Model-based Iterative Image Reconstruction Research Articles

Mitigating the Limited View Problem in Photoacoustic Tomography for a Planar Detection Geometry by Regularized Iterative Reconstruction.

The use of a planar detection geometry in photoacoustic tomography results in the so- called limited-view problem due to the finite extent of the acoustic detection aperture. When images are reconstructed using one-step reconstruction algorithms, image quality is compromised by the presence of streaking artefacts, reduced contrast, image distortion and reduced signal-to-noise ratio. To mitigate this, model-based iterative reconstruction approaches based on least squares minimisation with and without total variation regularization were evaluated using in-silico, experimental phantom, ex vivo and in vivo data. Compared to one-step reconstruction methods, it has been shown that iterative methods provide better image quality in terms of enhanced signal-to-artefact ratio, signal-to-noise ratio, amplitude accuracy and spatial fidelity. For the total variation approaches, the impact of the regularization parameter on image feature scale and amplitude distribution was evaluated. In addition, the extent to which the use of Bregman iterations can compensate for the systematic amplitude bias introduced by total variation was studied. This investigation is expected to inform the practical application of model-based iterative image reconstruction approaches for improving photoacoustic image quality when using finite aperture planar detection geometries.

Low-dose multi-detector computed tomography for periradicular infiltrations at the cervical and lumbar spine

Periradicular infiltrations are frequently performed in daily neuroradiological routine and are often guided by multi-detector computed tomography (MDCT), thus leading to radiation exposure. The purpose of this study was to evaluate MDCT with low dose (LD) and model-based iterative reconstruction for image-guided periradicular infiltrations at the cervical and lumbosacral spine. We retrospectively analyzed 204 MDCT scans acquired for the purpose of cervical or lumbosacral periradicular interventions, which were either derived from scanning with standard dose (SD; 40 mA and 120 kVp) or LD (20–30 mA and 120 kVp) using a 128-slice MDCT scanner. The SD cases were matched to the LD cases considering sex, age, level of infiltration, presence of spinal instrumentation, and body diameter. All images were reconstructed using model-based iterative image reconstruction and were evaluated by two readers (R1 and R2) using 5- or 3-point Likert scales (score of 1 reflects the best value per category). Furthermore, noise in imaging data was quantitatively measured by the standard deviation (StDev) of muscle tissue. The dose length product (DLP) was statistically significantly lower for LD scans (6.75 ± 6.43 mGy*cm vs. 10.16 ± 7.70 mGy*cm; p < 0.01; reduction of 33.5%). Image noise was comparable between LD and SD scans (13.13 ± 3.66 HU vs. 13.37 ± 4.08 HU; p = 0.85). Overall image quality was scored as good to very good with only minimal artifacts according to both readers, and determination of the nerve root was possible in almost all patients (LD vs. SD: p > 0.05 for all items). This resulted in high confidence for intervention planning as well as periprocedural intervention guidance for both SD and LD scans. The inter-reader agreement was at least substantial (weighted Cohen’s κ ≥ 0.62), except for confidence in intervention planning for LD scans (κ = 0.49). In conclusion, considerable dose reduction for planning and performing periradicular infiltrations with MDCT using model-based iterative image reconstruction is feasible and can be performed without clinically relevant drawbacks regarding image quality or confidence for planning.

Impact of dose reduction and iterative model reconstruction on multi-detector CT imaging of the brain in patients with suspected ischemic stroke

