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Objectives This study aimed to systematically investigate whether plaque autofluorescence properties assessed with intravascular fluorescence lifetime imaging (FLIm) can provide qualitative and quantitative information about intimal composition and improve the characterization of atherosclerosis lesions. Background Despite advances in cardiovascular diagnostics, the analytic tools and imaging technologies currently available have limited capabilities for evaluating in situ biochemical changes associated with luminal surface features. Earlier studies of small number of samples have shown differences among the autofluorescence lifetime signature of well-defined lesions, but a systematic pixel-level evaluation of fluorescence signatures associated with various histological features is lacking and needed to better understand the origins of fluorescence contrast. Methods Human coronary artery segments (n = 32) were analyzed with a bimodal catheter system combining multispectral FLIm with intravascular ultrasonography compatible with in vivo coronary imaging. Various histological components present along the luminal surface (200-μm depth) were systematically tabulated (12 sectors) from each serial histological section (n = 204). Morphological information provided by ultrasonography allowed for the accurate registration of imaging data with histology data. The relationships between histological findings and FLIm parameters obtained from 3 spectral channels at each measurement location (n = 33,980) were characterized. Results Our findings indicate that fluorescence lifetime from different spectral bands can be used to quantitatively predict the superficial presence of macrophage foam cells (mFCs) (area under the receiver-operator characteristic curve: 0.94) and extracellular lipid content in advanced lesions (lifetime increase in 540-nm band), detect superficial calcium (lifetime decrease in 450-nm band area under the receiver-operator characteristic curve: 0.90), and possibly detect lesions consistent with active plaque formation such as pathological intimal thickening and healed thrombus regions (lifetime increase in 390-nm band). Conclusions Our findings indicate that autofluorescence lifetime provides valuable information for characterizing atherosclerotic lesions in coronary arteries. Specifically, FLIm can be used to identify key phenomena linked with plaque progression (e.g., peroxidized-lipid–rich mFC accumulation and recent plaque formation).

Tumor-free surgical margins are critical in breast-conserving surgery. In up to 38% of the cases, however, patients undergo a second surgery since malignant cells are found at the margins of the excised resection specimen. Thus, advanced imaging tools are needed to ensure clear margins at the time of surgery. The objective of this study was to evaluate a random forest classifier that makes use of parameters derived from point-scanning label-free fluorescence lifetime imaging (FLIm) measurements of breast specimens as a means to diagnose tumor at the resection margins and to enable an intuitive visualization of a probabilistic classifier on tissue specimen. FLIm data from fresh lumpectomy and mastectomy specimens from 18 patients were used in this study. The supervised training was based on a previously developed registration technique between autofluorescence imaging data and cross-sectional histology slides. A pathologist’s histology annotations provide the ground truth to distinguish between adipose, fibrous, and tumor tissue. Current results demonstrate the ability of this approach to classify the tumor with 89% sensitivity and 93% specificity and to rapidly (∼ 20 frames per second) overlay the probabilistic classifier overlaid on excised breast specimens using an intuitive color scheme. Furthermore, we show an iterative imaging refinement that allows surgeons to switch between rapid scans with a customized, low spatial resolution to quickly cover the specimen and slower scans with enhanced resolution (400 μm per point measurement) in suspicious regions where more details are required. In summary, this technique provides high diagnostic prediction accuracy, rapid acquisition, adaptive resolution, nondestructive probing, and facile interpretation of images, thus holding potential for clinical breast imaging based on label-free FLIm.

Tumor removal typically involves electrocautery, but no studies to date have quantified the effect of electrocautery on fluorescence emission. Electrocautery was applied to N=4 locations of the oral cavity and striated leg muscle of a live Yorkshire pig. Autofluorescence of cauterized tissues and surrounding regions was measured at distinct time points up to 120 minutes following cauterization. The fluorescence lifetime was spectrally resolved in four spectral detection channels that maximized the signal emanating from endogenous fluorophores of interest. The autofluorescence emission (355 nm excitation) was temporally resolved using a high-speed digitizer; resulting fluorescence decay characteristics were retrieved using the Laguerre deconvolution technique. Histology was performed and co-registered with the autofluorescence data. Results show that cauterized tissue presents a distinct autofluorescence signature from surrounding regions immediately after cauterization. Differences become less evident with time. The autofluorescence-derived parameters suggest altered metabolism in peripheral regions compared to the region of maximal damage. Within the time-frame of this study, tissues investigated show variable degrees of recovery from the effects of electrocautery that can be monitored by changes in fluorescence lifetime characteristics. Our findings suggest delineation of pathologic conditions could be affected by tissue cauterization and that future studies in this area will be necessary.

