2.1.

Instrumentation

An overall view of the hardware requirements for LSCI is shown in Fig. 1(a), with dashed lines indicating how each component is integrated into the clinical instrument. A schematic of the adapted microscope (Zeiss OPMI Pentero, Carl Zeiss Meditec Inc., Oberkochen, Germany) is shown in Fig. 1(b), with a corresponding photograph shown in Fig. 1(c). A laser diode ($\lambda =660\mathrm{nm}$, $P=120\mathrm{mW}$, Thorlabs Inc., Newton, New Jersey) was incorporated into an add-on laser adapter (MM6 Micromanipulator, Carl Zeiss Meditec Inc.), which was attached to the bottom of the microscope head. The laser beam linearly travels through the adapter until it encounters a curved mirror, which directs the beam toward the cortex as shown in the laser adapter illustration of Fig. 1(a). The steering toggle on the laser adapter controls the angle of the mirror, which allows positioning of the beam on the tissue surface. The laser power was measured to be $28\mathrm{mW}/{\mathrm{cm}}^2$, which is far below the ANSI standard of $200\mathrm{mW}/{\mathrm{cm}}^2$ for maximum permissible exposure to a visible laser beam.²⁵ A camera (Basler A602f, Basler Vision Technologies, Ahrensburg, Germany) was connected to one of the side viewing ports using a C-mount camera adapter (Carl Zeiss Meditec, Inc.). The camera used in this study had a sensor size of $656\times 491\text{pixels}$, a pixel size of $9.9\mu \mathrm{m}$, and a frame rate of $100\text{images}/\text{second}$. This camera could be easily switched out for a camera model with a higher resolution or speed in future studies. The imaging optics built into the microscope were used to focus the camera and zoom onto an area of interest on the tissue surface. For this study, the microscope was set for maximum zoom and the camera field of view was $\sim 2\times 1.5\mathrm{cm}$, corresponding to $\sim 3\times$ demagnification relative to the camera sensor. The microscope’s depth of field for the settings used during image acquisition was measured to be $\sim 5.7\pm 0.6\mathrm{mm}$. The hardware modifications to the microscope were attached prior to the start of the surgery and did not interfere with sterile draping or with normal use of the microscope, as shown in Fig. 1(d).

Fig. 1

Zeiss OPMI Pentero neurosurgical microscope adapted to measure the cerebral blood flow intraoperatively using LSCI. (a) A schematic showing the hardware requirements for LSCI and how these are adapted for intraoperative use. The imaging optics are built-in to the microscope head, the camera is attached to a side viewing port, and the laser illumination is built in to an add-on laser adapter. Representative images from a clinical case illustrate the raw image recorded by the camera (Raw) and the corresponding speckle contrast image (LSCI). (b) A schematic of the intraoperative instrumentation shows the add-on attachments for LSCI, with drawings adapted from the Zeiss OPMI Pentero Manual Issue 9.3. (c) Photograph of the intraoperative instrumentation with all add-on components labeled. (d) The LSCI adapted microscope is covered in a sterile drape and used to assist with the tumor resection procedure without any interference from the additional hardware.

2.2.

Intraoperative Procedure

All experiments were performed during brain tumor resection surgeries at the NeuroTexas Institute in St. David’s Hospital, Austin, TX. The clinical study was approved by the Institutional Review Boards of the University of Texas at Austin and St. David’s Hospital and written informed consent was obtained for all patients. A summary of the patient details for the pilot clinical study is shown in Table 1. Immediately prior to LSCI imaging, the surgeon positioned the microscope over the cortical area of interest, adjusted the focus and zoom, and flushed the tissue with sterile saline to reduce specular reflections. LSCI images were recorded for $\sim 10-15\min$ with a camera exposure time of 5.0 ms. The camera exposure signal and the patient’s electrocardiogram (ECG) waveform were simultaneously recorded during image acquisition for retrospective cardiac cycle motion compensation. Baseline blood flow images were recorded for all patients, and in three cases, images were acquired before and after cortical stimulation with either bipolar cautery or the Ojemann cortical stimulator.

