Influence of MRI on target volume delineation and IMRT planning in nasopharyngeal carcinoma

MRI motion artifact reduction using a conditional diffusion probabilistic model (MAR-CDPM)

BACKGROUND

High-resolution magnetic resonance imaging (MRI) with excellent soft-tissue contrast is a valuable tool utilized for diagnosis and prognosis. However, MRI sequences with long acquisition time are susceptible to motion artifacts, which can adversely affect the accuracy of post-processing algorithms.

PURPOSE

This study proposes a novel retrospective motion correction method named "motion artifact reduction using conditional diffusion probabilistic model" (MAR-CDPM). The MAR-CDPM aimed to remove motion artifacts from multicenter three-dimensional contrast-enhanced T1 magnetization-prepared rapid acquisition gradient echo (3D ceT1 MPRAGE) brain dataset with different brain tumor types.

MATERIALS AND METHODS

This study employed two publicly accessible MRI datasets: one containing 3D ceT1 MPRAGE and 2D T2-fluid attenuated inversion recovery (FLAIR) images from 230 patients with diverse brain tumors, and the other comprising 3D T1-weighted (T1W) MRI images of 148 healthy volunteers, which included real motion artifacts. The former was used to train and evaluate the model using the in silico data, and the latter was used to evaluate the model performance to remove real motion artifacts. A motion simulation was performed in k-space domain to generate an in silico dataset with minor, moderate, and heavy distortion levels. The diffusion process of the MAR-CDPM was then implemented in k-space to convert structure data into Gaussian noise by gradually increasing motion artifact levels. A conditional network with a Unet backbone was trained to reverse the diffusion process to convert the distorted images to structured data. The MAR-CDPM was trained in two scenarios: one conditioning on the time step t $t$ of the diffusion process, and the other conditioning on both t $t$ and T2-FLAIR images. The MAR-CDPM was quantitatively and qualitatively compared with supervised Unet, Unet conditioned on T2-FLAIR, CycleGAN, Pix2pix, and Pix2pix conditioned on T2-FLAIR models. To quantify the spatial distortions and the level of remaining motion artifacts after applying the models, quantitative metrics were reported including normalized mean squared error (NMSE), structural similarity index (SSIM), multiscale structural similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multiscale gradient magnitude similarity deviation (MS-GMSD). Tukey's Honestly Significant Difference multiple comparison test was employed to quantify the difference between the models where p-value < 0.05 $ < 0.05$ was considered statistically significant.

RESULTS

Qualitatively, MAR-CDPM outperformed these methods in preserving soft-tissue contrast and different brain regions. It also successfully preserved tumor boundaries for heavy motion artifacts, like the supervised method. Our MAR-CDPM recovered motion-free in silico images with the highest PSNR and VIF for all distortion levels where the differences were statistically significant (p-values< 0.05 $< 0.05$ ). In addition, our method conditioned on t and T2-FLAIR outperformed (p-values< 0.05 $< 0.05$ ) other methods to remove motion artifacts from the in silico dataset in terms of NMSE, MS-SSIM, SSIM, and MS-GMSD. Moreover, our method conditioned on only t outperformed generative models (p-values< 0.05 $< 0.05$ ) and had comparable performances compared with the supervised model (p-values> 0.05 $> 0.05$ ) to remove real motion artifacts.

CONCLUSIONS

The MAR-CDPM could successfully remove motion artifacts from 3D ceT1 MPRAGE. It is particularly beneficial for elderly who may experience involuntary movements during high-resolution MRI imaging with long acquisition times.

Detailed analysis of failure patterns using deformable image registration in hypopharyngeal cancer patients treated with sequential boost intensity-modulated radiotherapy

INTRODUCTION

Sequential boost intensity-modulated radiotherapy (SQB-IMRT) uses two different planning CTs (pCTs) and treatment plans. SQB-IMRT is a form of adaptive radiotherapy that allows for responses to changes in the shape of the tumour and organs at risk (OAR). On the other hand, dose accumulation with the two plans can be difficult to evaluate. The purpose of this study was to analyse patterns of loco-regional failure using deformable image registration (DIR) in hypopharyngeal cancer patients treated with SQB-IMRT.

METHODS

Between 2013 and 2019, 102 patients with hypopharyngeal cancer were treated with definitive SQB-IMRT at our institution. Dose accumulation with the 1st and 2nd plans was performed, and the dose to the loco-regional recurrent tumour volume was calculated using the DIR workflow. Failure was classified as follows: (i) in-field (≥95% of the recurrent tumour volume received 95% of the prescribed dose); (ii) marginal (20-95%); or (iii) out-of-field (<20%).

RESULTS

After a median follow-up period of 25 months, loco-regional failure occurred in 34 patients. Dose-volume histogram analysis showed that all loco-regional failures occurred in the field within 95% of the prescribed dose, with no marginal or out-of-field recurrences observed.

CONCLUSION

The dosimetric analysis using DIR showed that all loco-regional failures were within the high-dose region. More aggressive treatment may be required for gross tumours.

Characterization of positioning uncertainties in PET-CT-MR trimodality solutions for radiotherapy

The purpose of this study was to evaluate the positioning uncertainties of two PET/CT‐MR imaging setups, C1 and C2. Because the PET/CT data were acquired on the same hybrid device with automatic image registration, experiments were conducted using CT‐MRI data. In C1, a transfer table was used, which allowed the patient to move from one imager to another while maintaining the same position. In C2, the patient stood up and was positioned in the same radiotherapy treatment position on each imager. The two setups provided a set of PET/CT and MR images. The accuracy of the registration software was evaluated on the CT‐MRI data of one patient using known translations and rotations of MRI data. The uncertainties on the two setups were estimated using a phantom and a cohort of 30 patients. The accuracy of the positioning uncertainties was evaluated using descriptive statistics and a t‐test to determine whether the mean shift significantly deviated from zero (p < 0.05) for each setup. The maximum registration errors were less than 0.97 mm and 0.6° for CT‐MRI registration. On the phantom, the mean total uncertainties were less than 2.74 mm and 1.68° for C1 and 1.53 mm and 0.33° for C2. For C1, the t‐test showed that the displacements along the z‐axis did not significantly deviate from zero (p = 0.093). For C2, significant deviations from zero were present for anterior‐posterior and superior‐inferior displacements. The mean total uncertainties were less than 4 mm and 0.42° for C1 and less than 1.39 mm and 0.27° for C2 in the patients. Furthermore, the t‐test showed significant deviations from zero for C1 on the anterior‐posterior and roll sides. For C2, there was a significant deviation from zero for the left‐right displacements.This study shows that transfer tables require careful evaluation before use in radiotherapy.

Dosimetric Accuracy of MR-Guided Online Adaptive Planning for Nasopharyngeal Carcinoma Radiotherapy on 1.5 T MR-Linac

Purpose

The aim of this study is to evaluate the dose accuracy of bulk relative electron density (rED) approach for application in 1.5 T MR-Linac and assess the reliability of this approach in the case of online adaptive MR-guided radiotherapy for nasopharyngeal carcinoma (NPC) patients.

Methods

Ten NPC patients formerly treated on conventional linac were included in this study, with their original planning CT and MRI collected. For each patient, structures such as the targets, organs at risk, bone, and air regions were delineated on the original CT in the Monaco system (v5.40.02). To simulate the online adaptive workflow, firstly all contours were transferred to MRI from the original CT using rigid registration in the Monaco system. Based on the structures, three different types of synthetic CT (sCT) were generated from MRI using the bulk rED assignment approach: the sCT_ICRU uses the rED values recommended by ICRU46, the sCT_tailor uses the patient-specific mean rED values, and the sCT_Homogeneity uses homogeneous water equivalent values. The same treatment plan was calculated on the three sCTs and the original CT. Dose calculation accuracy was investigated in terms of gamma analysis, point dose comparison, and dose volume histogram (DVH) parameters.

