Novel lung IMRT planning algorithms with nonuniform dose delivery strategy to account for respiratory motion

A Simulation Study of Tolerance of Breathing Amplitude Variations in Radiotherapy of Lung Cancer Using 4DCT and Time-Resolved 4DMRI

As patient breathing irregularities can introduce a large uncertainty in targeting the internal tumor volume (ITV) of lung cancer patients, and thereby affect treatment quality, this study evaluates dose tolerance of tumor motion amplitude variations in ITV-based volumetric modulated arc therapy (VMAT). A motion-incorporated planning technique was employed to simulate treatment delivery of 10 lung cancer patients' clinical VMAT plans using original and three scaling-up (by 0.5, 1.0, and 2.0 cm) motion waveforms from single-breath four-dimensional computed tomography (4DCT) and multi-breath time-resolved 4D magnetic resonance imaging (TR-4DMRI). The planning tumor volume (PTV = ITV + 5 mm margin) dose coverage (PTV D95%) was evaluated. The repeated waveforms were used to move the isocenter in sync with the clinical leaf motion and gantry rotation. The continuous VMAT arcs were broken down into many static beam fields at the control points (2°-interval) and the composite plan represented the motion-incorporated VMAT plan. Eight motion-incorporated plans per patient were simulated and the plan with the native 4DCT waveform was used as a control. The first (D95% ≤ 95%) and second (D95% ≤ 90%) plan breaching points due to motion amplitude increase were identified and analyzed. The PTV D95% in the motion-incorporated plans was 99.4 ± 1.0% using 4DCT, closely agreeing with the corresponding ITV-based VMAT plan (PTV D95% = 100%). Tumor motion irregularities were observed in TR-4DMRI and triggered D95% ≤ 95% in one case. For small tumors, 4 mm extra motion triggered D95% ≤ 95%, and 6-8 mm triggered D95% ≤ 90%. For large tumors, 14 mm and 21 mm extra motions triggered the first and second breaching points, respectively. This study has demonstrated that PTV D95% breaching points may occur for small tumors during treatment delivery. Clinically, it is important to monitor and avoid systematic motion increase, including baseline drift, and large random motion spikes through threshold-based beam gating.

Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network

Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung cancer treated with radiotherapy. Early prediction of AE, indicated by esophageal wall expansion, is critical, as it can facilitate the redesign of treatment plans to reduce radiation-induced esophageal toxicity in an adaptive radiotherapy (ART) workflow. We have developed a novel machine learning framework to predict the patient-specific spatial presentation of the esophagus in the weeks following treatment, using magnetic resonance imaging (MRI)/ cone-beam CT (CBCT) scans acquired earlier in the 6 week radiotherapy course. Our algorithm captures the response patterns of the esophagus to radiation on a patch level, using a convolutional neural network. A recurrence neural network then parses the evolutionary patterns of the selected features in the time series, and produces a predicted esophagus-or-not label for each individual patch over future weeks. Finally, the esophagus is reconstructed, using all the predicted labels. The algorithm is trained and validated by means of ∼ 250 000 patches taken from MRI scans acquired weekly from a variety of patients, and tested using both weekly MRI and CBCT scans under a leave-one-patient-out scheme. In addition, our approach is externally validated using a publicly available dataset (Hugo 2017). Using the first three weekly scans, the algorithm can predict the condition of the esophagus over the succeeding 3 weeks with a Dice coefficient of 0.83 ± 0.04, estimate esophagus volume highly (0.98), correlated with the actual volume, using our institutional MRI/CBCT data. When evaluated using the external weekly CBCT data, the averaged Dice coefficient is 0.89 ± 0.03. Our novel algorithm may prove useful in enabling radiation oncologists to monitor and detect AE in its early stages, and could potentially play an important role in the ART decision-making process.

