Maximum A Posteriori Algorithm Research Articles

Optimized equalization based on MAP detection for C-band bandwidth-limited IM/DD systems.

Entangled dynamic and deterministic inter-symbol interferences (ISIs) induced by complicated channel impairments, limit the transmission capacity of intensity modulation and direct detection (IM/DD) systems. This Letter proposes a colored noise-suppressed channel shortening filter (CNS-CSF)-enabled maximum a posteriori (MAP) estimation (CNS-CSF-MAP) scheme to disentangle and mitigate deterministic and dynamic ISIs, where the CNS-CSF is deployed to perform the optimized dynamic ISI equalization with equalization-enhanced noise suppression, and the subsequent MAP algorithm is used to eliminate the residual deterministic ISI. The performance of the CNS-CSF-MAP scheme is evaluated and demonstrated in a C-band 61-Gb/s 100-km optical on-off keying (OOK) IM/DD system. The experimental results show that the proposed CNS-CSF-MAP scheme reaches the 20% and 7% forward error correction (FEC) thresholds at received optical powers (ROPs) of -6.6 dBm and -4 dBm, achieving 0.5- and 1.5-dB gains over a conventional post-filter-enabled MAP (PF-MAP) scheme.

Verilog HDL using LTE Implementation MAP Algorithm

In many communication systems, turbo coding Techniques for Encoding and Decoding are employed to repair errors. As compared to other error correction codes, turbo codes provide great error correcting capabilities. For the implementation of the Turbo decoder, a Very Large Scale Integration (VLSI) architecture is suggested in this study. The Maximum-a-Posteriori (MAP) algorithm is employed at the decoder side, where soft-in-soft-out decoders, interleaves, and deinterleavers are all used. The usage of the MAP algorithm reduces the quantity of iterations necessary to decode the information bits being transferred. This research employs a system for the encoder component that consists of two recursive convolutional encoders and a pseudorandom interleaver on the encoder side. Tools from Octave and Xilinx Vivado are used for the Turbo encoding and decoding. The system is synthesised and implemented using a specialised integrated circuit.

A Literature Survey on Algorithms and Hardware Architectures of Max-Log-MAP Demapping

Emergent wireless communication standards, which are employed in different transmission environments, support various modulation schemes. High-order constellations are targeted to achieve high bandwidth efficiency. However, the complexity of the symbol-by-symbol Maximum A Posteriori (MAP) algorithm increases dramatically for these high-order modulation schemes. In order to reduce the hardware complexity, the suboptimal Max-Log-MAP, which is the direct transformation of the MAP algorithm into logarithmic domain, is alternatively implemented. In the literature, a great deal of research effort has been invested into Max-Log-MAP demapping. Several simplifications are presented to meet with specific constellations. In addition, the hardware implementations dedicated for Max-Log-MAP demapping vary greatly in terms of design choices, supported flexibility and performance criteria, making them a challenge to compare. This paper explores the published Max-Log-MAP algorithm simplifications and existing hardware demapper designs and presents an extensive review of the current literature. In-depth comparisons are drawn amongst the designs and different key performance characteristics are described, namely, achieved throughput, hardware resource requirements and flexibility. This survey should facilitate fair comparisons of future designs, as well as opportunities for improving the design of Max-Log-MAP demappers.

Artificial Neural Network Enhanced Bayesian PET Image Reconstruction.

In positron emission tomography (PET) image reconstruction, the Bayesian framework with various regularization terms has been implemented to constrain the radio tracer distribution. Varying the regularizing weight of a maximum a posteriori (MAP) algorithm specifies a lower bound of the tradeoff between variance and spatial resolution measured from the reconstructed images. The purpose of this paper is to build a patch-based image enhancement scheme to reduce the size of the unachievable region below the bound and thus to quantitatively improve the Bayesian PET imaging. We cast the proposed enhancement as a regression problem which models a highly nonlinear and spatial-varying mapping between the reconstructed image patches and an enhanced image patch. An artificial neural network model named multilayer perceptron (MLP) with backpropagation was used to solve this regression problem through learning from examples. Using the BrainWeb phantoms, we simulated brain PET data at different count levels of different subjects with and without lesions. The MLP was trained using the image patches reconstructed with a MAP algorithm of different regularization parameters for one normal subject at a certain count level. To evaluate the performance of the trained MLP, reconstructed images from other simulations and two patient brain PET imaging data sets were processed. In every testing cases, we demonstrate that the MLP enhancement technique improves the noise and bias tradeoff compared with the MAP reconstruction using different regularizing weights thus decreasing the size of the unachievable region defined by the MAP algorithm in the variance/resolution plane.