Results

Good agreement of dose distribution was observed between sCT_tailor and the original CT, with a gamma passing rate (3%/3 mm) of 97.81% ± 1.06%, higher than that of sCT_ICRU (94.27% ± 1.48%, p = 0.005) and sCT_Homogeneity (96.50% ± 1.02%, p = 0.005). For stricter criteria 1%/1 mm, gamma passing rates for plans on sCT_tailor, sCT_ICRU, and sCT_Homogeneity were 86.79% ± 4.31%, 79.81% ± 3.63%, and 77.56% ± 4.64%, respectively. The mean point dose difference in PTV_nx between sCT_tailor and planning CT was −0.14% ± 1.44%, much lower than that calculated on sCT_ICRU (−8.77% ± 2.33%) and sCT_Homogeneity (1.65% ± 2.57%), all with p < 0.05. The DVH differences for the plan based on sCT_tailor were much smaller than sCT_ICRU and sCT_Homogeneity.

Conclusions

The bulk rED-assigned sCT by adopting the patient-specific rED values can achieve a clinically acceptable level of dose calculation accuracy in the presence of a 1.5 T magnetic field, making it suitable for online adaptive MR-guided radiotherapy for NPC patients.

Artificial intelligence-based bone-enhanced magnetic resonance image-a computed tomography/magnetic resonance image composite image modality in nasopharyngeal carcinoma radiotherapy

Background

In the radiotherapy of nasopharyngeal carcinoma (NPC), magnetic resonance imaging (MRI) is widely used to delineate tumor area more accurately. While MRI offers the higher soft tissue contrast, patient positioning and couch correction based on bony image fusion of computed tomography (CT) is also necessary. There is thus an urgent need to obtain a high image contrast between bone and soft tissue to facilitate target delineation and patient positioning for NPC radiotherapy. In this paper, our aim is to develop a novel image conversion between the CT and MRI modalities to obtain clear bone and soft tissue images simultaneously, here called bone-enhanced MRI (BeMRI).

Methods

Thirty-five patients were retrospectively selected for this study. All patients underwent clinical CT simulation and 1.5T MRI within the same week in Shenzhen Second People's Hospital. To synthesize BeMRI, two deep learning networks, U-Net and CycleGAN, were constructed to transform MRI to synthetic CT (sCT) images. Each network used 28 patients' images as the training set, while the remaining 7 patients were used as the test set (~1/5 of all datasets). The bone structure from the sCT was then extracted by the threshold-based method and embedded in the corresponding part of the MRI image to generate the BeMRI image. To evaluate the performance of these networks, the following metrics were applied: mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR).

Results

In our experiments, both deep learning models achieved good performance and were able to effectively extract bone structure from MRI. Specifically, the supervised U-Net model achieved the best results with the lowest overall average MAE of 125.55 (P<0.05) and produced the highest SSIM of 0.89 and PSNR of 23.84. These results indicate that BeMRI can display bone structure in higher contrast than conventional MRI.

Conclusions

A new image modality BeMRI, which is a composite image of CT and MRI, was proposed. With high image contrast of both bone structure and soft tissues, BeMRI will facilitate tumor localization and patient positioning and eliminate the need to frequently check between separate MRI and CT images during NPC radiotherapy.

MR Imaging of Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma is endemic in parts of the world such as southern China and Southeast Asia. It is predominantly an undifferentiated carcinoma with a strong genetic basis and a close association with the Epstein-Barr virus. The ability of MR imaging to depict the boundaries of the primary tumor and its relationship with the complex structures of the skull base makes it the technique of choice for imaging of this disease in the head and neck. This article describes the MR imaging findings pertinent to staging and management and a new role of MR imaging in early cancer detection, in addition to a brief discussion of differential diagnoses.

Dosimetric evaluation of synthetic CT image generated using a neural network for MR-only brain radiotherapy

PURPOSE AND BACKGROUND

The magnetic resonance (MR)-only radiotherapy workflow is urged by the increasing use of MR image for the identification and delineation of tumors, while a fast generation of synthetic computer tomography (sCT) image from MR image for dose calculation remains one of the key challenges to the workflow. This study aimed to develop a neural network to generate the sCT in brain site and evaluate the dosimetry accuracy.

MATERIALS AND METHODS

A generative adversarial network (GAN) was developed to translate T1-weighted MRI to sCT. First, the "U-net" shaped encoder-decoder network with some image translation-specific modifications was trained to generate sCT, then the discriminator network was adversarially trained to distinguish between synthetic and real CT images. We enrolled 37 brain cancer patients acquiring both CT and MRI for treatment position simulation. Twenty-seven pairs of 2D T1-weighted MR images and rigidly registered CT image were used to train the GAN model, and the remaining 10 pairs were used to evaluate the model performance through the metric of mean absolute error. Furthermore, the clinical Volume Modulated Arc Therapy plan was calculated on both sCT and real CT, followed by gamma analysis and comparison of dose-volume histogram.

RESULTS

On average, only 15 s were needed to generate one sCT from one T1-weighted MRI. The mean absolute error between synthetic and real CT was 60.52 ± 13.32 Housefield Unit over 5-fold cross validation. For dose distribution on sCT and CT, the average pass rates of gamma analysis using the 3%/3 mm and 2%/2 mm criteria were 99.76% and 97.25% over testing patients, respectively. For parameters of dose-volume histogram for both target and organs at risk, no significant differences were found between both plans.

CONCLUSION

The GAN model can generate synthetic CT from one single MRI sequence within seconds, and a state-of-art accuracy of CT number and dosimetry was achieved.

Head and neck multi-organ auto-segmentation on CT images aided by synthetic MRI

PURPOSE

Because the manual contouring process is labor-intensive and time-consuming, segmentation of organs-at-risk (OARs) is a weak link in radiotherapy treatment planning process. Our goal was to develop a synthetic MR (sMR)-aided dual pyramid network (DPN) for rapid and accurate head and neck multi-organ segmentation in order to expedite the treatment planning process.

METHODS

Forty-five patients' CT, MR, and manual contours pairs were included as our training dataset. Nineteen OARs were target organs to be segmented. The proposed sMR-aided DPN method featured a deep attention strategy to effectively segment multiple organs. The performance of sMR-aided DPN method was evaluated using five metrics, including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and volume difference. Our method was further validated using the 2015 head and neck challenge data.

RESULTS

The contours generated by the proposed method closely resemble the ground truth manual contours, as evidenced by encouraging quantitative results in terms of DSC using the 2015 head and neck challenge data. Mean DSC values of 0.91 ± 0.02, 0.73 ± 0.11, 0.96 ± 0.01, 0.78 ± 0.09/0.78 ± 0.11, 0.88 ± 0.04/0.88 ± 0.06 and 0.86 ± 0.08/0.85 ± 0.1 were achieved for brain stem, chiasm, mandible, left/right optic nerve, left/right parotid, and left/right submandibular, respectively.

CONCLUSIONS

We demonstrated the feasibility of sMR-aided DPN for head and neck multi-organ delineation on CT images. Our method has shown superiority over the other methods on the 2015 head and neck challenge data results. The proposed method could significantly expedite the treatment planning process by rapidly segmenting multiple OARs.

Emerging radiotherapy technologies and trends in nasopharyngeal cancer

Technology has always driven advances in radiotherapy treatment. In this review, we describe the main technological advances in radiotherapy over the past decades for the treatment of nasopharyngeal cancer (NPC) and highlight some of the pressing issues and challenges that remain. We aim to identify emerging trends in radiation medicine. These include advances in personalized medicine and advanced imaging modalities, standardization of planning and delineation, assessment of treatment response and adaptive re‐planning, impact of particle therapy, and role of artificial intelligence or automation in clinical care. In conclusion, we expect significant improvement in the therapeutic ratio of radiotherapy treatment for NPC over the next decade.

Multimodality image registration in the head-and-neck using a deep learning-derived synthetic CT as a bridge

PURPOSE

To develop and demonstrate the efficacy of a novel head-and-neck multimodality image registration technique using deep-learning-based cross-modality synthesis.