Clinical evaluation of 4D MRI in the delineation of gross and internal tumor volumes in comparison with 4DCT

Purpose

To evaluate clinical utility of respiratory‐correlated (RC) four‐dimensional magnetic resonance imaging (4DMRI) for lung tumor delineation and motion assessment, in comparison with the current clinical standard of 4D computed tomography (4DCT).

Methods and Materials

A prospective T2‐weighted (T2w) RC‐4DMRI technique was applied to acquire coronal 4DMRI images for 14 lung cancer patients (16 lesions) during free breathing (FB) under an IRB‐approved protocol, together with a breath‐hold (BH) T1w 3DMRI and axial 4DMRI. Clinical simulation CT and 4DCT were acquired within 2 h. An internal navigator was applied to trigger amplitude‐binned 4DMRI acquisition whereas a bellows or real‐time position management (RPM) was used in the 4DCT reconstruction. Six radiation oncologists manually delineated the gross and internal tumor volumes (GTV and ITV) in 399 3D images using programmed clinical workflows under a tumor delineation guideline. The ITV was the union of GTVs within the breathing cycle without margin. Average GTV and motion range were assessed and ITV variation between 4DMRI and 4DCT was evaluated using the Dice similarity index, mean distance agreement (MDA), and volume difference.

Results

The mean tumor volume is similar between 4DCT (GTV^4DCT = 1.0, as the reference) and T2w‐4DMRI (GTV^T2wMR = 0.97), but smaller in T1w MRI (GTV^T1wMR = 0.76), suggesting possible peripheral edema around the tumor. Average GTV variation within the breathing cycle (22%) in 4DMRI is slightly greater than 4DCT (17%). GTV motion variation (−4 to 12 mm) and ITV variation (∆V^ITV=−25 to 95%) between 4DCT and 4DMRI are large, confirmed by relatively low ITV similarity (Dice = 0.72 ± 0.11) and large MDA = 2.9 ± 1.5 mm.

Conclusion

Average GTVs are similar between T2w‐4DMRI and 4DCT, but smaller by 25% in T1w BH MRI. Physician training and breathing coaching may be necessary to reduce ITV variability between 4DMRI and 4DCT. Four‐dimensional magnetic resonance imaging is a promising and viable technique for clinical lung tumor delineation and motion assessment.

Introduction of a pseudo demons force to enhance deformation range for robust reconstruction of super-resolution time-resolved 4DMRI

PURPOSE

The purpose of this study was to enhance the deformation range of demons-based deformable image registration (DIR) for large respiration-induced organ motion in the reconstruction of time-resolved four-dimensional magnetic resonance imaging (TR-4DMRI) for multi-breath motion simulation.

METHODS

A demons-based DIR algorithm was modified to enhance the deformation range for TR-4DMRI reconstruction using the super-resolution approach. A pseudo demons force was introduced to accelerate the coarse deformation in a multi-resolution (n = 3) DIR approach. The intensity gradient of a voxel was applied to its neighboring (5 × 5 × 5) voxels with a weight of Gaussian probability profile (σ = 1 voxel) to extend the demons force, especially on those voxels that have little intensity gradience but high-intensity difference. A digital 4DMRI phantom with 3-8 cm diaphragmatic motions was used for DIR comparison. Six volunteers were scanned with two high-resolution (highR: 2 × 2 × 2 mm³ ) breath-hold (BH) 3DMR images at full inhalation (BHI) and full exhalation (BHE) and low-resolution (lowR: 5 × 5 × 5 mm³ ) free-breathing (FB) 3DMR cine images (2 Hz) under an IRB-approved protocol. A cross-consistency check (CCC) (BHI→FB←BHE), with voxel intensity correlation (VIC) and inverse consistency error (ICE), was introduced for cross-verification of TR-4DMRI reconstruction.

