worse due to overlap between diaphragm hemispheres, and the increased amount of fatty tissue.

CONCLUSIONS

The neural network was able to track the diaphragm apex position successfully. This allows accurate assessment of the breathing phase, which can be used to estimate the position of the lung tumor in real time.

Investigating the impact of tumour motion on TomoTherapy stereotactic ablative body radiotherapy (SABR) deliveries on 3-dimensional and 4-dimensional computed tomography

TomoTherapy can provide highly accurate SABR deliveries, but currently it does not have any effective motion management techniques. Shallow breathing has been identified as one possible motion management solution on TomoTherapy, which has been made possible with the BreatheWell audiovisual biofeedback (AVB) device. Since both the shallow breathing technique and the clinical use of the BreatheWell device are novel, their implementation requires comprehensive verification and validation work. As the first stage of the validation, this paper investigates the impact of target motion on a TomoTherapy SABR delivery is assessed on both 3D CT and 4D CT using a 4D respiratory phantom. A dosimetric study on a 4D respiratory phantom was conducted, with the phantom's insert designed to move at four different amplitudes in the superior-inferior direction. SABR plans on 3D and 4D CT scans were created and measured. Critical plan statistics and measurement results were compared. It is found that for TomoTherapy SABR deliveries, by reducing the targets respiratory motion, target coverage, organ-at-risk (OAR) sparing, and delivery accuracy were improved.

The Practicality of ICRU and Considerations for Future ICRU Definitions

The International Commission on Radiation Units and Measurements (ICRU) volumes are standardized volume definitions used in radiation oncology practice that have evolved over time to account for advancements in technology and radiation planning. The current definitions have strengths but also practical limitations. The main limitation is related to the process of accounting for tumor motion during treatment. As radiotherapeutic techniques become more precise, motion interplay effects and anatomical changes during treatment must be taken into account to ensure accurate and safe delivery of treatment. Adaptive replanning can help to mitigate the effect of these uncertainties and widen the therapeutic ratio by maximizing dose to the tumor and protecting critical normal structures. As adaptive replanning becomes more common, standardization of how adaptive therapy is implemented and reported will become necessary.

Tracking tumor boundary using point correspondence for adaptive radio therapy

BACKGROUND AND OBJECTIVE

Tracking mobile tumor regions during the treatment is a crucial part of image-guided radiation therapy because of two main reasons which negatively affect the treatment process: (1) a tiny error will lead to some healthy tissues being irradiated; and (2) some cancerous cells may survive if the beam is not accurately positioned as it may not cover the entire cancerous region. However, tracking or delineation of such a tumor region from magnetic resonance imaging (MRI) is challenging due to photometric similarities of the region of interest and surrounding area as well as the influence of motion in the organs. The purpose of this work is to develop an approach to track the center and boundary of tumor region by auto-contouring the region of interest in moving organs for radiotherapy.

METHODS

We utilize a nonrigid registration method as well as a publicly available RealTITracker algorithm for MRI to delineate and track tumor regions from a sequence of MRI images. The location and shape of the tumor region in the MRI image sequence varies over time due to breathing. We investigate two approaches: the first one uses manual segmentation of the first frame during the pretreatment stage; and the second one utilizes manual segmentation of all the frames during the pretreatment stage.

RESULTS

We evaluated the proposed approaches over a sequence of 600 images acquired from 6 patients. The method that utilizes all the frames in the pretreatment stage with moving mesh based registration yielded the best performance with an average Dice Score of 0.89 ± 0.04 and Hausdorff Distance of 3.38 ± 0.10 mm.

CONCLUSIONS

This study demonstrates a promising boundary tracking tool for delineating the tumor region that can deal with respiratory movement and the constraints of adaptive radiation therapy.

Design and Evaluation of a MEMS Magnetic Field Sensor-Based Respiratory Monitoring and Training System for Radiotherapy

The patient’s respiratory pattern and reproducibility are important factors affecting the accuracy of radiotherapy for lung cancer or liver cancer cases. Therefore, respiration training is required to induce respiration regularity before radiotherapy. However, the need for specialized personnel, space, and time-consuming training represent limitations. To solve these problems, we have developed a respiratory monitoring and training system based on a micro-electro-mechanical-system (MEMS) magnetic sensor. This system consists of a small attaching magnet, a sensor, and a breathing pattern output device. In this study, we evaluated the performance of the signal measurement in the developed system based on the various respiratory cycles, the amplitudes, and the position angles of the magnet and the sensor. The system can provide a more accurate breathing signal graph with lower measurement error and higher spatial resolution than conventional sensor methods by using additional magnet. In addition, it is possible the patient to monitor and train breathing himself by making it easy to carry and use without restriction of time and space.

Evaluation of the 4D RADPOS dosimetry system for dose and position quality assurance of CyberKnife

PURPOSE

The Synchrony respiratory motion tracking of the CyberKnife system purports to provide real-time tumor motion compensation during robotic radiosurgery. Such a complex delivery system requires thorough quality assurance. In this work, RADPOS applicability as a dose and position quality assurance tool for CyberKnife treatments is assessed quantitatively for different phantom types and breathing motions, which increase in complexity to more closely resemble clinical situations.

