2.1.

Breast Margin Assessment Probe Instrumentation

The BMAP system leverages a custom 16-channel wide-field ($\mathrm{FOV}=256{\mathrm{c}\mathrm{m}}^2$) silicon-based photodiode array (PDA), a custom 16-channel transimpedance integrating amplifier, a xenon light source containing eight 10-nm bandpass optical channels, and a custom computer numeric controlled (CNC) imaging platform with adaptive pressure control. Compressive sensing is achieved through optimized bandpass filter center wavelengths (470, 480, 490, 500, 510, 560, 580, and 600 nm) informed by a genetic algorithm analysis of previous clinical data.¹¹ The lamp, imaging platform, and amplifier system are daisy chained to a universal serial bus (USB) hub within the amplifier box, allowing full system control via a laptop and a single USB connection. The control software was developed using the LabVIEW™G language; an object oriented framework was chosen to maintain scalability and incorporate a hardware abstraction layer. The system design is described in detail in the following sections and is diagrammed in Fig. 1.

Fig. 1

Diagram of the BMAP system: (a) The system components in clockwise order: a xenon light source with an 8-slot filter wheel containing narrow-band filters ($\mathrm{BP}=10\mathrm{nm}$) ($\lambda =[470(\mathrm{F}1)480490500510560580600]\mathrm{nm}$). Light is delivered to the backside of a PDA using a LG. The LG is coupled to an absorbing free-space tube expanded in (b), delivering low-angle light through the PD apertures. The PDA is clamped to a custom-made imaging platform enabling raster-scanning and controlled probe placement. The light source, imaging platform, and PDA are connected to an amplifier box containing an integrating DAQ and a USB hub to minimize the cable burden, resulting in a single USB connection to a laptop computer. A digital photograph of the system is shown in (c).

2.2.

Photodiode Array Design

In contrast to traditional fiber-optic probes, which require careful weaving and placement of glass fibers, the PDAs are constructed using standard silicon semiconductor fabrication techniques. The feature sizes of the individual photodiodes do not require highly specialized equipment and are achieved using photolithography. Moreover, changes in photodiode density, size, and pitch, are easily achieved with additional low-cost photomasks, affording a simple means to scalability. The specific details of the PDA fabrication process flow, photodiode responsivity, SNR, and cross talk considerations are detailed in Ref. 12. Briefly, the imaging array consists of 16 annular photodiodes formed on an $n$-type silicon epitaxial substrate and is arranged in a $4\times 4$ array with a 4.5 mm center-to-center pixel pitch, resulting in a $256{\mathrm{c}\mathrm{m}}^2$ field of view (FOV). Each of the annular photodiodes consists of a circular active area ($d=2.46\mathrm{mm}$) with an etched central aperture ($d=0.75\mathrm{mm}$). The patterned silicon wafer is diced into a rectangle and is epoxied and wire-bonded to a gold plated printed circuit board for structural support and traces for photocurrent readout. An example board-mounted PDA with respective geometry is shown in Fig. 2(a).

Fig. 2

Photodiode array design: (a) photo of board-mounted PDA from the bottom side with a corresponding diagram including dimensions and an expanded view of the photodiode layout, (b) illustration of light traversal from the LG to tissue, and (c) photo of the BMAP during acquisition on a foam resolution target. Note the near collimated beams within the sample emanating from the apertures. (d) A close-up cross-sectional view of the light traversal path including thickness dimensions of the board and silicon wafer. Note that the photodiode active area (shown in blue) has no thickness as these are just doped regions in the silicon wafer. Finally, (e) a close-up isometric view of a single photodiode as patterned on the silicon wafer. The leads for photocurrent readout are created by a metal evaporation process and have negligible thickness.

On average, the annular photodiodes were observed to have visible light responsivity greater than that of commonly available commercial silicon photodiodes. The measured photodiode responsivity ranged from 0.13 to $0.3\mathrm{A}/\mathrm{W}$ for $\lambda =400$ to 600 nm. The SNR, defined as $20·{\log }_{10}(I_{\mathrm{avg}}÷{\sigma }_I^2)$, was shown to be greater than 35 dB for channels across all wavelengths, limited by the xenon lamp noise. The SNR was determined by acquiring 10 repeated measures on a 10% Spectralon^TM reflectance imaging target. The interpixel cross talk for the central pixels is 10% in the worst case scenario: tissue-specific cross talk is highest for specimens with albedo [$a={\mu }_{\mathrm{s}}^{\prime }/({\mu }_{\mathrm{s}}^{\prime }+{\mu }_{\mathrm{a}})$] near 1 ($\ge 0.97$). Previous clinical studies conducted by our group indicate most breast tissue within an absorption range of (1.3 to $20.4{\mathrm{cm}}^{-1}$) and a scattering range of (6.5 to $11.3{\mathrm{cm}}^{-1}$); average interpixel tissue cross talk is $\le 4\%$ in these ranges.

