Interferometric Synthetic Aperture Microscopy (ISAM) is an optical imaging technique developed and demonstrated by Ralston, Marks, Carney, and Boppart. Rather than imaging a fluorescent sample, ISAM measures light scattered from an unstained object. ISAM is based on Optical Coherence Tomography (OCT), a maturing noninvasive technique that has already found application, particularly in opthamology. In addition, the Boppart group is actively pursuing the application of OCT and ISAM to other biomedical tasks, such as cancer detection in a variety of tissue types.

OCT and ISAM both rely on interferometric detection of scattered light. A broadband source is used and the resulting data are a function of two spatial variables and the wavelength - (x,y,λ). With the correct processing the data can be used to infer object structure in three spatial dimensions - (x,y,z). In OCT the image is reconstructed only where the probing light is in focus. The achievement of ISAM is to recognize that light outside of the focus can be computationally focused after data acquisition. This greatly improves the depth-of-field available. As in OCT, the processing used in ISAM requires interferometric detection of data, so that phase information is available. As the name of this technology indicates, ISAM has strong commonalities with Synthetic Aperture Radar (SAR). OCT can be viewed as analogous to standard radar.

Aberration, which causes a deviation of an ideal wavefront, is an important issue affecting image quality in many imaging modalities. With increased NA for higher transverse resolution, the optical wavefront is more susceptible to being distorted by the imperfections of the imaging optics and the sample itself. As a result, the resolution may decrease, as well as the contrast and the signal-to-noise ratio. OCT/OCM also share this limitation are directly affected by aberrations in the system optics or the sample. Sophisticated optical designs can suppress the static system aberrations, but they are not versatile for complex biological tissues that introduce unique, often dynamically changing, sample aberrations. Hardware-based adaptive optics (HAO) physically sense and correct the aberrations for improving the signal strength and resolution. The systems, however, remain expensive and complex. In addition, the aberration compensation can only be valid for one isoplanatic patch during the imaging procedure, which extends the time of the imaging session for the application of large volumetric imaging. We have utilized the phase information of OCT/OCM to compensate the aberrations through post-processing.

High-resolution in vivo imaging is of great importance for the fields of biology and medicine. The introduction of hardware- based adaptive optics (HAO) has pushed the limits of optical imaging, enabling high-resolution near diffraction-limited imaging of previously unresolvable structures. In ophthalmology, when combined with optical coherence tomography, HAO has enabled a detailed three-dimensional visualization of photoreceptor distributions and individual nerve fibre bundles in the living human retina. However, the introduction of HAO hardware and supporting software adds considerable complexity and cost to an imaging system, limiting the number of researchers and medical professionals who could benefit from the technology. We have demonstrated a fully automated computational approach that enables high-resolution in vivo ophthalmic imaging without the need for HAO. Our results highlight that computational methods in coherent microscopy are applicable in highly dynamic living systems.

In many optical imaging applications, it is necessary to correct for aberrations to obtain high quality images. Optical coherence tomography (OCT) provides access to the amplitude and phase of the backscattered optical field for three-dimensional (3D) imaging samples. Computational adaptive optics (CAO) modifies the phase of the OCT data in the spatial frequency domain to correct optical aberrations without using a deformable mirror, as is commonly done in hardware-based adaptive optics (AO). This provides improvement of image quality throughout the 3D volume, enabling imaging across greater depth ranges and in highly aberrated samples. However, the CAO aberration correction has a complicated relation to the imaging pupil and is not a direct measurement of the pupil aberrations. Here we present new methods for recovering the wavefront aberrations directly from the OCT data without the use of hardware adaptive optics. This enables both computational measurement and correction of optical aberrations.

In many optical imaging applications, it is necessary to overcome aberrations to obtain high-resolution images. Aberration correction can be performed by either physically modifying the optical wavefront using hardware components, or by modifying the wavefront during image reconstruction using computational imaging. Here we address a longstanding issue in computational imaging: photons that are not collected cannot be corrected. This severely restricts the applications of computational wavefront correction. Additionally, performance limitations of hardware wavefront correction leave many aberrations uncorrected. We combine hardware and computational correction to address the shortcomings of each method. Coherent optical backscattering data is collected using high-speed optical coherence tomography, with aberrations corrected at the time of acquisition using a wavefront sensor and deformable mirror to maximize photon collection. Remaining aberrations are corrected by digitally modifying the coherently-measured wavefront during imaging reconstruction. This strategy obtains high-resolution images with improved signal-to-noise ratio of in vivo human photoreceptor cells with more complete correction of ocular aberrations, and increased flexibility to image at multiple retinal depths, field locations, and time points. While our approach is not restricted to retinal imaging, this application is one of the most challenging for computational imaging due to the large aberrations of the dilated pupil, time-varying aberrations, and unavoidable eye motion. In contrast with previous computational imaging work, we have imaged single photoreceptors and their waveguide modes in fully dilated eyes with a single acquisition. Combined hardware and computational wavefront correction improves the image sharpness of existing adaptive optics systems, and broadens the potential applications of computational imaging methods.

Traditional wavefront-sensor-based adaptive optics (AO) techniques face numerous challenges that cause poor performance in scattering samples. Sensorless closed-loop AO techniques overcome these challenges by optimizing an image metric at different states of a deformable mirror (DM). This requires acquisition of a series of images continuously for optimization − an arduous task in dynamic in vivo samples.  To correct these OAs induced by tissue scattering, a sensorless AO algorithm, called AutoAO, was adapted. The optimal wavefront is estimated by performing CAO on an initial volume to minimize an image metric, and then the pattern is translated to the DM.  Unlike other commonly used sensorless AO algorithms that require a series of iterative images at different states of a deformable mirror (DM), AutoAO requires just one initial volume for optimization that was acquired within 1.25 s.  Our technique overcomes the disadvantages of sensor-based AO, reduces the number of image acquisitions compared to traditional sensorless AO, and retains the advantages of both computational and hardware-based AO.