S Jiang

The management of imaging dose during image-guided radiotherapy: Report of the AAPM Task Group 75

Radiographic image guidance has emerged as the new paradigm for patient positioning, target localization, and external beam alignment in radiotherapy. Although widely varied in modality and method, all radiographic guidance techniques have one thing in common--they can give a significant radiation dose to the patient. As with all medical uses of ionizing radiation, the general view is that this exposure should be carefully managed. The philosophy for dose management adopted by the diagnostic imaging community is summarized by the acronym ALARA, i.e., as low as reasonably achievable. But unlike the general situation with diagnostic imaging and image-guided surgery, image-guided radiotherapy (IGRT) adds the imaging dose to an already high level of therapeutic radiation. There is furthermore an interplay between increased imaging and improved therapeutic dose conformity that suggests the possibility of optimizing rather than simply minimizing the imaging dose. For this reason, the management of imaging dose during radiotherapy is a different problem than its management during routine diagnostic or image-guided surgical procedures. The imaging dose received as part of a radiotherapy treatment has long been regarded as negligible and thus has been quantified in a fairly loose manner. On the other hand, radiation oncologists examine the therapy dose distribution in minute detail. The introduction of more intensive imaging procedures for IGRT now obligates the clinician to evaluate therapeutic and imaging doses in a more balanced manner. This task group is charged with addressing the issue of radiation dose delivered via image guidance techniques during radiotherapy. The group has developed this charge into three objectives: (1) Compile an overview of image-guidance techniques and their associated radiation dose levels, to provide the clinician using a particular set of image guidance techniques with enough data to estimate the total diagnostic dose for a specific treatment scenario, (2) identify ways to reduce the total imaging dose without sacrificing essential imaging information, and (3) recommend optimization strategies to trade off imaging dose with improvements in therapeutic dose delivery. The end goal is to enable the design of image guidance regimens that are as effective and efficient as possible.

Prediction of respiratory tumour motion for real-time image-guided radiotherapy

Image guidance in radiotherapy and extracranial radiosurgery offers the potential for precise radiation dose delivery to a moving tumour. Recent work has demonstrated how to locate and track the position of a tumour in real-time using diagnostic x-ray imaging to find implanted radio-opaque markers. However, the delivery of a treatment plan through gating or beam tracking requires adequate consideration of treatment system latencies, including image acquisition, image processing, communication delays, control system processing, inductance within the motor, mechanical damping, etc. Furthermore, the imaging dose given over long radiosurgery procedures or multiple radiotherapy fractions may not be insignificant, which means that we must reduce the sampling rate of the imaging system. This study evaluates various predictive models for reducing tumour localization errors when a real-time tumour-tracking system targets a moving tumour at a slow imaging rate and with large system latencies. We consider 14 lung tumour cases where the peak-to-peak motion is greater than 8 mm, and compare the localization error using linear prediction, neural network prediction and Kalman filtering, against a system which uses no prediction. To evaluate prediction accuracy for use in beam tracking, we compute the root mean squared error between predicted and actual 3D motion. We found that by using prediction, root mean squared error is improved for all latencies and all imaging rates evaluated. To evaluate prediction accuracy for use in gated treatment, we present a new metric that compares a gating control signal based on predicted motion against the best possible gating control signal. We found that using prediction improves gated treatment accuracy for systems that have latencies of 200 ms or greater, and for systems that have imaging rates of 10 Hz or slower.

Synchronized moving aperture radiation therapy (SMART): average tumour trajectory for lung patients.

