The first patient treatment of computed tomography ventilation functional image-guided radiotherapy for lung cancer.

BACKGROUND AND PURPOSE: Radiotherapy that selectively avoids irradiating highly-functional lung regions may reduce pulmonary toxicity. We report on the first clinical implementation and patient treatment of lung functional image-guided radiotherapy using an emerging technology, computed tomography (CT) ventilation imaging. MATERIAL AND METHODS: A protocol was developed to investigate the safety and feasibility of CT ventilation functional image-guided radiotherapy. CT ventilation imaging is based on (1) deformable image registration of four-dimensional (4D) CT images, and (2) quantitative image analysis for regional volume change, a surrogate for ventilation. CT ventilation functional image-guided radiotherapy plans were designed to minimize specific lung dose-function metrics, including functional V20 (fV20), while maintaining target coverage and meeting standard constraints to other critical organs. RESULTS: CT ventilation functional image-guided treatment planning reduced the lung fV20 by 5% compared to an anatomic image-guided plan for an enrolled patient with stage IIIB non-small cell lung cancer. Although the doses to several other critical organs increased, the necessary constraints were all met. CONCLUSIONS: An emerging technology, CT ventilation imaging has been translated into the clinic and used in functional image-guided radiotherapy for the first time. This milestone represents an important first step toward hypothetically reduced pulmonary toxicity in lung cancer radiotherapy

Biological Dose Accumulation in Image-guided Radiotherapy

Dose accumulation (DA), the computation of the total delivered 3D dose distribution Da of a fractionated radiotherapy treatment using daily patient imaging, is getting established in clinical practice. Although the aim is to estimate the achieved biological outcome of the treatment, the principles of biological effect estimation are currently not followed consistently in the process. In this thesis, the biological effect accumulation approach and total biological dose (bEQD) were derived as a biologically consistent DA workflow. Clinical relevance of bEQD and its dependence on individual workflow aspects were investigated in data from three patient cohorts. It was found that Da systematically underestimates the obtained biological effect, which can be avoided by the use of bEQD. Results showed that this is strongest for late-responding organs at risk (OAR) with low �=� values in dose gradient regions around the target that are prone to organ motion. bEQD to Da deviations occurred locally, in so-called hotspots, showing individual cases of high difference magnitude but only small statistical impact. Hotspots of bEQD - Da deviation around 4 Gy in bladder and rectum were found in patients treated for prostate carcinoma. Hypofractionation increased these deviations strongly up to 8 Gy and also showed clinically relevant deviations in dose-volume analysis. Dose-response correlation in standard fractionation showed only little impact on the DA approaches. Workflow uncertainties are dominated by those from deformable image registration, which are in the same range as the difference between bEQD and Da. bEQD should be considered in the application of treatment adaptation, especially to avoid damage to OARs in individual cases

Acute toxicity in prostate cancer patients treated with and without image-guided radiotherapy

Image-guided radiotherapy (IGRT) increases the accuracy of treatment delivery through daily target localisation. We report on toxicity symptoms experienced during radiotherapy treatment, with and without IGRT in prostate cancer patients treated radically

Feasibility of intensity-modulated and image-guided radiotherapy for functional organ preservation in locally advanced laryngeal cancer

Purpose: The study aims to assess the feasibility of intensity-modulated and image-guided radiotherapy (IMRT, and IGRT, respectively) for functional preservation in locally advanced laryngeal cancer. A retrospective review of 27 patients undergoing concurrent chemoradiation for locally advanced laryngeal cancers (8 IMRT, 19 IGRT) was undertaken. In addition to regular clinical examinations, all patients had PET imaging at 4 months and 10 months after radiotherapy, then yearly. Loco-regional control, speech quality and feeding-tube dependency were assessed during follow-up visits. Results: At a median follow-up of 20 months (range 6-57 months), four out of 27 patients (14.8%) developed local recurrence and underwent salvage total laryngectomy. One patient developed distant metastases following salvage surgery. Among the 23 patients who conserved their larynx with no sign of recurrence at last follow-up, 22 (95%) reported normal or near normal voice quality, allowing them to communicate adequately. Four patients (14.8%) had long-term tube feeding-dependency because of severe dysphagia (2 patients) and chronic aspiration (2 patients, with ensuing death from aspiration pneumonia in one patient). Conclusions and Clinical Relevance: Functional laryngeal preservation is feasible with IMRT and IGRT for locally advanced laryngeal cancer. However, dysphagia and aspiration remain serious complications, due most likely to high radiation dose delivery to the pharyngeal musculatures. © 2012 Nguyen et al

Optimizing Magnetic Resonance Imaging for Image-Guided Radiotherapy

Magnetic resonance imaging (MRI) is playing an increasingly important role in image-guided radiotherapy. MRI provides excellent soft tissue contrast, and is flexible in characterizing various tissue properties including relaxation, diffusion and perfusion. This thesis aims at developing new image analysis and reconstruction algorithms to optimize MRI in support of treatment planning, target delineation and treatment response assessment for radiotherapy. First, unlike Computed Tomography (CT) images, MRI cannot provide electron density information necessary for radiation dose calculation. To address this, we developed a synthetic CT generation algorithm that generates pseudo CT images from MRI, based on tissue classification results on MRI for female pelvic patients. To improve tissue classification accuracy, we learnt a pelvic bone shape model from a training dataset, and integrated the shape model into an intensity-based fuzzy c-menas classification scheme. The shape-regularized tissue classification algorithm is capable of differentiating tissues that have significant overlap in MRI intensity distributions. Treatment planning dose calculations using synthetic CT image volumes generated from the tissue classification results show acceptably small variations as compared to CT volumes. As MRI artifacts, such as B1 filed inhomogeneity (bias field) may negatively impact the tissue classification accuracy, we also developed an algorithm that integrates the correction of bias field into the tissue classification scheme. We modified the fuzzy c-means classification by modeling the image intensity as the true intensity corrupted by the multiplicative bias field. A regularization term further ensures the smoothness of the bias field. We solved the optimization problem using a linearized alternating direction method of multipliers (ADMM) method, which is more computational efficient over existing methods. The second part of this thesis looks at a special MR imaging technique, diffusion-weighted MRI (DWI). By acquiring a series of DWI images with a wide range of b-values, high order diffusion analysis can be performed using the DWI image series and new biomarkers for tumor grading, delineation and treatment response evaluation may be extracted. However, DWI suffers from low signal-to-noise ratio at high b-values, and the multi-b-value acquisition makes the total scan time impractical for clinical use. In this thesis, we proposed an accelerated DWI scheme, that sparsely samples k-space and reconstructs images using a model-based algorithm. Specifically, we built a 3D block-Hankel tensor from k-space samples, and modeled both local and global correlations of the high dimensional k-space data as a low-rank property of the tensor. We also added a phase constraint to account for large phase variations across different b-values, and to allow reconstruction from partial Fourier acquisition, which further accelerates the image acquisition. We proposed an ADMM algorithm to solve the constrained image reconstruction problem. Image reconstructions using both simulated and patient data show improved signal-to-noise ratio. As compared to clinically used parallel imaging scheme which achieves a 4-fold acceleration, our method achieves an 8-fold acceleration. Reconstructed images show reduced reconstruction errors as proved on simulated data and similar diffusion parameter mapping results on patient data.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143919/1/llliu_1.pd

Optimization of image-guided radiotherapy (IGRT) for lung cancer

Senan, S. [Promotor]Slotman, B.J. [Promotor]Sörnsen De Koste, J.R. van [Copromotor