Implementation Of Low Complex SOVA In GNU Radio

Turbo codes are extensively used in wireless communication standards due to its capacity approaching performance. Maximum-A-Posteriori (MAP) algorithm and Soft Output Viterbi Algorithm (SOVA) are widely accepted decoding algorithms for Turbo codes. SOVA is a variant of Viterbi Algorithm and is less complex with moderate error correction performance as compared to MAP algorithm. In this paper, the branch symmetry property of radix-2 butterfly has been incorporated in to the SOVA algorithm to reduce the complexity of branch metric computation. The simulation results show that the computational complexity is reduced without degrading the error correction performance. Turbo CODEC block with SOVA and the low complex SOVA has been created in GNU Radio and the results were verified.

Target Tracking with Sensor Navigation Using Coupled RSS and AoA Measurements.

This work addresses the problem of tracking a signal-emitting mobile target in wireless sensor networks (WSNs) with navigated mobile sensors. The sensors are properly equipped to acquire received signal strength (RSS) and angle of arrival (AoA) measurements from the received signal, while the target transmit power is assumed not known. We start by showing how to linearize the highly non-linear measurement model. Then, by employing a Bayesian approach, we combine the linearized observation model with prior knowledge extracted from the state transition model. Based on the maximum a posteriori (MAP) principle and the Kalman filtering (KF) framework, we propose new MAP and KF algorithms, respectively. We also propose a simple and efficient mobile sensor navigation procedure, which allows us to further enhance the estimation accuracy of our algorithms with a reduced number of sensors. Model flaws, which result in imperfect knowledge about the path loss exponent (PLE) and the true mobile sensors’ locations, are taken into consideration. We have carried out an extensive simulation study, and our results confirm the superiority of the proposed algorithms, as well as the effectiveness of the proposed navigation routine.

An Iterative Parameter-Free MAP Algorithm With an Application to Forward Looking GPR Imaging

Ground-penetrating radar (GPR) has been used in a number of applications including the detection of landmines and improvised explosive devices. Most existing algorithms for forward looking GPR image reconstruction require that a hyperparameter or regularization parameter be chosen by the user. Selecting these parameters in an optimal fashion is a challenging problem that, in theory, can be addressed using cross validation and the L-curve method. Unfortunately, these hyperparameter selection methods are time consuming and not well suited for real-time applications. In this paper, we propose a hyperparameter-free algorithm that follows from a maximum a posteriori (MAP) formulation where the prior probability density function is obtained by the so-called “integrate-out” approach. First, we model the reflection coefficients as statistically independent random variables that are identically distributed and Laplacian with parameter, $\lambda $ . Then, we analytically integrate out the parameter, $\lambda $ , and optimize the resulting MAP objective function using the majorize–minimize optimization technique and soft-thresholding operator. We tested the algorithm using synthetic and real experimental data, and compared the results subjectively and objectively with the results obtained from well-known algorithms. Although the evaluation is not an exhaustive study, it demonstrates the feasibility of the proposed algorithm.

Performance analysis of a low‐complexity MAP algorithm for automatic modulation classification in adaptive OFDM systems

Automatic modulation classification (AMC) techniques enable the estimation of the bit allocation table in adaptive orthogonal frequency division multiplexing (OFDM) systems. In this study, the authors investigate the classification performance of a low-complexity maximum-a-posteriori (MAP) algorithm for AMC. Also, they derive an approximation of the probability of classification error in a subcarrier for this classifier. Furthermore, based on these theoretical derivations, a novel modified rate adaptive (RA) bit loading algorithm with additional classification error constraint is proposed. Numerical results show that the authors’ proposed method outperforms the RA algorithm significantly in terms of classification performance, while providing reasonable data throughput specifically at higher signal-to-noise ratios.

Sparsity-constrained PET image reconstruction with learned dictionaries

PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging.

Lesion quantification and detection in myocardial (18)F-FDG PET using edge-preserving priors and anatomical information from CT and MRI: a simulation study.

