A New Phantom for Individual Verification of the Dose Distribution in Precision Radiotherapy for Head-and-Neck Cancer.

BACKGROUND/AIM: Many patients with head-and-neck cancers receive radiotherapy. Treatment planning can be very complex in case of dental fillings or implants that cause metal artefacts. Verification of dose distributions may be performed using specific phantoms. This study aimed to develop a 3D-printed phantom that can be produced easily and cost-effectively. PATIENTS AND METHODS: The phantom was designed to allow fast adaption to a patient's individual situation with a particular focus on metal artefacts due to dental fillings. Bone and soft-tissue shells were 3D-printed and filled with tissue-equivalent materials. RESULTS: Attenuation properties of the tissue-equivalent structures in the phantom corresponded well to the structures of real human anatomy. In magnetic resonance (MR)-imaging, useful signals of the materials in the phantom were obtained. CONCLUSION: The phantom met the requirements including equivalence with human tissues and can be useful for highly individual treatment planning in precision-radiotherapy of head-and-neck cancers. It can be also used for scientific issues related to MR-imaging.

Improvement of dose calculation in radiation therapy due to metal artifact correction using the augmented likelihood image reconstruction.

BACKGROUND: Metal artifacts caused by high-density implants lead to incorrectly reconstructed Hounsfield units in computed tomography images. This can result in a loss of accuracy in dose calculation in radiation therapy. This study investigates the potential of the metal artifact reduction algorithms, Augmented Likelihood Image Reconstruction and linear interpolation, in improving dose calculation in the presence of metal artifacts. MATERIALS AND METHODS: In order to simulate a pelvis with a double-sided total endoprosthesis, a polymethylmethacrylate phantom was equipped with two steel bars. Artifacts were reduced by applying the Augmented Likelihood Image Reconstruction, a linear interpolation, and a manual correction approach. Using the treatment planning system Eclipse™, identical planning target volumes for an idealized prostate as well as structures for bladder and rectum were defined in corrected and noncorrected images. Volumetric modulated arc therapy plans have been created with double arc rotations with and without avoidance sectors that mask out the prosthesis. The irradiation plans were analyzed for variations in the dose distribution and their homogeneity. Dosimetric measurements were performed using isocentric positioned ionization chambers. RESULTS: Irradiation plans based on images containing artifacts lead to a dose error in the isocenter of up to 8.4%. Corrections with the Augmented Likelihood Image Reconstruction reduce this dose error to 2.7%, corrections with linear interpolation to 3.2%, and manual artifact correction to 4.1%. When applying artifact correction, the dose homogeneity was slightly improved for all investigated methods. Furthermore, the calculated mean doses are higher for rectum and bladder if avoidance sectors are applied. CONCLUSION: Streaking artifacts cause an imprecise dose calculation within irradiation plans. Using a metal artifact correction algorithm, the planning accuracy can be significantly improved. Best results were accomplished using the Augmented Likelihood Image Reconstruction algorithm.

The effects of metal artifact reduction on the retrieval of attenuation values.

BACKGROUND: The quality of CT slices can be drastically reduced in the presence of high-density objects such as metal implants within the patients' body due to the occurrence of streaking artifacts. Consequently, a delineation of anatomical structures might not be possible, which strongly influences clinical examination. PURPOSE: The aim of the study is to clinically evaluate the retrieval of attenuation values and structures by the recently proposed Augmented Likelihood Image Reconstruction (ALIR) and linear interpolation in the presence of metal artifacts. MATERIAL AND METHODS: A commercially available phantom was equipped with two steel inserts. At a position between the metal rods, which shows severe streaking artifacts, different human tissue-equivalent inserts are alternately mounted. Using a single-source computer tomograph, raw data with and without metal rods are acquired for each insert. Images are reconstructed using the ALIR algorithm and a filtered back projection with and without linear interpolation. Mean and standard deviation are compared for a region of interest in the ALIR reconstructions, linear interpolation results, uncorrected images with metal rods, and the images without metal rods, which are used as a reference. Furthermore, the reconstructed shape of the inserts is analyzed by comparing different profiles of the image. RESULTS: The measured mean and standard deviation values show that for all tissue classes, the metal artifacts could be reduced using the ALIR algorithm and the linear interpolation. Furthermore, the HU values for the different classes could be retrieved with errors below the standard deviation in the reference image. An evaluation of the shape of the inserts shows that the reconstructed object fits the shape of the insert accurately after metal artifact correction. Moreover, the evaluation shows a drop in the standard deviation for the ALIR reconstructed images compared to the reference images while reducing artifacts and keeping the shape of the inserts, which indicates a noise reduction ability of the ALIR algorithm. CONCLUSION: HU values, which are distorted by metal artifacts, can be retrieved accurately with the ALIR algorithm and the linear interpolation approach. After metal artifact correction, structures, which are not perceptible in the original images due to streaking artifacts, are reconstructed correctly within the image using the ALIR algorithm. Furthermore, the ALIR produced images with a reduced noise level compared to reference images and artifact images. Linear interpolation results in a distortion of the investigated shapes and features remaining streaking artifacts.

Augmented Likelihood Image Reconstruction.

The presence of high-density objects remains an open problem in medical CT imaging. Data of projections passing through objects of high density, such as metal implants, are dominated by noise and are highly affected by beam hardening and scatter. Reconstructed images become less diagnostically conclusive because of pronounced artifacts that manifest as dark and bright streaks. A new reconstruction algorithm is proposed with the aim to reduce these artifacts by incorporating information about shape and known attenuation coefficients of a metal implant. Image reconstruction is considered as a variational optimization problem. The afore-mentioned prior knowledge is introduced in terms of equality constraints. An augmented Lagrangian approach is adapted in order to minimize the associated log-likelihood function for transmission CT. During iterations, temporally appearing artifacts are reduced with a bilateral filter and new projection values are calculated, which are used later on for the reconstruction. A detailed evaluation in cooperation with radiologists is performed on software and hardware phantoms, as well as on clinically relevant patient data of subjects with various metal implants. Results show that the proposed reconstruction algorithm is able to outperform contemporary metal artifact reduction methods such as normalized metal artifact reduction.

3D reconstruction of 2D fluorescence histology images and registration with in vivo MR images: application in a rodent stroke model.

To validate and add value to non-invasive imaging techniques, the corresponding histology is required to establish biological correlates. We present an efficient, semi-automated image-processing pipeline that uses immunohistochemically stained sections to reconstruct a 3D brain volume from 2D histological images before registering these with the corresponding 3D in vivo magnetic resonance images (MRI). A multistep registration procedure that first aligns the "global" volume by using the centre of mass and then applies a rigid and affine alignment based on signal intensities is described. This technique was applied to a training set of three rat brain volumes before being validated on three normal brains. Application of the approach to register "abnormal" images from a rat model of stroke allowed the neurobiological correlates of the variations in the hyper-intense MRI signal intensity caused by infarction to be investigated. For evaluation, the corresponding anatomical landmarks in MR and histology were defined to measure the registration accuracy. A registration error of 0.249 mm (approximately one in-plane voxel dimension) was evident in healthy rat brains and of 0.323 mm in a rodent model of stroke. The proposed reconstruction and registration pipeline allowed for the precise analysis of non-invasive MRI and corresponding microstructural histological features in 3D. We were thus able to interrogate histology to deduce the cause of MRI signal variations in the lesion cavity and the peri-infarct area.