Non-contrast cerebral computed tomography (CT) is frequently performed as a first-line diagnostic approach in patients with suspected ischemic stroke. The purpose of this study was to evaluate the performance of hybrid and model-based iterative image reconstruction for standard-dose (SD) and low-dose (LD) non-contrast cerebral imaging by multi-detector CT (MDCT). We retrospectively analyzed 131 patients with suspected ischemic stroke (mean age: 74.2 ± 14.3 years, 67 females) who underwent initial MDCT with a SD protocol (300 mAs) as well as follow-up MDCT after a maximum of 10 days with a LD protocol (200 mAs). Ischemic demarcation was detected in 26 patients for initial and in 64 patients for follow-up imaging, with diffusion-weighted magnetic resonance imaging (MRI) confirming ischemia in all of those patients. The non-contrast cerebral MDCT images were reconstructed using hybrid (Philips “iDose4”) and model-based iterative (Philips “IMR3”) reconstruction algorithms. Two readers assessed overall image quality, anatomic detail, differentiation of gray matter (GM)/white matter (WM), and conspicuity of ischemic demarcation, if any. Quantitative assessment included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) calculations for WM, GM, and demarcated areas. Ischemic demarcation was detected in all MDCT images of affected patients by both readers, irrespective of the reconstruction method used. For LD imaging, anatomic detail and GM/WM differentiation was significantly better when using the model-based iterative compared to the hybrid reconstruction method. Furthermore, CNR of GM/WM as well as the SNR of WM and GM of healthy brain tissue were significantly higher for LD images with model-based iterative reconstruction when compared to SD or LD images reconstructed with the hybrid algorithm. For patients with ischemic demarcation, there was a significant difference between images using hybrid versus model-based iterative reconstruction for CNR of ischemic/contralateral unaffected areas (mean ± standard deviation: SD_IMR: 4.4 ± 3.1, SD_iDose: 3.5 ± 2.3, P < 0.0001; LD_IMR: 4.6 ± 2.9, LD_iDose: 3.2 ± 2.1, P < 0.0001). In conclusion, model-based iterative reconstruction provides higher CNR and SNR without significant loss of image quality for non-enhanced cerebral MDCT.

Deep Learning With Adaptive Hyper-Parameters for Low-Dose CT Image Reconstruction

Low-dose CT (LDCT) imaging is preferred in many applications to reduce the object's exposure to X-ray radiation. In recent years, one promising approach to image reconstruction in LDCT is the so-called optimization-unrolling-based deep learning approach, which replaces pre-defined image prior by learnable adaptive prior in some model-based iterative image reconstruction scheme (MBIR). While it is known that setting appropriate hyper-parameters in MBIR is challenging yet important to the reconstruction quality, it does not receive enough attention in the development of deep learning methods. This paper proposed a deep learning method for LDCT reconstruction that unrolls a half-quadratic splitting scheme. The proposed method not only introduces learnable image prior built on framelet filter bank, but also learns a network that automatically adjusts the hyper-parameters to fit noise level and the data for processing. As a result, only one universal model needs to be trained in our method to process the data taken under different dose levels. Experimental evaluation on clinical patient dataset showed that the proposed method outperformed both conventional and deep-learning-based solutions by a large margin.

Simultaneous high-resolution cardiac T1 mapping and cine imaging using model-based iterative image reconstruction.

To provide high-resolution cardiac T1 mapping of various cardiac phases and cine imaging within a single breath-hold using continuous golden ratio-based radial acquisition and model-based iterative image reconstruction. Data acquisition was performed continuously using golden ratio-based radial sampling and multiple inversion pulses were applied independent of the heart rate. Native T1 maps of diastole and systole were reconstructed with in-plane resolution of 1.3 × 1.3 mm2 using model-based iterative image reconstruction. Cine images with 30 cardiac phases were reconstructed from the same data using kt-SENSE. The method was evaluated in a commercially available T1 phantom and 10 healthy subjects. In vivo T1 assessment was carried out segment-wise. Evaluation in the phantom demonstrated accurate T1 times (R2 > 0.99) and insensitivity to the heart rate. In vivo T1 values did not differ between systole and diastole, and T1 times assessed by the proposed approach were longer than measured with a modified Look-Locker inversion recovery (MOLLI) sequence, except for lateral segments. Cine images had a consistent dark-blood contrast and functional assessment was in agreement with assessment based on Cartesian cine scans (difference in ejection fraction: 0.26 ± 2.65%, P = 0.65). The proposed approach provides native T1 maps of diastole and systole with high spatial resolution and cine images simultaneously within 16 s, which could strongly improve the scan efficiency.

Model-based Iterative CT Image Reconstruction on GPUs

Computed Tomography (CT) Image Reconstruction is an important technique used in a variety of domains, including medical imaging, electron microscopy, non-destructive testing and transportation security. Model-based Iterative Reconstruction (MBIR) using Iterative Coordinate Descent (ICD) is a CT algorithm that produces state-of-the-art results in terms of image quality. However, MBIR is highly computationally intensive and challenging to parallelize, and has traditionally been viewed as impractical in applications where reconstruction time is critical. We present the first GPU-based algorithm for ICD-based MBIR. The algorithm leverages the recently-proposed concept of SuperVoxels, and efficiently exploits the three levels of parallelism available in MBIR to better utilize the GPU hardware resources. We also explore data layout transformations to obtain more coalesced accesses and several GPU-specific optimizations for MBIR that boost performance. Across a suite of 3200 test cases, our GPU implementation obtains a geometric mean speedup of 4.43X over a state-of-the-art multi-core implementation on a 16-core iso-power CPU.