METHODS

Two radiotherapy treatment experiments were performed where dose and position were measured with the RADPOS probe housed within a Solid Water phantom. For the first experiment, a Solid Water breast phantom was irradiated using isocentric beam delivery while stationary or moving sinusoidally in the anterior/posterior direction. For the second experiment, a phantom consisting of a Solid Water tumor in lung equivalent material was irradiated using isocentric and non-isocentric beam delivery while either stationary or moving. The phantom movement was either sinusoidal or based on a real patient's breathing waveform. For each experiment, RADPOS dose measurements were compared to EBT3 GafChromic film dose measurements and the CyberKnife treatment planning system's (TPS) Monte Carlo and ray-tracing dose calculation algorithms. RADPOS position measurements were compared to measurements made by the CyberKnife system and to the predicted breathing motion models used by the Synchrony respiratory motion compensation.

RESULTS

For the static and dynamic (i.e., sinusoidal motion) cases of the breast experiment, RADPOS, film and the TPS agreed at the 2.0% level within 1.1 σ of estimated combined uncertainties. RADPOS position measurements were in good agreement with LED and fiducial position measurements, where the average standard deviation (SD) of the differences between any two of the three position datasets was ≤0.5 mm for all directions. For the 10 mm peak to peak amplitude sinusoidal motion of the breast experiment, the average Synchrony correlation errors were ≤0.2 mm, indicative of an accurate predictive model. For all the cases of the lung experiment, RADPOS and film measurements agreed with each other at the 2.0% level within 1.5 σ of estimated experimental uncertainties provided that the measurements were corrected for imaging dose. The measured dose for RADPOS and film were 4.0% and 3.4% higher, respectively, than the TPS for the most complex dynamic cases (i.e., irregular motion) considered for the lung experiment. Assessment of the Synchrony correlation models by RADPOS showed that model accuracy declined as motion complexity increased; the SD of the differences between RADPOS and model position data measurements was ≤0.8 mm for sinusoidal motion but increased to ≤2.6 mm for irregular patient waveform motion. These results agreed with the Synchrony correlation errors reported by the CyberKnife system.

CONCLUSIONS

RADPOS is an accurate and precise QA tool for dose and position measurements for CyberKnife deliveries with respiratory motion compensation.

Image-guided adaptive radiotherapy in patients with locally advanced non-small cell lung cancer: the art of PET

With a worldwide annual incidence of 1.8 million cases, lung cancer is the most diagnosed form of cancer in men and the third most diagnosed form of cancer in women. Histologically, 80-85% of all lung cancers can be categorized as non-small cell lung cancer (NSCLC). For patients with locally advanced NSCLC, standard of care is fractionated radiotherapy combined with chemotherapy. With the aim of improving clinical outcome of patients with locally advanced NSCLC, combined and intensified treatment approaches are increasingly being used. However, given the heterogeneity of this patient group with respect to tumor biology and subsequent treatment response, a personalized treatment approach is required to optimize therapeutic effect and minimize treatment induced toxicity. Medical imaging, in particular positron emission tomography (PET), before and during the course radiotherapy is increasingly being used to personalize radiotherapy. In this setting, PET imaging can be used to improve delineation of target volumes, employ molecularly-guided dose painting strategies, early response monitoring, prediction and monitoring of treatment-related toxicity. The concept of PET image-guided adaptive radiotherapy (IGART) is an interesting approach to personalize radiotherapy for patients with locally advanced NSCLC, which might ultimately contribute to improved clinical outcomes and reductions in frequency of treatment-related adverse events in this patient group. In this review, we provide a comprehensive overview of available clinical data supporting the use of PET imaging for IGART in patients with locally advanced NSCLC.

Evaluation of normal lung tissue complication probability in gated and conventional radiotherapy using the 4D XCAT digital phantom

PURPOSE

The present study was conducted to investigate normal lung tissue complication probability in gated and conventional radiotherapy (RT) as a function of diaphragm motion, lesion size, and its location using 4D-XCAT digital phantom in a simulation study.

MATERIALS AND METHODS

Different time series of 3D-CT images were generated using the 4D-XCAT digital phantom. The binary data obtained from this phantom were then converted to the digital imaging and communication in medicine (DICOM) format using an in-house MATLAB-based program to be compatible with our treatment planning system (TPS). The 3D-TPS with superposition computational algorithm was used to generate conventional and gated plans. Treatment plans were generated for 36 different XCAT phantom configurations. These included four diaphragm motions of 20, 25, 30 and 35 mm, three lesion sizes of 3, 4, and 5 cm in diameter and each tumor was placed in four different lung locations (right lower lobe, right upper lobe, left lower lobe and left upper lobe). The complication of normal lung tissue was assessed in terms of mean lung dose (MLD), the lung volume receiving ≥20 Gy (V20), and normal tissue complication probability (NTCP).

RESULTS

The results showed that the gated RT yields superior outcomes in terms of normal tissue complication compared to the conventional RT. For all cases, the gated radiation therapy technique reduced the mean dose, V20, and NTCP of lung tissue by up to 5.53 Gy, 13.38%, and 23.89%, respectively.

CONCLUSIONS

The results of this study showed that the gated RT provides significant advantages in terms of the normal lung tissue complication, compared to the conventional RT, especially for the lesions near the diaphragm.