The board-mounted PDA is fastened to custom-made plastic acrylonitrile butadiene styrene (ABS) components designed to optimize free-space light delivery to the tissue (detailed in Sec. 2.4). The generalized light delivery and collection model is diagrammed in Fig. 2(b). As indicated in the figure, light is launched from a simple light guide (LG) at the distal end of an absorbing tube to the backside of the silicon PDA. Light simultaneously passes through each of the 0.75-mm apertures to the probe–tissue interface on the PDA front side. When tissue is present, light enters and is multiply scattered or attenuated by absorption, a fraction of which will return to the front side of the PDA where it couples to one of the respective 16 annular photodiodes and is converted to a small electric current (photocurrent). The photocurrent is relayed to the transimpedance amplifier via two 20-pin ribbon cables, as seen in Fig. 2(c). A cross-sectional view of the photon path from source to detection is illustrated in Fig. 2(d), with an expanded view of a single annular photodiode diagrammed in Fig. 2(e).

2.3.

Photodiode Degradation

Routine clinical use revealed substantial PDA deterioration marked by severe degradation of pixel responsivity. The use of germicidal cleaning agents and rubbing of the surface caused physical degradation of the antireflection coating and patterned metal contacts [Figs. 3(a) and 3(b)]. As the contacts were degraded, the effective photodiode active area was reduced, resulting in decreased optical response following each use. The abrasive cleaning technique was necessary due to tissue proteins and the rapid clotting of blood in direct contact with the PDA; removal of these substances required substantial physical force. To address this challenge, a thin ($60\mu \mathrm{m}$) optically clear adhesive backed Teflon™ film was adhered to the PDA [Fig. 3(c)]. This coating did not interfere with the measured optical signal. An additional Tegaderm™ (3M) layer was used to protect and waterproof the printed circuit board surrounding the PDA, as shown in Fig. 3(c). Note that the Tegaderm layer is hole-punched in the center to create a window for the imaging array. Utilization of the Teflon film was shown to have a negligible effect on extracted tissue optical properties; the measured optical intensity [$I(\lambda )$] compared with and without Teflon had an root-mean-square deviation $<0.01$ and the extracted optical property accuracy remained the same ($\%\text{error}<10\%$). The influence of the Teflon film was determined in simulation and verified using the phantom study protocol discussed in Sec. 2.7. In addition to serving as a protective layer, the Teflon film was also shown to be reliably nonstick, effectively eliminating intermeasurement debris clinging to the probe from tissue contact. To validate this point, a clinical reproducibility study was performed wherein a single specimen was scanned three consecutive times ($n=10$). On average, the interscan deviation was found to be $\sim 1\%$.

Fig. 3

Prevention of photodiode damage: (a) digital image of an unused PDA pixel, (b) image of a damaged PDA pixel, and (c) diagram of the protective Teflon and Tegaderm films. The Teflon film ($\text{thickness}=60\mu \mathrm{m}$) is applied directly to the AR-coated PDA. A windowed Tegaderm film is layered over the entire surface of the probe bottom-side, leaving only the Teflon covered silicon exposed; this provides protection against biological compounds and cleaning agents.

2.4.

Light Source and Amplifier Design

A 150 W Xenon lamp (Asahi Spectra, Japan) is coupled to an absorbing (matte black inner surface) spacer tube ($\mathrm{ID}=30\mathrm{mm}$) using a hybrid LG ($\mathrm{NA}=0.57$). The optimal length of the absorbing tube was determined to be 70 mm as this provided low cross talk (4%) and nearly uniform illumination (center-pixel throughput ratio: 0.74).¹² Aperture throughput ranged from 17 to $22\mu \mathrm{W}$ per aperture while the total output from the hybrid LG ranged from 45 to 55 mW. An 8-slot filter wheel within the Asahi lamp houses eight thin-film bandpass filters ($\mathrm{BP}=10\mathrm{nm}$) across the visible spectrum ($\lambda =[470480490500510560580600]\mathrm{nm}$). A custom mounting strategy is employed to minimize inadvertent user error and provide maximal throughput, as described in Appendix A.1.

A custom integrating transimpedance amplifier (ITIA) system was developed to convert the raw photocurrent to easily measured voltage that can be read by a laptop computer. The amplifier design consists of a precision (16-bit) USB data acquisition system (DAQ) (NI-USB-6212, National Instruments, Austin, Texas) containing 16 separate analog voltage inputs, as well as a custom-made low-noise 16-channel ITIA board (printed by Advanced Circuits, LLC). The smallest measurable change in intensity corresponds to a $\mathrm{\Delta }\mathrm{IT}=10\mu \mathrm{s}$ with a noise floor at $500\mu \mathrm{s}$. Typically, an integration time of 50 ms was used for clinical measurements. An expanded description of the amplifier with circuit diagrams is shown in Appendix A.2.

2.5.