Table 1

Clinical patient overview for the pilot study. All patients were undergoing brain tumor resection procedures and were imaged either before or after the resection at the discretion of the surgeon.a

2.3.

Image Analysis

The raw images recorded by the camera were converted into speckle contrast images using Eq. (1),

The remaining image analysis was performed in postprocessing after the completion of each clinical case. To provide a more quantitative measure of blood flow, the speckle contrast images were converted to correlation time, ${\tau }_c$, using Eq. (2),

2.4.

Cardiac Filtering

To account for blood flow changes that occur during the cardiac cycle, an ad hoc ECG filter^31–33 was implemented to reduce fluctuations in recorded ${\tau }_c$ values. Briefly, the actual time of each image was determined from the recorded camera exposure signal and the actual time of each heartbeat was determined from the ECG waveform. A “normalized time” was assigned to each image frame based on the relative time of the image between adjacent $R$ peaks in the cardiac cycle. The measured ${\tau }_c$ was plotted against the normalized time for the first 25 heartbeats and a large-window moving average filter was used to compute the ad hoc filter function. This filter was applied to the entire data set by subtracting the filter value for the corresponding normalized time and adding back the median ${\tau }_c$ of the filter function. A small-window moving average filter ($\text{width}=0.15\mathrm{s}$) was used to reduce the noise in the filtered ${\tau }_c$ result. This cardiac filtering helps to reduce the variation in ${\tau }_c$ due to the cardiac cycle while retaining inherent variability due to physiological changes in flow.³¹

2.5.

Image Registration

Image registration was performed on the speckle contrast images using Elastix.³⁴ This program performs an automatic intensity-based image registration by adjusting the position of a moving image to match a fixed image using the transformation type, similarity measure, and optimization procedure specified by the user. The similarity measure was set to mutual information and the optimization procedure was set to adaptive stochastic gradient descent based on the registration performance. The transformation type was set to either translation transform for rigid registration or B-spline transform for nonrigid registration, and a comparison between the two transforms was performed. The $x$- and $y$-offsets needed for mapping each moving image back to the fixed image were output from Elastix in the translation transform parameter file. An overall total displacement was calculated for each registered image to assess the spatial shift between the original and registered images.

3.1.

Clinical Study Overview

The speckle contrast images serve as blood flow maps, where red regions correspond to faster flow and blue regions correspond to slower flow. Figure 2 shows the camera field of view (FOV) recorded by the Zeiss built-in color camera under xenon lamp illumination along with corresponding registered speckle contrast images for two patients. The speckle contrast images were also thresholded to display only speckle contrast values less than 40% of the maximum (fastest flows) and were overlaid on top of the color photographs. These image sets show excellent alignment of the anatomical vasculature and the blood flow maps seen in the LSCI images, confirming the performance of the microscope-integrated LSCI system. The overlay image also highlights a major benefit of integrating LSCI into the surgical microscope: LSCI can provide blood flow maps for the tissue region in the microscope’s FOV with the spatial resolution of the microscope.

Fig. 2

Comparison between color photographs taken under xenon lamp illumination using the built-in Zeiss color camera (a, d) and corresponding LSCI images (b, e) from patients 7 and 9, respectively. The color photographs were registered to match the orientation of the speckle contrast images for ease of comparison. The speckle contrast images are averaged over 30 frames with a field of view of $\sim 2\times 1.5\mathrm{cm}$. The scale bar in (a) applies to all images. The color map displays high flows in red (low speckle contrast) and low flows in blue (high speckle contrast), and the bright white areas are regions of specular reflection. The LSCI images were thresholded to display the speckle contrast values less than 40% of the maximum and are overlaid on top of the color photographs (c, f).

3.2.

Retrospective Motion Correction

The result of applying the ECG filtering procedure on original and registered image sets is shown for a single patient in Fig. 3, where the measured ${\tau }_c$ has been converted into CBF $\%\mathrm{\Delta }$ for ease of interpretation. Figure 3 shows the alignment of the minimum CBF with the ECG R-peak [Fig. 3(b)], the ECG filter shape generated from 25 heartbeats [Figs. 3(c) and 3(e)] and the filtered results [Figs. 3(d) and 3(f)]. Cardiac artifacts are clearly present both before and after registration from the measured CBF in blue. The filter shape is altered after registration, as tissue movement artifacts that result in sampling different tissue areas manifest as CBF changes. The filtered output reduces beat-to-beat variability from 12% to 3.0% [standard deviation of blue versus black curves in Fig. 3(f)], which is consistent with previously published results³¹ and allows the possibility of visualizing small changes in CBF. All plots in subsequent figures have been ECG filtered and display the equivalent of the black curves from Figs. 3(d) and 3(f).

