Ultrasound Tomography (USCT) is an emerging technology for early breast cancer detection. At Karlsruhe Institute of Technology we recently realized a new generation of full 3D USCT device with a pseudo-randomly sampled hemispherical aperture. In this paper we summarize first imaging results with phantoms and first volunteer images. Using a gelatin phantom with PVC inclusions we evaluated transmission imaging, which showed a deviation from the ground truth of less than 5 m/s in the sound speed and 0:2 dB/cm in the attenuation for the phantom body and less than 15 m/s and 0:2 dB/cm for an inclusion with a diameter of 2:2 cm. Geometric errors are in average in the range of 0:2 cm. For reflectivity imaging we showed that the point spread function is nearly isotropic and with an average of 0:26 mm close to the theoretical predictions for the current system. While the system is still in final commissioning, the results of the phantom and volunteer imaging are very promising: after further calibration and deeper analysis with phantoms we aim at starting a clinical study.
Breast cancer is the most common cancer for women worldwide. 3D Ultrasound Computed Tomography (3D USCT) is a novel imaging method for early breast cancer diagnosis, which allows reconstruction of quantitative tissue parameters like speed of sound and attenuation. For reconstruction we use the paraxial approximation of the Helmholtz equation as forward model. We have realized the forward solution, backprojection and reconstruction for a ring transducer arrangement. The reconstruction software was evaluated with data simulated with k-Wave, resulting in the mean error for the speed of sound map of 12.6 m/s for a pixel size of 0.3 mm. Spatial resolution was estimated with a resolution phantom containing circular inclusions with realistic speed of sound values for breast tissues, allowing maximum resolution of 2 mm. In this paper we show that our method has accurate forward solution, we present the new backprojection technique and initial results of reconstructing simulated data.
In a three-dimensional ultrasound computed tomography (3D USCT) system, system errors such as transducer delay, transducer position deviation and temperature error will affect the quality of reconstructed images. Most of the existing calibration works use iterative methods to solve large-scale systems of linear equations. In our case, the transducer delay and position deviation calibration problem of the considered 3D USCT system is essentially to solve a linear system containing about 840,000 equations and 11,500 unknowns. For such a large system, the existing iterative methods require a lot of computation time and the accuracy also needs to be improved. Considering that neural networks have the ability to find optimized solutions for large-scale linear systems, we propose a neural network method for transducer delay and position deviation calibration. We designed a neural network to calibrate both delay and position solutions, together during the network training. We test the method with simulated system data where we add transducer delays in the range of 0.7~1.3 μs, position deviation in the range of -1~1 mm for the X- and Y-axis, and -0.3~0.3 mm for the Z-axis. Results show that the mean delay error is reduced to 0.15 μs, and the mean position error is reduced to 0.15 mm, after a neural network calibration process which takes about 11 minutes. The delay calibration result is better than the existing Newton method in literature, while our method is especially less time-consuming.
A promising candidate for improved imaging of breast cancer is ultrasound tomography (USCT). To make full use of the 3D interaction of the ultrasound fields with the breast, we are focusing our research on full 3D USCT systems. While our previous 3DUSCTII device allowed nearly unfocussed emission and reception with approx. 600 emitters and 1400 receivers, the spatial sampling of the object is very sparse. In order to improve contrast in a sparse system, we realized an optimized pseudo-randomly sampled USCT device (3DUSCTIII) with approx. 2300 transducers. Additionally, the opening angle, the bandwidth and the active area of the transducers were improved. New front-end electronics with custom ASICs allow bidirectional operation of the transducers to acquire approx. six times more A-scans at one data acquisition step. This paper presents the setup of the new system and initial results acquired during the ongoing commissioning.