Feasibility of low-dose CT with model-based iterative image reconstruction in follow-up of patients with testicular cancer

PurposeWe examine the performance of pure model-based iterative reconstruction with reduced-dose CT in follow-up of patients with early-stage testicular cancer. MethodsSixteen patients (mean age 35.6±7.4years) with stage I or II testicular cancer underwent conventional dose (CD) and low-dose (LD) CT acquisition during CT surveillance. LD data was reconstructed with model-based iterative reconstruction (LD–MBIR). Datasets were objectively and subjectively analysed at 8 anatomical levels. Two blinded clinical reads were compared to gold-standard assessment for diagnostic accuracy. ResultsMean radiation dose reduction of 67.1% was recorded. Mean dose measurements for LD–MBIR were: thorax – 66±11mGycm (DLP), 1.0±0.2mSv (ED), 2.0±0.4mGy (SSDE); abdominopelvic – 128±38mGycm (DLP), 1.9±0.6mSv (ED), 3.0±0.6mGy (SSDE). Objective noise and signal-to-noise ratio values were comparable between the CD and LD–MBIR images. LD–MBIR images were superior (p<0.001) with regard to subjective noise, streak artefact, 2-plane contrast resolution, 2-plane spatial resolution and diagnostic acceptability. All patients were correctly categorised as positive, indeterminate or negative for metastatic disease by 2 readers on LD–MBIR and CD datasets. ConclusionsMBIR facilitated a 67% reduction in radiation dose whilst producing images that were comparable or superior to conventional dose studies without loss of diagnostic utility.

Contrast improvement of continuous wave diffuse optical tomography reconstruction by hybrid approach using least square and genetic algorithm.

Reconstruction of the absorption coefficient of tissue with good contrast is of key importance in functional diffuse optical imaging. A hybrid approach using model-based iterative image reconstruction and a genetic algorithm is proposed to enhance the contrast of the reconstructed image. The proposed method yields an observed contrast of 98.4%, mean square error of 0.638×10⁻³, and object centroid error of (0.001 to 0.22) mm. Experimental validation of the proposed method has also been provided with tissue-like phantoms which shows a significant improvement in image quality and thus establishes the potential of the method for functional diffuse optical tomography reconstruction with continuous wave setup. A case study of finger joint imaging is illustrated as well to show the prospect of the proposed method in clinical diagnosis. The method can also be applied to the concentration measurement of a region of interest in a turbid medium.

How Low Can We Go in Contrast-Enhanced CT Imaging of the Chest?: A Dose-Finding Cadaver Study Using the Model-based Iterative Image Reconstruction Approach

Dose reduction may compromise patients because of a decrease of image quality. Therefore, the amount of dose savings in new dose-reduction techniques needs to be thoroughly assessed. To avoid repeated studies in one patient, chest computed tomography (CT) scans with different dose levels were performed in corpses comparing model-based iterative reconstruction (MBIR) as a tool to enhance image quality with current standard full-dose imaging. Twenty-five human cadavers were scanned (CT HD750) after contrast medium injection at different, decreasing dose levels D0-D5 and respectively reconstructed with MBIR. The data at full-dose level, D0, have been additionally reconstructed with standard adaptive statistical iterative reconstruction (ASIR), which represented the full-dose baseline reference (FDBR). Two radiologists independently compared image quality (IQ) in 3-mm multiplanar reformations for soft-tissue evaluation of D0-D5 to FDBR (-2, diagnostically inferior; -1, inferior; 0, equal; +1, superior; and +2, diagnostically superior). For statistical analysis, the intraclass correlation coefficient (ICC) and the Wilcoxon test were used. Mean CT dose index values (mGy) were as follows: D0/FDBR=10.1±1.7, D1=6.2±2.8, D2=5.7±2.7, D3=3.5±1.9, D4=1.8±1.0, and D5=0.9±0.5. Mean IQ ratings were as follows: D0=+1.8±0.2, D1=+1.5±0.3, D2=+1.1±0.3, D3=+0.7±0.5, D4=+0.1±0.5, and D5=-1.2±0.5. All values demonstrated a significant difference to baseline (P<.05), except mean IQ for D4 (P=.61). ICC was 0.91. Compared to ASIR, MBIR allowed for a significant dose reduction of 82% without impairment of IQ. This resulted in a calculated mean effective dose below 1mSv.