Synchronized moving aperture radiation therapy (SMART) is a new technique for treating mobile tumours under development at Massachusetts General Hospital (MGH). The basic idea of SMART is to synchronize the moving radiation beam aperture formed by a dynamic multileaf collimator (DMLC) with the tumour motion induced by respiration. SMART is based on the concept of the average tumour trajectory (ATT) exhibited by a tumour during respiration. During the treatment simulation stage, tumour motion is measured and the ATT is derived. Then, the original IMRT MLC leaf sequence is modified using the ATT to compensate for tumour motion. During treatment, the tumour motion is monitored. The treatment starts when leaf motion and tumour motion are synchronized at a specific breathing phase. The treatment will halt when the tumour drifts away from the ATT and will resume when the synchronization between tumour motion and radiation beam is re-established. In this paper, we present a method to derive the ATT from measured tumour trajectory data. We also investigate the validity of the ATT concept for lung tumours during normal breathing. The lung tumour trajectory data were acquired during actual radiotherapy sessions using a real-time tumour-tracking system. SMART treatment is simulated by assuming that the radiation beam follows the derived ATT and the tumour follows the measured trajectory. In simulation, the treatment starts at exhale phase. The duty cycle of SMART delivery was calculated for various treatment times and gating thresholds, as well as for various exhale phases where the treatment begins. The simulation results show that in the case of free breathing, for 4 out of 11 lung datasets with tumour motion greater than 1 cm from peak to peak, the error in tumour tracking can be controlled to within a couple of millimetres while maintaining a reasonable delivery efficiency. That is to say, without any breath coaching/control, the ATT is a valid concept for some lung tumours. However, to make SMART an efficient technique in general, it is found that breath coaching techniques are required.

An experimental investigation on intra-fractional organ motion effects in lung IMRT treatments

Respiration-induced tumour motion can potentially compromise the use of intensity-modulated radiotherapy (IMRT) as a dose escalation tool for lung tumour treatment. We have experimentally investigated the intra-fractional organ motion effects in lung IMRT treatments delivered by multi-leaf collimator (MLC). An in-house made motor-driven platform, which moves sinusoidally with an amplitude of 1 cm and a period of 4 s, was used to mimic tumour motion. Tumour motion was simulated along cranial-caudal direction while MLC leaves moved across the patient from left to right, as in most clinical cases. The dose to a point near the centre of the tumour mass was measured according to geometric and dosimetric parameters from two five-field lung IMRT plans. For each field, measurement was done for two dose rates (300 and 500 MU min(-1)), three MLC delivery modes (sliding window, step-and-shoot with 10 and 20 intensity levels) and eight equally spaced starting phases of tumour motion. The dose to the measurement point delivered from all five fields was derived for both a single fraction and 30 fractions by randomly sampling from measured dose values of each field at different initial phases. It was found that the mean dose to a moving tumour differs slightly (<2-3%) from that to a static tumour. The variation in breathing phase at the start of dose delivery results in a maximum variation around the mean dose of greater than 30% for one field. The full width at half maximum for the probability distribution of the point dose is up to 8% for all five fields in a single fraction, but less than 1-2% after 30 fractions. In general, lower dose rate can reduce the motion-caused dose variation and therefore might be preferable for lung IMRT when no motion mitigation techniques are used. From the two IMRT cases we studied where tumour motion is perpendicular to MLC leaf motion, the dose variation was found to be insensitive to the MLC delivery mode.

Residual motion of lung tumours in gated radiotherapy with external respiratory surrogates