METHODS AND MATERIALS

Twenty-five head-and-neck patients received magnetic resonance (MR) and computed tomography (CT) (CTaligned ) scans on the same day with the same immobilization. Fivefold cross validation was used with all of the MR-CT pairs to train a neural network to generate synthetic CTs from MR images. Twenty-four of 25 patients also had a separate CT without immobilization (CTnon-aligned ) and were used for testing. CTnon-aligned 's were deformed to the synthetic CT, and compared to CTnon-aligned registered to MR. The same registrations were performed from MR to CTnon-aligned and from synthetic CT to CTnon-aligned . All registrations used B-splines for modeling the deformation, and mutual information for the objective. Results were evaluated using the 95% Hausdorff distance among spinal cord contours, landmark error, inverse consistency, and Jacobian determinant of the estimated deformation fields.

RESULTS

When large initial rigid misalignment is present, registering CT to MRI-derived synthetic CT aligns the cord better than a direct registration. The average landmark error decreased from 9.8 ± 3.1 mm in MR→CTnon-aligned to 6.0 ± 2.1 mm in CTsynth →CTnon-aligned deformable registrations. In the CT to MR direction, the landmark error decreased from 10.0 ± 4.3 mm in CTnon-aligned →MR deformable registrations to 6.6 ± 2.0 mm in CTnon-aligned →CTsynth deformable registrations. The Jacobian determinant had an average value of 0.98. The proposed method also demonstrated improved inverse consistency over the direct method.

CONCLUSIONS

We showed that using a deep learning-derived synthetic CT in lieu of an MR for MR→CT and CT→MR deformable registration offers superior results to direct multimodal registration.

Patch-based generative adversarial neural network models for head and neck MR-only planning

PURPOSE

To evaluate pix2pix and CycleGAN and to assess the effects of multiple combination strategies on accuracy for patch-based synthetic computed tomography (sCT) generation for magnetic resonance (MR)-only treatment planning in head and neck (HN) cancer patients.

MATERIALS AND METHODS

Twenty-three deformably registered pairs of CT and mDixon FFE MR datasets from HN cancer patients treated at our institution were retrospectively analyzed to evaluate patch-based sCT accuracy via the pix2pix and CycleGAN models. To test effects of overlapping sCT patches on estimations, we (a) trained the models for three orthogonal views to observe the effects of spatial context, (b) we increased effective set size by using per-epoch data augmentation, and (c) we evaluated the performance of three different approaches for combining overlapping Hounsfield unit (HU) estimations for varied patch overlap parameters. Twelve of twenty-three cases corresponded to a curated dataset previously used for atlas-based sCT generation and were used for training with leave-two-out cross-validation. Eight cases were used for independent testing and included previously unseen image features such as fused vertebrae, a small protruding bone, and tumors large enough to deform normal body contours. We analyzed the impact of MR image preprocessing including histogram standardization and intensity clipping on sCT generation accuracy. Effects of mDixon contrast (in-phase vs water) differences were tested with three additional cases. The sCT generation accuracy was evaluated using mean absolute error (MAE) and mean error (ME) in HU between the plan CT and sCT images. Dosimetric accuracy was evaluated for all clinically relevant structures in the independent testing set and digitally reconstructed radiographs (DRRs) were evaluated with respect to the plan CT images.

RESULTS

The cross-validated MAEs for the whole-HN region using pix2pix and CycleGAN were 66.9 ± 7.3 vs 82.3 ± 6.4 HU, respectively. On the independent testing set with additional artifacts and previously unseen image features, whole-HN region MAEs were 94.0 ± 10.6 and 102.9 ± 14.7 HU for pix2pix and CycleGAN, respectively. For patients with different tissue contrast (water mDixon MR images), the MAEs increased to 122.1 ± 6.3 and 132.8 ± 5.5 HU for pix2pix and CycleGAN, respectively. Our results suggest that combining overlapping sCT estimations at each voxel reduced both MAE and ME compared to single-view non-overlapping patch results. Absolute percent mean/max dose errors were 2% or less for the PTV and all clinically relevant structures in our independent testing set, including structures with image artifacts. Quantitative DRR comparison between planning CTs and sCTs showed agreement of bony region positions to <1 mm.

CONCLUSIONS

The dosimetric and MAE based accuracy, along with the similarity between DRRs from sCTs, indicate that pix2pix and CycleGAN are promising methods for MR-only treatment planning for HN cancer. Our methods investigated for overlapping patch-based HU estimations also indicate that combining transformation estimations of overlapping patches is a potential method to reduce generation errors while also providing a tool to potentially estimate the MR to CT aleatoric model transformation uncertainty. However, because of small patient sample sizes, further studies are required.

Synthetic CT Generation Based on T2 Weighted MRI of Nasopharyngeal Carcinoma (NPC) Using a Deep Convolutional Neural Network (DCNN)

Purpose: There is an emerging interest of applying magnetic resonance imaging (MRI) to radiotherapy (RT) due to its superior soft tissue contrast for accurate target delineation as well as functional information for evaluating treatment response. MRI-based RT planning has great potential to enable dose escalation to tumors while reducing toxicities to surrounding normal tissues in RT treatments of nasopharyngeal carcinoma (NPC). Our study aims to generate synthetic CT from T2-weighted MRI using a deep learning algorithm.

Methods: Thirty-three NPC patients were retrospectively selected for this study with local IRB's approval. All patients underwent clinical CT simulation and 1.5T MRI within the same week in our hospital. Prior to CT/MRI image registration, we had to normalize two different modalities to a similar intensity scale using the histogram matching method. Then CT and T2 weighted MRI were rigidly and deformably registered using intensity-based registration toolbox elastix (version 4.9). A U-net deep learning algorithm with 23 convolutional layers was developed to generate synthetic CT (sCT) using 23 NPC patients' images as the training set. The rest 10 NPC patients were used as the test set (~1/3 of all datasets). Mean absolute error (MAE) and mean error (ME) were calculated to evaluate HU differences between true CT and sCT in bone, soft tissue and overall region.

Results: The proposed U-net algorithm was able to create sCT based on T2-weighted MRI in NPC patients, which took 7 s per patient on average. Compared to true CT, MAE of sCT in all tested patients was 97 ± 13 Hounsfield Unit (HU) in soft tissue, 131 ± 24 HU in overall region, and 357 ± 44 HU in bone, respectively. ME was −48 ± 10 HU in soft tissue, −6 ± 13 HU in overall region, and 247 ± 44 HU in bone, respectively. The majority soft tissue and bone region was reconstructed accurately except the interface between soft tissue and bone and some delicate structures in nasal cavity, where the inaccuracy was induced by imperfect deformable registration. One patient example was shown with almost no difference in dose distribution using true CT vs. sCT in the PTV regions in the sinus area with fine bone structures.

Conclusion: Our study indicates that it is feasible to generate high quality sCT images based on T2-weighted MRI using the deep learning algorithm in patients with nasopharyngeal carcinoma, which may have great clinical potential for MRI-only treatment planning in the future.

Potential of MRI in Radiotherapy Mediated by Small Conjugates and Nanosystems

Radiation therapy has made tremendous progress in oncology over the last decades due to advances in engineering and physical sciences in combination with better biochemical, genetic and molecular understanding of this disease. Local delivery of optimal radiation dose to a tumor, while sparing healthy surrounding tissues, remains a great challenge, especially in the proximity of vital organs. Therefore, imaging plays a key role in tumor staging, accurate target volume delineation, assessment of individual radiation resistance and even personalized dose prescription. From this point of view, radiotherapy might be one of the few therapeutic modalities that relies entirely on high-resolution imaging. Magnetic resonance imaging (MRI) with its superior soft-tissue resolution is already used in radiotherapy treatment planning complementing conventional computed tomography (CT). Development of systems integrating MRI and linear accelerators opens possibilities for simultaneous imaging and therapy, which in turn, generates the need for imaging probes with therapeutic components. In this review, we discuss the role of MRI in both external and internal radiotherapy focusing on the most important examples of contrast agents with combined therapeutic potential.