BackgroundThe limited spatial resolution of the clinical PET scanners results in image blurring and does not allow for accurate quantification of very thin or small structures (known as partial volume effect). In cardiac imaging, clinically relevant questions, e.g. to accurately define the extent or the residual metabolic activity of scarred myocardial tissue, could benefit from partial volume correction (PVC) techniques.The use of high-resolution anatomical information for improved reconstruction of the PET datasets has been successfully applied in other anatomical regions. However, several concerns linked to the use of any kind of anatomical information for PVC on cardiac datasets arise. The moving nature of the heart, coupled with the possibly non-simultaneous acquisition of the anatomical and the activity datasets, is likely to introduce discrepancies between the PET and the anatomical image, that in turn might mislead lesion quantification and detection. Non-anatomical (edge-preserving) priors could represent a viable alternative for PVC in this case.In this work, we investigate and compare the regularizing effect of different anatomical and non-anatomical priors applied during maximum-a-posteriori (MAP) reconstruction of cardiac PET datasets. The focus of this paper is on accurate quantification and lesion detection in myocardial 18F-FDG PET.MethodsSimulated datasets, obtained with the XCAT software, are reconstructed with different algorithms and are quantitatively analysed.ResultsThe results of this simulation study show a superiority of the anatomical prior when an ideal, perfectly matching anatomy is used. The anatomical information must clearly differentiate between normal and scarred myocardial tissue for the PVC to be successful. In case of mismatched or missing anatomical information, the quality of the anatomy-based MAP reconstructions decreases, affecting both overall image quality and lesion quantification. The edge-preserving priors produce reconstructions with good noise properties and recovery of activity, with the advantage of not relying on an external, additional scan for anatomy.ConclusionsThe performance of edge-preserving priors is acceptable but inferior to those of a well-applied anatomical prior that differentiates between lesion and normal tissue, in the detection and quantification of a lesion in the reconstructed images. When considering bull’s eye plots, all of the tested MAP algorithms produced comparable results.

Performance of 3DOSEM and MAP algorithms for reconstructing low count SPECT acquisitions

PurposeLow count single photon emission computed tomography (SPECT) is becoming more important in view of whole body SPECT and reduction of radiation dose. In this study, we investigated the performance of several 3D ordered subset expectation maximization (3DOSEM) and maximum a posteriori (MAP) algorithms for reconstructing low count SPECT images. Materials and MethodsPhantom experiments were conducted using the National Electrical Manufacturers Association (NEMA) NU2 image quality (IQ) phantom. The background compartment of the phantom was filled with varying concentrations of pertechnetate and indiumchloride, simulating various clinical imaging conditions. Images were acquired using a hybrid SPECT/CT scanner and reconstructed with 3DOSEM and MAP reconstruction algorithms implemented in Siemens Syngo MI.SPECT (Flash3D) and Hermes Hybrid Recon Oncology (Hyrid Recon 3DOSEM and MAP). Image analysis was performed by calculating the contrast recovery coefficient (CRC),percentage background variability (N%), and contrast-to-noise ratio (CNR), defined as the ratio between CRC and N%. Furthermore, image distortion is characterized by calculating the aspect ratio (AR) of ellipses fitted to the hot spheres. Additionally, the performance of these algorithms to reconstruct clinical images was investigated. ResultsImages reconstructed with 3DOSEM algorithms demonstrated superior image quality in terms of contrast and resolution recovery when compared to images reconstructed with filtered-back-projection (FBP), OSEM and 2DOSEM. However, occurrence of correlated noise patterns and image distortions significantly deteriorated the quality of 3DOSEM reconstructed images. The mean AR for the 37, 28, 22, and 17mm spheres was 1.3, 1.3, 1.6, and 1.7 respectively. The mean N% increase in high and low count Flash3D and Hybrid Recon 3DOSEM from 5.9% and 4.0% to 11.1% and 9.0%, respectively. Similarly, the mean CNR decreased in high and low count Flash3D and Hybrid Recon 3DOSEM from 8.7 and 8.8 to 3.6 and 4.2, respectively. Regularization with smoothing priors could suppress these noise patterns at the cost of reduced image contrast. The mean N% was 6.4% and 6.8% for low count QSP and MRP MAP reconstructed images. Alternatively, regularization with an anatomical Bowhser prior resulted in sharp images with high contrast, limited image distortion, and low N% of 8.3% in low count images, although some image artifacts did occur. Analysis of clinical images suggested that the same effects occur in clinical imaging. ConclusionImage quality of low count SPECT acquisitions reconstructed with modern 3DOSEM algorithms is deteriorated by the occurrence of correlated noise patterns and image distortions. The artifacts observed in the phantom experiments can also occur in clinical imaging.

