Assessing quality assurance of multi-leaf collimator using the structural similarity index.

PURPOSE: This study aims to evaluate the viability of utilizing the Structural Similarity Index (SSI*) as an innovative imaging metric for quality assurance (QA) of the multi-leaf collimator (MLC). Additionally, we compared the results obtained through SSI* with those derived from a conventional Gamma index test for three types of Varian machines (Trilogy, Truebeam, and Edge) over a 12-week period of MLC QA in our clinic. METHOD: To assess sensitivity to MLC positioning errors, we designed a 1 cm slit on the reference MLC, subsequently shifted by 0.5-5 mm on the target MLC. For evaluating sensitivity to output error, we irradiated five 25 cm × 25 cm open fields on the portal image with varying Monitor Units (MUs) of 96-100. We compared SSI* and Gamma index tests using three linear accelerator (LINAC) machines: Varian Trilogy, Truebeam, and Edge, with MLC leaf widths of 1, 0.5, and 0.25 mm. Weekly QA included VMAT and static field modes, with Picket fence test images acquired. Mechanical uncertainties related to the LINAC head, electronic portal imaging device (EPID), and MLC during gantry rotation and leaf motion were monitored. RESULTS: The Gamma index test started detecting the MLC shift at a threshold of 4 mm, whereas the SSI* metric showed sensitivity to shifts as small as 2 mm. Moreover, the Gamma index test identified dose changes at 95MUs, indicating a 5% dose difference based on the distance to agreement (DTA)/dose difference (DD) criteria of 1 mm/3%. In contrast, the SSI* metric alerted to dose differences starting from 97MUs, corresponding to a 3% dose difference. The Gamma index test passed all measurements conducted on each machine. However, the SSI* metric rejected all measurements from the Edge and Trilogy machines and two from the Truebeam. CONCLUSIONS: Our findings demonstrate that the SSI* exhibits greater sensitivity than the Gamma index test in detecting MLC positioning errors and dose changes between static and VMAT modes. The SSI* metric outperformed the Gamma index test regarding sensitivity across these parameters.

Lung surface deformation prediction from spirometry measurement and chest wall surface motion.

PURPOSE: The authors have developed and evaluated a method to predict lung surface motion based on spirometry measurements, and chest and abdomen motion at selected locations. METHODS: A patient-specific 3D triangular surface mesh of the lung region was obtained at the end expiratory phase by the threshold-based segmentation method. Lung flow volume changes were recorded with a spirometer for each patient. A total of 192 selected points at a regular spacing of 2 × 2 cm matrix points were used to detect chest wall motion over a total area of 32 × 24 cm covering the chest and abdomen surfaces. QR factorization with column pivoting was employed to remove redundant observations of the chest and abdominal areas. To create a statistical model between the lung surface and the corresponding surrogate signals, the authors developed a predictive model based on canonical ridge regression. Two unique weighting vectors were selected for each vertex on the lung surface; they were optimized during the training process using all other 4D-CT phases except for the test inspiration phase. These parameters were employed to predict the vertex locations of a testing data set. RESULTS: The position of each lung surface mesh vertex was estimated from the motion at selected positions within the chest wall surface and from spirometry measurements in ten lung cancer patients. The average estimation of the 98th error percentile for the end inspiration phase was less than 1 mm (AP = 0.9 mm, RL = 0.6 mm, and SI = 0.8 mm). The vertices located at the lower region of the lung had a larger estimation error as compared with those within the upper region of the lung. The average landmark motion errors, derived from the biomechanical modeling using real surface deformation vector fields (SDVFs), and the predicted SDVFs were 3.0 and 3.1 mm, respectively. CONCLUSIONS: Our newly developed predictive model provides a noninvasive approach to derive lung boundary conditions. The proposed system can be used with personalized biomechanical respiration modeling to derive lung tumor motion during radiation therapy from noninvasive measurements.

Measurement of retinal arteriolar diameters from auto scale phase congruency with fuzzy weighting and L1 regularization.

Manual measurements of small changes in retinal vascular diameter are slow and may be subject to considerable observer-related biases. Among the conventional automatic methods the sliding linear regression filter (SLRF) demonstrates the least scattered and most repeatable coefficients. For optimal performance it relies on the choice of the correct filter scale for different vessel sizes. A small scale extracts fine details at the expense noise sensitivity, while large scales have poor edge localization. Here we use auto scale phase congruency to select the filter scales with fuzzy weighting to reduce noise, and L1 regularization for edge smoothing. Our method uses a one dimensional analysis normal to the vessel and so is faster than the 2D phase congruency. In 65 vessels randomly selected from 20 images the proposed method showed better repeatability and over three times less scattering than conventional SLRF.

A comparison between compressed sensing algorithms in electrical impedance tomography.

Electrical Impedance Tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. Conventional EIT reconstruction methods solve a linear model by minimizing the least squares error, i.e., the Euclidian or L2-norm, with regularization. Compressed sensing provides unique advantages in Magnetic Resonance Imaging (MRI) [1] when the images are transformed to a sparse basis. EIT images are generally sparser than MRI images due to their lower spatial resolution. This leads us to investigate ability of compressed sensing algorithms currently applied to MRI in EIT without transformation to a new basis. In particular, we examine four new iterative algorithms for L1 and L0 minimization with applications to compressed sensing and compare these with current EIT inverse L1-norm regularization methods. The four compressed sensing methods are as follows: (1) an interior point method for solving L1-regularized least squares problems (L1-LS); (2) total variation using a Lagrangian multiplier method (TVAL3); (3) a two-step iterative shrinkage / thresholding method (TWIST) for solving the L0-regularized least squares problem; (4) The Least Absolute Shrinkage and Selection Operator (LASSO) with tracing the Pareto curve, which estimates the least squares parameters subject to a L1-norm constraint. In our investigation, using 1600 elements, we found all four CS algorithms provided an improvement over the best conventional EIT reconstruction method, Total Variation, in three important areas: robustness to noise, increased computational speed of at least 40x and a visually apparent improvement in spatial resolution. Out of the four CS algorithms we found TWIST was the fastest with at least a 100x speed increase.

Dry electrode bio-potential recordings.

As wireless bio-medical long term monitoring moves towards personal monitoring it demands very high input impedance systems capable to extend the reading of bio-signal during the daily activities offering a kind of "stress free", convenient connection, with no need for skin preparation. In particular we highlight the development and broad applications of our own circuits for wearable bio-potential sensor systems enabled by the use of an FET based amplifier circuit with sufficiently high impedance to allow the use of passive dry electrodes which overcome the significant barrier of gel based contacts. In this paper we present the ability of dry electrodes in long term monitoring of ECG, EEG and fetal ECG.