Imaging Platform

A custom-made imaging platform was developed to provide robust quality control through automated positioning and probe handling. In addition to providing consistent contact pressure, the imaging platform is outfitted with stepper motor-controlled translational axes in each spatial dimension ($xyz$); this is utilized for subpixel sampling, implemented as a sequential raster scan. The imaging platform is diagrammed in detail in Appendix A.3. Pressure is measured as the total force applied to a custom-made copper base plate on which the tissue rests. The applied force is transduced by four discrete force sensors, each with a dynamic range of 1 to 44 N, converted to pressure based on the area of the probe in contact with the tissue. A board-level USB camera is embedded as an interchangeable component and is utilized to capture digital images of the tissue for size estimation and coregistration of optical data with tissue locations marked with ink for histopathological review. Each of the translational axes is a custom-made leadscrew-based linear actuator designed to provide fine incremental motion yet move rapidly as to not significantly slow the raster-scanning acquisition process. Positional uncertainty (backlash) was shown to be $\le 10\mu \mathrm{m}$ upon direction reversal.

The probe is attached to the $z$-axis of the platform with a keyed circular clamp matched to the absorbing tube described in Sec. 2.4. The smallest $\mathrm{\Delta }z$ was found to be $47\mu \mathrm{m}$, corresponding to a pressure resolution $\mathrm{\Delta }P=0.6\mathrm{mm}\mathrm{Hg}$ for the typical clinical specimen. Applied pressure and absolute position are coordinated by a dedicated embedded system to reduce PC computational load.

2.6.

Data Collection and Processing

In clinical practice, the probe is incrementally translated in both lateral directions and subsequently translated downward to establish contact with the specimen resting on the pressure sensitive base. Within each placement, 16 independent measures of diffuse reflectance are captured via the 16 annular photodiodes. The reflectance as a function of wavelength is achieved by serially capturing the integrated intensity for each bandpass filter [$I{(\lambda )}_s$], dividing this by the measured $I{(\lambda )}_c$ from a highly stable Spectralon lambertian surface ($\%R=99$), and adjusting for the respective integration times. Before each clinical scan, a software-automated calibration sequence that measures the Spectralon at three integration times ($\mathrm{IT}=25$, 50, and 100 ms) and three light intensity levels ($\mathrm{LI}=50\%$, 75%, and 100%) is executed; this sequence is used to decouple the source of day-to-day variations in measured Spectralon $I(\lambda )$ in addition to the wavelength-dependent source intensity. Intensity measurement calibration is then performed according to changes in the average slope of $I(\lambda ,\mathrm{IT})$ and $I(\lambda ,\mathrm{LI})$ as opposed to difference-based intensity levels. Note that a background measure is captured for each spectrum collected and is used to subtract stray light contributions from the intensity spectrum [$I{(\lambda )}_s=I(\lambda )-I{(\lambda )}_{\mathrm{bgd}}$]. The system response can vary due to light source degradation, changes in photodiode responsivity, scuffs on the Teflon film, and ambient conditions. The changes in system response found using the calibration scan are incorporated into the wavelength-dependent system scaling coefficients that were initially determined by the reference tissue phantom from the cross-validation tissue-simulating phantom study.

The result is a series of pressure bound multispectral images that are stitched into a high-resolution optical reflectance map [$R(\lambda ,x,y,P)$, where $P$ is typically 10 mm Hg]. The highly reproducible pressure application at each point provides a reasonably uniform surface and preserves the optical characteristics of the ex vivo tissue. Each clinical raster-scan typically consists of 36 separate placements conducted in a zigzag pattern where the placement pitch is 0.75 mm in $x$ and $y$, resulting in 576 independent measures of reflectance for each $256{\mathrm{c}\mathrm{m}}^2$ area, typically requiring 8 min or less depending on the specimen stiffness/elastic modulus. Each of the 576 reflectance spectra are run through a Monte-Carlo-based inverse model to obtain constituent optical property values [${\mu }_{\mathrm{s}}^{\prime }(\lambda )$ and ${\mu }_{\mathrm{a}}(\lambda )$], as described in Refs. 7, 13, and 14. Briefly, the inverse model utilizes a nonlinear least-squares fitting routine to best match a corrected reflectance spectrum to a Monte-Carlo-based reflectance look-up table established for the source-collection geometry and known optical interfaces. Absorber extinction spectra [$ϵ(\lambda )$] are tabulated for the known constituent absorbers and are utilized as inversion priors, such that extracted absorber matrix coefficients represent concentration. Reflectance spectra are scaled prior to inversion by calibrating to a previously measured reference known as a “reference phantom,” as described in Sec. 2.7.

2.7.