Fig. 3

The result of applying the ECG filtering process used to remove the pulsatile motion artifacts before and after translation-based image registration for patient 4. (a) The speckle contrast image is averaged over 30 frames with a field of view of $\sim 2\times 1.5\mathrm{cm}$ [same scale bar from Fig. 2(a)]. The ROI is shown in black, and the color map displays high flows in red (low speckle contrast) and low flows in blue (high speckle contrast). (b) The average CBF percent change ($\%\mathrm{\Delta }$) computed from the ROI is co-localized in time with the ECG waveform recorded for the patient, showing alignment of minimum CBF with the ECG R-peak. The CBF $\%\mathrm{\Delta }$ is calculated using an average of $\sim 5\mathrm{s}$ of baseline flow at the start of acquisition. (c) The ad hoc ECG filter shape generated from $\sim 25$ heartbeats from the original dataset. (d) The ECG filtered output is shown in red and the result after applying a small-window moving average filter to the filtered output is shown in black for the original dataset. (e) The ad hoc ECG filter shape generated from $\sim 25$ heartbeats from the translation-based registered dataset. (f) The ECG filtered results with and without the small-window moving average filter from the translation-based registered data set. The black curve from (d) or (f) will be displayed in future plots.

Translation rigid registration was performed for all image data because of the decreased complexity, reduced computation time, and preservation of image integrity. A comparison between the original and registered image sets is shown for three baseline cases in Fig. 4. These plots illustrate greater stability and reduced fluctuations in the ROI time courses after registration, especially for the vessel ROIs. This confirms that the CBF changes in the original time courses were solely due to tissue motion, causing vessel ROIs to sample the parenchyma, and vice versa. Compared to the dramatic improvement for vessel ROIs, registered parenchyma ROIs are visually similar to their originals, especially those placed farther from nearby vessels. A movie showing the original and translation-registered image sets for a single baseline case is included to demonstrate the effectiveness of image registration for correcting tissue motion artifacts in LSCI images (Video 1, left and center panels).

Fig. 4

A comparison between the time courses of ROIs before and after translation-based image registration for three baseline cases, patients 1, 7, and 9 for parts (a)–(c), (d)–(f), and (g)–(i), respectively. The images on the left (a, d, g) display the speckle contrast image at the start of image acquisition averaged over 30 frames with a field of view of $\sim 2\times 1.5\mathrm{cm}$ [same scale bar from Fig. 2(a)]. The color map displays high flows in red (low speckle contrast) and low flows in blue (high speckle contrast) with four numbered ROIs overlaid in black where analysis is taking place. The middle column of plots (b, e, h) displays the time course of the original recorded CBF percent change ($\%\mathrm{\Delta }$) during the course of the intraoperative acquisition. The right column of plots (c, f, i) displays the time course of the CBF $\%\mathrm{\Delta }$ after translation-based image registration of the image set. The CBF $\%\mathrm{\Delta }$ is calculated using an average of $\sim 5\mathrm{s}$ of baseline flow at the start of acquisition. Plots for the same patient were plotted with the same axes for easier comparison before and after registration.

Video 1

This video demonstrates the impact of both translation and B-spline-based image registration on a LSCI data set for monitoring CBF. The left frame shows the original image set from baseline case 7, the middle frame shows the translation-registered image set, and the right frame shows the B-spline registered image set, all with five-frame averaging. The location of the vasculature is stable after translation registration, which allows accurate quantitative monitoring of CBF changes in different tissue regions over time. B-spline registration provides both lateral image correction given by translation registration and axial correction for tissue pulsation and other nonrigid deformations (MOV, 12.8 MB) [URL: http://dx.doi.org/10.1117/1.NPh.1.1.015006.1].