Bayesian algorithm for high‐resolution DOA estimation

A maximum a posteriori (MAP) algorithm based on Bayesian criterion for high-resolution direction-of-arrival (DOA) estimation in array signal processing is proposed. The generalised Gaussian distribution was considered as the prior information of sources distribution for its wide applicability. The statistic parameter of the generalised Gaussian function can be transformed according to different sources distribution conditions. Only the sparse signal recovery problem is considered. It is found that with the generalised Gaussian sparse constraint, the proposed MAP algorithm provides high DOA estimation performance in the case of limited snapshots. Simulation results are given to verify the effectiveness and efficiency of the proposed algorithm.

Speech enhancement using Maximum A-Posteriori and Gaussian Mixture Models for speech and noise Periodogram estimation

In speech enhancement, Gaussian Mixture Models (GMMs) can be used to model the Probability Density Function (PDF) of the Periodograms of speech and different noise types. These GMMs are created by applying the Estimate Maximization (EM) algorithm on large datasets of speech and different noise type Periodograms and hence classify them into a small number of clusters whose centroid Periodograms are the mean vectors of the GMMs. These GMMs are used to realize the Maximum A-Posteriori (MAP) estimation of the speech and noise Periodograms present in a noisy speech observation. To realize the MAP estimation, use of a constrained optimization algorithm is proposed in which relatively good enhancement results with high processing times are attained. Due to the use of constraints in the optimization algorithm, incorrect estimation results may arise due to possible local maxima. A simple analytic MAP algorithm is proposed to attain global maximums in lower calculation times. With the new method the complicated MAP formula is simplified as much as possible to find the maxima, through solving a set of equations and not through conventional numerical methods used in optimization. This method results in excellent speech enhancement with a relatively short processing time.

Real-time maximum a-posteriori image reconstruction for fluorescence microscopy

Rapid reconstruction of multidimensional image is crucial for enabling real-time 3D fluorescence imaging. This becomes a key factor for imaging rapidly occurring events in the cellular environment. To facilitate real-time imaging, we have developed a graphics processing unit (GPU) based real-time maximum a-posteriori (MAP) image reconstruction system. The parallel processing capability of GPU device that consists of a large number of tiny processing cores and the adaptability of image reconstruction algorithm to parallel processing (that employ multiple independent computing modules called threads) results in high temporal resolution. Moreover, the proposed quadratic potential based MAP algorithm effectively deconvolves the images as well as suppresses the noise. The multi-node multi-threaded GPU and the Compute Unified Device Architecture (CUDA) efficiently execute the iterative image reconstruction algorithm that is ≈200-fold faster (for large dataset) when compared to existing CPU based systems.

ANFIS‐EM approach for PET brain image reconstruction

ABSTRACTIn this article, for the reconstruction of the positron emission tomography (PET) images, an iterative MAP algorithm was instigated with its adaptive neurofuzzy inference system based image segmentation techniques which we call adaptive neurofuzzy inference system based expectation maximization algorithm (ANFIS‐EM). This expectation maximization (EM) algorithm provides better image quality when compared with other traditional methodologies. The efficient result can be obtained using ANFIS‐EM algorithm. Unlike any usual EM algorithm, the predicted method that we call ANFIS‐EM minimizes the EM objective function using maximum a posteriori (MAP) method. In proposed method, the ANFIS‐EM algorithm was instigated by neural network based segmentation process in the image reconstruction. By the image quality parameter of PSNR value, the adaptive neurofuzzy based MAP algorithm and de‐noising algorithm compared and the PET input image is reconstructed and simulated in MATLAB/simulink package. Thus ANFIS‐EM algorithm provides 40% better peak signal to noise ratio (PSNR) when compared with MAP algorithm. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 1–6, 2015

Anatomy assisted PET image reconstruction incorporating multi-resolution joint entropy