Homogeneous Optical Property Validation

A set of 12 tissue-simulating phantoms was used to inform the homogeneous optical property extraction accuracy of the BMAP system. Each of the phantoms consisted of lyophilized human hemoglobin (Sigma-Aldrich Co. LLC, St. Louis, Missouri, H7379) and $1\mu \mathrm{m}$ polystyrene microspheres (Polysciences, Inc., Warrington, Pennsylvania, 07310-15) as analogs for tissue absorption and scattering, respectively. The phantoms were mixed in proportions to span optical property ranges: ${⟨{\mu }_{\mathrm{a}}⟩}_{\lambda }=0.5-5.5{\mathrm{cm}}^{-1}$ and ${⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }=2.9-9.5{\mathrm{cm}}^{-1}$ (averaged across all eight wavelengths used). The 12 phantoms were designed to subtend the 25th to 75th percentiles of ${⟨{\mu }_{\mathrm{a}}⟩}_{\lambda }$ and ${⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ observed in breast tissue. The Monte-Carlo model accuracy for the BMAP system was assessed by evaluating the RMS errors between the extracted and expected phantom optical properties as described in Ref. 7. The specific optical properties and phantom constituents are listed in Table 1. In addition to pipetting, the mass of each constituent was measured and recorded at each step of the phantom fabrication process to reduce discrepancies in optical property estimation from theoretical values. Each phantom was thoroughly mixed just prior to being measured to reduce the effects of solution settling. Ten repeated diffuse reflectance spectra were collected for all channels for each phantom. A leave-one-out cross-validation analysis, or “phantom study,” was performed using each of the 12 tissue-simulating phantoms as a reference phantom against the remaining 11. The reference phantom for clinical data inversion is selected by finding the phantom that, when used to scale the remaining 11, results in the lowest error in estimating ${\mu }_{\mathrm{s}}^{\prime }(\lambda )$ and ${\mu }_{\mathrm{a}}(\lambda )$ for the entire phantom set. With the Spectralon calibration measurement, the reference phantom captures the system-specific response for the $R(\lambda )\to [{\mu }_{\mathrm{a}}(\lambda ){\mu }_{\mathrm{s}}^{\prime }(\lambda )]$ isomorphism. The multiphantom cross-validation technique is a robust approach that eliminates random user error as the entire Monte-Carlo look-up table is only scaled by the offset provided by a single best (reference) phantom such that improperly mixed phantoms and phantoms that do not adequately capture the system response can be ignored when constructing the scaling coefficient matrix. Note that the optical properties of the unused phantoms are still adequately predicted when scaled by the reference phantom, suggesting that the system response is linear although subject to artifacts on either end of the detector dynamic range.

Table 1

Homogenous liquid tissue simulating phantoms.

2.8.

Resolution and Pressure Characterization

The optimal scanning resolution was determined using a high contrast, custom resolution target [Fig. 4(a)] patterned atop a diffuse surface (dense white foam) to inform best-case scenario resolution achieved under clinically acceptable time constraints, typically 25 to 30 min at the Duke University Medical Center. The custom target was constrained by the probe footprint and foam rigidity; the very small protrusions made by the wire-bonds from the silicon detector array to the printed circuit board would prevent total surface contact for any rigid object larger than $30\times 30\mathrm{mm}$; thus, a $25.4\times 25.4\mathrm{mm}$ target was fabricated. Furthermore features with, the target was optimized to only contain features with size scales relevant to the clinical needs of a margin assessment probe (0.25 to 3 mm). The target was scanned at multiple raster-scan rates, hereafter represented as sampling resolution in mm and referred to as ($\mathrm{\Delta }s$), and the time to complete the scan was automatically recorded for each scan.

Fig. 4

Pressure and resolution experimental design: (a) the resolution target used in nonvolumetric, high-contrast resolution characterization for clinical $\mathrm{\Delta }s$ optimization, (b) diagram of pressure experimental setup with layer and slab separated and (c) the anticipated effect as the pressure is increased; as the top layer is compressed, the distribution of photon paths include the cancer-like slab below the adipose layer. (d) Photographs of the physical phantoms used in the study; a top view of the slab and defect without the adipose layer is shown on the left and a side view of both the slab an layer (inside a container to maintain position) is shown on the right.

While characterizing the resolution for high-contrast target helps optimize $\mathrm{\Delta }s$, the question regarding the ability to resolve features with varying optical properties in tissue naturally arises. To characterize the volumetric, diffuse contrast (and, in a sense, resolution), three-dimensional (3-D) Monte-Carlo simulations were conducted for tissue slabs with contrasting perturbations of several sizes. These simulations were carried out using the same compressive sensing wavelengths leveraged by the BMAP system ($\lambda =[470480490500510560580600]\mathrm{nm}$). The Henyey–Greenstein phase function was assumed for ${\mu }_{\mathrm{s}}^{\prime }(\lambda )$. To incorporate the system response of the physical system in the simulated data, the phantom study reference phantom was also simulated; any wavelength-dependent differences between the simulated reflectance and actual reflectance can be used to scale all other simulated spectra. The large, semi-infinite slabs were designed to have optical properties that correspond to the median value of adipose tissue measured optical properties with our gold standard 49ch device (Table 2). The dimensions of the slabs are $25.4\times 25.4\times 20\mathrm{mm}$ ($lwh$), with a perturbation region in the center of the slab that extends 3 mm in depth and has an en face square surface with $l=w=[0.25,0.75,1.5]\mathrm{mm}$. Similarly, the optical properties of the perturbation were designed to match the median measured values of invasive carcinoma tissue (cancer) (Table 2). To adequately characterize the diffuse contrast, simulations were carried out for $\mathrm{\Delta }s=[0.25,0.75,1.5,4.5]\mathrm{mm}$ for each perturbation size.