Assessment of the Impact of Deformable Registration of Diagnostic MRI to Planning CT on GTV Delineation for Radiotherapy for Oropharyngeal Carcinoma in Routine Clinical Practice

Background: Aim of study was to assess impact of deformable registration of diagnostic MRI to planning CT upon gross tumour volume (GTV) delineation of oropharyngeal carcinoma in routine practice. Methods: 22 consecutive patients with oropharyngeal squamous cell carcinoma treated with definitive (chemo)radiotherapy between 2015 and 2016, for whom primary GTV delineation had been performed by a single radiation oncologist using deformable registration of diagnostic MRI to planning CT, were identified. Separate GTVs were delineated as part of routine clinical practice (all diagnostic imaging available side-by-side for each delineation) using: CT (GTV_CT), MRI (GTV_MR), and CT and MRI (GTV_CTMR). Volumetric and positional metric analyses were undertaken using contour comparison metrics (Dice conformity index, centre of gravity distance, mean distance to conformity). Results: Median GTV volumes were 13.7 cm³ (range 3.5–41.7), 15.9 cm³ (range 1.6–38.3), 19.9 cm³ (range 5.5–44.5) for GTV_CT, GTV_MR and GTV_CTMR respectively. There was no significant difference in GTV_CT and GTV_MR volumes; GTV_CTMR was found to be significantly larger than both GTV_MR and GTV_CT. Based on positional metrics, GTV_CT and GTV_MR were the least similar (mean Dice similarity coefficient (DSC) 0.71, 0.84, 0.82 for GTV_CT–GTV_MR, GTV_CTMR–GTV_CT and GTV_CTMR–GTV_MR respectively). Conclusions: These data suggest a complementary role of MRI to CT to reduce the risk of geographical misses, although they highlight the potential for larger target volumes and hence toxicity.

Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region

Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously published results and within the range of the IOV. While dosimetric differences could be as large as 23% of the clinical goal, treatment plans fulfilled all imposed clinical goals for the gold standard OARs. Correlations between geometric and dosimetric measures were low with R2  <  0.5. The geometric accuracy and the ability to achieve clinically acceptable treatment plans indicate the suitability of using atlas-based contours for RT treatment planning purposes. The low correlations between geometric and dosimetric measures suggest that geometric measures alone are not sufficient to predict the dosimetric impact of segmentation inaccuracies on treatment planning for the data utilised in this study.

Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region

Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously published results and within the range of the IOV. While dosimetric differences could be as large as 23% of the clinical goal, treatment plans fulfilled all imposed clinical goals for the gold standard OARs. Correlations between geometric and dosimetric measures were low with R²  <  0.5. The geometric accuracy and the ability to achieve clinically acceptable treatment plans indicate the suitability of using atlas-based contours for RT treatment planning purposes. The low correlations between geometric and dosimetric measures suggest that geometric measures alone are not sufficient to predict the dosimetric impact of segmentation inaccuracies on treatment planning for the data utilised in this study.

Tumor volume as a prognostic marker in p16-positive and p16-negative oropharyngeal cancer patients treated with definitive intensity-modulated radiotherapy

PURPOSE

To investigate the impact of primary gross tumor volume (pGTV) and nodal gross tumor volume (nGTV) in oropharyngeal squamous cell carcinoma (OPSCC) and the difference in their role between human papillomavirus (HPV)-positive and HPV-negative patients.

METHODS

The patient cohort consists of 91 OPSCC patients treated with definitive radiochemotherapy or radiotherapy using intensity-modulated radiotherapy (IMRT). All patients had a minimum follow-up of 31 months. Volume measurements were made from computer tomography (CT) scans and HPV status was assessed by p16 immunohistochemistry. The end points were as follows: overall survival (OS), disease-free survival (DFS) and locoregional control (LRC).

RESULTS

pGTV was a significant independent prognostic factor for overall survival (OS; p = 0.020) in p16-negative patients. nGTV of p16-negative tumors had significant prognostic value in all end points in multivariate analyses. High-stage (III-IVc) p16-negative tumors were only associated with significantly poorer OS (p = 0.046) but not with poorer LRC or DFS when compared with the low-stage (I-II) tumors. nGTV of p16-positive tumors was an independent prognostic factor for DFS (p = 0.005) and LRC (p = 0.007) in multivariate analyses.

CONCLUSION

pGTV may serve as an independent prognostic factor in p16-negative patients and nGTV may serve as an independent prognostic factor both in p16-positive and p16-negative patients treated with radiochemotherapy or radiotherapy using IMRT. Tumor volume may have an impact on selecting patients for de-escalation protocols in the future, both in p16-positive and p16-negative patients.

Deformable image registration in radiation therapy

The number of imaging data sets has significantly increased during radiation treatment after introducing a diverse range of advanced techniques into the field of radiation oncology. As a consequence, there have been many studies proposing meaningful applications of imaging data set use. These applications commonly require a method to align the data sets at a reference. Deformable image registration (DIR) is a process which satisfies this requirement by locally registering image data sets into a reference image set. DIR identifies the spatial correspondence in order to minimize the differences between two or among multiple sets of images. This article describes clinical applications, validation, and algorithms of DIR techniques. Applications of DIR in radiation treatment include dose accumulation, mathematical modeling, automatic segmentation, and functional imaging. Validation methods discussed are based on anatomical landmarks, physical phantoms, digital phantoms, and per application purpose. DIR algorithms are also briefly reviewed with respect to two algorithmic components: similarity index and deformation models.

CT- and MRI-based gross target volume comparison in vestibular schwannomas

AIM

This study represents an enumeration and comparison of gross target volumes (GTV) as delineated independently on contrast-enhanced computed tomography (CT) and T1 and T2 weighted magnetic resonance imaging (MRI) in vestibular schwannomas (VS).

BACKGROUND

Multiple imaging in radiotherapy improves target localization.

METHODS AND MATERIALS

42 patients of VS were considered for this prospective study with one patient showing bilateral tumor. The GTV was delineated separately on CT and MRI. Difference in volumes were estimated individually for all the 43 lesions and similarity was studied between CT and T1 and T2 weighted MRI.

RESULTS

The male to female ratio for VS was found to be 1:1.3. The tumor was right sided in 34.9% and left sided in 65.1%. Tumor volumes (TV) on CT image sets were ranging from 0.251 cc to 27.27 cc. The TV for CT, MRI T1 and T2 weighted were 5.15 ± 5.2 cc, 5.8 ± 6.23 cc, and 5.9 ± 6.13 cc, respectively. Compared to MRI, CT underestimated the volumes. The mean dice coefficient between CT versus T1 and CT versus T2 was estimated to be 68.85 ± 18.3 and 66.68 ± 20.3, respectively. The percentage of volume difference between CT and MRI (%VD: mean ± SD for T1; 28.84 ± 15.0, T2; 35.74 ± 16.3) and volume error (%VE: T1; 18.77 ± 10.1, T2; 23.17 ± 13.93) were found to be significant, taking the CT volumes as the baseline.

CONCLUSIONS

MRI with multiple sequences should be incorporated for tumor volume delineation and they provide a clear boundary between the tumor and normal tissue with critical structures nearby.

The emerging potential of magnetic resonance imaging in personalizing radiotherapy for head and neck cancer: an oncologist's perspective

Head and neck cancer (HNC) is a challenging tumour site for radiotherapy delivery owing to its complex anatomy and proximity to organs at risk (OARs) such as the spinal cord and optic apparatus. Despite significant advances in radiotherapy planning techniques, radiation-induced morbidities remain substantial. Further improvement would require high-quality imaging and tailored radiotherapy based on intratreatment response. For these reasons, the use of MRI in radiotherapy planning for HNC is rapidly gaining popularity. MRI provides superior soft-tissue contrast in comparison with CT, allowing better definition of the tumour and OARs. The lack of additional radiation exposure is another attractive feature for intratreatment monitoring. In addition, advanced MRI techniques such as diffusion-weighted, dynamic contrast-enhanced and intrinsic susceptibility-weighted MRI techniques are capable of characterizing tumour biology further by providing quantitative functional parameters such as tissue cellularity, vascular permeability/perfusion and hypoxia. These functional parameters are known to have radiobiological relevance, which potentially could guide treatment adaptation based on their changes prior to or during radiotherapy. In this article, we first present an overview of the applications of anatomical MRI sequences in head and neck radiotherapy, followed by the potentials and limitations of functional MRI sequences in personalizing therapy.