Motion management strategies and technical issues associated with stereotactic body radiotherapy of thoracic and upper abdominal tumors: A review from NRG oncology

The efficacy of stereotactic body radiotherapy (SBRT) has been well demonstrated. However, it presents unique challenges for accurate planning and delivery especially in the lungs and upper abdomen where respiratory motion can be significantly confounding accurate targeting and avoidance of normal tissues. In this paper, we review the current literature on SBRT for lung and upper abdominal tumors with particular emphasis on addressing respiratory motion and its affects. We provide recommendations on strategies to manage motion for different, patient-specific situations. Some of the recommendations will potentially be adopted to guide clinical trial protocols.

Does Motion Assessment With 4-Dimensional Computed Tomographic Imaging for Non-Small Cell Lung Cancer Radiotherapy Improve Target Volume Coverage?

Introduction:

Modern radiotherapy with 4-dimensional computed tomographic (4D-CT) image acquisition for non–small cell lung cancer (NSCLC) captures respiratory-mediated tumor motion to provide more accurate target delineation. This study compares conventional 3-dimensional (3D) conformal radiotherapy (3DCRT) plans generated with standard helical free-breathing CT (FBCT) with plans generated on 4D-CT contoured volumes to determine whether target volume coverage is affected.

Materials and methods:

Fifteen patients with stage I to IV NSCLC were enrolled in the study. Free-breathing CT and 4D-CT data sets were acquired at the same simulation session and with the same immobilization. Gross tumor volume (GTV) for primary and/or nodal disease was contoured on FBCT (GTV_3D). The 3DCRT plans were obtained, and the patients were treated according to our institution’s standard protocol using FBCT imaging. Gross tumor volume was contoured on 4D-CT for primary and/or nodal disease on all 10 respiratory phases and merged to create internal gross tumor volume (IGTV)_4D. Clinical target volume margin was 5 mm in both plans, whereas planning tumor volume (PTV) expansion was 1 cm axially and 1.5 cm superior/inferior for FBCT-based plans to incorporate setup errors and an estimate of respiratory-mediated tumor motion vs 8 mm isotropic margin for setup error only in all 4D-CT plans. The 3DCRT plans generated from the FBCT scan were copied on the 4D-CT data set with the same beam parameters. GTV_3D, IGTV_4D, PTV, and dose volume histogram from both data sets were analyzed and compared. Dice coefficient evaluated PTV similarity between FBCT and 4D-CT data sets.

Results:

In total, 14 of the 15 patients were analyzed. One patient was excluded as there was no measurable GTV. Mean GTV_3D was 115.3 cm³ and mean IGTV_4D was 152.5 cm³ (P = .001). Mean PTV_3D was 530.0 cm³ and PTV_4D was 499.8 cm³ (P = .40). Both gross primary and nodal disease analyzed separately were larger on 4D compared with FBCT. D95 (95% isodose line) covered 98% of PTV_3D and 88% of PTV_4D (P = .003). Mean dice coefficient of PTV_3D and PTV_4D was 84%. Mean lung V20 was 24.0% for the 3D-based plans and 22.7% for the 4D-based plans (P = .057). Mean heart V40 was 12.1% for the 3D-based plans and 12.7% for the 4D-based plans (P = .53). Mean spinal cord D_max was 2517 and 2435 cGy for 3D-based and 4D-based plans, respectively (P = .019). Mean esophageal dose was 1580 and 1435 cGy for 3D and 4D plans, respectively (P = .13).

Conclusions:

IGTV_4D was significantly larger than GTV_3D for both primary and nodal disease combined or separately. Mean PTV_3D was larger than PTV_4D, but the difference was not statistically significant. The PTV_4D coverage with 95% isodose line was inferior, indicating the importance of incorporating the true size and shape of the target volume. Relatively less dose was delivered to spinal cord and esophagus with plans based on 4D data set. Dice coefficient analysis for degree of similarity revealed that 16% of PTVs from both data sets did not overlap, indicating different anatomical positions of the PTV due to tumor/nodal motion during a respiratory cycle. All patients with lung cancer planned for radical radiotherapy should have 4D-CT simulation to ensure accurate coverage of the target volumes.

Lung tumor boundary tracking in MRI with moving mesh correspondences for adaptive radio therapy

Delineation of lung tumor regions from magnetic resonance imaging (MRI) poses many difficulties due to MR signal similarities of the region of interest and surrounding area as well as the influence of respiration. However, accurate segmentation of the tumor region is of utmost importance in planning a radiation therapy since a small error can result in some healthy tissues to receive excessive radiation. This study presents a semi-automated method to delineate lung tumor regions from a sequence of MRIs. The proposed method uses a non-rigid image registration framework to propagate the boundaries of the tumor region in MRI acquired during a radiation treatment stage, given manual segmentation on frames acquired during pretreatment stage. We investigate two approaches: 1) the first one utilizes manual segmentation of the first frame during the pretreatment stage; and 2) the second one utilizes manual segmentation of all the frames during the pretreatment stage. We evaluated the proposed approaches over a sequence of 400 images acquired from 4 patients. The proposed method based on the utilization of all the frames yielded a Dice score of 0.90 ± 0.04 and a Hausdorff distance of 1.17 ± 0.35 pixels (2.83 ± 0.79 mm) in comparison to expert manual segmentation.