Table 2

Tissue median values.

Clinically relevant pressure ranges were informed using layered gelatin-based ($60\mathrm{mg}/\mathrm{mL}$) solid tissue-simulating phantoms optimized to have a stress/strain ratio (Youngs Modulus) comparable to that observed during routine clinical use (0.08 to 11.3 kPa) and similar to those reported in literature.¹⁵ Note that because the imaging platform tracks the $xyz$ position for each measurement, tissue compression (via probe height) can be captured as a function of pressure during each clinical measurement. Solid phantom slabs were designed to simulate clinically relevant tissue geometry and optical properties: a 2-mm-thick slab was designed to have optical properties corresponding to purely adipose tissue to simulate a negative margin geometry; the 2-mm layer was placed on top of a larger $25.4\times 25.4\times 20$ $(l\times w\times h)\mathrm{mm}$ slab with optical properties correlated to invasive breast cancer. To corroborate changes due to pressure, the cancerous slab has an embedded defect ($4.5\times 4.5\times 4.5\mathrm{mm}$) with optical properties designed to match the reference phantom to provide a secondary source of contrast beneath the adipose layer [Fig. 4(b)]. The corresponding optical properties were informed using optical parameter distributions from previously acquired data for each tissue subtype (histopathologically verified by a practicing clinical pathologist). Note that the optical properties for the constructed solid phantoms deviate slightly from those in Table 2; the difference is due to mixture uncertainty and intentional enhancement of contrast. As a result, the cancer-like tissue has increased ${⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ and decreased [$\beta$-carotene], while the adipose tissue has further increased [$\beta$-carotene] and decreased [Hb]. A summary of the final optical properties can be observed in Table 3. To understand the effect of pressure on extracted optical properties, the layered sample was raster-scanned 12 times corresponding to pressures $P=[\mathrm{3,}6,\mathrm{9,}12,\mathrm{15,}18,\mathrm{21,}25,\mathrm{33,}37,\mathrm{43,}49]\mathrm{mm}\mathrm{Hg}$. Similar to clinical use, the layered sample was raster-scanned with $\mathrm{\Delta }s=0.75\mathrm{mm}$ (36 placements) using the zigzag pattern previously mentioned. The experimental design and hypothesized effect are outlined in Fig. 4(c). Photographs of the solid phantoms used in the study are shown in Fig. 4(d); the last photograph demonstrates the phantom under load. To preserve the integrity of the solid phantoms, each component was immersed in food-grade mineral oil when not in use once completely cured. The immersion in mineral oil prevents moisture from escaping the phantoms and drying them out, unpredictably changing the optical properties. Note that because collagen (in gelatin) is not soluble in any oil, or any of the phantom constituents, this immersion also prevents substantial penetration of the oil within the phantom. An additional benefit of this immersion is the prevention of bacterial or fungal growth; the phantoms do not mold or spoil over time. The major drawback of this immersion is the lack of oxygen available to the phantoms, necessarily causing oxy-hemoglobin to convert to deoxy-hemoglobin over time. To suspend the phantom oxygenation state, formalin (1 mL, 10% solution) was immersed into the phantoms immediately after curing (initial water volume was adjusted to account for the added formalin), causing the hemoglobin protein to become fixed in an oxygenated state. The phantoms were cast in a two stage process. First the phantoms were cast in a mold with a protrusion based on the size of the defect, resulting in a homogeneous slab with a hole where the defect should be. A thin layer of mineral oil was added to the exposed surfaces to prevent unwanted mixing and loss of moisture. The defect (hole) was then filled with the reference phantom-like gelatin-based solution and allowed to cure, with the formalin added immediately after and allowed to diffuse within the sample completely. After formalin diffusion, the completed phantom was immersed in mineral oil and stored at 4°C until used. The optical properties for the solid phantoms were measured randomly during a 1-month period, and no detectable changes (deviations $>5\%$) were observed. Finally, due to the fact that $\beta$-carotene is insoluble in water, Crocin (Sigma-Aldrich Co. LLC, St. Louis, Missouri) was used as an analog.

Table 3

Solid gelatin tissue phantoms.

2.9.

