Robust residual-guided iterative reconstruction for sparse-view CT in small animal imaging

Objective. We introduce a robust image reconstruction algorithm named residual-guided Golub–Kahan iterative reconstruction technique (RGIRT) designed for sparse-view computed tomography (CT), which aims at high-fidelity image reconstruction from a limited number of projection views. Approach. RGIRT utilizes an inner-outer dual iteration framework, with a flexible least square QR (FLSQR) algorithm implemented in the inner iteration and a restarted iterative scheme applied in the outer iteration. The inner FLSQR employs a flexible Golub–Kahan bidiagonalization method to reduce the size of the inverse problem, and a weighted generalized cross-validation method to adaptively estimate the regularization hyper-parameter. The inner iteration efficiently yields the intermediate reconstruction result, while the outer iteration minimizes the residual and refines the solution by using the result obtained from the inner iteration. Main results. The reconstruction performance of RGIRT is evaluated and compared to other reference methods (FBPConvNet, SART-TV, and FLSQR) using projection data from both numerical phantoms and real experimental Micro-CT data. The experimental findings, from testing various numbers of projection views and different noise levels, underscore the robustness of RGIRT. Meanwhile, theoretical analysis confirms the convergence of residual for our approach. Significance. We propose a robust iterative reconstruction algorithm for x-ray CT scans with sparse views, thereby shortening scanning time and mitigating excessive ionizing radiation exposure to small animals

Monitoring mouse brain perfusion with hybrid magnetic resonance optoacoustic tomography

Progress in brain research critically depends on the development of next-generation multi-modal imaging tools capable of capturing transient functional events and multiplexed contrasts noninvasively and concurrently, thus enabling a holistic view of dynamic events in vivo. Here we report on a hybrid magnetic resonance and optoacoustic tomography (MROT) system for murine brain imaging, which incorporates an MR-compatible spherical matrix array transducer and fiber-based light illumination into a 9.4 T small animal scanner. An optimized radiofrequency coil has further been devised for whole-brain interrogation. System's utility is showcased by acquiring complementary angiographic and soft tissue anatomical contrast along with simultaneous dual-modality visualization of contrast agent dynamics in vivo

Non-invasive visualization of amyloid-beta deposits in Alzheimer amyloidosis mice using magnetic resonance imaging and fluorescence molecular tomography

Abnormal cerebral accumulation of amyloid-beta peptide (Aβ) is a major hallmark of Alzheimer's disease. Non-invasive monitoring of Aβ deposits enables assessing the disease burden in patients and animal models mimicking aspects of the human disease as well as evaluating the efficacy of Aβ-modulating therapies. Previous in vivo assessments of plaque load have been predominantly based on macroscopic fluorescence reflectance imaging (FRI) and confocal or two-photon microscopy using Aβ-specific imaging agents. However, the former method lacks depth resolution, whereas the latter is restricted by the limited field of view preventing a full coverage of the large brain region. Here, we utilized a fluorescence molecular tomography (FMT)-magnetic resonance imaging (MRI) pipeline with the curcumin derivative fluorescent probe CRANAD-2 to achieve full 3D brain coverage for detecting Aβ accumulation in the arcAβ mouse model of cerebral amyloidosis. A homebuilt FMT system was used for data acquisition, whereas a customized software platform enabled the integration of MRI-derived anatomical information as prior information for FMT image reconstruction. The results obtained from the FMT-MRI study were compared to those from conventional planar FRI recorded under similar physiological conditions, yielding comparable time courses of the fluorescence intensity following intravenous injection of CRANAD-2 in a region-of-interest comprising the brain. In conclusion, we have demonstrated the feasibility of visualizing Aβ deposition in 3D using a multimodal FMT-MRI strategy. This hybrid imaging method provides complementary anatomical, physiological and molecular information, thereby enabling the detailed characterization of the disease status in arcAβ mouse models, which can also facilitate monitoring the efficacy of putative treatments targeting Aβ

Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging

Multispectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain. It can be greatly augmented by magnetic resonance imaging (MRI) which offers excellent soft-tissue contrast and high-resolution brain anatomy. Nevertheless, registration of MSOT-MRI images remains challenging, chiefly due to the entirely different image contrast rendered by these two modalities. Previously reported registration algorithms mostly relied on manual user-dependent brain segmentation, which compromised data interpretation and quantification. Here we propose a fully automated registration method for MSOT-MRI multimodal imaging empowered by deep learning. The automated workflow includes neural network-based image segmentation to generate suitable masks, which are subsequently registered using an additional neural network. The performance of the algorithm is showcased with datasets acquired by cross-sectional MSOT and high-field MRI preclinical scanners. The automated registration method is further validated with manual and half-automated registration, demonstrating its robustness and accuracy

STIFT: a modular software platform for simulation, optimization and reconstruction in fluorescence molecular tomography

