Xavier Geets

Gradient-based delineation of the primary GTV on FDG-PET in non-small cell lung cancer: A comparison with threshold-based approaches, CT and surgical specimens.

PURPOSE The aim of this study was to validate a gradient-based segmentation method for GTV delineation on FDG-PET in NSCLC through surgical specimen, in comparison with threshold-based approaches and CT. MATERIALS AND METHODS Ten patients with stage I-II NSCLC were prospectively enrolled. Before lobectomy, all patients underwent contrast enhanced CT and gated FDG-PET. Next, the surgical specimen was removed, inflated with gelatin, frozen and sliced. The digitized slices were used to reconstruct the 3D macroscopic specimen. GTVs were manually delineated on the macroscopic specimen and on CT images. GTVs were automatically segmented on PET images using a gradient-based method, a source to background ratio method and fixed threshold values at 40% and 50% of SUV(max). All images were finally registered. Analyses of raw volumes and logarithmic differences between GTVs and GTV(macro) were performed on all patients and on a subgroup excluding the poorly defined tumors. A matching analysis between the different GTVs was also conducted using Dices similarity index. RESULTS Considering all patients, both lung and mediastinal windowed CT overestimated the macroscopy, while FDG-PET provided closer values. Among various PET segmentation methods, the gradient-based technique best estimated the true tumor volume. When analysis was restricted to well defined tumors without lung fibrosis or atelectasis, the mediastinal windowed CT accurately assessed the macroscopic specimen. Finally, the matching analysis did not reveal significant difference between the different imaging modalities. CONCLUSIONS FDG-PET improved the GTV definition in NSCLC including when the primary tumor was surrounded by modifications of the lung parenchyma. In this context, the gradient-based method outperformed the threshold-based ones in terms of accuracy and robustness. In other cases, the conventional mediastinal windowed CT remained appropriate.

Physical radiotherapy treatment planning based on functional PET/CT data

Positron emission tomography (PET) provides molecular information about the tumor microenvironment in addition to anatomical imaging. Hence, it seems to be highly beneficial to integrate PET data into radiotherapy (RT) treatment planning. Functional PET images can be used in RT planning following different strategies, with different orders of complexity. In a first instance, PET imaging data can be used for better target volume delineation. A second strategy, dose painting by contours (DPBC), consists of creating an additional PET-based target volume which will then be treated with a higher dose level. In contrast, dose painting by numbers (DPBN) aims for a locally varying dose prescription according to the variation of the PET signal. For both dose painting approaches, isotoxicity planning strategies should be applied in order not to compromise organs at risk compared to conventional modern RT treatment. In terms of physical dose painting treatment planning, several factors that may introduce limitations and uncertainties are of major importance. These are the PET voxel size, uncertainties due to image acquisition and reconstruction, a reproducible image registration, inherent biological uncertainties due to biological and chemical tracer characteristics, accurate dose calculation algorithms and radiation delivery techniques able to apply highly modulated dose distributions. Further research is necessary in order to investigate these factors and their influence on dose painting treatment planning and delivery thoroughly. To date, dose painting remains a theoretical concept which needs further validation. Nevertheless, molecular imaging has the potential to significantly improve target volume delineation and might also serve as a basis for treatment alteration in the future.

Assessment by a deformable registration method of the volumetric and positional changes of target volumes and organs at risk in pharyngo-laryngeal tumors treated with concomitant chemo-radiation

PURPOSE Anatomic changes occur during radiation therapy (RT) for head and neck (H&N) tumors. This study aims at quantifying the volumetric and positional changes of gross tumor volumes (GTV), clinical target volumes (CTV), and organs at risk (OAR). Anatomic (CT) and functional (FDG-PET) imaging were used for the delineation of the GTVs. MATERIALS AND METHODS Ten patients with H&N tumors treated by chemo-RT were used. Contrast-enhanced CT and FDG-PET were acquired prior and during RT following delivery of mean doses of 14.2, 24.5, 35.0, and 44.9 Gy. CT-based GTVs were manually delineated, and PET-based GTVs were segmented using a gradient-based segmentation method. Pre-treatment prophylactic dose CTVs were manually delineated on the pre-treatment CT using consistent and reproducible guidelines. Per-treatment prophylactic CTVs were obtained with an automatic re-contouring method based on deformable registration. For the therapeutic dose CTVs, a 5 mm margin was applied around the corresponding GTVs. OARs such as the parotid glands and the submandibular glands were manually delineated on the pre-treatment CT. OARs on the per-treatment CT were automatically delineated using the method used for prophylactic CTVs. The mean slopes of the relative change in volume over time and the mean displacements of the center of mass after 44.9 Gy were calculated for each volume. RESULTS Regarding volumetric changes, CT-based and PET-based primary tumor GTVs decreased at a mean rate of 3.2% and 3.9%/treatment day (td), respectively; nodal GTVs decreased at a mean rate of 2.2%/td. This led to a corresponding decrease of the CT-based and PET-based therapeutic CTVs by 2.4% and 2.5%/td, respectively. CT- and PET-based prophylactic tumor CTVs decreased by an average of 0.7% and 0.5%/td, respectively. No difference in volume shrinkage was observed between CT- and PET-based volumes. The ipsilateral and contralateral parotid glands showed a mean decrease of 0.9% and 1.0%/td, respectively. The ipsilateral and contralateral submandibular glands shrank at a mean rate of 1.5% and 1.3%/td, respectively. Regarding positional changes, CT-based GTVs showed a lateral shift of 1.3 mm, PET-based GTVs a posterior shift of 3.4mm and the nodal GTVs a medial shift of 1.0 mm, translating into parallel shifts of the therapeutic CTVs. The ipsilateral prophylactic nodal CTV shifted medially by 1.8 mm. The ipsilateral parotid gland shifted medially by 3.4 mm. The ipsilateral submandibular gland showed a medial shift of 1.7 mm and a superior shift of 2.7 mm. The contralateral submandibular gland only showed a superior shift of 1.7 mm. CONCLUSIONS Volumetric and positional changes in TVs and OARs were observed during concomitant chemo-RT suggesting that adaptive strategies, where patients are re-imaged and possibly re-planned during treatment, are worth evaluating.