RESULTS

Using the digital phantom, the maximum deformable magnitude is doubled using the modified DIR from 3 to 6 cm at the diaphragm. In six human subjects, the first 15-iteration DIR using the pseudo force deforms 200 ± 150% more than the original force, and succeeds in all 12 cases, whereas the original demons-based DIR failed in 67% of tested cases. Using the pseudo force, high VIC (>0.9) and small ICE (1.6 ± 0.6 mm) values are observed for DIR of BHI&BHE, BHI→FB, and BHE→FB. The CCC identifies four questionable cases, in which two cases need further DIR refinement, without missing true negative.

CONCLUSIONS

The introduction of a pseudo demons force enhances the largest deformation magnitude up to 6 cm. The cross-consistency check ensures the quality of TR-4DMRI reconstruction. Further investigation is ongoing to fully characterize TR-4DMRI for potential multi-breathing-cycle radiotherapy simulation.

Predictive treatment management: incorporating a predictive tumor response model into robust prospective treatment planning for non-small cell lung cancer

PURPOSE

We hypothesized that a treatment planning technique that incorporates predicted lung tumor regression into optimization, predictive treatment planning (PTP), could allow dose escalation to the residual tumor while maintaining coverage of the initial target without increasing dose to surrounding organs at risk (OARs).

METHODS AND MATERIALS

We created a model to estimate the geometric presence of residual tumors after radiation therapy using planning computed tomography (CT) and weekly cone beam CT scans of 5 lung cancer patients. For planning purposes, we modeled the dynamic process of tumor shrinkage by morphing the original planning target volume (PTVorig) in 3 equispaced steps to the predicted residue (PTVpred). Patients were treated with a uniform prescription dose to PTVorig. By contrast, PTP optimization started with the same prescription dose to PTVorig but linearly increased the dose at each step, until reaching the highest dose achievable to PTVpred consistent with OAR limits. This method is compared with midcourse adaptive replanning.

RESULTS

Initial parenchymal gross tumor volume (GTV) ranged from 3.6 to 186.5 cm(3). On average, the primary GTV and PTV decreased by 39% and 27%, respectively, at the end of treatment. The PTP approach gave PTVorig at least the prescription dose, and it increased the mean dose of the true residual tumor by an average of 6.0 Gy above the adaptive approach.

CONCLUSIONS

PTP, incorporating a tumor regression model from the start, represents a new approach to increase tumor dose without increasing toxicities, and reduce clinical workload compared with the adaptive approach, although model verification using per-patient midcourse imaging would be prudent.

Reduction of irregular breathing artifacts in respiration-correlated CT images using a respiratory motion model

PURPOSE

Respiration-correlated CT (RCCT) images produced with commonly used phase-based sorting of CT slices often exhibit discontinuity artifacts between CT slices, caused by cycle-to-cycle amplitude variations in respiration. Sorting based on the displacement of the respiratory signal yields slices at more consistent respiratory motion states and hence reduces artifacts, but missing image data (gaps) may occur. The authors report on the application of a respiratory motion model to produce an RCCT image set with reduced artifacts and without missing data.

METHODS

Input data consist of CT slices from a cine CT scan acquired while recording respiration by monitoring abdominal displacement. The model-based generation of RCCT images consists of four processing steps: (1) displacement-based sorting of CT slices to form volume images at 10 motion states over the cycle; (2) selection of a reference image without gaps and deformable registration between the reference image and each of the remaining images; (3) generation of the motion model by applying a principal component analysis to establish a relationship between displacement field and respiration signal at each motion state; (4) application of the motion model to deform the reference image into images at the 9 other motion states. Deformable image registration uses a modified fast free-form algorithm that excludes zero-intensity voxels, caused by missing data, from the image similarity term in the minimization function. In each iteration of the minimization, the displacement field in the gap regions is linearly interpolated from nearest neighbor nonzero intensity slices. Evaluation of the model-based RCCT examines three types of image sets: cine scans of a physical phantom programmed to move according to a patient respiratory signal, NURBS-based cardiac torso (NCAT) software phantom, and patient thoracic scans.

