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Accurate spatial dose delivery in radiotherapy is frequently complicated due to changes in the patient's internal anatomy during and in-between therapy segments. The recent introduction of hybrid MRI radiotherapy systems allows unequaled soft-tissue visualization during radiation delivery and can be used for dose reconstruction to quantify the impact of motion. To this end, knowledge of anatomical deformations obtained from continuous monitoring during treatment has to be combined with information on the spatio-temporal dose delivery to perform motion-compensated dose accumulation (MCDA). Here, the influence of the choice of deformable image registration algorithm, dose warping strategy, and magnetic resonance image resolution and signal-to-noise-ratio on the resulting MCDA is investigated. For a quantitative investigation, four 4D MRI-datasets representing typical patient observed motion patterns are generated using finite element modeling and serve as a gold standard. Energy delivery is simulated intra-fractionally in the deformed image space and, subsequently, MCDA-processed. Finally, the results are substantiated by comparing MCDA strategies on clinically acquired patient data. It is shown that MCDA is needed for correct quantitative dose reconstruction. For prostate treatments, using the energy per mass transfer dose warping strategy has the largest influence on decreasing dose estimation errors. [ABSTRACT FROM AUTHOR]

Image-guided radiotherapy (IGRT) allows observation of the location and shape of the tumor and organs-at-risk (OAR) over the course of a radiation cancer treatment. Such information may in turn be used for reducing geometric uncertainties during therapeutic planning, dose delivery and response assessment. However, given the multiple imaging modalities and/or contrasts potentially included within the imaging protocol over the course of the treatment, the current manual approach to determining tissue displacement may become time-consuming and error prone. In this context, variational multi-modal deformable image registration (DIR) algorithms allow automatic estimation of tumor and OAR deformations across the acquired images. In addition, they require short computational times and a low number of input parameters, which is particularly beneficial for online adaptive applications, which require on-the-fly adaptions with the patient on the treatment table. However, the majority of such DIR algorithms assume that all structures across the entire field-of-view (FOV) undergo a similar deformation pattern. Given that various anatomical structures may behave considerably different, this may lead to the estimation of anatomically implausible deformations at some locations, thus limiting their validity. Therefore, in this paper we propose an anatomically-adaptive variational multi-modal DIR algorithm, which employs a regionalized registration model in accordance with the local underlying anatomy. The algorithm was compared against two existing methods which employ global assumptions on the estimated deformations patterns. Compared to the existing approaches, the proposed method has demonstrated an improved anatomical plausibility of the estimated deformations over the entire FOV as well as displaying overall higher accuracy. Moreover, despite the more complex registration model, the proposed approach is very fast and thus suitable for online scenarios. Therefore, future adaptive IGRT workflows may benefit from an anatomically-adaptive registration model for precise contour propagation and dose accumulation, in areas showcasing considerable variations in anatomical properties. [ABSTRACT FROM AUTHOR]

4D-MRI is a promising tool for organ exploration, target delineation and treatment planning. Intra-scan motion artifacts may be greatly reduced by increasing the imaging frame rate. However, poor signal-to-noise ratios (SNR) are observed when increasing spatial and/or frame number per physiological cycle, in particular in the abdomen. In the current work, the proposed 4D-MRI method favored spatial resolution, frame number, isotropic voxels and large field-of-view (FOV) during MR-acquisition. The consequential SNR penalty in the reconstructed data is addressed retrospectively using an iterative back-projection (IBP) algorithm. Practically, after computing individual spatial 3D deformations present in the images using a deformable image registration (DIR) algorithm, each 3D image is individually enhanced by fusing several successive frames in its local temporal neighborood, these latter being likely to cover common independent informations. A tuning parameter allows one to freely readjust the balance between temporal resolution and precision of the 4D-MRI. The benefit of the method was quantitatively evaluated on the thorax of 6 mice under free breathing using a clinically acceptable duration. Improved 4D cardiac imaging was also shown in the heart of 1 mice. Obtained results are compared to theoretical expectations and discussed. The proposed implementation is easily parallelizable and optimized 4D-MRI could thereby be obtained with a clinically acceptable duration. [ABSTRACT FROM AUTHOR]

Poster discussion: PH: Adaptive radiotherapy and interfraction motion management 2 PD-0557: Biomechanical quality assurance criteria for deformable registration in image guided radiotherapy C. Zachiu, B. Denis de Senneville, M. Ries, B.W. Raaymakers. [Extracted from the article]

• A new fast method – using as a starting-point a 3D modified PatchMatch algorithm – is proposed to align the field-of-view in medical 3D images. • The modified PatchMatch algorithm is combined with a well-adapted multi-modal image similarity metric in order to cope with multi-modal medical images. • The performance – in terms of registration accuracy and computational needs – is analyzed, and demonstrated to be compatible with clinical constraints in the practical case of on-line irreversible electroporation procedures. • The benefit of proposed approach is evaluated for a potential pre-conditioning of a more complex multi-modal deformable image registration algorithm. Various multi-modal imaging sensors are currently involved at different steps of an interventional therapeutic work-flow. Cone beam computed tomography (CBCT), computed tomography (CT) or Magnetic Resonance (MR) images thereby provides complementary functional and/or structural information of the targeted region and organs at risk. Merging this information relies on a correct spatial alignment of the observed anatomy between the acquired images. This can be achieved by the means of multi-modal deformable image registration (DIR), demonstrated to be capable of estimating dense and elastic deformations between images acquired by multiple imaging devices. However, due to the typically different field-of-view (FOV) sampled across the various imaging modalities, such algorithms may severely fail in finding a satisfactory solution. In the current study we propose a new fast method to align the FOV in multi-modal 3D medical images. To this end, a patch-based approach is introduced and combined with a state-of-the-art multi-modal image similarity metric in order to cope with multi-modal medical images. The occurrence of estimated patch shifts is computed for each spatial direction and the shift value with maximum occurrence is selected and used to adjust the image field-of-view. The performance of the proposed method – in terms of both registration accuracy and computational needs – is analyzed in the practical case of on-line irreversible electroporation procedures. In total, 30 pairs of pre-/per-operative IRE images are considered to illustrate the efficiency of our algorithm. We show that a regional registration approach using voxel patches provides a good structural compromise between the voxel-wise and "global shifts" approaches. The method was thereby beneficial for CT to CBCT and MRI to CBCT registration tasks, especially when highly different image FOVs are involved. Besides, the benefit of the method for CT to CBCT and MRI to CBCT image registration is analyzed, including the impact of artifacts generated by percutaneous needle insertions. Additionally, the computational needs using commodity hardware are demonstrated to be compatible with clinical constraints in the practical case of on-line procedures. The proposed patch-based workflow thus represents an attractive asset for DIR at different stages of an interventional procedure. [ABSTRACT FROM AUTHOR]