Diffeomorphic registration of images with variable contrast enhancement

Nonrigid image registration is widely used to estimate tissue deformations in highly deformable anatomies. Among the existing methods, nonparametric registration algorithms such as optical flow, or Demons, usually have the advantage of being fast and easy to use. Recently, a diffeomorphic version of the Demons algorithm was proposed. This provides the advantage of producing invertible displacement fields, which is a necessary condition for these to be physical. However, such methods are based on the matching of intensities and are not suitable for registering images with different contrast enhancement. In such cases, a registration method based on the local phase like the Morphons has to be used. In this paper, a diffeomorphic version of the Morphons registration method is proposed and compared to conventional Morphons, Demons, and diffeomorphic Demons. The method is validated in the context of radiotherapy for lung cancer patients on several 4D respiratory-correlated CT scans of the thorax with and without variable contrast enhancement.

Edge-Preserving Filtering of Images with Low Photon Counts

Edge-preserving filters such as local M-smoothers or bilateral filtering are usually designed for Gaussian noise. This paper investigates how these filters can be adapted in order to efficiently deal with Poissonian noise. In addition, the issue of photometry invariance is addressed by changing the way filter coefficients are normalized. The proposed normalization is additive, instead of being multiplicative, and leads to a strong connection with anisotropic diffusion. Experiments show that ensuring the photometry invariance leads to comparable denoising performances in terms of the root mean square error computed on the signal.

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

Purpose The purpose of this educational report is to provide an overview of the present state‐of‐the‐art PET auto‐segmentation (PET‐AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET‐AS algorithm for a particular application. Approach A brief description of the different types of PET‐AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET‐AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET‐AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto‐segmentation are addressed. Findings A large number of PET‐AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi‐automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal‐to‐noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol. Conclusions Available comparison studies suggest that PET‐AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET‐AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET‐AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET‐AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET‐AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members.

Efficient multi-modal dense field non-rigid registration: alignment of histological and section images.

We describe a new algorithm for non-rigid registration capable of estimating a constrained dense displacement field from multi-modal image data. We applied this algorithm to capture non-rigid deformation between digital images of histological slides and digital flat-bed scanned images of cryotomed sections of the larynx, and carried out validation experiments to measure the effectiveness of the algorithm. The implementation was carried out by extending the open-source Insight ToolKit software. In diagnostic imaging of cancer of the larynx, imaging modalities sensitive to both anatomy (such as MRI and CT) and function (PET) are valuable. However, these modalities differ in their capability to discriminate the margins of tumor. Gold standard tumor margins can be obtained from histological images from cryotomed sections of the larynx. Unfortunately, the process of freezing, fixation, cryotoming and staining the tissue to create histological images introduces non-rigid deformations and significant contrast changes. We demonstrate that the non-rigid registration algorithm we present is able to capture these deformations and the algorithm allows us to align histological images with scanned images of the larynx. Our non-rigid registration algorithm constructs a deformation field to warp one image onto another. The algorithm measures image similarity using a mutual information similarity criterion, and avoids spurious deformations due to noise by constraining the estimated deformation field with a linear elastic regularization term. The finite element method is used to represent the deformation field, and our implementation enables us to assign inhomogeneous material characteristics so that hard regions resist internal deformation whereas soft regions are more pliant. A gradient descent optimization strategy is used and this has enabled rapid and accurate convergence to the desired estimate of the deformation field. A further acceleration in speed without cost of accuracy is achieved by using an adaptive mesh refinement strategy.