Clinical Data Collection

Diffuse reflectance spectra were collected from excised breast tissue specimens from a total of three margins in three separate patients for the proof-of-concept data shown in this paper. This study was performed in strict accordance with Duke University Institutional Review Board protocol: Pro00028284. Patients, who were 18 years and older and having the lumpectomy procedure, granted written consent under the approved protocol. Specimen orientation for lumpectomies (partial mastectomies) was determined according to surgically placed reference features including: a surgical wire inserted into the center of the tumor, colored sutures, and surgical clips. Specimen faces were defined as the faces of a cube and labeled relative to the specimen orientation in situ; the six measurable faces are hereafter referred to as the superior, inferior, posterior, anterior, medial, and lateral margins. Immediately following tissue resection, partial mastectomy specimens were sent to radiology for an intraoperative mammography to verify removal of the tumor mass. Upon return, the specimen was placed on the pressure-sensing base of the imaging platform and oriented accordingly. Following orientation, the raster-scanning procedure was initiated and the diffuse reflectance spectra were collected. Once the scanning procedure was completed, a site-level inking procedure was performed wherein 6 to 10 sites were randomly marked using tattoo ink (typically orange in color) with the aid of a coregistration structure that physically relayed the central location of each optical channel to the respective point of contact with the specimen. A single margin was inked for postoperative pathological assessment. Margin inking was followed by the acquisition of a digital image using the previously mentioned (Sec. 2.5) USB digital camera assembly. Tissue optical property maps were reconstructed postmeasurement using the inverse Monte-Carlo model discussed previously. To establish optical extraction equivalence to previous generation devices, the samples in this work were measured using both the BMAP system and our gold-standard 49ch system described in Sec. 1 and Ref. 16. These measurements were performed by swapping the probes only; the tissue specimen was not moved or realigned. The on-board positioning tracking of the imaging platform was used to determine the corresponding regions measured by each probe, which share a central optical axis as mentioned in Sec. 2.5.

3.1.

Optical Property Extraction Accuracy

Using the seventh (middle) phantom (${⟨{\mu }_{\mathrm{a}}⟩}_{\lambda }=3.5$, ${⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }=6{\mathrm{cm}}^{-1}$) as a scaling reference, the average percent error across all phantoms and pixels in extracting ${\mu }_{\mathrm{s}}^{\prime }$ and ${\mu }_{\mathrm{a}}$ was found to be $5.69\pm 1.88\%$ and $8.91\pm 1.72\%$, respectively. The extracted phantom values are plotted against the expected values in Fig. 5. These error ranges are comparable to our current gold standard device, as shown in Ref. 16. The deviation of the reference phantom measured reflectance from the expected reflectance produced by the forward Monte-Carlo model is used to calibrate all measured spectra for inversion and accounts for the system response not captured by the forward model, as described in Ref. 7. Note that because the PDA geometry captures light near the input source, the ${\mu }_{\mathrm{s}}^{\prime }(\lambda )$ is sensitive to the scattering phase function; this is discussed in additional detail in Sec. 4.

Fig. 5

Homogeneous optical property extraction error: (a) average percent error in ${\mu }_{\mathrm{s}}^{\prime }$ and (b) average percent error in ${\mu }_{\mathrm{a}}$. The diagonal line indicates perfect agreement. Error bars represent the standard deviation of the extracted values across pixels. Wavelength averaged ${\mu }_{\mathrm{s}}^{\prime }$ and ${\mu }_{\mathrm{a}}$ for the phantom used as a reference were 6 and $3.5{\mathrm{cm}}^{-1}$, respectively. Note that the indicated error includes the use of the Teflon film mentioned in Sec. 2.2.

3.2.

Resolution Characterization

From a clinical timing perspective, an upsample factor of 6 or $\mathrm{\Delta }s=0.75\mathrm{mm}$ proved to be ideal; additional gains in two-dimensional (2-D) feature resolution beyond this $\mathrm{\Delta }s$ were minimal due to the large illumination apertures (0.75 mm), while the total scan time was substantially increased (9.61 to 13.1 min) [Fig. 6(a)]. Note that because the target is not completely opaque (there is some diffusion of light), the feature resolution does not strictly follow the Whittaker–Shannon sampling theorem; for a $\mathrm{\Delta }s=0.75\mathrm{mm}$, 1 mm features are adequately resolved without aliasing.

Fig. 6

BMAP resolution characterization: (a) Reflectance measured on a high contrast resolution target for a series of raster-scan rates with increasing $\mathrm{\Delta }s$ from left to right. The time required for each scan is indicated below the respective image and (b) line scans across simulated diffuse, tissue-like slabs each with an embedded heterogeneity. The center position of the simulated photodiode is represented by $\chi$. Within each plot, line scans are shown as the extracted $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ for three heterogeneity sizes ($\mathrm{\Delta }b=[0.50,0.75,1.5]\mathrm{mm}$). Heterogeneities are centered at $\chi =0$. From left to right, $\mathrm{\Delta }s$ is increased from 0.25 to 4.5 mm, depicting the loss of contrast for each heterogeneity as a function of sampling rate. The error-bars represent all possible $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ estimates (uncertainty) derived from the inverse model for the simulated spectra.