Ultrasound transmission tomography promises a high potential and novel imaging method for early breast cancer diagnosis; it can quantitatively characterize tissues or materials by the attenuation and speed of sound (SoS). Reconstruction of ultrasound transmission tomography is an inverse problem that can be solved iteratively based on a paraxial approximation of the Helmholtz equation as forward model, which is highly non-linear and time-consuming. In order to address these problems and reconstruct desired images, we design a dual domain network architecture for ultrasound transmission tomography reconstruction. It can enhance the information of measurement domain and directly reconstruct from pressure field measurements without using any initialization of reconstruction and fully connected layer. We train the network on simulated ImageNet data and transfer it for ultrasound transmission tomography images to avoid overfitting when the amount of ultrasound transmission tomography images is limited. Our experimental results demonstrate that a dual domain network produces significant improvements over state-of-the-art methods. It improves the measured structural similarity measure (SSIM) from 0.54 to 0.90 and normalized root mean squared error (nRMSE) from 0.49 to 0.01 on average concerning the SoS reconstruction, and from 0.46 to 0.98 for SSIM, from 353 to 0.03 for nRMSE on average concerning the attenuation reconstruction.
For Ultrasound Tomography reflectivity imaging Synthetic Aperture Focusing Technique (SAFT) is often applied. Phase aberration correction is required to achieve images with high resolution and high contrast, for which a sound speed map is required. For USCT systems these sound speed maps are usually reconstructed using the transmission data from the raw data set, which is also used for reflection tomography. We compare straight and bent ray phase aberration correction SAFT algorithms with respect to different reconstruction algorithms to derive the sound speed map. Evaluations are carried out based on a simulated phantom and measured data from the Multimodal Ultrasound Breast Imaging System (MUBI). Phase aberration correction enables recovering the contrast of the image, while without SAFT results in considerably unfocused inner structures. By applying a reconstructed sound speed map however the local contrast cannot be fully recovered compared to the ideal case. Introducing bent ray transmission reconstruction approaches based on the Fast Marching or B´ezier curve method in all cases improves the results over the straight ray transmission tomography.
In ultrasound transmission tomography, image reconstruction is an inverse problem which is solved iteratively based on a forward model that simulates the wave propagation of ultrasound. A commonly used forward model is paraxial approximation of the Helmholtz equation, which is time-consuming. Hence developing optimizers that minimize the number of forward solutions is crucial to achieve clinically acceptable reconstruction time, while the state-of-the-art methods in this field such as Gauss-Newton conjugate gradient (CG) and nonlinear CG are not capable of reaching this goal. To that end, we focus on Jacobian-free optimizers or accelerators in this paper, since the computation of the Jacobian is expensive. We investigate the limited memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm as a preconditioning technique due to its ability to efficiently approximate inverse Hessian without performing forward model or its adjoint. We show L-BFGS can reach a speedup of more than one order of magnitude for the noise-free case, while the method still halves the reconstruction time in presence of noise in the data. The performance drop is explained by perturbed gradients due to noise in the data. We also show when used alone as a quasi-Newton method, L-BFGS is competitive with the accelerated CG based methods regarding the number of iterations, and outperforms them regarding reconstruction time.
In Ultrasound computer tomography (USCT) Synthetic aperture focusing technique (SAFT) is often applied for reflectivity image reconstruction. Phase aberration correction is essential to cope with the large sound speed differences in water and the different human tissues. In this paper we compare two approaches for phase aberration correction: a straight ray approximation using the Bresenham algorithm (B-SAFT) and a bent ray approximating using a multi-stencil Fast Marching Method (FMM-SAFT). The analysis is carried out with simulated point scatterers and simulated phantoms to measure the effect on the image resolution and contrast. The method is additionally applied to experimental data. B-SAFT degrades the image resolution and contrast in cases of large sound speed differences of objects and if the reconstructed point is close to a boundary where a change in impedance is present. FMM-SAFT is able to recover the image quality in these cases if the sound speed distribution is known accurately and with high resolution. If these requirements cannot be met, B-SAFT proved to be more robust.
Ultrasound Computer Tomography is an exciting new technology mostly aimed at breast cancer imaging. Due to the complex interaction of ultrasound with human tissue, the large amount of raw data, and the large volumes of interest, both image acquisition and image reconstruction are challenging. Following the idea of open science, the long term goal of the USCT reference database is establishing open and easy to use data and code interfaces and stimulating the exchange of available reconstruction algorithms and raw data sets of different USCT devices. The database was established with freely available and open licensed USCT data for comparison of reconstruction algorithms, and will be maintained and updated. Additionally, the feedback about data and system architecture of the scientists working on reconstruction methods will be published to help to drive further development of the various measurement setups.
