2.1.

Phantom Design

In this study, we created a multilayer head phantom based on physiologically realistic specifications that incorporates a range of hematoma classifications. We considered several distinct types of intracranial hematomas, including epidural, subdural, subarachnoid, and intracerebral. The first three are more commonly induced through moderate to severe TBI. Based on prior CT imaging studies, we have attempted to identify idealized structures to represent each of these four hematoma types.¹⁶ This approach is in line with phantoms described in standards for established medical imaging modalities that use idealized inclusions to represent tissue structures for basic performance testing^17,18 that can be easily replicated. The specific designs selected are described in Fig. 1 and Table 1, and include (1) epidural/subdural, (2) subarachnoid, and (3) intracerebral hematomas. Our model of the head includes four base layers: scalp, skull, CSF, and cerebral tissue (comprised of multiple identical layers). For the epidural and subdural hematoma, a blood-simulating layer spanning the full width of the phantom is included between the skull and CSF layers. For the subarachnoid hematoma, a CSF-diluted blood layer is represented in the space between the skull and cerebral tissue. Although it is known that subarachnoid hematomas are diluted by CSF,¹⁹ the representative dilution rate or hemoglobin concentrations are not readily available in literature. We used 60% blood concentration as a worst-case estimation based on CT (x-ray computed tomography) numbers reported previously for subarachnoid hematomas²⁰ and subdural hematomas.²¹ Intracerebral hematomas are simulated using disks within the cerebral tissue.

Fig. 1

Schematic of phantoms representing (a) epi/subdural, (b) subarachnoid, and (c) intracerebral hematomas. Dark-shaded regions in (a) and (c) represent blood, light-shaded region in (b) represents blood–CSF mixture; hashed regions represent CSF layer. Layer thicknesses: 3 mm for scalp, 5 mm for skull, and 3 mm for CSF.

Table 1

Hematoma geometries incorporated into phantoms.

2.2.

Phantom Fabrication

Multilayer phantoms were fabricated using two primary matrix materials—polydimethylsiloxane (PDMS) and polyurethane (PU). PDMS (Dow Corning Sylgard 184, Auburn, Michigan), which has an index of refraction ($n$) of 1.4, was used for all layers except for the skull layer.¹⁷ Titanium dioxide particles (${\mathrm{TiO}}_2$, anatase powder, Sigma-Aldrich, St. Louis, Missouri) and India ink were used to tune the scattering coefficient and absorption coefficient of PDMS layers. Stock solution of Sylgard 184 base with titanium dioxide or India ink at high concentration was prepared by mixing and sonicating using a probe tip sonicator (Q500 Sonicator, Qsonica, Newtown, Connecticut) for 10 h. To allow heat generated from the sonication to dissipate, the sample was placed in a room temperature water bath, and the sonication was done with a 10-s on and 20-s off cycle. The stock solutions were added to PDMS base at desired concentration and mixed with curing agent at 10:1 ratio. The mixture was poured into molds and kept in vacuum for an hour to remove air bubbles.

The molds for intracerebral hematomas were polystyrene petri dishes of 5- or 8.5-cm diameter. For brain layers, a glass petri dish of 12.5-cm diameter was used. For brain layers containing hematoma, the cured hematoma phantom was placed at the bottom of the mold, and uncured PDMS representing brain was poured over the hematoma at desired volume (calculated by measured weight) and placed in an oven at 75°C for 4 h to cure. It should be noted that a meniscus shape is formed due to surface tension along the lateral edge of the phantom when PDMS is cured with the surface open to air. Thus, for thinner layers—i.e., epi and subdural hematoma, scalp, and CSF layers (3-mm thickness)—a different molding approach was used to ensure uniform layer thickness. A 15-cm-diameter petri dish was used as the mold. Near the side of the petri dish cap, three-dimensional (3-D) printed spacers at desired thickness were glued to the dish. PDMS mixture was poured into the petri dish cap, and the petri dish bottom that has a slightly smaller diameter was placed on top of the spacers. The mold was then put in the oven to cure the PDMS. For thin hematomas ($<1\text{-}\mathrm{mm}$ thick), a spin coater (WS-650MZ-23NPP Spin Coater, Laurell Technologies Corporation, North Wales, Pennsylvania) running at 500 RPM for 30 s was used to apply a thin PDMS layer ($\sim 0.2\text{-}\mathrm{mm}$ thickness) to the underside of a polystyrene petri dish, before curing in the oven. Layers were applied and cured repeatedly until the desired thickness hematoma was achieved, determined by weight.