The future of image-guided radiotherapy will be MR guided

Advances in image-guided radiotherapy (RT) have allowed for dose escalation and more precise radiation treatment delivery. Each decade brings new imaging technologies to help improve RT patient setup. Currently, the most frequently used method of three-dimensional pre-treatment image verification is performed with cone beam CT. However, more recent developments have provided RT with the ability to have on-board MRI coupled to the teleradiotherapy unit. This latest tool for treating cancer is known as MR-guided RT. Several varieties of these units have been designed and installed in centres across the globe. Their prevalence, history, advantages and disadvantages are discussed in this review article. In preparation for the next generation of image-guided RT, this review also covers where MR-guided RT might be heading in the near future.

Spectral characterization of tissues in high spectral and spatial resolution MR images: Implications for a classification-based synthetic CT algorithm

PURPOSE

To characterize the spectral parameters of tissues with high spectral and spatial resolution magnetic resonance images to be used as a foundation for a classification-based synthetic CT algorithm.

METHODS

A phantom was constructed consisting of a section of fresh beef leg with bone embedded in 1% agarose gel. The high spectral and spatial (HiSS) resolution MR imaging sequence used had 1.0 mm in-plane resolution and 11.1 Hz spectral resolution. This sequence was used to image the phantom and one patient. Post-processing was performed off-line with IDL and included Fourier transformation of the time-domain data, labeling of fat and water peaks, and fitting the magnitude spectra with Lorentzian functions. Images of the peak height and peak integral of both the water and fat resonances were generated and analyzed. Several regions-of-interest (ROIs) were identified in phantom: bone marrow, cortical bone, adipose tissue, muscle, agar gel, and air; in the patient, no agar gel was present but an ROI of saline in the bladder was analyzed. All spectra were normalized by the noise within each voxel; thus, all parameters are reported in terms of signal-to-noise (SNR). The distributions of tissue spectral parameters were analyzed and scatterplots generated. Water peak height in cortical bone was compared to air using a nonparametric t-test. Composition of the various ROIs in terms of water, fat, or fat and water was also reported.

RESULTS

In phantom, the scatterplot of peak height (water versus fat) showed good separation of bone marrow and adipose tissue. Water versus fat integral scatterplot showed better separation of muscle and cortical bone than the peak height scatterplot. In the patient data, the distributions of water and fat peak heights were similar to that in phantom, with more overlap of bone marrow and cortical bone than observed in phantom. The relationship between bone marrow and cortical bone for peak integral was better separated than those of peak heights in the patient data. For both the phantom and patient, there was a significant amount of overlap in spectral parameters of cortical bone versus air.

CONCLUSION

These results show promising results for utilizing HiSS imaging in a classification-based synthetic CT algorithm. Cortical bone and air overlap was expected due to the short T2* of bone; reducing early echo times would improve the SNR in bone and image data from these early echoes could help differentiate these tissue types. Further studies need to be done with the goal of better separation of air and bone, and to extend the concept to volumetric imaging before it can be clinically applied.

Clinical outcome and morbidity in pediatric patients with nasopharyngeal cancer treated with chemoradiotherapy

OBJECTIVES

The aim of the study was to evaluate the outcome and treatment-related morbidity in pediatric patients with nasopharyngeal carcinoma (NPC) treated with chemoradiotherapy.

METHODS

We did a retrospective review of 41 pediatric patients diagnosed with NPC between 2000 and 2013. The majority of the patients were treated with neoadjuvant chemotherapy followed by concurrent chemoradiation with the dose of 70 Gy in 35 fractions. Eight patients were treated with intensity-modulated radiation therapy, while the remaining with three-dimensional conformal radiation therapy or two-dimensional simulation technique.

RESULTS

The median age of the patients was 14 years (range 6-20 years). Most of the patients had locoregionally advanced disease (stage III/IVA/IVB). The histology of all the cases was undifferentiated carcinoma. Immunohistochemistry for the Epstein-Barr virus-Latent membrane protein 1 was positive in nine of the 13 tested cases. The median follow-up for all and the surviving patients was 26.6 months (range 2-140.8) and 51.2 months, respectively. The 3-year overall survival (OS) and event-free survival (EFS) rates were estimated at 83.7% (95% confidence interval [CI]: 64.8-93%) and 55.8% (95%CI: 38.7-69.8%), respectively. Distant metastases were the predominant pattern of failure. Treatment response showed an independent association with OS. T classification (T1/T2 vs. T3/T4) was significantly associated with EFS. Xerostomia, hypothyroidism, dental caries, neck fibrosis, trismus, and dysphagia were the common late effects in survivors. Radiation myelitis was observed in one patient.

CONCLUSIONS

Treatment with neoadjuvant chemotherapy followed by concurrent chemoradiation provides good survival outcomes in pediatric NPC. The quality of life of the survivors is a pertinent area that necessitates consideration.

Functional magnetic resonance imaging techniques and their development for radiation therapy planning and monitoring in the head and neck cancers

Radiation therapy (RT), in particular intensity-modulated radiation therapy (IMRT), is becoming a more important nonsurgical treatment strategy in head and neck cancer (HNC). The further development of IMRT imposes more critical requirements on clinical imaging, and these requirements cannot be fully fulfilled by the existing radiotherapeutic imaging workhorse of X-ray based imaging methods. Magnetic resonance imaging (MRI) has increasingly gained more interests from radiation oncology community and holds great potential for RT applications, mainly due to its non-ionizing radiation nature and superior soft tissue image contrast. Beyond anatomical imaging, MRI provides a variety of functional imaging techniques to investigate the functionality and metabolism of living tissue. The major purpose of this paper is to give a concise and timely review of some advanced functional MRI techniques that may potentially benefit conformal, tailored and adaptive RT in the HNC. The basic principle of each functional MRI technique is briefly introduced and their use in RT of HNC is described. Limitation and future development of these functional MRI techniques for HNC radiotherapeutic applications are discussed. More rigorous studies are warranted to translate the hypotheses into credible evidences in order to establish the role of functional MRI in the clinical practice of head and neck radiation oncology.

The feasibility of atlas-based automatic segmentation of MRI for H&N radiotherapy planning

Atlas‐based autosegmentation is an established tool for segmenting structures for CT‐planned head and neck radiotherapy. MRI is being increasingly integrated into the planning process. The aim of this study is to assess the feasibility of MRI‐based, atlas‐based autosegmentation for organs at risk (OAR) and lymph node levels, and to compare the segmentation accuracy with CT‐based autosegmentation. Fourteen patients with locally advanced head and neck cancer in a prospective imaging study underwent a T1‐weighted MRI and a PET‐CT (with dedicated contrast‐enhanced CT) in an immobilization mask. Organs at risk (orbits, parotids, brainstem, and spinal cord) and the left level II lymph node region were manually delineated on the CT and MRI separately. A ‘leave one out’ approach was used to automatically segment structures onto the remaining images separately for CT and MRI. Contour comparison was performed using multiple positional metrics: Dice index, mean distance to conformity (MDC), sensitivity index (Se Idx), and inclusion index (Incl Idx). Automatic segmentation using MRI of orbits, parotids, brainstem, and lymph node level was acceptable with a DICE coefficient of 0.73−0.91, MDC 2.0−5.1 mm, Se Idx 0.64−0.93, Incl Idx 0.76−0.93. Segmentation of the spinal cord was poor (Dice coefficient 0.37). The process of automatic segmentation was significantly better on MRI compared to CT for orbits, parotid glands, brainstem, and left lymph node level II by multiple positional metrics; spinal cord segmentation based on MRI was inferior compared with CT. Accurate atlas‐based automatic segmentation of OAR and lymph node levels is feasible using T1‐MRI; segmentation of the spinal cord was found to be poor. Comparison with CT‐based automatic segmentation suggests that the process is equally as, or more accurate, using MRI. These results support further translation of MRI‐based segmentation methodology into clinical practice.

