Francesc Massanes

Compute-unified device architecture implementation of a block-matching algorithm for multiple graphical processing unit cards

In this paper we describe and evaluate a fast implementation of a classical block matching motion estimation algorithm for multiple Graphical Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) computing engine. The implemented block matching algorithm (BMA) uses summed absolute difference (SAD) error criterion and full grid search (FS) for finding optimal block displacement. In this evaluation we compared the execution time of a GPU and CPU implementation for images of various sizes, using integer and non-integer search grids.The results show that use of a GPU card can shorten computation time by a factor of 200 times for integer and 1000 times for a non-integer search grid. The additional speedup for non-integer search grid comes from the fact that GPU has built-in hardware for image interpolation. Further, when using multiple GPU cards, the presented evaluation shows the importance of the data splitting method across multiple cards, but an almost linear speedup with a number of cards is achievable.In addition we compared execution time of the proposed FS GPU implementation with two existing, highly optimized non-full grid search CPU based motion estimations methods, namely implementation of the Pyramidal Lucas Kanade Optical flow algorithm in OpenCV and Simplified Unsymmetrical multi-Hexagon search in H.264/AVC standard. In these comparisons, FS GPU implementation still showed modest improvement even though the computational complexity of FS GPU implementation is substantially higher than non-FS CPU implementation.We also demonstrated that for an image sequence of 720×480 pixels in resolution, commonly used in video surveillance, the proposed GPU implementation is sufficiently fast for real-time motion estimation at 30 frames-per-second using two NVIDIA C1060 Tesla GPU cards.

GPU based calculation of a SPECT projection operator for content adaptive mesh model

In this paper we explore the use of a graphical processing unit (GPU) in a fast calculation of a projection operator using a ray casting algorithm for a content-adaptive mesh model (CAMM). Previously we had introduced 2D and 3D tomographic image reconstruction using a CAMM for emission tomography (EM). The proposed GPU based projection operator calculation is fast and allows incorporation of a non-uniform attenuation and distance-dependent spatial resolution of the imaging system. The GPU allows for fast computation, therefore establishing a necessary step for the future practical development of a CAMM tomographic reconstruction.

Parallel computation of a SPECT projection operator for a content adaptative mesh model

In this paper we explore a parallel implementation for fast calculation of a tomographic projection operator for content-adaptive mesh model (CAMM) image reconstruction. Previously we introduced 2D and 3D tomographic image reconstruction using a CAMM for single positron emission computed tomography (SPECT). The proposed parallel method is fast and allows incorporation of a non-uniform attenuation and distance-dependent spatial resolution of the imaging system. This implementation establishes a necessary step for the future development and practical use of the CAMM tomo-graphic reconstruction.

Motion perception in medical imaging

A potential drawback of image noise suppression in medical image sequence processing is a possible loss of the apparent motion: making objects appears to move slower or less then they move in reality. For medical imaging application this can be of critical importance, for example myocardium motion in cardiac gated single photon emission computed tomography (SPECT) imaging can differentiate viable muscle from scar tissue. Therefore, in this work we design a set of experiments to measure how human observers perceive apparent motion in the presence of image degradations like noise and blur. In addition we will try to identify relevant image features, based on a visual attention model and a block matching motion estimation method that would allow development of an accurate numerical observer capable of predicting human observer motion perception.

Evaluation of CNN as anthropomorphic model observer

Model observers (MO) are widely used in medical imaging to act as surrogates of human observers in task-based image quality evaluation, frequently towards optimization of reconstruction algorithms. In this paper, we explore the use of convolutional neural networks (CNN) to be used as MO. We will compare CNN MO to alternative MO currently being proposed and used such as the relevance vector machine based MO and channelized Hotelling observer (CHO). As the success of the CNN, and other deep learning approaches, is rooted in large data sets availability, which is rarely the case in medical imaging systems task-performance evaluation, we will evaluate CNN performance on both large and small training data sets.

Calculations of a SPECT projection operator on a graphical processing unit

In this paper we explore the capabilities of a graphical processing unit (GPU) for the fast calculation of a tomographic projection operator in content-adaptive mesh models (CAMM). We explore the use of two distinct methods, ray-tracing and mesh element projection, both implemented on classical computers (CPU) and GPU. Both methods have already been proposed in the literature for 2D and 3D emission tomography (EM) image reconstruction using a CAMM, however, there was no clear comparison between both methods in terms of computational efficiency, which is an aim of this paper.