PU (WC781, BJB Enterprises, Tustin, California; $n=1.47$) was used to make the layer mimicking skull as it is not easily compressed as PDMS.¹⁸ The same titanium oxide particles were added to PU as scattering agent, and plastic black colorant (BPC, M-F Manufacturing Co., Fort Worth, Texas) was used as absorbing agent. The molding and curing methods used were the same as fabrication of the PDMS layers, except for different ratio between part A and part B of the PU (1:0.85) and time for curing ($\sim 16\mathrm{h}$). For intracerebral hematoma phantoms [Fig. 1(c)], the CSF layer was fabricated first, and then the skull and scalp layers were successively poured on top and cured, resulting in a single component containing the scalp/skull/CSF layers, minimizing assembly effort and any air gap between the layers.

2.3.

Evaluating the Impact of Hemoglobin Oxygenation

Measurements of the hematoma detector’s light source with a high sensitivity spectrometer (QE65 Pro Scientific Grade Spectrometer, Ocean Optics, Dunedin, Florida) indicated a center wavelength of 805 nm and a bandwidth of 2 nm (full-width-half-maximum). Thus, the device’s output is close to the near-infrared (NIR) oxy-/deoxy-hemoglobin isosbestic point at 800 nm. In a prior study, Zhang et al.¹⁴ noted that a wavelength of 805 nm “is suitable for hematoma detection since…the signal detected will not be affected by differences in oxygen.” However, no data were provided to support this assertion. The actual isosbestic point is at 798 nm, while at 805 nm the ${\mu }_{\mathrm{a}}$ of oxy-Hb ($4.6{\mathrm{cm}}^{-1}$) is 18% greater than that of deoxy-Hb ($3.9{\mathrm{cm}}^{-1}$).²² Thus, variations in saturation might introduce some degree of confounding effect in detected signals. To assess the robustness of the hematoma detector to blood oxygenation, we measured a phantom incorporating hemoglobin solutions with different saturation levels. Specifically, we used a 3-D printed phantom with ${\mu }_{\mathrm{s}}^{\prime }=10{\mathrm{cm}}^{-1}$, as measured with a spectrophotometer and inverse adding doubling (IAD) software. The phantom was designed with a disk-shaped void space (10 mL, 8.5-cm diameter, and 1.8-mm thick) and a removable top layer (1.0 mm thick) above the void space. The phantom included a removable 1-mm-thick top layer above the void space. A commercially available hemoglobin solution (Multi-4 CO-Oximeter Control, Level 2, Hb $\text{concentration}=13.8\mathrm{g}/\mathrm{dL}$, Instrumentation Laboratory Co., Bedford, Massachusetts) was used to represent the hematoma. The saturation level of the solution was changed by adding yeast and validated through CO-oximetry (Avoximeter 4000, Accriva diagnostics, San Diego, California). Measurements were performed for saturation values ranging from 3% to 99%.

2.4.

Optical Property Characterization

For phantoms of each tissue type, a 1-mm-thick sample of 4-cm diameter was made from the same batch of uncured mixture for OP measurement. The total reflectance and transmittance were measured using a 150-mm integrating sphere and spectrophotometer (Lambda 1050 UV/VIS/NIR, Perkin Elmer, Inc. Waltham, Massachusetts), and IAD software²³ was then used to calculate the absorption (${\mu }_{\mathrm{a}}$) and reduced scattering coefficients (${\mu }_{\mathrm{s}}^{\prime }$). The OPs used in the phantoms are shown in Table 2 and are based largely on clinical frequency-domain NIRS measurements in the adult head from a prior study.²⁴

Table 2

Thickness and OPs of tissue phantom layers24

2.5.