As seen in the left frame of Video 1 and in the original plots from Fig. 4, the lateral displacement caused by tissue motion can be a significant source of noise, especially for vessel tissue regions. This displacement was quantified as the total spatial shift of each registered image relative to the fixed image at the start of acquisition. Across seven cases, the mean lateral displacement ranged from $0.12\pm 0.078$ to $0.64\pm 0.15\mathrm{mm}$, and the maximum displacement ranged from 0.41 to 1.9 mm. To illustrate how tissue motion affects the measured CBF changes, the relationship between the change in ${\tau }_c$ and displacement is illustrated in Fig. 5 for two cases corresponding to Figs. 4(b) and 4(e). The displacement is co-localized in time with the change in ${\tau }_c$, showing excellent correspondence between the magnitude of the spatial shift and the change in flow measured by LSCI. Pearson’s correlation coefficient indicates a positive correlation between the displacement and change in flow, with $r\ge 0.9$ for two of the ROIs.

Fig. 5

(a, c) The displacement is co-localized in time with the change in correlation time ($\mathrm{\Delta }{\tau }_c$) for two ROIs in Figs. 4(b) and 4(e), respectively. Two ROIs are shown for each case to improve the visibility on the plots, and the colors correspond to those in Fig. 4. The black dashed line corresponds to the displacement. (b, d) Correlation between the displacement and $\mathrm{\Delta }{\tau }_c$ for (a, c), respectively, with the Pearson’s correlation coefficient listed for each ROI.

Quantitative analysis confirms that both vessel and parenchyma regions had a statistically significant noise reduction after ECG filtering plus registration (Fig. 6). Noise was quantified as the standard deviation of the CBF % over the recorded time course for each ROI analyzed. Noise levels were compared for the original recorded images versus ECG filtering alone, image registration alone, and ECG filtering plus image registration for 16 ROIs across four baseline cases. ECG filtering alone as well as combined with image registration are the two scenarios highlighted in the plots from Fig. 4. As shown in Fig. 6, ECG filtering alone causes a statistically significant noise reduction in the parenchyma regions ($15\%\pm 3\%$ to $8.3\%\pm 2.7\%$, $p<{10}^{-5}$). Registration alone causes a statistically significant noise reduction in the vessel regions relative to ECG filtering alone ($38\%\pm 25\%$ to $16\%\pm 5.8\%$, $p<0.05$), as well as a large reduction relative to the vessel noise in the original set. However, the most dramatic noise reduction is seen after combining both ECG filtering and image registration, which had a statistically significant reduction for all tissue regions. Combined ECG filtering and image registration significantly reduced the noise across all ROIs to $6.2\%\pm 2.6\%$. This was statistically significant compared to the original set ($25\%\pm 27\%$, $p<0.05$), ECG filtering alone ($20\%\pm 21\%$, $p<0.05$), and image registration alone ($15\%\pm 4\%$, $p{<10}^{-5}$).

Fig. 6

Quantification of noise (standard deviation of CBF %) before and after both ECG filtering and image registration across ROI groups from baseline cases 1, 5, 7, and 9. Statistical significance is calculated using a right-tailed paired $t$-test. Sample sizes are $N=6$ for vessel ROIs, $N=10$ for parenchyma ROIs, and $N=16$ for all ROIs. Orig. = original data set; ECG = ECG filtering only; Reg. = image registration only; and Both = ECG filtering plus image registration.

For the three cortical stimulation cases, a comparison between the original and registered images is shown in Fig. 7. Similar to the original baseline cases, large-scale fluctuations resulting from tissue motion make it difficult to accurately quantify and interpret the flow dynamics, especially in Figs. 7(b) and 7(e). After registration, the noise is largely suppressed and clear trends in CBF change are easily visualized in each case. Quantification of the flow change confirms that region 5 experienced the smallest change from baseline in all cases with a flow reduction of 24%, flow increase of 31%, and flow decrease of 46% for Figs. 7(c), 7(f), and 7(i), respectively. This improved sensitivity to small changes in flow increases LSCI’s utility as a CBF monitoring tool. Removing the tissue motion artifact also increases the accuracy of the results and patient safety, as the original region 5 time courses misleadingly suggested larger flow reductions in Figs. 7(b) and 7(e) that may have prompted unnecessary surgical action.