A promising approach in PET image reconstruction is to incorporate high resolution anatomical information (measured from MR or CT) taking the anato-functional similarity measures such as mutual information or joint entropy (JE) as the prior. These similarity measures only classify voxels based on intensity values, while neglecting structural spatial information. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction algorithm wherein the JE measure is supplied by spatial information generated using wavelet multi-resolution analysis. The proposed wavelet-based JE (WJE) MAP algorithm involves calculation of derivatives of the subband JE measures with respect to individual PET image voxel intensities, which we have shown can be computed very similarly to how the inverse wavelet transform is implemented. We performed a simulation study with the BrainWeb phantom creating PET data corresponding to different noise levels. Realistically simulated T1-weighted MR images provided by BrainWeb modeling were applied in the anatomy-assisted reconstruction with the WJE-MAP algorithm and the intensity-only JE-MAP algorithm. Quantitative analysis showed that the WJE-MAP algorithm performed similarly to the JE-MAP algorithm at low noise level in the gray matter (GM) and white matter (WM) regions in terms of noise versus bias tradeoff. When noise increased to medium level in the simulated data, the WJE-MAP algorithm started to surpass the JE-MAP algorithm in the GM region, which is less uniform with smaller isolated structures compared to the WM region. In the high noise level simulation, the WJE-MAP algorithm presented clear improvement over the JE-MAP algorithm in both the GM and WM regions. In addition to the simulation study, we applied the reconstruction algorithms to real patient studies involving DPA-173 PET data and Florbetapir PET data with corresponding T1-MPRAGE MRI images. Compared to the intensity-only JE-MAP algorithm, the WJE-MAP algorithm resulted in comparable regional mean values to those from the maximum likelihood algorithm while reducing noise. Achieving robust performance in various noise-level simulation and patient studies, the WJE-MAP algorithm demonstrates its potential in clinical quantitative PET imaging.

Development and evaluation of convergent and accelerated penalized SPECT image reconstruction methods for improved dose-volume histogram estimation in radiopharmaceutical therapy.

Three-dimensional (3D) dosimetry has the potential to provide better prediction of response of normal tissues and tumors and is based on 3D estimates of the activity distribution in the patient obtained from emission tomography. Dose-volume histograms (DVHs) are an important summary measure of 3D dosimetry and a widely used tool for treatment planning in radiation therapy. Accurate estimates of the radioactivity distribution in space and time are desirable for accurate 3D dosimetry. The purpose of this work was to develop and demonstrate the potential of penalized SPECT image reconstruction methods to improve DVHs estimates obtained from 3D dosimetry methods. The authors developed penalized image reconstruction methods, using maximum a posteriori (MAP) formalism, which intrinsically incorporate regularization in order to control noise and, unlike linear filters, are designed to retain sharp edges. Two priors were studied: one is a 3D hyperbolic prior, termed single-time MAP (STMAP), and the second is a 4D hyperbolic prior, termed cross-time MAP (CTMAP), using both the spatial and temporal information to control noise. The CTMAP method assumed perfect registration between the estimated activity distributions and projection datasets from the different time points. Accelerated and convergent algorithms were derived and implemented. A modified NURBS-based cardiac-torso phantom with a multicompartment kidney model and organ activities and parameters derived from clinical studies were used in a Monte Carlo simulation study to evaluate the methods. Cumulative dose-rate volume histograms (CDRVHs) and cumulative DVHs (CDVHs) obtained from the phantom and from SPECT images reconstructed with both the penalized algorithms and OS-EM were calculated and compared both qualitatively and quantitatively. The STMAP method was applied to patient data and CDRVHs obtained with STMAP and OS-EM were compared qualitatively. The results showed that the penalized algorithms substantially improved the CDRVH and CDVH estimates for large organs such as the liver compared to optimally postfiltered OS-EM. For example, the mean squared errors (MSEs) of the CDRVHs for the liver at 5 h postinjection obtained with CTMAP and STMAP were about 15% and 17%, respectively, of the MSEs obtained with optimally filtered OS-EM. For the CDVH estimates, the MSEs obtained with CTMAP and STMAP were about 16% and 19%, respectively, of the MSEs from OS-EM. For the kidneys and renal cortices, larger residual errors were observed for all algorithms, likely due to partial volume effects. The STMAP method showed promising qualitative results when applied to patient data. Penalized image reconstruction methods were developed and evaluated through a simulation study. The study showed that the MAP algorithms substantially improved CDVH estimates for large organs such as the liver compared to optimally postfiltered OS-EM reconstructions. For small organs with fine structural detail such as the kidneys, a large residual error was observed for both MAP algorithms and OS-EM. While CTMAP provided marginally better MSEs than STMAP, given the extra effort needed to handle misregistration of images at different time points in the algorithm and the potential impact of residual misregistration, 3D regularization methods, such as that used in STMAP, appear to be a more practical choice.