Near-Infrared Spectroscopy Measurements

The clinical hematoma detector used in this study (Fig. 2) evaluates the presence of hematomas based on spatial variations in signal attenuation. The device has one illumination fiber and one collection fiber; each has a diameter of 2 mm and the fiber separation distance is 4 cm. While the device’s primary function involves providing the user with a warning indicator when differences in signal attenuation increase beyond the threshold level, it also provides quantitative attenuation data for each measurement in terms of OD. Unless otherwise indicated, the measurements in our study were performed with the detector mounted on a custom-designed scaffold to ensure that pressure was consistent, yet light enough to minimize compression of the top phantom layer. Change in optical density ($\mathrm{\Delta }\mathrm{OD}$) was calculated by measuring normal and hematoma phantoms with the detector.

Fig. 2

Photographs of phantom layers incorporating (a) 5- and (b) 8.5-cm-diameter hematoma-simulating inclusions, as well as the (c) hematoma detector on its scaffold and (d) during a handheld measurement.

2.6.

Measurement Repeatability

A variety of factors can impact the repeatability of hematoma detection during phantom testing and real-world use. Improved understanding of these factors can help to optimize test methods and facilitate practical interpretation of the results. The repeatability of phantom measurements was evaluated by acquiring normal brain phantom OD data under six different scenarios. Although the hematoma detector is intended to be used as a handheld device, for standard performance testing, it is desirable to separate user-induced variability from other sources of error. Therefore, a custom holder was fabricated to fix the detector position relative to the phantom and ensure a small, yet consistent level of pressure. The weight of the detector on the phantom was measured as 190 g (1.86 kN). Both handheld and holder-mounted measurements were performed for each of the following cases: (1) repeat acquisitions without moving the device, (2) removal and replacement of the device on the phantom between acquisitions, and (3) removal and restacking of phantom layers between acquisitions.

2.7.

Effect of Hematoma Morphology on Detectability

Variations in hematoma detectability due to changes in morphology were explored by varying the geometry of epidural/subdural, subarachnoid, and intracerebral hematoma phantoms (Fig. 1). For the epi/subdural phantom geometry and subarachnoid geometry, the hematoma thickness was varied from 1 to 4 mm. To assess device sensitivity to intracerebral hematomas, the depth, diameter, and thickness of hematoma inclusions were varied. Specifically, hematoma depths of 0, 0.55, 1, 1.55, 2, and 3 cm below the CSF layer were measured, corresponding to total depths of 1.1 to 4.1 cm. At each depth, hematoma volumes of 3, 5, 10, and 30 mL were implemented. To achieve these volumes, inclusion thicknesses of 1.5, 2.5, 5.1, and 15.3 mm for the 5-cm-diameter hematoma, and 0.5, 0.9, 1.8, and 5.3 mm for the 8.5-cm-thick hematoma were used. For the 5-cm-diameter hematoma, two additional hematoma volumes were measured, 1 and 1.8 mL (with thicknesses of 0.5 and 0.9 mm), to better visualize thickness effects and provide similar thicknesses for comparison to the 8.5-cm-diameter hematomas. The $\mathrm{\Delta }\mathrm{OD}$ between normal phantoms and each individual hematoma case was quantified and compared.

2.8.

Lateral Sensitivity

To elucidate the impact of lateral offset on hematoma detectability, a test method based on the intracerebral hematoma geometry (Fig. 1) was developed. Measurements were performed using four different phantom layers incorporating 5-cm-diameter hematomas shifted laterally from the center by 0, 1, 2, or 3 cm (Fig. 3). Two depth scenarios were studied: 0.0 and 0.55 cm below the CSF layer (1.1 or 1.65 cm below the phantom surface). Thus, measurements were made at seven locations in the $X$ direction [along the S–D axis, as in Fig. 3(a)] and the $Y$ direction [perpendicular to the S–D axis, as in Fig. 3(b)]. In all measurements, the detector was centered on the phantom as illustrated. Since the detector had an S–D separation distance of 4 cm, for all hematoma positions along the S–D axis [Fig. 10(a)] either the source, detector, or both fibers were positioned above the hematoma, we did not extend measurements to the point at which neither fiber was positioned over the hematoma ($X=4.5\mathrm{cm}$) as limited by the diameter of the phantom. For displacements perpendicular to the S–D axis, however, the hematoma was below the S–D axis as long as the hematoma lateral offset was 2.5 cm or less.