In tissue, the smallest cancer-like heterogeneity that we can reasonably expect to detect and correctly classify is roughly 0.75 mm for a $\mathrm{\Delta }s=0.75\mathrm{mm}$. As shown in Fig. 6(b), for $\mathrm{\Delta }s=0.75\mathrm{mm}$, the cancer-like heterogeneity has enough contrast against the background (adipose-like tissue) to manifest as an area with $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }\le 2$, the utilized threshold differentiating likely cancer from likely normal tissue in this work. Furthermore, the benefit of subpixel sampling via raster-scanning is clearly presented in Fig. 6(b); the uncertainty (indicated by the error bars) in the heterogeneity size, position, and optical property values decreases nonlinearly with decreasing $\mathrm{\Delta }s$. At the native probe resolution ($\mathrm{\Delta }s=4.5\mathrm{mm}$), it is only possible to correctly classify the largest heterogeneity ($\mathrm{\Delta }b=1.5\mathrm{mm}$), and the likelihood of doing so is only 33%.

For the purposes of detecting residual disease at the tumor margin, it is sufficient to merely detect its presence in a binary sense; the precise position, optical parameter value, and size are not critical to be determined as long as positive margins are not missed. An expanded view of the expected contrast in tissue is shown in Figs. 7(a)–7(c). The possible values in estimating $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ for three separate sized cancer-like heterogeneities can be seen as a function of both $\mathrm{\Delta }s$ and probe position ($\chi$) for a single en face line scan across the heterogeneity. The cancerous heterogeneities and adipose slabs have a true value of $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }=0.9$ and $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }=2.97$, respectively, and the gray plane represents the $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }=2$ threshold utilized in this paper. As the figure shows, the 0.25-mm heterogeneity ($\mathrm{\Delta }b=0.25\mathrm{mm}$) [Fig. 7(a)] cannot be resolved with the current PDA geometry, even if sampled at $\mathrm{\Delta }s=0.25\mathrm{mm}$. For the 0.75-mm heterogeneity ($\mathrm{\Delta }b=0.75\mathrm{mm}$) in Fig. 7(b), it can be seen that there is only a small subregion of $\mathrm{\Delta }s$ that can adequately differentiate the heterogeneity from the adipose background (the region below the gray plane). The effect of $\mathrm{\Delta }s$ on the possible estimated $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ values is condensed in a box-and-whisker style plot in Figs. 7(e) and 7(f).

Fig. 7

Contrast in tissue. (a) Possible $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ values for a line scan ($\chi$) across a rectangular volume ($l\times w\times h=0.25\times 0.25\times 3\mathrm{mm}$) with cancer-like optical properties surrounded by a semi-infinite adipose slab (the geometry is shown in Sec. 2.8), shown as a function of $\mathrm{\Delta }s$. Similarly, (b) and (c) represent possible $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ values for perturbations in increasing size (0.75 and 1.5 mm, respectively). The center position of the simulated photodiode is represented by $\chi$. The gray plane represents the threshold for which the heterogeneity would be correctly classified as “likely cancer.” In (d) through (f), the possible $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ estimates are condensed in box-and-whisker plot format. The blue line represents the true $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ value for the cancer-like heterogeneity and the expected median value for the lower row of boxes. The dashed red line indicates the threshold for which the heterogeneity would be correctly diagnostically classified. The orange line represents the true $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ value for the adipose slab and the expected median value for the upper row of boxes.

3.3.

Effects of Pressure

The pressure applied at the probe–tissue interface was observed to cause substantial deviations in optical parameter estimates for the solid tissue phantoms, as described in Sec. 2.8. As shown in Fig. 8(a), the estimated [$\beta$-carotene] at pressures below 5 mm Hg was observed to be wildly inconsistent, suggesting insufficient or partial contact with the layered phantom; however, at pressures $\ge 15\mathrm{mm}\mathrm{Hg}$, the estimates for [$\beta$-carotene] begin to decrease as the 2-mm layer begins to compress and the slab below is included in the photon path; both regions below the adipose layer contain much less of the $\beta$-carotene analog (Crocin). A similar effect was observed for ${⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ and is shown in Fig. 8(b). The optical changes suggest a window where optical property fidelity is maintained, roughly between 6 and 15 mm Hg. Thus, for routine clinical use, a pressure of 10 mm Hg was used.

Fig. 8

Pressure-induced optical parameter deviations. (a) Map of [$\beta$-carotene] concentration of the layered phantom with a two region slab [high scattering low beta, low scattering low beta (center region)]. Likewise, (b) shows the wavelength averaged ${\mu }_{\mathrm{s}}^{\prime }$ (${\mathrm{cm}}^{-1}$) and reveals a similar trend as the center region has a slightly reduced ${⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ (7 to 6.2). (c) Shows the amount of compression during the experiment with the phantom diagrammed in the plot; yellow represents the adipose layer, red the center region, and orange the remaining cancer slab. (d) Shows the $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ trend for the center region indicated by the box labeled 1. The yellow line represents the expected value. (e) Similarly shows the trend for the region indicated by box 2 and not influenced by the center region. Note the distinct drop in the $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ ratio beyond 15 mm Hg; this is due to the highly scattering nature of the cancer slab, having a ${⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ of $15{\mathrm{cm}}^{-1}$.