The bottom dose limit for soft tissue evaluation in contrast enhanced CT of the chest: A dose finding cadaver study using a model-based iterative image reconstruction approach

The bottom dose limit for soft tissue evaluation in contrast enhanced CT of the chest: A dose finding cadaver study using a model-based iterative image reconstruction approach

Dose-reduced CT with model-based iterative reconstruction in evaluations of hepatic steatosis: How low can we go?

PurposeTo determine whether dose-reduced CT with model-based iterative image reconstruction (MBIR) is a useful tool with which to diagnose hepatic steatosis. Materials and methodsThis prospective clinical study approved by our Institutional Review Board included 103 (67 men and 36 women; mean age, 64.3 years) patients who provided written informed consent to undergo unenhanced CT. Images of reference-dose CT (RDCT) with filtered back projection (R-FBP) and low- and ultralow-dose CT (dose-length product; 24 and 9% of that of RDCT) with MBIR (L-MBIR and UL-MBIR) were reconstructed. Mean CT numbers of liver (CT[L]) and spleen (CT[S]), and quotient (CT[L/S]) of CT[L] and CT[S] were calculated from selected regions of interest. Bias and limits of agreement (LOA) of CT[L] and CT[L/S] in L-MBIR and UL-MBIR (vs. R-FBP) were assessed using Bland–Altman analyses. Diagnostic methods for hepatic steatosis of CT[L]<48 Hounsfield units (HU) and CT[L/S]<1.1 were applied to L-MBIR and UL-MBIR using R-FBP as the reference standard. ResultsBias was larger for CT[L] in UL-MBIR than in L-MBIR (−3.18HU vs. −1.73HU). The LOA of CT[L/S] was larger for UL-MBIR than for L-MBIR (±0.425 vs. ±0.245) and outliers were identified in CT[L/S] of UL-MBIR. Accuracy (0.92–0.95) and the area under the receiver operating characteristics curve (0.976–0.992) were high for each method, but some were slightly lower in UL-MBIR than L-MBIR. ConclusionDose-reduced CT reconstructed with MBIR is applicable to diagnose hepatic steatosis, however, a low dose of radiation might be preferable.

Projection Error Propagation-based Regularization (PEPR) method for resistivity reconstruction in Electrical Impedance Tomography (EIT)

A novel Projection Error Propagation-based Regularization (PEPR) method is proposed to improve the image quality in Electrical Impedance Tomography (EIT). PEPR method defines the regularization parameter as a function of the projection error developed by difference between experimental measurements and calculated data. The regularization parameter in the reconstruction algorithm gets modified automatically according to the noise level in measured data and ill-posedness of the Hessian matrix. Resistivity imaging of practical phantoms in a Model Based Iterative Image Reconstruction (MoBIIR) algorithm as well as with Electrical Impedance Diffuse Optical Reconstruction Software (EIDORS) with PEPR. The effect of PEPR method is also studied with phantoms with different configurations and with different current injection methods. All the resistivity images reconstructed with PEPR method are compared with the single step regularization (STR) and Modified Levenberg Regularization (LMR) techniques. The results show that, the PEPR technique reduces the projection error and solution error in each iterations both for simulated and experimental data in both the algorithms and improves the reconstructed images with better contrast to noise ratio (CNR), percentage of contrast recovery (PCR), coefficient of contrast (COC) and diametric resistivity profile (DRP).

Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone‐beam CT

Image reconstruction and motion model estimation in four-dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4D-CBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR). The proposed SMEIR algorithm consists of two alternating steps: (1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and (2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction technique (SART) coupled with total variation minimization. During the forward- and backprojection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The quality of reconstructed 4D images and the accuracy of tumor motion trajectory are assessed by comparing with those resulting from conventional sequential 4D-CBCT reconstructions (FDK and total variation minimization) and motion estimation (demons algorithm). The performance of the SMEIR algorithm is further evaluated by reconstructing a lung cancer patient 4D-CBCT. Image quality of 4D-CBCT is greatly improved by the SMEIR algorithm in both phantom and patient studies. When all projections are used to reconstruct a 3D-CBCT by FDK, motion-blurring artifacts are present, leading to a 24.4% relative reconstruction error in the NACT phantom. View aliasing artifacts are present in 4D-CBCT reconstructed by FDK from 20 projections, with a relative error of 32.1%. When total variation minimization is used to reconstruct 4D-CBCT, the relative error is 18.9%. Image quality of 4D-CBCT is substantially improved by using the SMEIR algorithm and relative error is reduced to 7.6%. The maximum error (MaxE) of tumor motion determined from the DVF obtained by demons registration on a FDK-reconstructed 4D-CBCT is 3.0, 2.3, and 7.1 mm along left-right (L-R), anterior-posterior (A-P), and superior-inferior (S-I) directions, respectively. From the DVF obtained by demons registration on 4D-CBCT reconstructed by total variation minimization, the MaxE of tumor motion is reduced to 1.5, 0.5, and 5.5 mm along L-R, A-P, and S-I directions. From the DVF estimated by SMEIR algorithm, the MaxE of tumor motion is further reduced to 0.8, 0.4, and 1.5 mm along L-R, A-P, and S-I directions, respectively. The proposed SMEIR algorithm is able to estimate a motion model and reconstruct motion-compensated 4D-CBCT. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation.

Improving Conductivity Image Quality Using Block Matrix-based Multiple Regularization (BMMR) Technique in EIT: A Simulation Study

Abstract A Block Matrix based Multiple Regularization (BMMR) technique is proposed for improving conductivity image quality in Electrical Impedance Tomography (EIT). The response matrix (JTJ) has been partitioned into several sub-block matrices and the largest element of each sub-block matrix has been chosen as regularization parameter for the nodes contained by that sub-block. Simulated boundary data are generated for circular domains with circular inhomogeneities of different geometry and the conductivity images are reconstructed in a Model Based Iterative Image Reconstruction (MoBIIR) algorithm. Conductivity images are reconstructed with BMMR technique and the results are compared with the Single-step Tikhonov Regularization (STR) and modified Levenberg-Marquardt Regularization (LMR) methods. Results show that the BMMR technique improves the impedance image and its spatial resolution for single and multiple inhomogeneity phantoms of different geometries. It is observed that the BMMR technique reduces the projection error as well as the solution error and improves the conductivity reconstruction in EIT. Results also show that the BMMR method improves the image contrast and inhomogeneity conductivity profile by reducing background noise for all the phantom configurations.

Dynamic optical imaging of vascular and metabolic reactivity in rheumatoid joints

Dynamic optical imaging is increasingly applied to clinically relevant areas such as brain and cancer imaging. In this approach, some external stimulus is applied and changes in relevant physiological parameters (e.g., oxy- or deoxyhemoglobin concentrations) are determined. The advantage of this approach is that the prestimulus state can be used as a reference or baseline against which the changes can be calibrated. Here we present the first application of this method to the problem of characterizing joint diseases, especially effects of rheumatoid arthritis (RA) in the proximal interphalangeal finger joints. Using a dual-wavelength tomographic imaging system together with previously implemented model-based iterative image reconstruction schemes, we have performed initial dynamic imaging case studies on a limited number of healthy volunteers and patients diagnosed with RA. Focusing on three cases studies, we illustrated our major finds. These studies support our hypothesis that differences in the vascular reactivity exist between affected and unaffected joints.

Diffuse optical tomography through solving a system of quadratic equations: theory and simulations

This paper discusses the iterative solution of the nonlinear problem of optical tomography. In the established forward model-based iterative image reconstruction (MOBIIR) method a linear perturbation equation containing the first derivative of the forward operator is solved to obtain the update vector for the optical properties. In MOBIIR, the perturbation equation is updated by recomputing the first derivative after each update of the optical properties. In the method presented here a nonlinear perturbation equation, containing terms up to the second derivative, is used to iteratively solve for the optical property updates. Through this modification, reconstructions with reasonable contrast recovery and accuracy are obtained without the need for updating the perturbation equation and therefore eliminating the outer iteration of the usual MOBIIR algorithm. To improve the performance of the algorithm the outer iteration is reintroduced in which the perturbation equation is recomputed without re-estimating the derivatives and with only updated computed data. The system of quadratic equations is solved using either a modified conjugate gradient descent scheme or a two-step linearized predictor–corrector scheme. A quick method employing the adjoint of the forward operator is used to estimate the derivatives. By solving the nonlinear perturbation equation it is shown that the iterative scheme is able to recover large contrast variations in absorption coefficient with improved noise tolerance in data. This ability has not been possible so far with linear algorithms. This is demonstrated by presenting results of numerical simulations from objects with inhomogeneous inclusions in absorption coefficient with different contrasts and shapes.