Due to respiration, many tumours in the thorax and abdomen may move as much as 3 cm peak-to-peak during radiation treatment. To mitigate motion-induced irradiation of normal lung tissue, clinics have employed external markers to gate the treatment beam. This technique assumes that the correlation between the external surface and the internal tumour position remains constant inter-fractionally and intra-fractionally. In this work, a study has been performed to assess the validity of this correlation assumption for external surface based gated radiotherapy, by measuring the residual tumour motion within a gating window. Eight lung patients with implanted fiducial markers were studied at the NTT Hospital in Sapporo, Japan. Synchronized internal marker positions and external abdominal surface positions were measured during the entire course of treatment. Stereoscopic imaging was used to find the internal markers in four dimensions. The data were used retrospectively to assess conventional external surrogate respiratory-gated treatment. Both amplitude- and phase-based gating methods were investigated. For each method, three gating windows were investigated, each giving 40%, 30% and 20% duty cycle, respectively. The residual motion of the internal marker within these six gating windows was calculated. The beam-to-beam variation and day-to-day variation in the residual motion were calculated for both gating modalities. We found that the residual motion (95th percentile) was between 0.7 and 5.8 mm, 0.8 and 6.0 mm, and 0.9 and 6.2 mm for 20%, 30% and 40% duty cycle windows, respectively. Five of the eight patients showed less residual motion with amplitude-based gating than with phase-based gating. Large fluctuations (>300%) were seen in the residual motion between some beams. Overall, the mean beam-to-beam variation was 37% and 42% from the previous treatment beam for amplitude- and phase-based gating, respectively. The day-to-day variation was 29% and 34% from the previous day for amplitude- and phase-based gating, respectively. Although gating reduced the total tumour motion, the residual motion behaved unpredictably. Residual motion during treatment could exceed that which might have been considered in the treatment plan. Treatment margins that account for motion should be individualized and daily imaging should be performed to ensure that the residual motion is not exceeding the planned motion on a given day.

Implementation and evaluation of various demons deformable image registration algorithms on a GPU

Online adaptive radiation therapy (ART) promises the ability to deliver an optimal treatment in response to daily patient anatomic variation. A major technical barrier for the clinical implementation of online ART is the requirement of rapid image segmentation. Deformable image registration (DIR) has been used as an automated segmentation method to transfer tumor/organ contours from the planning image to daily images. However, the current computational time of DIR is insufficient for online ART. In this work, this issue is addressed by using computer graphics processing units (GPUs). A gray-scale-based DIR algorithm called demons and five of its variants were implemented on GPUs using the compute unified device architecture (CUDA) programming environment. The spatial accuracy of these algorithms was evaluated over five sets of pulmonary 4D CT images with an average size of 256 x 256 x 100 and more than 1100 expert-determined landmark point pairs each. For all the testing scenarios presented in this paper, the GPU-based DIR computation required around 7 to 11 s to yield an average 3D error ranging from 1.5 to 1.8 mm. It is interesting to find out that the original passive force demons algorithms outperform subsequently proposed variants based on the combination of accuracy, efficiency and ease of implementation.

A Monte Carlo dose calculation tool for radiotherapy treatment planning

A Monte Carlo user code, MCDOSE, has been developed for radiotherapy treatment planning (RTP) dose calculations. MCDOSE is designed as a dose calculation module suitable for adaptation to host RTP systems. MCDOSE can be used for both conventional photon/electron beam calculation and intensity modulated radiotherapy (IMRT) treatment planning. MCDOSE uses a multiple-source model to reconstruct the treatment beam phase space. Based on Monte Carlo simulated or measured beam data acquired during commissioning, source-model parameters are adjusted through an automated procedure. Beam modifiers such as jaws, physical and dynamic wedges, compensators, blocks, electron cut-outs and bolus are simulated by MCDOSE together with a 3D rectilinear patient geometry model built from CT data. Dose distributions calculated using MCDOSE agreed well with those calculated by the EGS4/DOSXYZ code using different beam set-ups and beam modifiers. Heterogeneity correction factors for layered-lung or layered-bone phantoms as calculated by both codes were consistent with measured data to within 1%. The effect of energy cut-offs for particle transport was investigated. Variance reduction techniques were implemented in MCDOSE to achieve a speedup factor of 10-30 compared to DOSXYZ.