Fractionated Lung IMPT Treatments

Treatment uncertainties in radiotherapy are either systematic or random. This study evaluates the sensitivity of fractionated intensity-modulated proton therapy (IMPT) lung treatments to systematic and random setup uncertainties. Treatments in which single-field homogeneity was restricted to within ±20% (IMPT20%) were compared to full IMPT (IMPTfull) for 10 patients with lung cancer. Four-dimensional Monte Carlo calculations were performed using patient computed tomography geometries with ±5 mm systematic or random setup uncertainties applied over a 35 × 2.5Gy(RBE) treatment course. Fifty fractionated courses were simulated for each patient using both IMPT delivery methods with random setup uncertainties applied each fraction and for 3 energy-dependent spot sizes (big spots, σ≈18-9 mm; intermediate spots, σ≈11-5 mm; and small spots, σ≈4-2 mm). These results were compared to Monte Carlo recalculations of the original treatment plan assuming zero setup uncertainty. Results are presented as the difference in equivalent uniform dose (ΔEUD), V95 (ΔV95), and target dose homogeneity (ΔD1-D99). Over the whole patient cohort, the ΔEUD was 2.0 ± 0.5 (big spots), 1.9 ± 0.7 (intermediate spots), and 1.3 ± 0.4 (small spots) times more sensitive to ±5 mm systematic setup uncertainties in IMPTfull compared to IMPT20%. IMPTfull is 1.9 ± 0.9 (big spots), 2.1 ± 1.1 (intermediate spots), and 1.5 ± 0.6 (small spots) times more sensitive to random setup uncertainties than IMPT20% over a fractionated treatment course. The ΔV95 is at least 1.4 times more sensitive to systematic and random setup uncertainties for IMPTfull for all spot sizes considered. The ΔD1-D99 values coincided within uncertainty limits for both IMPT delivery methods for the 3 spot sizes considered, with higher mean values always observed for IMPTfull. The paired t-test indicated that variations observed between IMPTfull and IMPT20% were significantly different for the majority of scenarios. Significantly larger variations were observed in ΔEUD and ΔV95 in IMPTfull lung treatments in addition to higher mean ΔD1−D99. The steep intra-target dose gradients in IMPTfull make it more susceptible to systematic and random setup uncertainties.

Breathing guidance in radiation oncology and radiology: A systematic review of patient and healthy volunteer studies

PURPOSE

The advent of image-guided radiation therapy has led to dramatic improvements in the accuracy of treatment delivery in radiotherapy. Such advancements have highlighted the deleterious impact tumor motion can have on both image quality and radiation treatment delivery. One approach to reducing tumor motion irregularities is the use of breathing guidance systems during imaging and treatment. These systems aim to facilitate regular respiratory motion which in turn improves image quality and radiation treatment accuracy. A review of such research has yet to be performed; it was therefore their aim to perform a systematic review of breathing guidance interventions within the fields of radiation oncology and radiology.

METHODS

From August 1-14, 2014, the following online databases were searched: Medline, Embase, PubMed, and Web of Science. Results of these searches were filtered in accordance to a set of eligibility criteria. The search, filtration, and analysis of articles were conducted in accordance with preferred reporting items for systematic reviews and meta-analyses. Reference lists of included articles, and repeat authors of included articles, were hand-searched.

RESULTS

The systematic search yielded a total of 480 articles, which were filtered down to 27 relevant articles in accordance to the eligibility criteria. These 27 articles detailed the intervention of breathing guidance strategies in controlled studies assessing its impact on such outcomes as breathing regularity, image quality, target coverage, and treatment margins, recruiting either healthy adult volunteers or patients with thoracic or abdominal lesions. In 21/27 studies, significant (p < 0.05) improvements from the use of breathing guidance were observed.

CONCLUSIONS

There is a trend toward the number of breathing guidance studies increasing with time, indicating a growing clinical interest. The results found here indicate that further clinical studies are warranted that quantify the clinical impact of breathing guidance, along with the health technology assessment to determine the advantages and disadvantages of breathing guidance.

Impact of microscopic disease extension, extra-CTV tumour islets, incidental dose and dose conformity on tumour control probability

The impact of microscopic disease extension (MDE), extra-CTV tumour islets (TIs), incidental dose and dose conformity on tumour control probability (TCP) is analyzed using insilico simulations in this study. MDE in the region in between GTV and CTV is simulated inclusive of geometric uncertainties (GE) using spherical targets and spherical dose distribution. To study the effect of incidental dose on TIs and the effect of dose-response curve (DRC) on tumour control, islets were randomly distributed and TCP was calculated for various dose levels by rescaling the dose. Further, the impact of dose conformity on required PTV margins is also studied. The required PTV margins are ~2 mm lesser than assuming a uniform clonogen density if an exponential clonogen density fall off in the GTV-CTV is assumed. However, margins are almost equal if GE is higher in both cases. This shows that GE has a profound impact on margins. The effect of TIs showed a bi-phasic relation with increasing dose, indicating that patients with islets not in the beam paths do not benefit from dose escalation. Increasing dose conformity is also found to have considerable effect on TCP loss especially for larger GE. Further, smaller margins in IGRT should be used with caution where uncertainty in CTV definition is of concern.