Fig. 3

Variations in hematoma inclusion position during lateral sensitivity measurements. Initially centered with the source and detector fibers, inclusions were repositioned either (a) along the S–D axis ($X$ direction) or (b) perpendicular to this axis ($Y$ direction). The hematoma inclusion was 5 cm in diameter, located 0 or 0.55 cm below the CSF layer; S–D separation distance was 4 cm.

2.9.

Statistical Analysis

All measurements were repeated four times and the standard deviations are shown as error bars in figures. All statistical analysis was performed in Excel, using Excel Data Analysis tool box. Analyses performed include exponential/logarithmic regression with analysis of variance (ANOVA) outputs to evaluate correlation between two variables, and a paired sample $t$-test with null hypothesis in which the mean difference between two sets of data is zero.

3.1.

Effect of Hematoma Oxygenation

Measurements of phantoms containing hematoma-simulating inclusions filled with a human hemoglobin solution at different oxygen saturation (${\mathrm{S}}_{\mathrm{t}}{\mathrm{O}}_{\mathrm{2}}$) levels showed small differences in $\mathrm{\Delta }\mathrm{OD}$ (Fig. 4). Saturation level appeared to exhibit minimal effect on signal attenuation, which is supported by $p\text{-}\text{value}=0.73$ from regression analysis/ANOVA. These data confirm that the proximity of the illumination wavelength to the NIR isosbestic point provides insensitivity to oxygenation level, as stated by Zhang et al.¹⁴ Furthermore, it supports the validity of a phantom-based approach that involves a stable and neutral absorber rather than oxygenated and deoxygenated hemoglobin.

Fig. 4

Effect of hemoglobin oxygenation level (${\mathrm{S}}_{\mathrm{t}}{\mathrm{O}}_{\mathrm{2}}$)—as measured by a CO-oximeter—on hematoma detector measurements.

3.2.

Measurement Repeatability

Results illustrating measurement variability based on the six measurement configurations described in Sec. 2 are shown in Fig. 5. For consecutive handheld measurements without any movement of the detector, the error level was moderately low, however, much greater than when the scaffold was used. When the detector was removed and reapplied to the phantom surface, variability increased by a factor of 3 to 4. Finally, when the phantom was restacked and the detector reapplied to the phantom, the standard deviation increased again by a factor of 2 or 3. These results indicate that using a scaffold may slightly reduce the variability of phantom measurements, and that variability overall was low, even when restacking of the phantom layers was considered.

Fig. 5

Repeatability of hematoma detector OD measurements ($n=5$) for four different acquisition protocols.

3.3.

Visualization of Near-Infrared Light Distributions

To provide initial qualitative insights into light–tissue interactions, a mobile phone camera (based on a color CMOS sensor) with NIR blocking filter removed (Eigen Imaging, Inc., San Diego, California) was used to record videos during the measurement process. Images were acquired of the top/side [Fig. 6(a)] of the phantom, as well as below the hematoma layer for 0.55- and 3-mm-thick intracerebral hematomas (i.e., imaging bottom side of the phantom with the nonhematoma brain layers removed). It should be noted that if the image in Fig. 6(a) was normalized to the maximum value near the illumination fiber, the rapid decay in light intensity as a function of distance would preclude visualization of light in more distant regions. By manually controlling exposure, it was possible to visualize the wider light propagation pattern, although the region near the illumination fiber was saturated. In the other two figures, the effects of hematoma thickness on light attenuation are illustrated. For the thinner hematoma [Fig 6(b)], significant attenuation has occurred, but some light that has penetrated directly below the illumination fiber is apparent. However, for the thicker hematoma [Fig 6(c)], only light that has diffused around the inclusion is visible.

Fig. 6

Visualization of NIR light in the phantom during hematoma detector operation, as acquired from (a) above and to the side of the phantom; as well as directly below the hematoma layer for a 5-cm-diameter intracerebral hematoma [Fig. 1(c)] with thicknesses of (b) 0.5 and (c) 3 mm.

3.4.