Assessment of tumor motion reproducibility with audio-visual coaching through successive 4D CT sessions

This study aimed to compare combined audio‐visual coaching with audio coaching alone and assess their respective impact on the reproducibility of external breathing motion and, one step further, on the internal lung tumor motion itself, through successive sessions. Thirteen patients with NSCLC were enrolled in this study. The tumor motion was assessed by three to four successive 4D CT sessions, while the breathing signal was measured from magnetic sensors positioned on the epigastric region. For all sessions, the breathing was regularized with either audio coaching alone (AC, n=5) or combined with a real‐time visual feedback (A/VC, n=8) when tolerated by the patients. Peak‐to‐peak amplitude, period and signal shape of both breathing and tumor motions were first measured. Then, the correlation between the respiratory signal and internal tumor motion over time was evaluated, as well as the residual tumor motion for a gated strategy. Although breathing and tumor motions were comparable between AC and AV/C groups, A/VC approach achieved better reproducibility through sessions than AC alone (mean tumor motion of 7.2 mm±1 vs. 8.6 mm±1.8 mm, and mean breathing motion of 14.9 mm±1.2 mm vs. 13.3 mm±3.7 mm, respectively). High internal/external correlation reproducibility was achieved in the superior‐inferior tumor motion direction for all patients. For the anterior‐posterior tumor motion direction, better correlation reproducibility has been observed when visual feedback has been used. For a displacement‐based gating approach, A/VC might also be recommended, since it led to smaller residual tumor motion within clinically relevant duty cycles. This study suggests that combining real‐time visual feedback with audio coaching might improve the reproducibility of key characteristics of the breathing pattern, and might thus be considered in the implementation of lung tumor radiotherapy. PACS number: 87

Validation of the mid-position strategy for lung tumors in helical TomoTherapy

PURPOSE To compare the mid-position (MidP) strategy to the conventional internal target volume (ITV) for lung tumor management in helical TomoTherapy, using 4D Monte Carlo (MC) plan simulations. MATERIALS AND METHODS For NSCLC patients treated by SBRT (n = 8) or SIB-IMRT (n = 7), target volumes and OARs were delineated on a contrast-enhanced CT, while 4D-CT was used to generate either ITV or MidP volumes with deformable registrations. PTV margins were added. Conformity indexes, volumetric and dosimetric parameters were compared for both strategies. Dose distributions were also computed using a 4D MC model (TomoPen) to assess how intra-fraction tumor motion affects tumor coverage, with and without interplay effect. RESULTS PTVs derived from MidP were on average 1.2 times smaller than those from ITV, leading to lower doses to OARs. Planned dose conformity to TVs was similar for both strategies. 4D MC computation showed that ITV ensured adequate TV coverage (D95 within 1% of clinical requirements), while MidP failed in 3 patients of the SBRT group (D95 to the TV lowered by 4.35%, 2.16% and 2.61%) due to interplay effect in one case and to breathing motion alone in the others. CONCLUSIONS Compared to the ITV, the MidP significantly reduced PTV and doses to OARs. MidP is safe for helical delivery except for very small tumors (<5 cc) with large-amplitude motion (>10mm) where the ITV might remain the most adequate approach.

Evaluation of the radiobiological impact of anatomic modifications during radiation therapy for head and neck cancer: can we simply summate the dose?

BACKGROUND AND PURPOSE Adaptive strategies in radiotherapy (RT) require the knowledge of the total dose given to every organ of the body. Because of anatomical changes and setup errors non-rigid registration is necessary to map the different dose fractions to a common reference. This study evaluates practically if the accumulation of all of these registered dose fractions must take radiobiology into account in a classical clinical setting. MATERIALS AND METHODS Ten patients with head and neck tumors treated by chemo-RT were used. Contrast-enhanced CT scans were acquired prior and during RT following delivery of mean doses of 14.2, 24.5, 35.0 and 44.9 Gy and the planned pre-treatment helical tomotherapy sinograms were applied on the per-treatment CTs to create a series of per-treatment dose distributions corresponding to each per-treatment CT image. In order to calculate the cumulative dose distribution, the per-treatment dose maps were non-rigidly deformed by using the deformation map computed by a non-rigid registration. The deformed dose maps were then summed in two ways: one while taking radiobiology into account and one without. These two strategies were compared using clinical surrogates in the target volumes (TV) and in surrounding organs at risk (OAR). RESULTS The differences between the strategies, while statistically significant (p<0.05), are clinically irrelevant. In the OARs, the mean differences stay in the 0.01-0.07 Gy range for the total dose. In the targets, all mean differences stay in the 0.001-0.012 Gy range. However, some local high difference spots appear leading to punctual errors as high as 2.5 Gy. CONCLUSION If using current radiotherapy practices and clinical recommendations based on dose surrogates computed globally on OARs and TVs, one does not need to take radiobiological effects into account while accumulating total dose as these lead to very small differences compared to a simple accumulation technique consisting of a linear sum of the dose fractions. However, care must be taken if other adaptive strategies, based on local rather than global information, are used.