2.1.

Framework of Registration

We have developed RegOA, an open-source software toolbox for OAT-MRI registration based on MATLAB (MathWorks, Massachusetts, version: R2018a). The framework of RegOA is shown in Fig. 1. Raw data containing two-dimensional (2-D) or three-dimensional (3-D) MRI and OAT image stacks were preprocessed, which included adaptive filtering and segmentation of the region of interest. The preprocessed images were then registered using a two-step strategy based on MI (or M3 method, explained in “registration algorithm”). The registration results can be visualized and evaluated with metrics such as TRE and EER.²⁵ We have also implemented registration of OAT images to a standard MRI brain atlas for further analysis. Finally, RegOA allows exporting registered results in a NIfTI format to other widely used imaging analysis toolbox, such as SPM²⁷ and AFNI,²⁶ for volume-based morphometry and voxel-based analysis.

Fig. 1

The workflow for automatic registration. The gray boxes indicate dataset generated at different steps in the workflow and the white boxes represent the corresponding image processing technique.

2.1.1.

Preprocessing

Each slice of MRI and OAT stacks was preprocessed to remove noise and to segment the subject from the background. Many sophisticated nonlinear filters have been developed for denoising MRI images; here, we have adopted a 2-D adaptive low-pass Wiener filter that estimates the local mean and variance around each pixel and removes the stochastic noise in MR images without losing important features of the subject.^28–30 Then the MRI slices were segmented using an active contour model or “snakes,” which evolves the segmentation result iteratively (details in supplementary material, Sec. 1 in Ref. 31). The initial contour is provided by the user by drawing a coarse contour around the object. The maximum iteration cycle number was set to 50. OAT data displayed ripple-shaped artifacts arising from limitation of OAT instrumentation, reconstruction algorithm, and minor speckle noise. Therefore, a “Canny edge” detector (Gaussian filter, size $7\times 7\text{pixels}$) instead of the low-pass Wiener filter was applied to OAT images to identify clean and well-defined edges of the object and successfully discard fictitious edges from the image.³² Thereafter, segmentation using snakes identical to the one used in MRI preprocessing was applied (supplementary Fig. 2 in Ref. 33).

Fig. 2

The GUI of registration toolbox RegOA. The blue panels (1) are displayed images, including loaded OAT and MRI data and the registration result. The drop-down button (2) controls different display options for (1). Status lamps (3) display the status of a certain function: green notifies accomplishment while red denotes an unfinished state. Normal buttons are for functions, including loading OAT and MRI datasets, preprocessing, registration, exporting files in NIfTI format, evaluation of registration quality, and image saving.

2.2.

Phantom

To assess the performance of the OAT-MRI registration method, a phantom with fiducial markers was measured using OAT and MRI systems. The dimensions of the phantom were $13\times 14.5\times 40{\mathrm{mm}}^3$, with a curved top surface of 30-mm diameter mimicking a mouse head. A cross section of the phantom is shown in Fig. 3(a) with three parallel holes of 1.4-mm diameter for incorporating contrast agents. The phantom was made of 50 mL water with 3% agar and 3 mL intralipid (Sigma-Aldrich, Switzerland) using a home-designed mould [Fig. 3(b)]. Three polyester tubes (diameter: 1.2 mm) were inserted into the mould before pouring heated agar solution. A solution containing optoacoustic visible gold nanoparticle Ntracker DM50 ($10\mu \mathrm{L}$, Nanopartz, excitation peak: 775 nm) and MRI contrast agent gadolinium-DTPA ($5\mu \mathrm{L}$, Guerbet, France) was prepared in $200\mu \mathrm{L}$ phosphate-buffered saline (PBS) and injected into polyester tubes after cooling.

Fig. 3

Phantom design and fabrication: (a) the cross section of the phantom with three inclusions and (b) an aluminum mould used for fabricating the phantom. Three polyester tubes were inserted as the contrast inclusions.

2.3.

Animal Model

All procedures conformed to the national guidelines of the Swiss Federal Act on animal protection and were approved by the Cantonal Veterinary Office Zurich (Permit No. 18-2014). Three C57BL/6J mice (Janvier, France), weighting 20 to 25 g, 8 to 10 weeks of age were used. Animals were housed in ventilated cages inside a temperature-controlled room, under a 12-h dark/light cycle. Pelleted food (3437PXL15, CARGILL) and water were provided ad libitum.

2.4.

Ex Vivo Mouse Brain

A whole mouse brain was imaged ex vivo with OAT after removal of the skull. The brain was embedded in agar 3% in PBS (pH 7.4). A tube for holding the mouse brain was made from agar using a mold consisting of a 20- and a 5-mL syringe that were aligned coaxially resulting in an agar tube of $25/15\text{-}\mathrm{mm}$ outer/inner diameter. The mouse brain was placed in coronal orientation inside the agar tube, which was filled with PBS for removing any residual air. The agar tube was removed from syringe prior to OAT and MR imaging.