PACS number(s): 87.55.de

Uncertainties in target volume delineation in radiotherapy - are they relevant and what can we do about them?

Background

Modern radiotherapy techniques enable delivery of high doses to the target volume without escalating dose to organs at risk, offering the possibility of better local control while preserving good quality of life. Uncertainties in target volume delineation have been demonstrated for most tumour sites, and various studies indicate that inconsistencies in target volume delineation may be larger than errors in all other steps of the treatment planning and delivery process. The aim of this paper is to summarize the degree of delineation uncertainties for different tumour sites reported in the literature and review the effect of strategies to minimize them.

Conclusions

Our review confirmed that interobserver variability in target volume contouring represents the largest uncertainty in the process for most tumour sites, potentially resulting in a systematic error in dose delivery, which could influence local control in individual patients. For most tumour sites the optimal combination of imaging modalities for target delineation still needs to be determined. Strict use of delineation guidelines and protocols is advisable both in every day clinical practice and in clinical studies to diminish interobserver variability. Continuing medical education of radiation oncologists cannot be overemphasized, intensive formal training on interpretation of sectional imaging should be included in the program for radiation oncology residents.

Different methods for target volume delineation of glandular breast tissue following breast-conserving surgery in breast cancer: A comparative study

The present study aimed to investigate an optimal and feasible method for delineating the target volume of glandular breast tissue following breast-conserving surgery. A total of 15 patients who underwent radiotherapy following breast-conserving surgery were recruited into the study. Clinical target volume was delineated by the following three methods based on computed tomography (CT): Anatomical landmarks (CTVan), breast palpation (CTVpa) and CT scan images (CTVgl). The target volume, degree of inclusion (DI) and conformal index (CI) defined by these methods were compared. The difference was significant between CTVan and CTVgl, and CTVpa and CTVgl (P<0.0001). The CI between CTVan and CTVpa was 0.644±0.122, significantly higher than that between CTVan and CTVgl (0.264±0.108; P<0.0001) or between CTVpa and CTVgl (0.328±0.115; P<0.0001). The DI of CTVpa in CTVan was 0.890±0.08 and the opposite was 0.709±0.144, while that of DI of CTVgl in CTVan or CTVpa was 0.994±0.005 and 0.989±0.008, respectively. The boundary difference between CTVan and CTVpa was 3.35±7.23, 5.57±13.37, 1.75±11.62 and 11.25±4.07 mm for the medial, lateral, cephalic and caudal boundaries, respectively. A significant difference was observed in the target volume of the breast defined by the three methods. The target volume defined by CTVgl was significantly smaller than that identified by the other two methods. Overall, the combination of palpation marks and anatomical landmarks to define the contouring scope of the breast was indicated to be a relatively rational method for delineating the target volume of the breast.

The relevance of MRI for patient modeling in head and neck hyperthermia treatment planning: a comparison of CT and CT-MRI based tissue segmentation on simulated temperature

PURPOSE

In current clinical practice, head and neck (H&N) hyperthermia treatment planning (HTP) is solely based on computed tomography (CT) images. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast over CT. The purpose of the authors' study is to investigate the relevance of using MRI in addition to CT for patient modeling in H&N HTP.

METHODS

CT and MRI scans were acquired for 11 patients in an immobilization mask. Three observers manually segmented on CT, MRI T1 weighted (MRI-T1w), and MRI T2 weighted (MRI-T2w) images the following thermo-sensitive tissues: cerebrum, cerebellum, brainstem, myelum, sclera, lens, vitreous humor, and the optical nerve. For these tissues that are used for patient modeling in H&N HTP, the interobserver variation of manual tissue segmentation in CT and MRI was quantified with the mean surface distance (MSD). Next, the authors compared the impact of CT and CT and MRI based patient models on the predicted temperatures. For each tissue, the modality was selected that led to the lowest observer variation and inserted this in the combined CT and MRI based patient model (CT and MRI), after a deformable image registration. In addition, a patient model with a detailed segmentation of brain tissues (including white matter, gray matter, and cerebrospinal fluid) was created (CT and MRIdb). To quantify the relevance of MRI based segmentation for H&N HTP, the authors compared the predicted maximum temperatures in the segmented tissues (Tmax) and the corresponding specific absorption rate (SAR) of the patient models based on (1) CT, (2) CT and MRI, and (3) CT and MRIdb.

RESULTS

In MRI, a similar or reduced interobserver variation was found compared to CT (maximum of median MSD in CT: 0.93 mm, MRI-T1w: 0.72 mm, MRI-T2w: 0.66 mm). Only for the optical nerve the interobserver variation is significantly lower in CT compared to MRI (median MSD in CT: 0.58 mm, MRI-T1w: 1.27 mm, MRI-T2w: 1.40 mm). Patient models based on CT (Tmax: 38.0 °C) and CT and MRI (Tmax: 38.1 °C) result in similar simulated temperatures, while CT and MRIdb (Tmax: 38.5 °C) resulted in significantly higher temperatures. The SAR corresponding to these temperatures did not differ significantly.

CONCLUSIONS

Although MR imaging reduces the interobserver variation in most tissues, it does not affect simulated local tissue temperatures. However, the improved soft-tissue contrast provided by MRI allows generating a detailed brain segmentation, which has a strong impact on the predicted local temperatures and hence may improve simulation guided hyperthermia.

Investigation on the performance of dedicated radiotherapy positioning devices for MR scanning for prostate planning

The purpose of this study was to investigate performance of the couch and coil mounts designed for MR‐simulation prostate scanning using data from ten volunteers. Volunteers were scanned using the standard MR scanning protocol with the MR coil directly strapped on the external body and the volunteer lying on the original scanner table. They also were scanned using a MR‐simulation table top and pelvic coil mounts. MR images from both setups were compared in terms of body contour variation and image quality effects within particular organs of interest. Six‐field conformal plans were generated on the two images with assigned bulk density for dose calculation. With the MR‐simulation devices, the anterior skin deformation was reduced by up to 1.7 cm. The hard tabletop minimizes the posterior body deformation which can be up to 2.3 cm on the standard table, depending on the weight of volunteer. The image signal‐to‐noise ratio reduced by 14% and 25% on large field of view (FOV) and small FOV images, respectively, after using the coil mount; the prostate volume contoured on two images showed difference of 1.05±0.66 cm3. The external body deformation caused a mean dose reduction of 0.6±0.3 Gy, while the coverage reduced by 22%±13% and 27%±6% in V98 and V100, respectively. A dedicated MR simulation setup for prostate radiotherapy is essential to ensure the agreement between planning anatomy and treatment anatomy. The image signal was reduced after applying the coil mount, but no significant effect was found on prostate contouring.

PACS numbers: 87.55.D‐, 87.61.‐c, 87.57.C‐

The value of magnetic resonance imaging for radiotherapy planning

The success of highly conformal radiotherapy techniques in the sparing of normal tissues or in dose escalation, or both, relies heavily on excellent imaging. Because of its superior soft tissue contrast, magnetic resonance imaging is increasingly being used in radiotherapy treatment planning. This review discusses the current clinical evidence to support the pivotal role of magnetic resonance imaging in radiation oncology.