Synthetic Aperture Focusing Technique (SAFT) allows fast data acquisition and optimally focused images. The computational burden for 3D imaging is large as for each voxel the delay for each acquired A-scan has to be calculated, e.g. O(N5) for N3 voxels and N2 A-scans. For 3D reconstruction of objects which are large in terms of the wavelength, e.g. ≥ (100 λ)3, the computation of one volume takes several days on a current multicore PC. If the 3D distribution of the speed of sound is applied to correct the delays, the computation time increases further. In this work a time of flight interpolation based GPU implementation (TOFI-SAFT) is presented which accelerates our previous GPU implementation of speed of sound corrected SAFT by a factor of 7 to 16 min. with only minor reduction of image quality.
Ultrasound Computer Tomography (USCT) is a promising new imaging system for breast cancer diagnosis. An essential step before further processing is to remove the water background from the reconstructed images. In this paper we present a fully-automated image segmentation method based on three-dimensional active contours. The active contour method is extended by applying gradient vector flow and encoding the USCT aperture characteristics as additional weighting terms. A surface detection algorithm based on a ray model is developed to initialize the active contour, which is iteratively deformed to capture the breast outline in USCT reflection images. The evaluation with synthetic data showed that the method is able to cope with noisy images, and is not influenced by the position of the breast and the presence of scattering objects within the breast. The proposed method was applied to 14 in-vivo images resulting in an average surface deviation from a manual segmentation of 2.7 mm. We conclude that automated segmentation of USCT reflection images is feasible and produces results comparable to a manual segmentation. By applying the proposed method, reproducible segmentation results can be obtained without manual interaction by an expert.
In the past years we have perceived within the USCT research community a demand for freely available USCT data sets.
Inspired by the idea of Open Science, this collection of data sets could stimulate the collaboration and the exchange of
ideas and experiences between USCT researchers. In addition, it may lead to comprehensive comparison of different
reconstruction algorithms and their results. Finally, by collecting feedback from the users about data and system
architecture, valuable information is gathered for further development of measurement setups. For the above reasons, we
have initiated a digital portal with several reference data sets and access scripts under free licenses. To kick off this
initiative, we organized a USCT data challenge event at SPIE Medical Imaging 2017.
Ultrasound Computer Tomography (USCT) is a promising new imaging method for breast cancer diagnosis. We developed a 3D USCT system and tested it in a pilot study with encouraging results: 3D USCT was able to depict two carcinomas, which were present in contrast enhanced MRI volumes serving as ground truth. To overcome severe differences in the breast shape, an image registration was applied. We analyzed the correlation between average sound speed in the breast and the breast density estimated from segmented MRIs and found a positive correlation with R=0.70. Based on the results of the pilot study we now carry out a successive clinical study with 200 patients. For this we integrated our reconstruction methods and image post-processing into a comprehensive workflow. It includes a dedicated DICOM viewer for interactive assessment of fused USCT images. A new preview mode now allows intuitive and faster patient positioning. We updated the USCT system to decrease the data acquisition time by approximately factor two and to increase the penetration depth of the breast into the USCT aperture by 1 cm. Furthermore the compute-intensive reflectivity reconstruction was considerably accelerated, now allowing a sub-millimeter volume reconstruction in approximately 16 minutes. The updates made it possible to successfully image first patients in our ongoing clinical study.
In our first clinical study with a full 3D Ultrasound Computer Tomography (USCT) system patient data was acquired in eight minutes for one breast. In this paper the patient movement during the acquisition was analyzed quantitatively and as far as possible corrected in the resulting images. The movement was tracked in ten successive reflectivity reconstructions of full breast volumes acquired during 10 s intervals at different aperture positions, which were separated by 41 s intervals. The mean distance between initial and final position was 2.2 mm (standard deviation (STD) ± 0.9 mm, max. 4.1 mm, min. 0.8 mm) and the average sum of all moved distances was 4.9 mm (STD ± 1.9 mm, max. 8.8 mm, min. 2.7 mm). The tracked movement was corrected by summing successive images, which were transformed according to the detected movement. The contrast of these images increased and additional image content became visible.