2.5.

Optoacoustic Tomography

For OAT imaging of both phantom and brain, slice-by-slice 2-D imaging setting was used. The in-plane resolution is $100\times 100\mu \mathrm{m}$². For imaging of the phantom with fiducial markers, laser excitation pulses of 9 ns were delivered at seven wavelengths (680, 715, 730, 775, 800, 850, and 900 nm), $\text{step size}=0.3\mathrm{mm}$ moving along horizontal direction, $\text{field of view}=20\mathrm{mm}\times 20\mathrm{mm}$, 10 averages, and $\text{scan time}=20\min$. For ex vivo mouse brain, the agar tube containing mouse brain was fixed into the supplied rigid phantom holder and placed into the imaging chamber of the OAT system. Laser excitation pulses of 9 ns were delivered at six wavelengths (680, 715, 730, 760, 800, and 850 nm) in coronal orientation, $\text{step size}=0.3\mathrm{mm}$ moving along horizontal direction, $\text{field of view}=20\mathrm{mm}\times 20\mathrm{mm}$, resolution $100\mu \mathrm{m}$, 10 averages, and $\text{scan time}=20\min$. For in vivo mouse brain, laser excitation pulses of 9 ns were delivered at five wavelengths (715, 730, 760, 800, and 850 nm) in coronal orientation, $\text{step size}=0.3\mathrm{mm}$ moving along horizontal direction, $\text{field of view}=20\mathrm{mm}\times 20\mathrm{mm}$, 10 averages, and $\text{scan time}=20\min$.

2.6.

Magnetic Resonance Imaging

All MRI scans were performed on a 7/16 small animal MR Pharmascan (Bruker Biospin GmbH, Ettlingen, Germany) equipped with an actively shielded gradient capable of switching $760\mathrm{mT}/\mathrm{m}$ with a $80\text{-}\mu \mathrm{s}$ rise time and operated by a ParaVision 6.0 software platform (Bruker Biospin GmbH, Ettlingen, Germany). A circular polarized volume resonator was used for signal transmission, and an actively decoupled mouse brain quadrature surface coil with integrated combiner and preamplifier was used for signal receiving.

For imaging of phantom with fiducial marker, ${\mathrm{T}}_1$-weighted MR imaging was performed. A 3-D volume was acquired using fast low-angle shot sequence in combination with slice selective excitation of a 20-mm-thick volume. The imaging parameters were: $\text{echo time}=3.493\mathrm{ms}$, $\text{repetition time}=50\mathrm{ms}$; $\text{flip}\text{angle}=20\mathrm{deg}$; $\text{field of view}=20\times 20\times 30{\mathrm{mm}}^3$, $\text{matrix}=100\times 100\times 150$, giving an isotropic spatial $\text{resolution}=200\times 200\times 200\mu {\mathrm{m}}^3$, $\text{average}=1$, axial orientation, and $\text{scan time}=13\min$ 50 s.

Both the in vivo and ex vivo ${\mathrm{T}}_2$-weighted MR images of mouse brain/head were obtained using a 2-D spin echo sequence (Turbo rapid acquisition with refocused echoes) with imaging parameters: RARE $\text{factor}=8$, $\text{echotime}=36\mathrm{ms}$, $\text{repetition time}=2627.7\mathrm{ms}$, 6 averages, slice $\text{thickness}=0.7\mathrm{mm}$, no slice gap, $\text{field of view}=20\times 20{\mathrm{mm}}^2$, $\text{matrix}=512\times 512$, giving an in-plane spatial $\text{resolution}=39\times 39\mu {\mathrm{m}}^2$, out-of-plane $\text{resolution}=0.7\mathrm{mm}$ (slice thickness), within a $\text{scan time}=12\min$ 36 s. For ex vivo MRI, the whole mouse brain was placed in a 10-mL syringe filled with perfluoropolyether (Fomblin Y, LVAC 16/6, average molecular weight 2700, Sigma-Aldrich, Switzerland). For in vivo MRI, mice were anesthetized with an initial dose of 4% isoflurane (Abbott, Cham, Switzerland) in oxygen/air ($200/800\mathrm{mL}/\min$) mixture and were maintained at 1.5% isoflurane in oxygen/air ($100/400\mathrm{mL}/\min$). Mice were next placed in prone position on a water-heated support to keep body temperature within $36.5°\mathrm{C}\pm 0.5°\mathrm{C}$, monitored with a rectal temperature probe.

2.7.

