Because contemporary intraoperative tumor detection modalities, such as intraoperative MRI, are not ubiquitously available and can disrupt surgical workflow, there is an imperative for an accessible diagnostic device that can meet the surgeon’s needs in identifying tissue types. The objective of this paper is to determine the efficacy of a novel non-contact tumor detection device for metastatic melanoma boundary identification in a tissue-mimicking phantom, evaluate the identification of metastatic melanoma boundaries in ex vivo mouse brain tissue, and find the error associated with identifying this boundary. To validate the spatial and fluorescence resolution of the device, tissue-mimicking phantoms were created with modifiable optical properties. Phantom tissue provided ground truth measurements for fluorophore concentration differences with respect to spatial dimensions. Modeling metastatic disease, ex vivo melanoma brain metastases were evaluated to detect differences in fluorescence between healthy and neoplastic tissue. This analysis includes determining required-to-observe fluorescence differences in tissue. H&E staining confirmed tumor presence in mouse tissue samples. The device detected a difference in normalized average fluorescence intensity in all three phantoms. There were differences in fluorescence with the presence and absence of melanin. The estimated tumor boundary of all tissue phantoms was within 0.30 mm of the ground truth tumor boundary for all boundaries. Likewise, when applied to the melanoma-bearing brains from ex vivo mice, a difference in normalized fluorescence intensity was successfully detected. The potential prediction window for the tumor boundary location is less than 1.5 mm for all ex vivo mouse brain tumors boundaries. We present a non-contact, laser-induced fluorescence device that can identify tumor boundaries based on changes in laser-induced fluorescence emission intensity. The device can identify phantom ground truth tumor boundaries within 0.30 mm using instantaneous rate of change of normalized fluorescence emission intensity and can detect endogenous fluorescence differences in melanoma brain metastases in ex vivo mouse tissue.
Intraoperative imaging of brain tumors using spectral signatures of tissue, based on injected fluorescent dye such as 5-ALA, has enabled surgeons to target residual malignant tissue near the boundaries of the tumor cavity where extent of resection is most difficult. This paper presents a novel approach to intraoperative tumor boundary detection based on a moving excitation laser crossing a tumor boundary while measuring spectral signatures generated. In prior work, we have characterized the intrinsic spectral signatures of glioblastoma and healthy brain tissue from in vivo mouse models within the 400 to 700 nm range given a 405 nm excitation source at a single spot, without the use of injected dye. In this work, we present a theoretical model of expected spectral signature observations for a moving excitation laser across a tumor boundary based on discretized contribution of known spectral signatures (i.e. GBM, healthy) within the region of the laser spot on the surface of a tissue. This approach allows for improved intraoperative boundary detection despite having a laser spot size larger than the desired resolution of detection.
The removal of tissue with a laser scalpel is a complex process that is affected by the laser incidence angle on the surface of the tissue. Current models of laser ablation, however, do not account for the angle of incidence, assuming that it is always normal to the surface. In order to improve ablation modeling in soft tissue, this work characterizes photoablation crater profiles at incidence angles ranging from 0 degrees to 45 degrees off perpendicular. Simulated results, based on a discretized steady-state ablation model, are generated for comparison based on the assumption that material removal occurs in the direction of the laser. Experiments in an agarose-based, homogeneous soft tissue phantom are performed with a carbon dioxide (CO2) laser. Surface profiles of the craters are acquired using a micro x-ray computed tomography scanner (Micro-CT) and compared to results from the simulation. The difference of the simulated and experimental results are measured and the error analysis is reported.
The ability to differentiate healthy and tumorous tissue is vital during the surgical removal of tumors. This ability is especially critical during neurosurgical tumor resection due to the risk associated with removing healthy brain tissue. In this paper, we present an epifluorescence spectroscopy guided device that is not only capable of autonomously classifying a region of tissue as tumorous or healthy in real-time–but is also able to differentiate between different tumor types. For this study, glioblastoma and melanoma were chosen as the two different tumor types. Six mice were utilized in each of the three classes (healthy, glioblastoma, melanoma) for a total of eighteen mice. A “one-vs-the-all” approach was used to create a multi-class classifier. The multi-class classifier was capable of classifying with 100% accuracy. Future work includes increasing the number of mice in each of the three tumor classes to create a more robust classifier and expanding the number of tumor types beyond glioblastoma and melanoma.
Robotic and robotic-assisted surgeries are becoming more prevalent with the promise of improving surgical outcomes through increased precision, reduced operating times, and minimally invasive procedures. The handheld laser scalpel in neurosurgery has been shown to provide a more gentle approach to tissue manipulation on or near critical structures over classical tooling, though difficulties of control have prevented large scale adoption of the tool. This paper presents a novel approach to generating a cutting path for the volumetric resection of tissue using a computer-guided laser scalpel. A soft tissue ablation simulator is developed and used in conjunction with an optimization routine to select parameters which maximize the total resection of target tissue while minimizing the damage to surrounding tissue. The simulator predicts the ablative properties of tissue from an interrogation cut for tuning and simulates the removal of a tumorous tissue embedded on the surface of healthy tissue using a laser scalpel. We demonstrate the ability to control depth and smoothness of cut using genetic algorithms to optimize the ablation parameters and cutting path. The laser power level, cutting rate and spacing between cuts are optimized over multiple surface cuts to achieve the desired resection volumes.
An optically tunable, solid tissue phantom was developed in order to aid in the verification and validation of non-destructive cancer detection technologies based on fluorescence spectroscopy. The solid tissue phantom contained agarose, hemoglobin, Intralipid, NADH, and FAD. The redox ratio of the solid phantoms were shown to be tunable; thus, indicating that these phantoms could be used to tailor specific optical conditions that mimic cancerous and healthy tissues. Therefore, this solid tissue phantom can serve as a suitable test bed to evaluate fluorescence spectroscopy based cancer detection devices.
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