A hybrid approach for fusing 4D-MRI temporal information with 3D-CT for the study of lung and lung tumor motion

PURPOSE

Accurate visualization of lung motion is important in many clinical applications, such as radiotherapy of lung cancer. Advancement in imaging modalities [e.g., computed tomography (CT) and MRI] has allowed dynamic imaging of lung and lung tumor motion. However, each imaging modality has its advantages and disadvantages. The study presented in this paper aims at generating synthetic 4D-CT dataset for lung cancer patients by combining both continuous three-dimensional (3D) motion captured by 4D-MRI and the high spatial resolution captured by CT using the authors' proposed approach.

METHODS

A novel hybrid approach based on deformable image registration (DIR) and finite element method simulation was developed to fuse a static 3D-CT volume (acquired under breath-hold) and the 3D motion information extracted from 4D-MRI dataset, creating a synthetic 4D-CT dataset.

RESULTS

The study focuses on imaging of lung and lung tumor. Comparing the synthetic 4D-CT dataset with the acquired 4D-CT dataset of six lung cancer patients based on 420 landmarks, accurate results (average error <2 mm) were achieved using the authors' proposed approach. Their hybrid approach achieved a 40% error reduction (based on landmarks assessment) over using only DIR techniques.

CONCLUSIONS

The synthetic 4D-CT dataset generated has high spatial resolution, has excellent lung details, and is able to show movement of lung and lung tumor over multiple breathing cycles.

The impact of cine EPID image acquisition frame rate on markerless soft-tissue tracking

PURPOSE

Although reduction of the cine electronic portal imaging device (EPID) acquisition frame rate through multiple frame averaging may reduce hardware memory burden and decrease image noise, it can hinder the continuity of soft-tissue motion leading to poor autotracking results. The impact of motion blurring and image noise on the tracking performance was investigated.

METHODS

Phantom and patient images were acquired at a frame rate of 12.87 Hz with an amorphous silicon portal imager (AS1000, Varian Medical Systems, Palo Alto, CA). The maximum frame rate of 12.87 Hz is imposed by the EPID. Low frame rate images were obtained by continuous frame averaging. A previously validated tracking algorithm was employed for autotracking. The difference between the programmed and autotracked positions of a Las Vegas phantom moving in the superior-inferior direction defined the tracking error (δ). Motion blurring was assessed by measuring the area change of the circle with the greatest depth. Additionally, lung tumors on 1747 frames acquired at 11 field angles from four radiotherapy patients are manually and automatically tracked with varying frame averaging. δ was defined by the position difference of the two tracking methods. Image noise was defined as the standard deviation of the background intensity. Motion blurring and image noise are correlated with δ using Pearson correlation coefficient (R).

RESULTS

For both phantom and patient studies, the autotracking errors increased at frame rates lower than 4.29 Hz. Above 4.29 Hz, changes in errors were negligible withδ < 1.60 mm. Motion blurring and image noise were observed to increase and decrease with frame averaging, respectively. Motion blurring and tracking errors were significantly correlated for the phantom (R = 0.94) and patient studies (R = 0.72). Moderate to poor correlation was found between image noise and tracking error with R -0.58 and -0.19 for both studies, respectively.

CONCLUSIONS

Cine EPID image acquisition at the frame rate of at least 4.29 Hz is recommended. Motion blurring in the images with frame rates below 4.29 Hz can significantly reduce the accuracy of autotracking.

Modeling and incorporating cardiac-induced lung tissue motion in a breathing motion model

PURPOSE

The purpose of this work is to develop a cardiac-induced lung motion model to be integrated into an existing breathing motion model.

METHODS

The authors' proposed cardiac-induced lung motion model represents the lung tissue's specific response to the subject's cardiac cycle. The model is mathematically defined as a product of a converging polynomial function h of the cardiac phase (c) and the maximum displacement y(X0) of each voxel (X0) among all the cardiac phases. The function h(c) was estimated from cardiac-gated MR imaging of ten healthy volunteers using an Akaike Information Criteria optimization algorithm. For each volunteer, a total of 24 short-axis and 18 radial planar views were acquired on a 1.5 T MR scanner during a series of 12-15 s breath-hold maneuvers. Each view contained 30 temporal frames of equal time-duration beginning with the end-diastolic cardiac phase. The frames in each of the planar views were resampled to create a set of three-dimensional (3D) anatomical volumes representing thoracic anatomy at different cardiac phases. A 3D multiresolution optical flow deformable image registration algorithm was used to quantify the difference in tissue position between the end-diastolic cardiac phase and the remaining cardiac phases. To account for image noise, voxel displacements whose maximum values were less than 0.3 mm, were excluded. In addition, the blood vessels were segmented and excluded in order to eliminate registration artifacts caused by blood-flow.

RESULTS

The average cardiac-induced lung motions for displacements greater than 0.3 mm were found to be 0.86 ± 0.74 and 0.97 ± 0.93 mm in the left and right lungs, respectively. The average model residual error for the ten healthy volunteers was found to be 0.29 ± 0.08 mm in the left lung and 0.38 ± 0.14 mm in the right lung for tissue displacements greater than 0.3 mm. The relative error decreased with increasing cardiac-induced lung tissue motion. While the relative error was > 60% for submillimeter cardiac-induced lung tissue motion, the relative error decreased to < 5% for cardiac-induced lung tissue motion that exceeded 10 mm in displacement.