Fig. 7

A comparison between the time courses of ROIs before and after translation-based image registration for three cases with cortical stimulation, patients 4, 6, and 10 for parts (a)–(c), (d)–(f), and (g)–(i), respectively. The images on the left (a, d, g) display the speckle contrast image at the start of image acquisition averaged over 30 frames with a field of view of $\sim 2\times 1.5\mathrm{cm}$ [same scale bar from Fig. 2(a)]. The color map displays high flows in red (low speckle contrast) and low flows in blue (high speckle contrast) with five numbered ROIs overlaid in black where analysis is taking place. The dotted black oval indicates the location of stimulation in the field of view, and the yellow star indicates the time that the stimulation occurred. In (a) and (g), bipolar cautery was used with forceps close together (one oval). In (d), the Ojemann cortical stimulator was used, and the two ovals show the locations of both bipolar stimulators. The middle column of plots (b, e, h) displays the time course of the original recorded CBF percent change ($\%\mathrm{\Delta }$) during the course of the intraoperative acquisition. The right column of plots (c, f, i) displays the time course of the CBF $\%\mathrm{\Delta }$ after translation-based image registration of the image set. The CBF $\%\mathrm{\Delta }$ is calculated using an average of $\sim 5\mathrm{s}$ of baseline flow before stimulation occurs. Plots for the same patient were plotted with the same axes for easier comparison before and after registration.

Since brain tissue may deform after skull removal, a nonrigid transform may be required to account for tissue deformation. Because of increased computational costs, we performed B-spline nonrigid registration on a subset of the pilot study data from four baseline patients for comparison to the translation transform, with results shown for a single patient in Fig. 8. Similar to Fig. 4, both registration methods greatly reduce the fluctuations and improve stability, with the most significant impact on the vessel ROI and visible improvement on parenchyma regions placed near vessels. When Figs. 8(c) and 8(d) are directly compared, the time courses are visually similar between the two registration methods. Video 1 (middle and right panels) includes a comparison of translation and B-spline registered image sets for a single baseline case to demonstrate the effectiveness of nonrigid image registration for correcting axial tissue deformation artifacts in the LSCI image set.

Fig. 8

A comparison between translation-based rigid registration and B-spline-based nonrigid registration illustrated with the time courses of ROIs for one case, patient 5. The image in (a) shows the speckle contrast image at the start of image acquisition averaged over 30 frames with a cropped field of view of $\sim 1.6\times 1.2\mathrm{cm}$ (scale bar included). The color map displays high flows in red (low speckle contrast) and low flows in blue (high speckle contrast) with four numbered ROIs overlaid in black where analysis is taking place. (b) The original recorded CBF percent change ($\%\mathrm{\Delta }$) during the course of the intraoperative acquisition. (c) The time course of the CBF $\%\mathrm{\Delta }$ after translation-based image registration of the image set. (d) The time course of the CBF $\%\mathrm{\Delta }$ after B-spline-based image registration of the image set. All plots are displayed with the same axes for easier comparison before and after registration and between the two registration methods.

Quantitatively, the average percent difference between rigid and nonrigid registration methods is displayed in Table 2 for the ROIs shown in Figs. 4(a), 4(d), 4(g), and 8(a). Although B-spline registration is effective for removing residual nonrigid tissue deformation artifacts that occur in the axial direction, the difference compared to translation registration was minimal, with $<1\%$ difference for 11/16 ROIs and $<3\%$ difference for 15/16 ROIs. Region 4 in Fig. 8(a) had the largest discrepancy with a 7% difference. However, the improvement of B-spline registration was only moderate relative to translation registration when viewing region 4 over time. Overall, the lower complexity translation registration had excellent agreement with B-spline registration and the improved motion correction indicated by the small percent differences would not change the course of treatment.

Table 2

Average and standard deviation of the percent difference between translation and B-spline registration over the time course of image acquisition. The regions correspond to the four numbered ROIs shown in Figs. 4(a), 4(d), 4(g), and 8(a).