Marker-based C-arm self-calibration with unknown calibration pattern.

BACKGROUND: Accurate tomographic reconstructions require the knowledge of the actual acquisition geometry. Many mobile C-arm CT scanners have poorly reproducible acquisition geometries and thus need acquisition-specific calibration procedures. Most of geometric self-calibration methods based on projection data either need prior information or are limited to the estimation of a low number of geometric calibration parameters. Other self-calibration methods generally use a calibration pattern with known geometry and are hardly implementable in practice for clinical applications. PURPOSE: We present a three-step marker based self-calibration method which does not require the prior knowledge of the calibration pattern and thus enables the use of calibration patterns with arbitrary markers positions. METHODS: The first step of the method aims at detecting the set of markers of the calibration pattern in each projection of the CT scan and is performed using the YOLO (You Only Look Once) Convolutional Neural Network. The projected marker trajectories are then estimated by a sequential projection-wise marker association scheme based on the Linear Assignment Problem which uses Kalman filters to predict the markers 2D positions in the projections. The acquisition geometry is finally estimated from the marker trajectories using the Bundle-adjustment algorithm. RESULTS: The calibration method has been tested on realistic simulated images of the ICRP (International Commission on Radiological Protection) phantom, using calibration patterns with 10 and 20 markers. The backprojection error was used to evaluate the self-calibration method and exhibited sub-millimeter errors. Real images of two human knees with 10 and 30 markers calibration patterns were then used to perform a qualitative evaluation of the method, which showed a remarkable artifacts reduction and bone structures visibility improvement. CONCLUSIONS: The proposed calibration method gave promising results that pave the way to patient-specific geometric self-calibrations in clinics.

A tomographic reconstruction algorithm for cross-sectional imaging of IMRT beams from six projections.

Objective. Patient-specific Quality Assurance (QA) measurements are of key importance in radiotherapy for safe and efficient treatment delivery and allow early detection of clinically relevant errors. Such QA processes remain challenging to implement for complex Intensity Modulated Radiation Therapy (IMRT) radiotherapy fields delivered using a multileaf collimator (MLC) which often feature small open segments and raise QA issues similar to those encountered in small field dosimetry. Recently, detectors based on long scintillating fibers have been proposed to measure a few parallel projections of the irradiation field with good performance for small field dosimetry. The purpose of this work is to develop and validate a novel approach to reconstruct MLC-shaped small irradiation fields from six projections.Approach. The proposed field reconstruction method uses a limited number of geometric parameters to model the irradiation field. These parameters are iteratively estimated with a steepest descent algorithm. The reconstruction method was first validated on simulated data. Real data were measured with a water-equivalent slab phantom equipped with a detector made of 6 scintillating-fiber ribbons placed at 1 m from the source. A radiochromic film was used to acquire a reference measurement of a first dose distribution in the slab phantom at the same source-to-detector distance and the treatment planning system (TPS) provided the reference for another dose distribution. In addition, simulated errors introduced on the delivered dose, field location and field shape were used to evaluate the ability of the proposed method to efficiently identify a deviation between the planned and delivered treatments.Main results. For a first small IMRT segment, 3%/3 mm, 2%/2 mm and 2%/1 mm gamma analysis conducted between the reconstructed dose distribution and the dose measured with radiochromic film exhibited pass rates of 100%, 99.9% and 95.7%, respectively. For a second and smaller IMRT segment, the same gamma analysis performed between the reconstructed dose distribution and the reference provided by the TPS showed pass rates of 100%, 99.4% and 92.6% for the 3%/3 mm, 2%/2 mm and 2%/1 mm gamma criteria, respectively. Gamma analysis of the simulated treatment delivery errors showed the ability of the reconstruction algorithm to detect a 3% deviation between the planned and delivered doses, as well as shifts lower than 7 mm and 3 mm when considering an individual leaf and a whole field shift, respectively.Significance. The proposed method allows accurate tomographic reconstruction of IMRT segments by processing projections measured with six scintillating-fiber ribbons and is suitable for water-equivalent real-time small IMRT segments QA.

A novel QA phantom based on scintillating fiber ribbons with implementation of 2D dose tomography for small-field radiotherapy.

PURPOSE: To develop a novel instrument for real-time quality assurance (QA) procedures in radiotherapy. The system implements a scintillation-based phantom and associated signal acquisition and processing modules and aims to monitor two-dimensional (2D) dose distributions of small fields. MATERIALS AND METHODS: For the proposed phantom, we have designed and realized a prototype implementing six high-resolution tissue-equivalent scintillating fiber ribbons stacked with in-plane 30° rotated orientations from each other. Each ribbon output is coupled to a silicon photodiode linear array (with an element pitch of 400 µm) to detect scintillating signal, which represents the projected irradiation profile perpendicular to the ribbon's orientation. For the system providing six acquired projected dose profiles at different orientations, we have developed a two-step signal processing method to perform 2D dose reconstruction. The first step is to determine irradiation field geometry parameters using a tomographic geometry approach, and the second one is to perform specific penumbra estimation. The QA system prototype has been tested on a Novalis TrueBeam STX with a 6-MV photon beam for small elliptic fields defined by 5- and 10-mm cone collimators and for 10 × 10- and 20 × 10-mm2 rectangular fields defined by the micro-multileaf collimator. Gamma index analysis using EBT3 films as reference has been carried out with tight 2%-dose-difference (DD)/700-µm-distance-to-agreement (DTA) as well as 1%-DD/1-mm-DTA criteria for evaluating the system performances. The testing also includes an evaluation of the proposed two-step field reconstruction method in comparison with two conventional methods: filtered back projection (FBP) and simultaneous iterative reconstruction technique (SIRT). RESULTS: The reconstructed 2D dose distributions have gamma index pass rates higher than 95% for all the tested configurations as compared with EBT3 film measurements with both 2%-DD/700-µm-DTA and 1%-DD/1-mm criteria. 2D global gamma analysis shows that the two-step and FBP radiation field reconstruction methods systematically outperform the SIRT approach. Moreover, higher gamma index success rates are obtained with the two-step method than with FBP in the case of the fields defined with the stereotactic cones. CONCLUSIONS: The proposed small-field QA system makes a use of six water-equivalent scintillating detectors (fiber ribbons) to acquire dose distribution. The developed two-step signal processing method performs tomographic 2D dose reconstruction. A system prototype has been built and tested using hospital facilities with small rectangular and elliptic fields. Testing results show 2D reconstructed dose distributions with high accuracy and resolution. Such a system could potentially be an alternative approach to film dosimetry for small-field QA, which is still widely used as reference in clinical practice.

Scatter Correction for Spectral CT Using a Primary Modulator Mask.

The problem of scattered radiation correction in computed tomography (CT) is well known because scatter induces a bias, a loss of contrast and artifacts. Numerous strategies have been proposed in conventional CT (using energy-integrating detectors) but the problem is still open in the field of spectral CT, a new imaging technique based on energy-selective photon counting detectors. The aim of the present study is to introduce a scatter correction method adapted to multi-energy imaging and based on the use of a primary modulator mask. The main contributions are a correction matrix, which compensates for the effect of the mask, a scatter model based on B-splines and a cost function based on the mask structures and robust to the object structures. The performances of the method have been evaluated on both simulated and experimental data. The mean relative error was reduced from 20% in the lower energy-bins without correction to 4% with the proposed technique, which is close to the error caused by statistical noise.