CONCLUSIONS

The authors' studies implied that modeling and including cardiac-induced lung motion would improve breathing motion model accuracy for tissues with cardiac-induced motion greater than 0.3 mm.

A respiratory compensating system: design and performance evaluation

This study proposes a respiratory compensating system which is mounted on the top of the treatment couch for reverse motion, opposite from the direction of the targets (diaphragm and hemostatic clip), in order to offset organ displacement generated by respiratory motion. Traditionally, in the treatment of cancer patients, doctors must increase the field size for radiation therapy of tumors because organs move with respiratory motion, which causes radiation‐induced inflammation on the normal tissues (organ at risk (OAR)) while killing cancer cells, and thereby reducing the patient's quality of life. This study uses a strain gauge as a respiratory signal capture device to obtain abdomen respiratory signals, a proposed respiratory simulation system (RSS) and respiratory compensating system to experiment how to offset the organ displacement caused by respiratory movement and compensation effect. This study verifies the effect of the respiratory compensating system in offsetting the target displacement using two methods. The first method uses linac (medical linear accelerator) to irradiate a 300 cGy dose on the EBT film (GAFCHROMIC EBT film). The second method uses a strain gauge to capture the patients' respiratory signals, while using fluoroscopy to observe in vivo targets, such as a diaphragm, to enable the respiratory compensating system to offset the displacements of targets in superior‐inferior (SI) direction. Testing results show that the RSS position error is approximately 0.45 ~ 1.42 mm, while the respiratory compensating system position error is approximately 0.48 ~ 1.42 mm. From the EBT film profiles based on different input to the RSS, the results suggest that when the input respiratory signals of RSS are sine wave signals, the average dose (%) in the target area is improved by 1.4% ~ 24.4%, and improved in the 95% isodose area by 15.3% ~ 76.9% after compensation. If the respiratory signals input into the RSS respiratory signals are actual human respiratory signals, the average dose (%) in the target area is improved by 31.8% ~ 67.7%, and improved in the 95% isodose area by 15.3% ~ 86.4% (the above rates of improvements will increase with increasing respiratory motion displacement) after compensation. The experimental results from the second method suggested that about 67.3% ~ 82.5% displacement can be offset. In addition, gamma passing rate after compensation can be improved to 100% only when the displacement of the respiratory motion is within 10 ~ 30 mm. This study proves that the proposed system can contribute to the compensation of organ displacement caused by respiratory motion, enabling physicians to use lower doses and smaller field sizes in the treatment of tumors of cancer patients.

PACS number: 87.19. Wx; 87.55. Km

A novel fast helical 4D-CT acquisition technique to generate low-noise sorting artifact-free images at user-selected breathing phases

PURPOSE

To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing motion model to remove sorting artifacts.

METHODS AND MATERIALS

Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific motion model parameters were determined using a breathing motion model. The tissue locations predicted by the motion model in the 25 images were compared against the deformably registered tissue locations, allowing a model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The motion model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling.

RESULTS

Images produced using the model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing motion model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm.

CONCLUSIONS

The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact-free images at a patient dose similar to or less than current 4D-CT techniques.

Thoracic tumor volume delineation in 4D-PET/CT by low dose interpolated CT for attenuation correction

PURPOSE

4D-PET/CT imaging is an excellent solution for reducing the breathing-induced effects in both CT and PET images. In 4D-PET/CT, 4D-CT images are selected to match those of 4D-PET phase by phase and the corresponding phases are used for attenuation correction in 4D-PET. However, the high radiation dose that patients acquire while undergoing 4D-CT imaging for diagnostic purposes remains a concern. This study aims to assess low-dose interpolated CT (ICT) for PET attenuation correction (PETICT) in thoracic tumor volume delineation.

METHODS AND MATERIALS

Twelve thoracic cancer patients (10 esophageal and 2 lung cancer cases) were recruited. All patients underwent 4D-PET/CT scans. The optical flow method based on image intensity gradient was applied to calculate the motion displacement in three dimensions for each voxel on two original extreme CT phases in the respiratory cycle, end-inspiration and end-expiration. The interpolated CTs were generated from two phases of the original 4D-CT using motion displacement.

RESULTS

Tumor motion due to respiration was estimated in the anterior-posterior dimension, the lateral dimension and the superior-inferior dimension by the optical flow method. The PETICT and ICT (4D-PET ICT/ICT) matched each other spatially in all the phases. The distortion of tumor shape and size resulting from respiratory motion artifacts were not observed in 4D-PETICT. The tumor volume measured by 4D-PET ICT/ICT correlated to the tumor volume measured by 4D-PET/CT (p = 0.98).

CONCLUSIONS

4D-PETICT consistently represented the interpretation of FDG uptake as effectively as 4D-PET. 4D-PET ICT/ICT is a low-dose alternative to 4D-CT and significantly improves the interpretation of PET and CT images, while solving the respiratory motion problem as effectively as 4D-PET/CT.