Effect of Cerebrospinal Fluid Layer

As a test of the significance of the CSF layer on NIRS measurements, we compared attenuation levels for intracerebral hematomas with and without a 3-mm-thick CSF layer. Measurements were performed for two hematomas, 5-cm diameter with 5.1-mm thickness (10 mL volume) and 8.5-cm diameter with 5.3-mm thickness (30 mL volume), varying in depth from 0 to 3 mm below the CSF layer or skull layer when no CSF layer was present. Although adding/removing the CSF layer would change the depth of the hematoma from the phantom surface, since CSF layer is clear, it is not expected to attenuate the optical signal. Results (Fig. 7) showed greater $\mathrm{\Delta }\mathrm{OD}$ values for hematomas in phantoms without a CSF layer compared to those with a CSF layer—by up to 0.5 for 5-cm-diameter hematomas and up to 0.8 for 8.5-cm-diameter hematomas. As depth increased, the fractional impact of the CSF layer on attenuation by the hematoma remained roughly constant. A paired two-sample $t$-test resulted in $p$-values of 0.0001 and 0.0004 for 5- and 8.5-cm-diameter hematomas, respectively, showing that there is a statistically significant difference between the $\mathrm{\Delta }\mathrm{OD}$ values obtained with and without a CSF layer.

Fig. 7

Effect of CSF layer presence on system sensitivity to 5- and 8.5-cm-diameter intracerebral hematomas as a function of depth.

3.5.

Epi/Subdural and Subarachnoid Hematoma Detection

Results were generated that illustrate the effect of hematoma type—epidural/subdural, subarachnoid, and intracerebral—on device sensitivity. A comparison of superficial hematomas—epidural/subdural and subarachnoid—is shown in Fig. 8. High sensitivity to all of these superficial hematomas cases was indicated, in that $\mathrm{\Delta }\mathrm{OD}$ values were consistently above 1.2. For epidural/subdural hematomas, no increase in $\mathrm{\Delta }\mathrm{OD}$ was seen beyond a thickness of 2 mm and a regression analysis indicated that overall, signals did not correlate strongly with inclusion thickness across the 1- to 4-mm depth range (logarithmic regression, $R^2=0.715$). For subarachnoid hematomas, signals increased with hematoma thickness up to 3 mm, and a stronger statistical correlation was shown (logarithmic regression, $R^2=0.996$). The limited correlation for epi/subdural hematomas was likely due to the strong attenuation of light as it propagated both into and out of the tissue, and the fact that little light could propagate laterally to the detection fiber in the superficial layers alone. As a result, the reflectance signal quickly approached the limit of the dynamic range of the device and showed a maximum $\mathrm{\Delta }\mathrm{OD}$ of $\sim 2.3$.

Fig. 8

NIRS system sensitivity to epi/subdural and subarachnoid hematomas over a range of hematoma thicknesses.

3.6.

Intracerebral Hematoma Detection

The effects of intracerebral hematoma depth, diameter, and thickness on detectability are presented in Fig. 9. Hematoma depth had perhaps the clearest impact, with exponential regressions of the attenuation ($\mathrm{\Delta }\mathrm{OD}$) versus depth relationship producing $R^2$ values in the range of 0.97 to 0.99 for both 5- and 8.5-cm-diameter inclusions. By identifying where these regression lines crossed the $\mathrm{\Delta }\mathrm{OD}=0.2$ threshold, it was possible to estimate that maximum detection depth ranged from 0.6 to 1.2 cm (with increasing inclusion thickness) for the 5-cm hematoma cases tested and from 0.9 to 1.4 for the 8.5-cm cases. All hematomas deeper than 1.55 cm, as well as very thin hematomas at 1-cm depth, were not detectable ($\mathrm{\Delta }\mathrm{OD}<0.2$). Therefore, since the bottom of the CSF layer was located 1.1 cm below the surface, the detection depth of the detector from the tissue surface was $\sim 1.7$ to 2.5 cm, depending on hematoma size.

Fig. 9

Effect of volume/thickness and depth on detectability in intracerebral hematoma phantoms. Results are presented for hematoma diameters of (a) 5.0 and (b) 8.5 cm. Note that distance from phantom surface through CSF layer is 1.1 cm.