The physical amount of compression was observed to be nearly 10 mm for the greatest pressures used ($\ge 15\mathrm{mm}\mathrm{Hg}$) [Fig. 8(c)]. Distributions of $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ are shown in Figs. 8(d) and 8(e) for $4.5\times 4.5\mathrm{mm}$ regions of the parameter maps in (a) and (b), indicated as box (1) and (2), corresponding to areas of the large phantom slab containing optical properties representing the reference phantom (low ${⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ and no [$\beta$-carotene]) and cancerous tissue (high ${⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ and low [$\beta$-carotene]), respectively. As seen in 8(e), the $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ values drop below the actual value (2.97) at pressures beyond 15 mm Hg, suggesting that the “clear” margin would not be read as clear adipose tissue, but perhaps with some fibroglandular content.

3.4.

Clinical Validation

Clinical specimens (three total) measured using the BMAP system were found to exhibit similar optical parameter distributions to those captured with the gold standard 49ch system and were consistent with our previously published results.^8,9,16 The clinical utility of the BMAP on a representative clinical specimen with a close but negative margin is shown in Figs. 9(a)–9(d). The labeled sites correspond to histopathology-verified site-level data reviewed by an expert pathologist. The high-resolution parameter maps [Fig. 9(d)] reveal a level of detail that is not apparent in the low-resolution reflectance image [Fig. 9(c)], demonstrating the importance of subpixel sampling. Congruent with previously published results, areas corresponding to fibroglandular tissue (region 3) have markedly lower $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ values compared to regions of adipose tissue (region 4). Note that this stark contrast is lost in the low-resolution parameter map [Fig. 9(c)]. Region 5 corresponds to a site with invasive ductal carcinoma 1.8 mm distal (in depth) to the margin surface; the entire region contains markedly lower $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ values relative to other sites; this is aligned with a generally accepted notion of stromal modification near malignant sites.^17–19 The mammographic breast density (MBD) for the sample was reported to be high ($\mathrm{MBD}=4$). MBD scores are indicated as 1-fatty, 2-scattered fibrous tissue, 3-heterogeneously dense, and 4-extremely dense. High MBD has a known association with increased breast cancer risk, particularly in premenopausal patients.^20,21 Figures 9(e)–9(h) show an additional clinical specimen with a high $\mathrm{MBD}=3$. Note the overall lower distribution of $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ values, which again corroborates significant stromal involvement for dense, fibrous regions of tissue due to extensive collagen involvement.

Fig. 9

Clinical margin data: (a) digital image of a close margin (BMAP-2) showing sites inked for pathological review, (b) digital image with $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ map overlayed, (c), $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ map with $\mathrm{\Delta }s=4.5\mathrm{mm}$, and (d), $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ map with $\mathrm{\Delta }s=0.75\mathrm{mm}$ with outlines corresponding to inked regions. FA, fibro-adipose; FG, fibroglandular; and IDC, invasive ductal carcinoma. Similarly, (e) a digital image of an additional close but negative margin with (f) the corresponding wide-field $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ map acquired with the 49ch gold-standard clinical system. The black dashed box indicates the region also measured by the BMAP system. (g) A cropped and zoomed map of the coregistered region captured with the 49ch device (49ch-1) and (h) the corresponding $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ map captured with the BMAP device (BMAP-1). In (i) through (l), the digital, 49ch full FOV, 49ch-cropped (49ch-3), and corresponding BMAP parameter map (BMAP-3) are shown for a low-density margin ($\mathrm{MBD}=2$). In (m), the $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ cumulative distributions for each of the 49ch/BMAP pairs with the mammographic breast density (MBD) indicated. Finally, (n) the $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ distributions of the coregistered regions shown in a condensed box-and-whisker format.

The similarity to the 49ch gold-standard device is also demonstrated in Figs. 9(e)–9(h). Figure 9(f) shows a full margin scan captured with the 49ch device of a mammographically dense breast ($\mathrm{MBD}=3$); the region measured by both devices is indicated by the dashed black box. A zoomed and cropped 49ch parameter map of the black box region is shown in Fig. 9(g); note the similarity to the same parameter map captured with the BMAP system shown in Fig. 9(h). A similar representation is shown on a low-density breast ($\mathrm{MBD}=2$) in Figs. 9(i)–9(l). As a corollary to the previously mentioned hypothesis, the overall increased $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ values indicate reduced fibroglandular extent and a greater percentage of fatty tissue; indeed the measurement distribution shown here results from both increased [$\beta$-carotene] and decreased ${⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$.

A summary of the extracted $[\beta \text{-}\text{carotene}]/{⟨{\mu }_{\mathrm{s}}^{\prime }⟩}_{\lambda }$ values for each of the three margins is shown in Fig. 9(m) as CDFs. For each margin, an additional CDF corresponding to the same region measured with our gold standard 49ch device is overlaid to demonstrate the equivalence in extracted optical parameter value distributions. The similarity of these optical parameter distributions is alternatively quantified and is shown in the form of box-and-whisker plots in Fig. 9(n).