Assessment of respiration-induced motion and its impact on treatment outcome for lung cancer

This study presented the analysis of free-breathing lung tumor motion characteristics using GE 4DCT and Varian RPM systems. Tumor respiratory movement was found to be associated with GTV size, the superior-inferior tumor location in the lung, and the attachment degree to rigid structure (e.g., chest wall, vertebrae, or mediastinum), with tumor location being the most important factor among the other two. Improved outcomes in survival and local control of 43 lung cancer patients were also reported. Consideration of respiration-induced motion based on 4DCT for lung cancer yields individualized margin and more accurate and safe target coverage and thus can potentially improve treatment outcome.

Evaluation of a lung tumor autocontouring algorithm for intrafractional tumor tracking using low-field MRI: a phantom study

PURPOSE

The first aim of this study is to investigate the feasibility of online autocontouring of tumor in low field MR images (0.2 and 0.5 T) by means of a phantom and simulation study for tumor-tracking in linac-MR systems. The second aim of this study is to develop an MR compatible, lung tumor motion phantom.

METHODS

An autocontouring algorithm was developed to determine both the position and shape of a lung tumor from each intra fractional MR image. To initiate the algorithm, an expert user contours the tumor and its maximum anticipated range of motion (herein termed the Background) using pretreatment scan data. During treatment, the algorithm processes each intrafractional MR image and automatically contours the tumor. To evaluate this algorithm, the authors built a phantom that replicates the low field contrast parameters (proton density, T(1), T(2)) of lung tumors and healthy lung parenchyma. This phantom allows simulation of MR images with the expected lung tumor CNR at 0.2 and 0.5 T by using a single 3 T scanner. Dynamic bSSFP images (approximately 4 images per second) are acquired while the phantom undergoes a series of preprogrammed motions based on patient lung tumor motion data. These images are autocontoured off-line using our algorithm. The fidelity of autocontouring is assessed by comparing autocontoured tumor shape and its centroid position to the actual tumor shape and its position.

RESULTS

The algorithm successfully contoured the shape of a moving tumor model from dynamic MR images acquired every 275 ms. Dice's coefficients of > 0.96 and > 0.93 are achieved in 0.5 and 0.2 T equivalent images, respectively. Also, the algorithm tracked tumor position during dynamic studies, with root mean squared error (RMSE) values of < 0.55 and < 0.92 mm for 0.5 and 0.2 T equivalent images, respectively. Autocontouring speed is approximately 5 ms for each image.

CONCLUSIONS

Dice's coefficients of > 0.96 and > 0.93 are achieved between autocontoured and real tumor shapes, and the position of a tumor can be tracked with RMSE values of < 0.55 and < 0.92 mm in 0.5 and 0.2 T equivalent images, respectively. These results demonstrate the feasibility of lung tumor autocontouring in low field MR images, and, by extension, intrafractional lung tumor tracking with our laboratory's linac-MR system.

A novel technique for markerless, self-sorted 4D-CBCT: feasibility study

PURPOSE

Four-dimensional CBCT (4D-CBCT) imaging in the treatment room can provide verification of moving targets, facilitating the potential for margin reduction and consequent dose escalation. Reconstruction of 4D-CBCT images requires correlation of respiratory phase with projection acquisition, which is often achieved with external surrogate measures of respiration. However, external measures may not be a direct representation of the motion of the internal anatomy and it is therefore the aim of this work to develop a novel technique for markerless, self-sorted 4D-CBCT reconstruction.

METHODS

A novel 4D-CBCT reconstruction technique based on the principles of Fourier transform (FT) theory was investigated for markerless extraction of respiratory phase directly from projection data. In this FT technique, both phase information (FT-phase) and magnitude information (FT-magnitude) were separately implemented in order to discern projections corresponding to peak inspiration, which then facilitated the proceeding sort and bin processes involved in retrospective 4D image reconstruction. In order to quantitatively evaluate the accuracy of the Fourier methods, peak-inspiration projections identified each by FT-phase and FT-magnitude were compared to those manually identified by visual tracking of structures. The average phase difference as assigned by each method vs the manual technique was calculated per projection dataset. The percentage of projections that were assigned within 10% phase of each other was also computed. Both Fourier methods were tested on two phantom datasets, programmed to exhibit sinusoidal respiratory cycles of 2.0 cm in amplitude with respiratory cycle lengths of 3 and 6 s, respectively. Additionally, three sets of patient projections were studied. All of the data were previously acquired at slow-gantry speeds ranging between 0.6°/s and 0.7°/s over a 200° rotation. Ten phase bins with 10% phase windows were selected for 4D-CBCT reconstruction of one phantom and one patient case for visual and quantitative comparison. Line profiles were plotted for the 0% and 50% phase images as reconstructed by the manual technique and each of the Fourier methods.