3D Ultrasound Computer Tomography (USCT) is a new imaging method for breast cancer diagnosis. In the current state of development it is essential to correlate USCT with a known imaging modality like MRI to evaluate how different tissue types are depicted. Due to different imaging conditions, e.g. with the breast subject to buoyancy in USCT, a direct correlation is demanding. We present a 3D image registration method to reduce positioning differences and allow direct side-by-side comparison of USCT and MRI volumes. It is based on a two-step approach including a buoyancy simulation with a biomechanical model and free form deformations using cubic B-Splines for a surface refinement. Simulation parameters are optimized patient-specifically in a simulated annealing scheme. The method was evaluated with in-vivo datasets resulting in an average registration error below 5mm. Correlating tissue structures can thereby be located in the same or nearby slices in both modalities and three-dimensional non-linear deformations due to the buoyancy are reduced. Image fusion of MRI volumes and USCT sound speed volumes was performed for intuitive display. By applying the registration to data of our first in-vivo study with the KIT 3D USCT, we could correlate several tissue structures in MRI and USCT images and learn how connective tissue, carcinomas and breast implants observed in the MRI are depicted in the USCT imaging modes.
A promising candidate for improved imaging of breast cancer is ultrasound computer tomography (USCT). The aim of this work was to design a new aperture for our full 3D USCT which extends the properties of the current aperture to a larger ROI fitting the buoyant breast in water and decreasing artifacts in transmission tomography. The optimization resulted in a larger opening angle of the transducers, a larger diameter of the aperture and an approximately homogeneous distribution of the transducers, with locally random distances. The developed optimization methods allow us to automatically generate an optimized aperture for given diameters of apertures and transducer arrays, as well as quantitative comparison to other arbitrary apertures. Thus, during the design phase of the next generation KIT 3D USCT, the image quality can be balanced against the specification parameters and given hardware and cost limitations. The methods can be applied for general aperture optimization, only limited by the assumptions of a hemispherical aperture and circular transducer arrays.
Ultrasound Computer Tomography is an upcoming imaging modality for early breast cancer detection. For evaluation of the method, comparison with the standard method X-ray mammography is of strongest interest. To overcome the significant differences in dimensionality and compression state of the breast, in earlier work a registration method based on biomechanical modeling of the breast was proposed. However only homogeneous models could be applied, i.e. inner structures of the breast were neglected. In this work we extend the biomechanical modeling of the breast by estimating patient-specific tissue parameters automatically from the speed of sound volume. Two heterogeneous models are proposed modeling a quadratic and an exponential relationship between speed of sound and tissue stiffness. The models were evaluated using phantom images and clinical data. The size of all lesions is better preserved using heterogeneous models, especially using an exponential relationship. The presented approach yields promising results and gives a physical justification to our registration method. It can be considered as a first step towards a realistic modeling of the breast.
KEYWORDS: MATLAB, 3D image processing, Image restoration, Reconstruction algorithms, Signal processing, 3D image reconstruction, Image processing, Breast, 3D acquisition, 3D metrology
3D ultrasound computer tomography (3D USCT) promises reproducible high-resolution images for early detection of
breast tumors. The synthetic aperture focusing technique (SAFT) used for image reconstruction is highly computeintensive but suitable for an accelerated execution on GPUs. In this paper we investigate how a previous implementation of the SAFT algorithm in CUDA C can be further accelerated and integrated into the existing MATLAB signal and image processing chain for 3D USCT. The focus is on an efficient preprocessing and preparation of data blocks in MATLAB as well as an improved utilisation of special hardware like the texture fetching units on GPUs. For 64 slices with 1024×1024 pixels each the overall runtime of the reconstruction including data loading and preprocessing could be decreased from 35 hours with CPU to 2.4 hours with eight GPUs.
3D ultrasound computer tomography (USCT) requires a large number of transducers approx. two orders of magnitude larger than in a 2D system. Technical feasibility limits the number of transducer positions to a much smaller number resulting in a sparse aperture and causing artifacts due to grating lobe effects in the images. Usually, grating lobes are suppressed by using a non-sparse geometry. Thus, there is no quantitative estimation method available how much the image contrast is degraded when a sparse aperture is applied and how much the contrast is improved when adding more transducers, changing the overall aperture or the object. In this paper the effect of the grating lobes on the image quality was analyzed for a spherical, a hemispherical and the semi-ellipsoidal USCT aperture: The background noise due to grating lobes is very similar for the three apertures and mainly influenced by the sparseness and the imaged object. A model for noise reduction was fitted to simulated and experimental data, and can be used to predict the peak-signal-to-noise- ratio for a given object and number of aperture positions.