RESULTS

Comparison in physical motion phantom shows that object distortion caused by variable motion amplitude in phase-based sorting is visibly reduced with model-based RCCT. Comparison of model-based RCCT to original NCAT images as ground truth shows best agreement at motion states whose displacement-sorted images have no missing slices, with mean and maximum discrepancies in lung of 1 and 3 mm, respectively. Larger discrepancies correlate with motion states having a larger number of missing slices in the displacement-sorted images. Artifacts in patient images at different motion states are also reduced. Comparison with displacement-sorted patient images as a ground truth shows that the model-based images closely reproduce the ground truth geometry at different motion states.

CONCLUSIONS

Results in phantom and patient images indicate that the proposed method can produce RCCT image sets with reduced artifacts relative to phase-sorted images, without the gaps inherent in displacement-sorted images. The method requires a reference image at one motion state that has no missing data. Highly irregular breathing patterns can affect the method's performance, by introducing artifacts in the reference image (although reduced relative to phase-sorted images), or in decreased accuracy in the image prediction of motion states containing large regions of missing data.

A 4D IMRT planning method using deformable image registration to improve normal tissue sparing with contemporary delivery techniques

We propose a planning method to design true 4-dimensional (4D) intensity-modulated radiotherapy (IMRT) plans, called the t4Dplan method, in which the planning target volume (PTV) of the individual phases of the 4D computed tomography (CT) and the conventional PTV receive non-uniform doses but the cumulative dose to the PTV of each phase, computed using deformable image registration (DIR), are uniform. The non-uniform dose prescription for the conventional PTV was obtained by solving linear equations that required motion-convolved 4D dose to be uniform to the PTV for the end-exhalation phase (PTV50) and by constraining maximum inhomogeneity to 20%. A plug-in code to the treatment planning system was developed to perform the IMRT optimization based on this non-uniform PTV dose prescription. The 4D dose was obtained by summing the mapped doses from individual phases of the 4D CT using DIR. This 4D dose distribution was compared with that of the internal target volume (ITV) method. The robustness of the 4D plans over the course of radiotherapy was evaluated by computing the 4D dose distributions on repeat 4D CT datasets. Three patients with lung tumors were selected to demonstrate the advantages of the t4Dplan method compared with the commonly used ITV method. The 4D dose distribution using the t4Dplan method resulted in greater normal tissue sparing (such as lung, stomach, liver and heart) than did plans designed using the ITV method. The dose volume histograms of cumulative 4D doses to the PTV50, clinical target volume, lung, spinal cord, liver, and heart on the 4D repeat CTs for the two patients were similar to those for the 4D dose at the time of original planning.

Inverse planning for IMRT with nonuniform beam profiles using total-variation regularization (TVR)

PURPOSE

Radiation therapy with high dose rate and flattening filter-free (FFF) beams has the potential advantage of greatly reduced treatment time and out-of-field dose. Current inverse planning algorithms are, however, not customized for beams with nonuniform incident profiles and the resultant IMRT plans are often inefficient in delivery. The authors propose a total-variation regularization (TVR)-based formalism by taking the inherent shapes of incident beam profiles into account.

METHODS

A novel TVR-based inverse planning formalism is established for IMRT with nonuniform beam profiles. The authors introduce a TVR term into the objective function, which encourages piecewise constant fluence in the nonuniform FFF fluence domain. The proposed algorithm is applied to lung and prostate and head and neck cases and its performance is evaluated by comparing the resulting plans to those obtained using a conventional beamlet-based optimization (BBO).

RESULTS

For the prostate case, the authors' algorithm produces acceptable dose distributions with only 21 segments, while the conventional BBO requires 114 segments. For the lung case and the head and neck case, the proposed method generates similar coverage of target volume and sparing of the organs-at-risk as compared to BBO, but with a markedly reduced segment number.

CONCLUSIONS

TVR-based optimization in nonflat beam domain provides an effective way to maximally leverage the technical capacity of radiation therapy with FFF fields. The technique can generate effective IMRT plans with improved dose delivery efficiency without significant deterioration of the dose distribution.