Inter‐laboratory comparison of channelized hotelling observer computation

Purpose The task‐based assessment of image quality using model observers is increasingly used for the assessment of different imaging modalities. However, the performance computation of model observers needs standardization as well as a well‐established trust in its implementation methodology and uncertainty estimation. The purpose of this work was to determine the degree of equivalence of the channelized Hotelling observer performance and uncertainty estimation using an intercomparison exercise. Materials and Methods Image samples to estimate model observer performance for detection tasks were generated from two‐dimensional CT image slices of a uniform water phantom. A common set of images was sent to participating laboratories to perform and document the following tasks: (a) estimate the detectability index of a well‐defined CHO and its uncertainty in three conditions involving different sized targets all at the same dose, and (b) apply this CHO to an image set where ground truth was unknown to participants (lower image dose). In addition, and on an optional basis, we asked the participating laboratories to (c) estimate the performance of real human observers from a psychophysical experiment of their choice. Each of the 13 participating laboratories was confidentially assigned a participant number and image sets could be downloaded through a secure server. Results were distributed with each participant recognizable by its number and then each laboratory was able to modify their results with justification as model observer calculation are not yet a routine and potentially error prone. Results Detectability index increased with signal size for all participants and was very consistent for 6 mm sized target while showing higher variability for 8 and 10 mm sized target. There was one order of magnitude between the lowest and the largest uncertainty estimation. Conclusions This intercomparison helped define the state of the art of model observer performance computation and with thirteen participants, reflects openness and trust within the medical imaging community. The performance of a CHO with explicitly defined channels and a relatively large number of test images was consistently estimated by all participants. In contrast, the paper demonstrates that there is no agreement on estimating the variance of detectability in the training and testing setting.

A resampling comparison of CHO's detectability index bias and uncertainty

Model observers have gained popularity as a surrogate approach for image quality assessment, they are often used for the optimization of the reconstruction algorithm. The most widespread model observer is the channelized Hotelling observer (CHO) that allows measuring the image quality by calculating the detectability index (or associated area under receiver operating characteristic curve). In this work we have chosen to explore different resampling methods used to estimate the CHO performance and uncertainty. In this paper, using data from the inter-laboratory comparison of the computation of CHO model observer study, we established a simulation framework to fully evaluate different resampling methods, namely, leave-one out and bootstrapping with replacement to estimate the CHO’s detectability index bias and uncertainty. For this particular study, we focus our experiments on datasets with a few data samples, 200 normal and 200 abnormal images.

Full receiver operating characteristic curve estimation using two alternative forced choice studies

Abstract. Task-based medical image quality is typically measured by the degree to which a human observer can perform a diagnostic task in a psychophysical human observer study. During a typical study, an observer is asked to provide a numerical score quantifying his confidence as to whether an image contains a diagnostic marker or not. Such scores are then used to measure the observers’ diagnostic accuracy, summarized by the receiver operating characteristic (ROC) curve and the area under ROC curve. These types of human studies are difficult to arrange, costly, and time consuming. In addition, human observers involved in this type of study should be experts on the image genre to avoid inconsistent scoring through the lengthy study. In two-alternative forced choice (2AFC) studies, known to be faster, two images are compared simultaneously and a single indicator is given. Unfortunately, the 2AFC approach cannot lead to a full ROC curve or a set of image scores. The aim of this work is to propose a methodology in which multiple rounds of the 2AFC studies are used to re-estimate an image confidence score (a.k.a. rating, ranking) and generate the full ROC curve. In the proposed approach, we treat image confidence score as an unknown rating that needs to be estimated and 2AFC as a two-player match game. To achieve this, we use the ELO rating system, which is used for calculating the relative skill levels of players in competitor-versus-competitor games such as chess. The proposed methodology is not limited to ELO, and other rating methods such as TrueSkill™, Chessmetrics, or Glicko can be also used. The presented results, using simulated data, indicate that a full ROC curve can be recovered using several rounds of 2AFC studies and that the best pairing strategy starts with the first round of pairing abnormal versus normal images (as in the classical 2AFC approach) followed by a number of rounds using random pairing. In addition, the proposed method was tested in a pilot human observer study. These pilot results indicate that three to five rounds of 2AFC studies require less human observer time than a full scoring study and that the re-estimated ROC curves and associated area under ROC curve values have high statistical agreement with the full scoring study.

Motion compensated reconstruction of 4D SPECT using parallel computation and deformable content adaptive mesh

In this abstract, we present preliminary evaluation of a full 4D reconstruction method aimed at cardiac gated emission tomography. The proposed method uses content adaptive mesh model (CAMM) to represent the 3D volumetric data. The nodes of the CAMM are then moved from a gate to gate, according to the motion obtained from a motion estimation algorithm, to form 4D deformable content adaptive mesh model (DCAMM). The use of a CAMM as well as DCAMM allows incorporating all data degradation models, namely objecting attenuation and detector-collimator spatial response, referred to as distance dependent blur. The parallel implementation, using OpenCL, was essential to shorten the needed computational time and make this a practical reconstruction methodology. In this work we successfully tested proposed parallel implementation by reconstructing images obtained from a realistic cardiac gated SPECT simulation using SIMIND Monte Carlo package and NCAT phantom.