Feasibility and limitations of bulk density assignment in MRI for head and neck IMRT treatment planning

Head and neck cancers centered at the base of skull are better visualized on MRI than on CT. The purpose of this investigation was to investigate the accuracy of bulk density assignment in head and neck intensity‐modulated radiation therapy (IMRT) treatment plan optimization. Our study investigates dose calculation differences between density‐assigned MRI and CT, and identifies potential limitations related to dental implants and MRI geometrical distortion in the framework of MRI‐only‐based treatment planning. Bulk density assignment was performed and applied onto MRI to generate three MRI image sets with increasing levels of heterogeneity for seven patients: 1) MRIW: all water‐equivalent; 2) MRIW + B: included bone with density of 1.53 g/cm3; and 3) MRIW + B + A: included bone and air. Using identical planning and optimization parameters, MRI‐based IMRT plans were generated and compared to corresponding, forward‐calculated, CT‐based plans on the basis of target coverage, isodose distributions, and dose‐volume histograms (DVHs). Phantom studies were performed to assess the magnitude and spatial dependence of MRI geometrical distortion. MRIW‐based dose calculations overestimated target coverage by 16.1%. Segmentation of bone reduced differences to within 2% of the coverage area on the CT‐based plan. Further segmentation of air improved conformity near air–tissue interfaces. Dental artifacts caused substantial target coverage overestimation even on MRIW + B + A. Geometrical distortion was less than 1 mm in an imaging volume 20 × 20 × 20 cm3 around scanner isocenter, but up to 4 mm at 17 cm lateral to isocenter. Bulk density assignment in the framework of MRI‐only IMRT head and neck treatment planning is a feasible method with certain limitations. Bone and teeth account for the majority of density heterogeneity effects. While soft tissue is well visualized on MRI compared to CT, dental implants may not be visible on MRI and must be identified by other means and assigned appropriate density for accurate dose calculation. Far off‐center geometrical distortion of the body contour near the shoulder region is a potential source of dose calculation inaccuracy.

PACS numbers: 87.61.‐c, 87.55.‐D

MRI to delineate the gross tumor volume of nasopharyngeal cancers: which sequences and planes should be used?

Background

Magnetic resonance imaging (MRI) has been found to be better than computed tomography for defining the extent of primary gross tumor volume (GTV) in advanced nasopharyngeal cancer. It is routinely applied for target delineation in planning radiotherapy. However, the specific MRI sequences/planes that should be used are unknown.

Methods

Twelve patients with nasopharyngeal cancer underwent primary GTV evaluation with gadolinium-enhanced axial T1 weighted image (T1) and T2 weighted image (T2), coronal T1, and sagittal T1 sequences. Each sequence was registered with the planning computed tomography scans. Planning target volumes (PTVs) were derived by uniform expansions of the GTVs. The volumes encompassed by the various sequences/planes, and the volumes common to all sequences/planes, were compared quantitatively and anatomically to the volume delineated by the commonly used axial T1-based dataset.

Results

Addition of the axial T2 sequence increased the axial T1-based GTV by 12% on average (p = 0.004), and composite evaluations that included the coronal T1 and sagittal T1 planes increased the axial T1-based GTVs by 30% on average (p = 0.003). The axial T1-based PTVs were increased by 20% by the additional sequences (p = 0.04). Each sequence/plane added unique volume extensions. The GTVs common to all the T1 planes accounted for 38% of the total volumes of all the T1 planes. Anatomically, addition of the coronal and sagittal-based GTVs extended the axial T1-based GTV caudally and cranially, notably to the base of the skull.

Conclusions

Adding MRI planes and sequences to the traditional axial T1 sequence yields significant quantitative and anatomically important extensions of the GTVs and PTVs. For accurate target delineation in nasopharyngeal cancer, we recommend that GTVs be outlined in all MRI sequences/planes and registered with the planning computed tomography scans.

[Radioanatomy of rhinopharyngeal carcinoma]

Rhinopharyngeal cancer is one of the best indications for conformal radiotherapy with modulated intensity. Due to the high dose gradient, accurate delineation of target volumes and organs at risk is a critical success factor with this technology. This requires a good knowledge of rhinopharyngeal radioanatomy and optimal imaging techniques.

Intensity-modulated radiation therapy for head and neck cancer

Radiotherapy planning studies have confirmed dosimetric advantages of intensity-modulated radiation therapy over conventional and conformal radiation therapy. Utilization of intensity-modulated radiation therapy is ideal in head and neck cancer patients. Critical structures can be spared due to sharp dose gradients and limited organ motion with correct immobilization. Initial clinical results have shown excellent locoregional control, in part due to the delivery of high doses to the target volume. Reductions in acute toxicities and xerostomia through parotid sparing have been notable benefits. However, long-term outcomes with regards to local control and late toxicities with intensity-modulated radiation therapy are still lacking. This review focuses on the implementation of intensity-modulated radiation therapy for the treatment of head and neck cancers, with a specific focus on set-up uncertainties, dose prescription and target volume determination and delineation.

Tumor volume is an independent prognostic indicator of local control in nasopharyngeal carcinoma patients treated with intensity-modulated radiotherapy

Background

To retrospectively analyze whether primary tumor volume and primary nodal volume could be considered independent prognostic factors for nasopharyngeal carcinoma treated with intensity-modulated radiation therapy.

Methods

Three hundred sixty-three consecutive nasopharyngeal carcinoma (NPC) patients who were stage I-IVa+b and treated with intensity-modulated radiotherapy (IMRT) in our center from October 2003 to October 2005 were analyzed retrospectively. The predictive ability of gender, age, T and N stage, combined chemotherapy, primary tumor and nodal volume for the 5-year local control (LC), distant-metastasis free survival (DMFS) and overall survival (OS) rate were investigated. Primary tumor and nodal volume were measured based on registration of magnetic resonance imaging (MRI) with contrast-enhanced computed tomography (CT) images. The Kaplan–Meier method was used for survival analysis, the log-rank test was used for univariate analyses and the Cox proportional hazard model was used for multivariate prognostic analyses.

Results

The mean value of primary tumor and nodal volume were 31.5 ml and 9.7 ml. The primary tumor and nodal volume were respectively divided into four groups for analysis (primary tumor volume: TV1≤20 ml, 20<TV2≤30 ml, 30<TV3≤40 ml, TV4>40 ml; primay nodal volume: NV1≤5 ml, 5<NV2≤10 ml, 10<NV3≤20 ml, NV4>20 ml). In univariate analysis, the 5-year LC and DMFS rate for TV4 was significantly decreased compared to the other groups (LC: p<0.001, DMFS: p=0.001), the 5-year OS rate for TV3 and TV4 were significantly decreased compared to other two subgroups (p=0.002) and the 5-year regional control (RC), DMFS and OS rate for NV3 and NV4 were significantly less than NV1 and NV2 (RC: p=0.002, DMFS: p=0.01, OS: p=0.014). Multivariate analysis showed that TV>40 ml was an adverse prognostic factor for the 5-year local regional control (LRC) rate (RR 2.454, p=0.002). Primary nodal volume had no statistical significance in predicting 5-year LRC, DMFS and OS rate in multivariate analysis.

Conclusions

Primary tumor volume could predict LRC rate of NPC patients, and the primary tumor volume of 40 ml may be the cut-off. Primary nodal volume may have predictive significance, but more data are needed. These factors should be considered in the TNM staging system of NPC for better estimates of prognosis.

Commissioning of a new wide-bore MRI scanner for radiotherapy planning of head and neck cancer

OBJECTIVE

A combination of CT and MRI is recommended for radiotherapy planning of head and neck cancers, and optimal spatial co-registration is achieved by imaging in the treatment position using the necessary immobilisation devices on both occasions, something which requires wide-bore scanners. Quality assurance experiments were carried out to commission a newly installed 1.5-T wide-bore MRI scanner and a dedicated, flexible six-channel phased array head and neck coil.

METHODS

Signal-to-noise ratio (SNR) and spatial signal uniformity were quantified using a homogeneous aqueous phantom, and geometric distortion was quantified using a phantom with water-filled fiducials in a grid pattern. Volunteer scans were also used to determine the in vivo image quality. Clinically relevant T1 weighted and T2 weighted fat-suppressed sequences were assessed in multiple scan planes (both sequences fast spin echo based). The performance of two online signal uniformity correction schemes, one utilising low-resolution reference scans and the other not utilising low-resolution reference scans, was compared.