Speed of sound imaging is an important modality used in medical ultrasound applications. We developed a 3D
ultrasound computer tomograph (3D USCT) which is capable of reflection and transmission tomography. Most
3D tomography reconstruction methods like the algebraic reconstruction technique rely on the assumption that
the transmission rays propagate straightly from emitter to receiver, which is not valid for ultrasound. Due to
refractions in the tissue the rays are bent rather than straight. To overcome this problem we use a 3D Eikonal
solver that calculates the bent ray paths for the transmission pulses and include it into our Compressive Sampling
reconstruction framework. Using an iterative scheme we show results for synthetic and real data. The shape and
the outline of the phantoms reconstructed with the bent-ray method match the reflection reconstructions better
and for synthetic data the speed of sound is closer to the speed of sound in the phantom by approximately 1.2
m/s.
A promising candidate for improved imaging of breast cancer is ultrasound computer tomography (USCT).
Current experimental USCT systems are still focused in elevation dimension resulting in a large slice thickness,
limited depth of field, loss of out-of-plane reflections, and a large number of movement steps to acquire a stack
of images. 3DUSCT emitting and receiving spherical wave fronts overcomes these limitations. We built an
optimized 3DUSCT with nearly isotropic 3DPSF, realizing for the first time the full benefits of a 3Dsystem.
In this paper results of the 3D point spread function measured with a dedicated phantom and images acquired
with a clinical breast phantom are presented. The point spread function could be shown to be nearly isotropic
in 3D, to have very low spatial variability and fit the predicted values. The contrast of the phantom images
is very satisfactory in spite of imaging with a sparse aperture. The resolution and imaged details of the
reflectivity reconstruction are comparable to a 3TeslaMRI volume of the breast phantom. Image quality and
resolution is isotropic in all three dimensions, confirming the successful optimization experimentally.
A promising candidate for improved imaging of breast cancer is ultrasound computer tomography (USCT).
Current experimental USCT systems are still focused in elevation dimension resulting in a large slice thickness,
limited depth of field, loss of out-of-plane reflections, and a large number of movement steps to acquire a stack
of images. 3DUSCT emitting and receiving spherical wave fronts overcomes these limitations. We built an
optimized 3DUSCT with nearly isotropic 3D point spread function, realizing for the first time the full benefits
of a 3D system. The 3DUSCT II is based on a semi-ellipsoidal transducer holder cut from polyoxymethylene.
The aperture is implemented together with water supply, disinfection unit, temperature control, and movement
mechanics in a patient bed. 2041 transducers are mounted in the aperture holder grouped into transducer array
systems with embedded amplifiers and emitter electronics. The data acquisition is carried out with 480 parallel
channels at 20MHz and with 12 bit resolution. 3.5 million A-Scans with 20 GByte of raw data are acquired for
one breast volume. With data acquisition time of less than two minutes for one breast volume, the new system
enables the next step of our research: a first clinical study.
KEYWORDS: Point spread functions, Transducers, 3D image processing, Receivers, Imaging systems, Ultrasonography, 3D modeling, Breast, Signal attenuation, Ultrasonics
The point spread function (PSF) of an imaging system may be used as measure for the imaging quality. The PSF usually depends on position and an several other system parameters. Our current 3D imaging system for ultrasound computer tomography consists of a rotatable cylinder with approx. 2000 ultrasound transducers. 3D images are reconstructed by means of synthetic aperture focusing technique (SAFT) using all available emitter-receiver-combinations. No analytical solution exists for determining the spatially varying PSF for arbitrary placement of the transducers.
This work derives a new numerical approach for the approximation of the 3D PSF for arbitrary transducer geometries including the beam pattern of the ultrasound transducers, a directional point scatterer model, damping of the breast and arbitrary pulse shapes.
As an exemplary application the spatially varying 3D PSF of the current cylindrical geometry is analyzed under idealized conditions (point sources, no damping, and isotropic scattering) and compared to non-idealized results of the PSF analysis. The results show the necessity to take the system specific parameters into account for a realistic
prognosis of 3D imaging performance.