Optoacoustic Tomography Reconstruction

OAT images were reconstructed using a model-based linear algorithm using MSOT Viewer (iThera Medical GmbH, Germany).³⁶ For in vivo mouse imaging data, linear unmixing was applied to resolve signals from oxygenated and deoxygenated-hemoglobin. For phantom imaging data, linear unmixing was applied to resolve signal from gold nanoparticle. For ex vivo brain imaging data, background image was resolved.

2.8.

Evaluation of Registration

We have assessed the performance of the three registration methods (M1, M2, and M3) using OAT and MR images of one phantom and three mouse head/brains. The three mouse brain registration tasks were (1) ex vivo OAT—ex vivo MRI, (2) ex vivo OAT–in vivo MRI, and (3) in vivo OAT–in vivo MRI. Task 2 aims to test if toolbox RegOA has a certain degree of freedom in registering different brain regions of interest. The registration performance was evaluated by the metrics of TRE and EER for each pair of landmarks. TRE is defined as the Euclidean distance between points (targets) that are used for registration algorithm.²⁵ EER, the fraction of Euclidean error remaining after registration, is defined as one TRE gap between two corresponding targets divided by their original distance before registration.²⁵

For phantom validation, the three inclusions were clearly visible in both OAT and MR images. The center of each inclusion served as ground-truth position of the registration targets. For mouse brain study, natural landmarks were manually selected as registration targets. Five anatomical landmarks were chosen (Fig. 4). We also tested the robustness of M3 by fixing the MR image and rotating the OAT image by an angle ranging from $-90\mathrm{deg}$ to 90 deg, with an incremental step of 10 deg. Bilinear interpolation was applied for rotating the image. The original distance, TRE, and EER for five pairs of registration targets were calculated.

Fig. 4

Registration targets in four datasets for assessing the performance of different registration methods. Three inclusions served as registration targets for phantom study, and five natural landmarks were selected as targets based on the anatomy of in vivo and ex vivo mouse brain. The upper row shows the MR images of (a) phantom, (b) ex vivo brain, (c) segmented brain from in vivo case, and (d) in vivo head of mouse, with red crosses indicating the targets. The bottom row shows the paired OAT images of the upper row (e) phantom, (f) and (g) ex vivo brain, and (h) in vivo head of mouse, with blue crosses indicating the targets.

2.9.

Exporting, Registration, and Comparison with AFNI

We exported the preprocessed OAT and MR images as NIfTI format and loaded them to the program “3DAllineate” from the AFNI software package ( http://afni.nimh.nih.gov) to register in 2-D the OAT image to the corresponding MR image slice. Affine transformation was performed using two passes: a coarse pass with larger shifts followed by a fine pass to prevent local minima. MI was selected as the similarity metric. Other parameters were left at the default setting.

2.10.

Registration with a Brain Atlas

Spatial normalization of individual neuroimaging data by mapping to a standard reference atlas is commonly adopted when analyzing datasets across multiple subjects.^37,38 Here, we tested the feasibility to register the ex vivo OAT brain image with a widely used high-resolution volumetric atlas segmented into 62 structures based on average MRI of 40 adult C57Bl/6J mice.³⁷

3.1.

Evaluation of Registration Accuracy

We have tested three different MI-based registration methods (M1 to M3) using phantom with fiducial markers, as well as ex vivo and in vivo mouse brain. Both M1 and M2 performed poorly; the transformed OAT images were either highly distorted in the corner [in Figs. 5(b), 5(g), and 5(l)] or misaligned to a false anatomical structure [in Figs. 5(m), 5(q), and 5(r)]. Compared with M1 and M2, the first step of M3 has achieved higher success rate of registration already based on visual evaluation. The selected target pairs, either the fiducials in the phantom [Fig. 5(d)] or the landmarks in the brain [Figs. 5(i), 5(n), and 5(s)], were matched accurately in OAT and MR images. The second step of M3 [Figs. 5(e), 5(j), 5(o), and 5(t)] led to further significant improvements of registration accuracy when the registered object contains heterogeneous inner structure. For example, in the case of in vivo brain registration, the lower boundary of brain was better aligned in M3-step 2 [Fig. 5(t)] than in M3-step 1 [Fig. 5(s)].

Fig. 5

Comparison of different registration methods in different registration tasks. Rows (1) to (4) show the overlaid OAT (“HOT” scale) and MRI (gray) images of four tasks: (1) phantom, (2) ex vivo OAT–ex vivo MRI mouse brain, (3) ex vivo OAT–in vivo MR images of mouse brain, and (4) in vivo OAT–in vivo MRI mouse brain. The columns (left to right) display before registration and resulting images using M1, M2, and M3 registration, respectively. M1 and M2 resulted in misalignment due to the interference from the background noise and overdependence on the texture of inner structure obtained from different modalities, having different interpretation of intensity values.