The impact of hematoma thickness was also strong, over a limited range of values. For 5-cm-diameter hematomas, $\mathrm{\Delta }\mathrm{OD}$ increased until the thickness reached about 2.5 mm (10-mL volume), and regression analyses based on a logarithmic fit over this range showed relatively high $R^2$ values (0.98, 0.96, 0.97, 0.82, 0.64, and 0.85). For 8.5-cm-diameter hematomas, significant increases in $\mathrm{\Delta }\mathrm{OD}$ were seen up to thicknesses of 1.8 mm, and logarithmic regression also tended to showed high $R^2$ values (0.83, 0.94, 0.99, 0.44, 0.85, and 0.81). The magnitude of attenuation changes with thickness decreased rapidly with depth; as a result, the best regression fits were found for more superficial hematomas (e.g., $R^2>0.9$ for depths of 0 and 0.55). Additionally, for corresponding thicknesses, 8.5-cm-diameter hematomas showed greater $\mathrm{\Delta }\mathrm{OD}$ than those with a diameter of 5 cm (one-tailed, paired sample $t$-test produced a $p<0.05$ over depths of 0 to 1.55 cm).

3.7.

Lateral Variations in Sensitivity

Another key performance issue is how sensitivity is impacted by lateral positioning of the detector relative to the hematoma. The effect of positioning—both along and perpendicular to the S–D axis (as illustrated in Fig. 3)—is presented in terms of $\mathrm{\Delta }\mathrm{OD}$ (Fig. 10) for 5-cm-diameter hematomas. Results for positions along the S–D axis [Fig. 10(a)] indicated that $\mathrm{\Delta }\mathrm{OD}$ for the 0-cm depth hematoma decreased from a maximum of $\sim 0.7$ at the center position to 0.47 at a lateral offset of 3 cm in the positive $X$ direction and to 0.39 in the negative $X$ direction. For the deeper hematoma, the maximum $\mathrm{\Delta }\mathrm{OD}$ was 0.3, decreasing to 0.21 at a lateral offset of 3 cm. These results indicate that although superficial hematomas provided greater attenuation than deeper ones for all lateral positions, the magnitude of this difference decreases with lateral displacement. While both hematomas were in the detectable range for all positions in Fig. 10(a), results indicated that the deeper inclusion would only be detectable to $X$ offsets of $\pm 3\mathrm{cm}$ and the superficial one to $\pm 4\mathrm{cm}$. Thus, in practice, detection with this device would only be likely if a significant portion of the hematoma is positioned between the fibers. Additionally, our results indicated a slight asymmetry in the lateral sensitivity curve, with ΔOD appearing slightly larger when only the detection fiber was above the hematoma, as compared to when only the source fiber was above the hematoma (one-tailed, paired sample $t$-test: $p=0.003$). The experiment was repeated by rotating the probe 180 deg to the phantom and the same asymmetry pattern depending on the hematoma location relative to S–D was observed, confirming that the trend is not due to inhomogeneity of phantom (data not shown). However, for the deeper hematoma, this effect was diminished to the point where it was not statistically significant (one-tailed, paired sample $t$-test: $p=0.46$).

Fig. 10

Effect of device-hematoma lateral offset (a) along and (b) perpendicular to the S–D axis (as illustrated in Fig. 3). Results are presented for a 5-cm-diameter intracerebral hematoma located at depths of 0 and 0.55 cm below the CSF. The red dashed line indicates the detection threshold. Note that in (a), some part of the hematoma is directly below one of the fibers as long as the distance from the center is $<4.5\mathrm{cm}$, whereas in (b), some part of the hematoma is directly below the S–D axis as long as the distance from the center is $<2.5\mathrm{cm}$.

In the perpendicular offset scenario [Fig 10(b)], $\mathrm{\Delta }\mathrm{OD}$ remained relatively constant until beyond a 1-cm offset for both hematomas. Beyond this point, ΔOD showed a strong decrease with position for the more superficial hematoma, falling to near the detection threshold at 3 cm. For the deeper hematoma, $\mathrm{\Delta }\mathrm{OD}$ decreased from a maximum near 0.3 to 0.15 at 3-cm displacement. Although the deeper hematoma became undetectable when the S–D axis was no longer above the inclusion (2.5 cm from center), the superficial hematoma was still detectable for another 0.5 cm. Thus overall, the device’s detection region appears elliptical in shape, slightly larger along the S–D axis than perpendicular to it, and decreasing strongly with lesion depth.