Diffuse optical tomography using intensity measurements and the a priori acquired regions of interest: theory and simulations

Light transmission data collected around an object show large variation with source–detector separation owing to the presence of single or multiple inhomogeneous regions in the object. This variation in the measured intensity is made use of to reconstruct regions of the inhomogeneous inclusions. In addition, it is possible to select a set of data from the above which is most likely least affected by the presence of the inhomogeneity, and estimate reasonably accurately the background optical properties from it. The reconstructed region is found to always contain the inhomogeneity and is of size approximately 140% by area of the inhomogeneity. With the regions to be reconstructed a priori known, a model-based iterative reconstruction procedure for reconstructing the optical properties of the region converged five times faster than without such information. It is also shown that whereas for the full object, a view-based propagation–backpropagation reconstruction procedure failed to converge, owing to large underdeterminacy of the problem, a smaller problem attempting to reconstruct a priori specified regions of interest converged and did so faster than a non-view-based approach for similar objects. Reconstruction results are presented from simulated transmitted intensity data from the following objects with regions of inhomogeneity in both absorption and scattering: (i) single centrally located inhomogeneity, (ii) two off-centred inhomogeneous regions of equal size and contrast (iii) two off-centred inhomogeneous regions of unequal size and equal contrast and (iv) two off-centred inhomogeneous regions of unequal size and contrast. Whereas the model-based iterative image reconstruction procedure gave good convergence in the first, second and third cases, in the fourth case the reconstructions failed to recover the exact numerical value of the optical properties in the higher contrast region.

Sagittal laser optical tomography for imaging of rheumatoid finger joints

We present a novel optical tomographic imaging system that was designed to determine two-dimensional spatial distribution of optical properties in a sagittal plane through finger joints. The system incorporates a single laser diode and a single silicon photodetector into a scanning device that records spatially resolved light intensities as they are transmitted through a finger. These data are input to a model-based iterative image reconstruction (MOBIIR) scheme, which uses the equation of radiative transfer (ERT) as a forward model for light propagation through tissue. We have used this system to obtain tomographic images of six proximal interphalangeal finger joints from two patients with rheumatoid arthritis. The optical images were compared to clinical symptoms and ultrasound images.

Three-dimensional optical tomography with the equation of radiative transfer

We report on the derivation and implementation of the first three-dimensional optical tomographic image reconstruction scheme that is based on the time-independent equation of radiative transfer (ERT) and allows for arbitrarily shaped medium boundaries and arbitrary spatial material distributions. The scheme builds on the concept of model-based iterative image reconstruction, in which a forward model provides prediction of detector readings, and a gradient-based updating scheme minimizes an appropriately de- fined objective function. The forward model is solved by using an even-parity formulation of the ERT, which lends itself to a finite- element discretization method. The finite-element technique pro- vides the suitable framework for predicting light propagation in arbi- trarily shaped three-dimensional media. For an efficient way of calculating the gradient of the objective function we have imple- mented an adjoint differentiation scheme. Initial reconstruction re- sults using synthetic data from simple media and a three- dimensional mesh of the human forehead illustrate the performance of the code. © 2003 SPIE and IS&T. (DOI: 10.1117/1.1587730)

Laser Imaging Techniques for Follow-up Analysis of Joint Inflammation in Patients with Rheumatoid Arthritis

Summary Rheumatoid arthritis (RA) is the most frequent chronic inflammatory arthropathy that often leads to joint destruction. Essential for the progress of the disease are both an early diagnosis and a sensitive follow-up of synovitis. In this paper, we present new laser based imaging techniques for the transillumination of small finger joints. The “Laserscan” technique allows the transillumination of small finger joints using red laser light with a wavelength of 675nm. The laser is positioned above the finger joint and a CCD camera detects the scattering distribution below the joint. After processing the data through a statistical machine learning method, we could show in a preliminary clinical study that it was possible to correctly classify the inflammatory status of 62 of 72 joints from 22 patients with RA compared to precise clinical examination. Sagittal optical tomography is a further development of “Laserscan”. In this technique, a red laser is moved in sagittal plane above the proximal interphalangeal finger joint while a detector underneath the joint measures the scattered light distribution in the same plane. Using model-based iterative image reconstruction algorithms, sagittal cross-sectional images are obtained based on the acquired data. These images reflect the distribution of scattering and absorption properties within and around the joint cavity of a first healthy volunteer.