Ultrasound computer tomography is an imaging method capable of
producing volume images with high spatial resolution. The imaged
object is enclosed by a cylindrical array of transducers. While
one transducer emits a spherical wavefront (pulse), all other
transducers are recording the radiofrequency (RF) a-scans
simultaneously. Then another transducer acts as the emitter and so
on.
In this paper we describe the image reconstruction method and an
enhanced algorithm for the a-scan preprocessing. The image
reconstruction is based on a 'full aperture sum-and-delay'
algorithm evaluating the reflected and scattered signals in the
a-scans. The a-scans are modelled as the tissue response of the
imaged object convoluted with the shape of the ultrasound pulse,
which is determined by the transfer function of the transducers
and the excitation. Spiking deconvolution and blind deconvolution
with different parameters are used to build inverse filters of the
ultrasound pulse. Applying the inverse filters to the a-scans
results in sharper signals which are used for image
reconstruction. Smallest scatterers of 0.1 mm size corresponding
to one fifth of the used ultrasound wavelength are visible in the
reconstructed images. Compared to conventional b-scans the
resulting images show an approximately tenfold better resolution.
Ultrasound computer tomography is an imaging method capable of producing volume images with both high spatial and temporal resolution. The promising results of a 2D experimental setup of an ultrasound computer tomography system with at least 0.25 mm resolution encouraged us to build a new 3D demonstration system. It consists of three parts: a tank containing the sensor system, a data acquisition hardware and a computer workstation for image reconstruction and visualization. For the sensor system we developed and manufactured our own low-cost transducer array emitting or receiving ultrasound signals in three dimensions. To optimize the transducer geometry in respect to aperture angle and pressure amplitude the pressure field was simulated using the ultrasound simulation program Field II. Each transducer arrays system carries 8 emitting and 32 receiving elements with integrated amplifier and address electronics. 192 A-scans can be recorded in parallel by the data acquisition hardware. 48 multiplexing steps are needed to store all A-scans of the 1536 receiving transducers. After recording the data is transmitted to the computer workstation.
In breast cancer diagnosis, ultrasound examination provides useful additional diagnostic information. Moreover ultrasound does not harm biological tissue and can be applied frequently. But conventional ultrasound imaging methods lack both high spatial and temporal resolution. Usually, the scanner is operated manually and the tissue is deformed while getting as close as possible to the regions of interest. Therefore, image contents and image quality depend strongly on the operator. Exact measurement of tissue structures, like tumor size, is not possible. Instead of a manually controlled linear transducer array, we use ultrasound computer tomography (USCT) to image a volume directly. A few thousand ultrasound transducers are arranged in a cylindrical array around a tank containing the object to be examined coupled by water. Every single transducer is small enough to emit an almost spherical sound wave. While one transducer is transmitting, all others receive simultaneously. Afterwards a different transducer emits the next pulse. For volume reconstruction every transmitted, scattered and reflected signal is used. This new method allows reproducible image sequences with enhanced spatial and temporal resolution. For the benefit of more reconstructed 3D images per second, spatial resolution may be reduced offline. First tests with our prototype in a ring-shaped geometry have even showed nylon threads (0.4 mm) and an image quality superior to clinical ultrasound scanners.
X-ray mammography is one of the most significant diagnosis methods in early detection of breast cancer. Usually two X- ray images from different angles are taken from each mamma to make even overlapping structures visible. X-ray mammography has a very high spatial resolution and can show microcalcifications of 50 - 200 micron in size. Clusters of microcalcifications are one of the most important and often the only indicator for malignant tumors. These calcifications are in some cases extremely difficult to detect. Computer assisted diagnosis of digitized mammograms may improve detection and interpretation of microcalcifications and cause more reliable diagnostic findings. We build a low-cost mammography workstation to detect and classify clusters of microcalcifications and tissue densities automatically. New in this approach is the estimation of the 3D formation of segmented microcalcifications and its visualization which will put additional diagnostic information at the radiologists disposal. The real problem using only two or three projections for reconstruction is the big loss of volume information. Therefore the arrangement of a cluster is estimated using only the positions of segmented microcalcifications. The arrangement of microcalcifications is visualized to the physician by rotating.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.