RESULTS

Geometric distortions, for a ±35-kHz bandwidth, were <1 mm for locations within 10 cm of the isocentre rising to 1.8 mm at 18 cm away. SNR was above 50, and uniformity in the axial plane was 71% and 95% before and after uniformity correction, respectively.

CONCLUSION

The combined performance of the wide-bore scanner and the dedicated coil was adjudged adequate, although superior-inferior spatial coverage was slightly limited in the lower neck.

ADVANCES IN KNOWLEDGE

These results will be of interest to the increasing number of oncology centres that are seeking to incorporate MRI into planning practice using dedicated equipment.

MRI simulation for radiotherapy treatment planning

Over the last two decades, the computed tomography simulator became the standard of the contemporary radiotherapy treatment planning (RTP) process. Along the same time, the superb soft tissue contrast of magnetic resonance imaging (MRI) was widely incorporated into RTP through the process of image coregistration. This review summarizes the efforts of incorporation of MRI data into target definition process for RTP based on gained clinical evidence so far and opens a question whether the time is up for bringing a MRI-simulator as an additional standard imaging tool into radiation oncology departments.

Improving target definition for head and neck radiotherapy: a place for magnetic resonance imaging and 18-fluoride fluorodeoxyglucose positron emission tomography?

Defining the target for head and neck radiotherapy is a critical issue with the introduction of steep dose gradients associated with intensity-modulated radiotherapy. Tumour delineation inaccuracies are a major source of error in radiotherapy planning. The integration of 18-fluoride fluorodeoxyglucose positron emission tomography ((18)FDG-PET) and magnetic resonance imaging directly into the radiotherapy planning process has the potential to greatly improve target identification/selection and delineation. This raises a range of new issues surrounding image co-registration, delineation methodology and the use of functional data and treatment adaptation. This overview will discuss the practical aspects of integrating (18)FDG-PET and magnetic resonance imaging into head and neck radiotherapy planning.

Contribution of PET-CT to staging, gross tumour volume definition, planning and response assessment in IMRT for nasopharyngeal carcinoma

The effectiveness of PET-CT (positron emission tomography–computed tomography) was investigated for staging target delineation compared with CT-MR (computed tomography–magnetic resonance) and early response of intensity-modulated radiotherapy (IMRT). Gross tumour volume–clinical target volume (GTV-CTV) differences between PET-CT and CT-MR for 14 nasopharyngeal carcinoma (NPC) patients were compared. Evaluation of doses of organs at risk (OARs) was done by IMRT plans. Responses of IMRT were evaluated with both sets. PET-CT changed MR-based TNM (Tumour Lymph Nodes Metastasis) in 11 of 14 patients. The median GTVNP (nasopharyx gross tumour volume) was 49.25 and 18.8 cm3 for CT-MR and PET-CT, respectively. In eight cases, GTVNP in the PET-CT was smaller than the CT-MR. The PET-CT presented a larger GTVNP than the CT-MR for six cases. Mean doses for the parotid glands were found to be higher than in CT-MR-based plan in one patient although he had smaller GTVNP at the PET-CT. The median follow-up was 16 months. Only one patient experienced recurrence in the CTVNP (nasopharyx clinical target volume). MR showed a decrease in the size-number of lymph nodes in four patients whereas PET-CT showed no uptake. All patients had positive responses to IMRT in their second control MR and PET-CT. PET-CT could improve tumour delineation. This enables an increase in dose inside the CTV. PET-CT provided significant information on the control scans for most of our patients whose MR imaging showed residual or recurrence.

Magnetic resonance imaging for adaptive cobalt tomotherapy: A proposal

Magnetic resonance imaging (MRI) provides excellent soft tissue contrast for oncology applications. We propose to combine a MRI scanner with a helical tomotherapy (HT) system to enable daily target imaging for improved conformal radiation dose delivery to a patient. HT uses an intensity-modulated fan-beam that revolves around a patient, while the patient slowly advances through the plane of rotation, yielding a helical beam trajectory. Since the use of a linear accelerator to produce radiation may be incompatible with the pulsed radiofrequency and the high and pulsed magnetic fields required for MRI, it is proposed that a radioactive Cobalt-60 ((60)Co) source be used instead to provide the radiation. An open low field (0.25 T) MRI system is proposed where the tomotherapy ring gantry is located between two sets of Helmholtz coils that can generate a sufficiently homogenous main magnetic field.It is shown that the two major challenges with the design, namely acceptable radiation dose rate (and therefore treatment duration) and moving parts in strong magnetic field, can be addressed. The high dose rate desired for helical tomotherapy delivery can be achieved using two radiation sources of 220TBq (6000Ci) each on a ring gantry with a source to axis-of-rotation distance of 75 cm. In addition to this, a dual row multi-leaf collimator (MLC) system with 15 mm leaf width at isocentre and relatively large fan beam widths between 15 and 30 mm per row shall be employed. In this configuration, the unit would be well-suited for most pelvic radiotherapy applications where the soft tissue contrast of MRI will be particularly beneficial. Non-magnetic MRI compatible materials must be used for the rotating gantry. Tungsten, which is non-magnetic, can be used for primary collimation of the fan-beam as well as for the MLC, which allows intensity modulated radiation delivery. We propose to employ a low magnetic Cobalt compound, sycoporite (CoS) for the Cobalt source material itself.Rotational delivery is less susceptible to problems related to the use of a low energy megavoltage photon source while the helical delivery reduces the negative impact of the relatively large penumbra inherent in the use of Cobalt sources for radiotherapy. On the other hand, the use of a (60)Co source ensures constant dose rate with gantry rotation and makes dose calculation in a magnetic field as easy as the range of secondary electrons is limited.The MR-integrated Cobalt tomotherapy unit, dubbed 'MiCoTo,' uses two independent physical principles for image acquisition and treatment delivery. It would offer excellent target definition and will allow following target motion during treatment using fast imaging techniques thus providing the best possible input for adaptive radiotherapy. As an additional bonus, quality assurance of the radiation delivery can be performed in situ using radiation sensitive gels imaged by MRI.

Nasopharyngeal cancer around the Mediterranean area: role of newer radiation techniques

Primary radiotherapy has been the mainstay of treatment of patients with nondisseminated nasopharyngeal cancer (NPC). Novel techniques, such as intensity modulated and image guided radiotherapy has the capability to generate steep dose gradients, leading to an improved therapeutic index, especially in NPC. Although it is widely accepted as the treatment of choice in NPC in the modern world, in developing countries the financing of these innovative delivery systems still continues to be a major problem. The purpose of this article is to discuss the difficulties one may experience during the transition from 2D way of thinking to the 3D conformal era and to review the clinical outcome and toxicity profile of these promising new radiation techniques.

Imaging in radiation oncology: a perspective

This paper reviews the integration of imaging and radiation oncology, and discusses challenges and opportunities for improving the practice of radiation oncology with imaging.

An inherent goal of radiation therapy is to deliver enough dose to the tumor to eradicate all cancer cells or to palliate symptoms, while avoiding normal tissue injury. Imaging for cancer diagnosis, staging, treatment planning, and radiation targeting has been integrated in various ways to improve the chance of this occurring. A large spectrum of imaging strategies and technologies has evolved in parallel to advances in radiation delivery. The types of imaging can be categorized into offline imaging (outside the treatment room) and online imaging (inside the treatment room, conventionally termed image-guided radiation therapy). The direct integration of images in the radiotherapy planning process (physically or computationally) often entails trade-offs in imaging performance. Although such compromises may be acceptable given specific clinical objectives, general requirements for imaging performance are expected to increase as paradigms for radiation delivery evolve to address underlying biology and adapt to radiation responses. This paper reviews the integration of imaging and radiation oncology, and discusses challenges and opportunities for improving the practice of radiation oncology with imaging.