To quantitatively assess the accuracy of registration methods (M1 to M3), TRE and EER were computed for the four cases (Table 1). M1 results in high value of TRE and EER values (Fig. 5, second column), whereas M2 registration using preprocessed data yielded lower mean values of TRE and EER. However, the standard deviation of EER was still large compared with the mean value of the respective metrics. For example, the M2 EER for phantom study was $105.22\pm 819.54$ with a standard deviation 8 times larger than the mean value. This indicates that at least one of the registration pairs was significantly misaligned [as observed in Fig. 5(c)]. Both steps 1 and 2 of M3 performed better than M1 and M2, with decreased mean values and standard deviations of TRE and EER. M3-step 2 further decreased TRE and EER based on the M3-step 1. For instance, in the case of ex vivo brain registration, the standard deviation of TRE and EER decreased from 5.20 and 42.87 to 2.81 and 23.52, respectively, after step 2. The in vivo brain registration showed a decrease in both mean value and standard deviation for TRE and EER from steps 1 to 2 (TRE: $17.11\pm 11.36$ to $10.84\pm 8.34$; EER: $32.51\pm 14.96$ to $19.70\pm 11.19$).

Table 1

Registration evaluation by measuring TRE and EER for different registration methods (M1, M2, and step 1 and step 2 of M3) and different cases (phantom, ex vivo brains, ex vivo/in vivo brains, and in vivo brains). The numbers of target pairs (T. pair) and the original distance for target pairs are given to better compare them to the TRE after registration. Data were presented as mean value ± standard deviation.

3.2.

Evaluation of Registration Robustness

The robustness of the registration method M3 was evaluated by rotating OAT image relative to the MR image by an angle ranging from $-90\mathrm{deg}$ to 90 deg [Figs. 6(c)–6(f)]. Upon rotating the OAT relative to the MR images, the initial distance varies in a sinusoidal manner [Fig. 6(a)]. Nevertheless, following the two-step registration procedure, the dependence of TRE (blue) on the rotation angle is weak with values $<40\text{pixels}$, showing a significant decrease compared with the original distance. The second metric EER displayed a stronger dependence on the rotation angle than TRE, especially for the angle in the range of 20 deg to 50 deg. Lowest value of initial distance was observed in this range of the rotation angle, suggesting that OAT and MRI were already well-matched and thus resulted in high values of EER. The mean value and standard deviation for TRE and EER over the 180 deg were $17.42\pm 6.38$ and $22.81\pm 10.68$, respectively.

Fig. 6

Robustness test of registration method M3 at different rotational angles using in vivo OAT–in vivo MRI mouse brain images. OAT image was rotated with an angle ranging from $-90\mathrm{deg}$ to 90 deg. (a) Variation of original distance and TRE between corresponding targets in OAT and MR images; (b) variation of EER; (c)–(f) exemplary OAT images at $-90\mathrm{deg}$, $-45\mathrm{deg}$, 45 deg, and 90 deg of rotation; (g)–(j) overlaid OAT/MR images after registration. Ori. Dis: original distance.

3.3.

Comparison of RegOA and AFNI

We have compared the performance of RegOA with an established registration tool used for aligning MRI-based brain data, AFNI. Accordingly, we have exported preprocessed datasets of the in vivo mouse brain study to AFNI and performed AFNI inbuilt MI-based registration. MI-based registration by AFNI [Fig. 7(a)] yielded registration results comparable to M2 by RegOA [Fig. 7(b)]; the alignment of OAT image to the MRI reference image was unsatisfactory, which becomes obvious when comparing the cortical surfaces. In contrast, the two-step M3 registration by RegOA yielded superior results, i.e., the contour in OAT image was matched better with that of MR image [Fig. 7(c)].

Fig. 7

Comparison of registration results using AFNI and RegOA for ex vivo OAT–ex vivo MRI mouse brain images. Images show the overlaid OAT (HOT scale) and MRI (gray) images after registration using (a) AFNI MI-based, (b) M2, and (c) M3 (after step 2).

3.4.

Registration with a Brain Atlas

Instead of mapping OAT mouse brain image to its own MR image, registering it to a standard anatomical atlas would be helpful for analyzing large data series and use of predefined regions of interest for quantitative analysis. Figure 8 shows the labeled regions in the brain atlas (indicated with different colors), MRI anatomical reference (Dorr’s), OAT image after M3 registration, and overlaid images.

Fig. 8

Registration of OAT of mouse brain with MRI atlas and corresponding segmentation. (a) MRI atlas Dorr’s C57B6J mice, (b) overlaid OAT image, (c) labeled segmentation based on the (a), and (d) overlaid OAT image with labeled segmentation. The regions of cortex and hippocampus were indicated by the contour.