RESULTS

As compared with the manual technique, the FT-phase method resulted in average phase differences of 1.8% for the phantom with the 3 s respiratory cycle, 3.9% for the phantom with the 6 s respiratory cycle, 2.9% for patient 1, 5.0% for patient 2, and 3.8% for patient 3. For the FT-magnitude method, these numbers were 2.1%, 4.0%, 2.9%, 5.3%, and 3.5%, respectively. The percentage of projections that were assigned within 10% phase by the FT-phase method as compared to the manual technique for the five datasets were 100.0%, 100.0%, 97.6%, 93.4%, and 94.1%, respectively, whereas for the FT-magnitude method these percentages were 98.1%, 92.3%, 98.7%, 87.3%, and 95.7%. Reconstructed 4D phase images for both the phantom and patient case were visually and quantitatively equivalent between each of the Fourier methods vs the manual technique.

CONCLUSIONS

A novel technique employing the basics of Fourier transform theory was investigated and demonstrated to be feasible in achieving markerless, self-sorted 4D-CBCT reconstruction.

Markerless EPID image guided dynamic multi-leaf collimator tracking for lung tumors

Compensation of target motion during the delivery of radiotherapy has the potential to improve treatment accuracy, dose conformity and sparing of healthy tissue. We implement an online image guided therapy system based on soft tissue localization (STiL) of the target from electronic portal images and treatment aperture adaptation with a dynamic multi-leaf collimator (DMLC). The treatment aperture is moved synchronously and in real time with the tumor during the entire breathing cycle. The system is implemented and tested on a Varian TX clinical linear accelerator featuring an AS-1000 electronic portal imaging device (EPID) acquiring images at a frame rate of 12.86 Hz throughout the treatment. A position update cycle for the treatment aperture consists of four steps: in the first step at time t = t0 a frame is grabbed, in the second step the frame is processed with the STiL algorithm to get the tumor position at t = t0, in a third step the tumor position at t = ti + δt is predicted to overcome system latencies and in the fourth step, the DMLC control software calculates the required leaf motions and applies them at time t = ti + δt. The prediction model is trained before the start of the treatment with data representing the tumor motion. We analyze the system latency with a dynamic chest phantom (4D motion phantom, Washington University). We estimate the average planar position deviation between target and treatment aperture in a clinical setting by driving the phantom with several lung tumor trajectories (recorded from fiducial tracking during radiotherapy delivery to the lung). DMLC tracking for lung stereotactic body radiation therapy without fiducial markers was successfully demonstrated. The inherent system latency is found to be δt = (230 ± 11) ms for a MV portal image acquisition frame rate of 12.86 Hz. The root mean square deviation between tumor and aperture position is smaller than 1 mm. We demonstrate the feasibility of real-time markerless DMLC tracking with a standard LINAC-mounted (EPID).

Improvement of internal tumor volumes of non-small cell lung cancer patients for radiation treatment planning using interpolated average CT in PET/CT

Respiratory motion causes uncertainties in tumor edges on either computed tomography (CT) or positron emission tomography (PET) images and causes misalignment when registering PET and CT images. This phenomenon may cause radiation oncologists to delineate tumor volume inaccurately in radiotherapy treatment planning. The purpose of this study was to analyze radiology applications using interpolated average CT (IACT) as attenuation correction (AC) to diminish the occurrence of this scenario. Thirteen non-small cell lung cancer patients were recruited for the present comparison study. Each patient had full-inspiration, full-expiration CT images and free breathing PET images by an integrated PET/CT scan. IACT for AC in PET_IACT was used to reduce the PET/CT misalignment. The standardized uptake value (SUV) correction with a low radiation dose was applied, and its tumor volume delineation was compared to those from HCT/PET_HCT. The misalignment between the PET_IACT and IACT was reduced when compared to the difference between PET_HCT and HCT. The range of tumor motion was from 4 to 17 mm in the patient cohort. For HCT and PET_HCT, correction was from 72% to 91%, while for IACT and PET_IACT, correction was from 73% to 93% (*p<0.0001). The maximum and minimum differences in SUV_max were 0.18% and 27.27% for PET_HCT and PETIACT, respectively. The largest percentage differences in the tumor volumes between HCT/PET and IACT/PET were observed in tumors located in the lowest lobe of the lung. Internal tumor volume defined by functional information using IACT/PET_IACT fusion images for lung cancer would reduce the inaccuracy of tumor delineation in radiation therapy planning.

Respiratory Gating for Radiotherapy: Main Technical Aspects and Clinical Benefits

Respiratory-gated radiotherapy offers a significant potential for improvement in the irradiation of tumor sites affected by respiratory motion such as lung, breast, and liver tumors. An increased conformality of irradiation fields leading to decreased complication rates of organs at risk is expected. Five main strategies are used to reduce respiratory motion effects: integration of respiratory movements into treatment planning, forced shallow breathing with abdominal compression, breath-hold techniques, respiratory gating techniques, and tracking techniques. Measurements of respiratory movements can be performed either in a representative sample of the general population, or directly on the patient before irradiation. Reduction of breathing motion can be achieved by using either abdominal compression, breath-hold techniques, or respiratory gating techniques. Abdominal compression can be used to reduce diaphragmatic excursions. Breath-hold can be achieved with active techniques, in which airflow of the patient is temporarily blocked by a valve, or passive techniques, in which the patient voluntarily breath-holds. Respiratory gating techniques use external devices to predict the phase of the breathing cycle while the patient breathes freely. Another approach is tumor-tracking technique, which consists of a real-time localization of a constantly moving tumor. This work describes these different strategies and gives an overview of the literature.