Internal-external correlation investigations of respiratory induced motion of lung tumors

In gated radiation therapy procedures, the lung tumor position is used directly (by implanted radiopaque markers) or indirectly (by external surrogate methods) to decrease the volume of irradiated healthy tissue. Due to a risk of pneumothorax, many clinics do not implant fiducials, and the gated treatment is primarily based on a respiratory induced external signal. The external surrogate method relies upon the assumption that the internal tumor motion is well correlated with the external respiratory induced motion, and that this correlation is constant in time. Using a set of data that contains synchronous internal and external motion traces, we have developed a dynamic data analysis technique to study the internal-external correlation, and to quantitatively estimate its underlying time behavior. The work presented here quantifies the time dependent behavior of the correlation between external respiratory signals and lung implanted fiducial motion. The corresponding amplitude mismatch is also reported for the lung patients studied. The information obtained can be used to improve the accuracy of tumor tracking. For the ten patients in this study, the SI internal-external motion is well correlated, with small time shifts and corresponding amplitude mismatches. Although the AP internal-external motion reveals larger time shifts than along the SI direction, the corresponding amplitude mismatches are below 5 mm.

Low Dose CT Reconstruction via Edge-preserving Total Variation Regularization

High radiation dose in computed tomography (CT) scans increases the lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with total variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data acquired at low mAs levels in order to reduce the imaging dose. Nonetheless, the low-contrast structures tend to be smoothed out by the TV regularization, posing a great challenge for the TV method. To solve this problem, in this work we develop an iterative CT reconstruction algorithm with edge-preserving TV (EPTV) regularization to reconstruct CT images from highly undersampled data obtained at low mAs levels. The CT image is reconstructed by minimizing energy consisting of an EPTV norm and a data fidelity term posed by the x-ray projections. The EPTV term is proposed to preferentially perform smoothing only on the non-edge part of the image in order to better preserve the edges, which is realized by introducing a penalty weight to the original TV norm. During the reconstruction process, the pixels at the edges would be gradually identified and given low penalty weight. Our iterative algorithm is implemented on graphics processing unit to improve its speed. We test our reconstruction algorithm on a digital NURBS-based cardiac-troso phantom, a physical chest phantom and a Catphan phantom. Reconstruction results from a conventional filtered backprojection (FBP) algorithm and a TV regularization method without edge-preserving penalty are also presented for comparison purposes. The experimental results illustrate that both the TV-based algorithm and our EPTV algorithm outperform the conventional FBP algorithm in suppressing the streaking artifacts and image noise under a low-dose context. Our edge-preserving algorithm is superior to the TV-based algorithm in that it can preserve more information of low-contrast structures and therefore maintain acceptable spatial resolution.

Synchronized moving aperture radiation therapy (SMART): improvement of breathing pattern reproducibility using respiratory coaching

Recently, at Massachusetts General Hospital (MGH) we proposed a new treatment technique called synchronized moving aperture radiation therapy (SMART) to account for tumour motion during radiotherapy. The basic idea of SMART is to synchronize the moving radiation beam aperture formed by a dynamic multileaf collimator with the tumour motion induced by respiration. The two key requirements for being able to successfully use SMART in clinical practice are the precise and fast detection of tumour position during the simulation/treatment and the good reproducibility of the tumour motion pattern. To fulfil the first requirement, an integrated radiotherapy imaging system is currently being developed at MGH. The results of a previous study show that breath coaching techniques are required to make SMART an efficient technique in general. In this study, we investigate volunteer and patient respiratory coaching using a commercial respiratory gating system as a respiration coaching tool. Five healthy volunteers, observed during six sessions, and 33 lung cancer patients, observed during one session when undergoing 4D CT scans, were investigated with audio and visual promptings, with free breathing as a control. For all five volunteers, breath coaching was well tolerated and the intra- and inter-session reproducibility of the breathing pattern was greatly improved. Out of 33 patients, six exhibited a regular breathing pattern and needed no coaching, four could not be coached at all due to the patients medical condition or had difficulty following the instructions, 13 could only be coached with audio instructions and 10 could follow the instructions of and benefit from audio-video coaching. We found that, for all volunteers and for those patients who could be properly coached, breath coaching improves the duty cycle of SMART treatment. However, about half of the patients could not follow both audio and video instructions simultaneously, suggesting that the current coaching technique requires improvements.