Comparative Study of Simplified MAP Algorithms and an Implementation of Non-Parallel-Radix-2 Turbo Decoder

This paper presents hardware and bit-error-rate (BER) performance analysis of simplified maximum-a-posteriori (MAP) algorithms based on piece-wise-linear-approximations and Maclaurin-series-expansion for the turbo codes. From this comparative study, a simplified MAP algorithm with optimal BER performance is selected and an architecture suitable for high speed application is suggested for the design of soft-input-soft-output (SISO) unit. Subsequently, a quantitative model is proposed for estimating the amount of memory required by SISO unit in terms of sliding window size, data width of internal metrics and total number of systematic and parity bits. Thereafter, a non-parallel-radix-2 architecture of turbo decoder which incorporates SISO unit and quadratic-permutation-polynomial interleaver is presented. Application-specific-integrated-circuit (ASIC) implementation of this turbo decoder is carried out in 130 nm complementary-metal-oxide-semiconductor (CMOS) technology node and its power consumption, design area and operating clock frequency are reported. Finally, a comparison with similar contributions in the literature has shown that the implemented turbo decoder achieves energy efficiency of 0.28 nJ/b/iteraions. Similarly, it has achieved a highest throughput of 28 Mbps among radix-2 and radix-4 non-parallel turbo decoders.

PET image reconstruction using ANN

ABSTRACTThe aim of this study is to improve the positron emission tomography (PET) image quality for medical diagnosis. The statistical reconstructions on the maximum a posteriori (MAP) algorithm often results in a blurring effect, which fails to determine the toughness class in the reconstructed image. The development of new reconstruction algorithms for PET is an active field of research. In this article, artificial neural network (ANN) is proposed for replicating the output image, which is generated from the acquired projection data with the corresponding angles using the PET images. This article proposes the advantage of arranging the neural network to stock up the information of the continuous capacity. This reduces the storage space and recuperates as much sequence of the continuous quantity as possible. The performance of image quality parameters using ANN is better when compared with MAP, FBP‐NN (filtered back projection with nearest neighbor interpolation). Thus ANN provides 63% better peak signal to noise ratio (PSNR) when compared with FBP‐NN and 47% better when compared to MAP. Thus, ANN is better than FBP and MAP algorithm, by providing better PSNR. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 249–255, 2014

SU‐E‐J‐170: Beyond Single‐Cycle 4DCT: Maximum a Posteriori (MAP) Reconstruction‐Based Binning‐Free Multicycle 4DCT for Lung Radiotherapy

Purpose:Thoracic motion changes from cycle‐to‐cycle and day‐to‐day. Conventional 4DCT does not capture these cycle to cycle variations. We present initial results of a novel 4DCT reconstruction technique based on maximum a posteriori (MAP) reconstruction. The technique uses the same acquisition process (and therefore dose) as a conventional 4DCT in order to create a high spatiotemporal resolution cine CT that captures several breathing cycles.Methods:Raw 4DCT data were acquired from a lung cancer patient. The continuous 4DCT was reconstructed using MAP algorithm which uses the raw, time‐stamped CT data to reconstruct images while simultaneously estimating deformation in the subject's anatomy. This framework incorporates physical effects such as hysteresis and is robust to detector noise and irregular breathing patterns. The 4D image is described in terms of a 3D reference image defined at one end of the hysteresis loop, and two deformation vector fields (DVFs) corresponding to inhale motion and exhale motion respectively. The MAP method uses all of the CT projection data and maximizes the log posterior in order to iteratively estimate a timevariant deformation vector field that describes the entire moving and deforming volume.Results:The MAP 4DCT yielded CT‐quality images for multiple cycles corresponding to the entire duration of CT acquisition, unlike the conventional 4DCT, which only yielded a single cycle. Variations such as amplitude and frequency changes and baseline shifts were clearly captured by the MAP 4DCConclusion:We have developed a novel, binning‐free, parameterized 4DCT reconstruction technique that can capture cycle‐to‐cycle variations of respiratory motion. This technique provides an invaluable tool for respiratory motion management research. This work was supported by funding from the National Institutes of Health and VisionRT Ltd.Amit Sawant receives research funding from Varian Medical Systems, Vision RT and Elekta