Fluorescence molecular tomography (FMT) provides molecular information on tracer bio-distribution in the organism similar to positron emission tomography (PET). Instead of using radioactive tracers in PET, FMT non-invasively resolves the three-dimensional distribution of fluorescent probes in vivo. Thus, it can be considered an optical version of PET. Nevertheless, FMT has not reached the popularity of PET in molecular imaging community due to a variety of reasons. Although both image reconstruction algorithms and instrumentation for FMT have evolved over the past decade, none of the setups has been widely accepted as a standard molecular imaging tool for routine biomedical research. Light when penetrating living tissue is exponentially attenuated and highly scattered, rendering the FMT image reconstruction an ill-posed problem. 3D recovery of fluorescent dye distribution calls for an elaborated combination of a forward model accounting for optical heterogeneity and irregular object shapes and an inversion technique guaranteeing both reasonable computational time and accuracy. Given the intrinsic complexity of the problem, it becomes exceedingly difficult for non-expert users to choose a proper reconstruction setting, which is inherently linked to the data acquisition procedures, and hence depends on the configuration of the measurement setup, parameter choices, e.g., the optimal illumination pattern. Also, it should be noted that the temporal resolution of FMT is limited by the sequential nature of the point illumination method. To systematically address those problems, we propose the Smart ToolkIt for Fluorescence Tomography (STIFT), a modular software platform for three workflows: simulation, optimization and reconstruction. The software should be simple to operate for non-expert users. STIFT should be able to handle non-contact FMT experiments in an object of irregular geometry and heterogeneous composition. This has been achieved by implementing a finite-element method (FEM) approach for modeling diffusive light propagation within the object. For inversion, Tikhonov regularization has been used, which allows for incorporation of prior, such as anatomical information. The simulation mode allowed predicting and optimizing the performance of an experimental protocol for an assumed object configuration. In a second step the software performance was evaluated in phantom experiments. Both simulation and phantom experiments demonstrated that STIFT can be used to optimize FMT measurement, in particular for optimizing illumination patterns. Robust reconstruction results have been obtained for both homogeneous and heterogeneous tissue mimicking phantoms containing fluorescent inclusions. Following the establishment and phantom validation of the software platform, STIFT was applied to two biological problems: imaging the fluorophore distribution in a subcutaneous tumor corresponding to a focal signal with high target-to-background ratio, and imaging the distribution of fluorescently labeled aggregated proteins in the mouse brain corresponding to a diffuse signal with weak-to-medium target-to-background ratio. Mice bearing subcutaneous mammary tumors were imaged after injecting ProSense 680, a probe for assessing protease activity, whereas cerebral $\beta$-amyloid plaque distribution was assessed in aged ArcA$\beta$ transgenic mice following administration of an amyloid-sensitive fluorescent probe, CRANAD-2. A sequential multi-modal strategy combining FMT yielding molecular and MRI yielding structural readouts was applied. Image registration, FMT reconstruction and result visualization were all based on STIFT. We further extended the functionality of FMT to investigate dynamic problems, e.g., for assessing tumor vascular parameters such as permeability and perfusion. Dynamic fluorescence reflectance imaging (dFRI) was integrated into the hybrid FMT/MRI system to achieve high temporal resolution. Excellent correspondence between uptake curves of magnetic enhancement agents and fluorescent probes has been found with correlation coefficients R $>$ 0.98, indicating that processes associated with tumor angiogenesis can be non-invasively monitored using dFRI, though the spatial resolution was inferior to that of MRI. The dFRI measurements are intrinsically two-dimensional, which might be adequate under certain conditions. Yet, full 3D characterization of dynamic processes would be attractive though limited by the poor FMT temporal resolution due to its intrinsic sequential data acquisition. A generalized fast reconstruction algorithm has been proposed by assuming changes in concentration during a time interval to be linear, i.e., described by a first order rate constant according to $C_{recon}(t_0+\Delta t) = C_{recon}(t_0)+ \Delta tK$ yielding a $K$-map. This new unknown variable serves as prior information to guide the design of an optimal illumination pattern, though this would require reconstruction in real time. Simulation showed that for a dye bolus moving within an imaging cube, $K$-map-based reconstruction performs superior compared to the conventional frame-by-frame reconstruction. Phantom validation and in vivo experiments will be required to confirm the performance of the algorithms under ‘real world’ conditions. In summary, the availability of a user-friendly software platform such as STIFT enabling simulation, optimization and reconstruction should facilitate integrating a three-dimensional optical technique like FMT into a workflow typically for preclinical research involving in vivo imaging followed by tissue sectioning and in vitro imaging using a standard microscopy for validation purposes. Thus, software platform such as STIFT is expected to fill the gap between the sophisticated high-end techniques and reconstruction algorithms used in specialized imaging centers and in vivo imaging tasks run in a typical in vivo biology laboratory. As STIFT is of modular design, novel developments, such as improved forward modeling approaches or inversion tools, could be integrated in a more or less straightforward manner, invisible to the routine user. This may also include extensions towards dynamic imaging

Noninvasive detection of acute cerebral hypoxia and subsequent matrix-metalloproteinase activity in a mouse model of cerebral ischemia using multispectral-optoacoustic-tomography

Oxygen metabolism and matrix metalloproteinases (MMPs) play important roles in the pathophysiology of cerebral ischemia. Using multispectral optoacoustic tomography (MSOT) imaging, we visualized in vivo changes in cerebral tissue oxygenation during 1 h of transient middle cerebral artery occlusion (tMCAO) and at 48 h after reperfusion together with MMP activity using an MMP-activatable probe. The deoxyhemoglobin, oxyhemoglobin, and MMP signals were coregistered with structural magnetic resonance imaging data. The ipsi-/contralateral ratio of tissue oxygen saturation (SO2) was significantly reduced during 1 h of tMCAO and recovered after 48 h of reperfusion in tMCAO compared with sham-operated mice (n  =  8 to 10 per group). A higher ipsi-/contralateral MMP signal ratio was detected at 48 h after reperfusion in the lesioned brain regions of tMCAO compared with the sham-operated animal (n  =  4 to 6 per group). Ex vivo near-infrared fluorescence imaging of MMP signal in brain slices was used to validate in vivo MSOT measurements. In conclusion, noninvasive MSOT imaging can provide visualization of hemodynamic alterations and MMP activity in a mouse model of cerebral ischemia