Current clinical brain imaging techniques used for surgical planning of tumor resection lack intraoperative and real-time feedback; hence surgeons ultimately rely on subjective evaluation to identify tumor areas and margins. We report a fluorescence lifetime imaging (FLIm) instrument (excitation: 355 nm; emission spectral bands: 390/40 nm, 470/28 nm, 542/50 nm, and 629/53 nm) that integrates with surgical microscopes to provide real-time intraoperative augmentation of the surgical field of view with fluorescent derived parameters encoding diagnostic information. We show the functionality and safety features of this instrument during neurosurgical procedures in patients undergoing craniotomy for the resection of brain tumors and/or tissue with radiation damage. We demonstrate in three case studies the ability of this instrument to resolve distinct tissue types and pathology including cortex, white matter, tumor, and radiation-induced necrosis. In particular, two patients with effects of radiation induced necrosis exhibited longer fluorescence lifetimes and increased optical redox ratio on the necrotic tissue with respect to non-affected cortex, and an oligodendroglioma resected from a third patient reported shorter fluorescence life-time and a decrease in optical redox ratio than the surrounding white matter. These results encourage the use of FLIm as a label-free and non-invasive intraoperative tool for neurosurgical guidance.

Fluorescence lifetime imaging (FLIm) was integrated with a da Vinci Surgical System (Intuitive Surgical, Inc.) for exploration of head and neck cancer (N=25 patients). In vivo results showed FLIm distinguished between tumor and healthy tissue.

Re-excision rates for breast cancer lumpectomy procedures are currently nearly 25% due to surgeons relying on inaccurate or incomplete methods of evaluating specimen margins. The objective of this study was to determine if cancer could be automatically detected in breast specimens from mastectomy and lumpectomy procedures by a classification algorithm that incorporated parameters derived from fluorescence lifetime imaging (FLIm). This study generated a database of co-registered histologic sections and FLIm data from breast cancer specimens (N = 20) and a support vector machine (SVM) classification algorithm able to automatically detect cancerous, fibrous, and adipose breast tissue. Classification accuracies were greater than 97% for automated detection of cancerous, fibrous, and adipose tissue from breast cancer specimens. The classification worked equally well for specimens scanned by hand or with a mechanical stage, demonstrating that the system could be used during surgery or on excised specimens. The ability of this technique to simply discriminate between cancerous and normal breast tissue, in particular to distinguish fibrous breast tissue from tumor, which is notoriously challenging for optical techniques, leads to the conclusion that FLIm has great potential to assess breast cancer margins. Identification of positive margins before waiting for complete histologic analysis could significantly reduce breast cancer re-excision rates.

The current standard of care for early stages of breast cancer is breast-conserving surgery (BCS). BCS involves a lumpectomy procedure, during which the tumor is removed with a rim of normal tissue-if cancer cells found in that rim of tissue, it is called a positive margin and means part of the tumor remains in the breast. Currently there is no method to determine if cancer cells exist at the margins of lumpectomy specimens aside from time-intensive histology methods that result in reoperations in up to 38% of cases. We used fluorescence lifetime imaging (FLIm) to measure time-resolved autofluorescence from N=13 ex vivo human breast cancer specimens (N=10 patients undergoing lumpectomy or mastectomy) and compared our results to histology. Tumor (both invasive and ductal carcinoma in situ), fibrous tissue, fat and fat necrosis have unique fluorescence signatures. For instance, between 500-580 nm, fluorescence lifetime of tumor was shortest (4.7 ± 0.4 ns) compared to fibrous tissue (5.5 ± 0.7 ns) and fat (7.0 ± 0.1 ns), P

In order to objectively assess the laryngeal vibratory behavior, endoscopic high-speed cameras capture several thousand frames per second of the vocal folds during phonation. However, judging all inherent clinically relevant features is a challenging task and requires well-founded expert knowledge. In this study, an automated wavelet-based analysis of laryngeal high-speed videos based on phonovibrograms is presented. The phonovibrogram is an image representation of the spatiotemporal pattern of vocal fold vibration and constitutes the basis for a computer-based analysis of laryngeal dynamics. The features extracted from the wavelet transform are shown to be closely related to a basic set of video-based measurements categorized by the European Laryngological Society for a subjective assessment of pathologic voices. The wavelet-based analysis further offers information about irregularity and lateral asymmetry and asynchrony. It is demonstrated in healthy and pathologic subjects as well as for a surgical group that was examined before and after the removal of a vocal fold polyp. The features were found to not only classify glottal closure characteristics but also quantify the impact of pathologies on the vibratory behavior. The interpretability and the discriminative power of the proposed feature set show promising relevance for a computer-assisted diagnosis and classification of voice disorders.

Summary Introduction In a recent publication, the phasegram, a bifurcation diagram over time, has been introduced as an intuitive visualization tool for assessing the vibratory states of oscillating systems. Here, this nonlinear dynamics approach is augmented with quantitative analysis parameters, and it is applied to clinical laryngeal high-speed video (HSV) endoscopic recordings of healthy and pathological phonations. Methods HSV data from a total of 73 females diagnosed as healthy (n = 42), or with functional dysphonia (n = 15) or with unilateral vocal fold paralysis (n = 16), were quantitatively analyzed. Glottal area waveforms (GAW) and left and right hemi-GAWs (hGAW) were extracted from the HSV recordings. Based on Poincare sections through phase space-embedded signals, two novel quantitative parameters were computed: the phasegram entropy (PE) and the phasegram complexity estimate (PCE), inspired by signal entropy and correlation dimension computation, respectively. Results Both PE and PCE assumed higher average values (suggesting more irregular vibrations) for the pathological as compared with the healthy participants, thus significantly discriminating healthy group from the paralysis group ( P = 0.02 for both PE and PCE). Comparisons of individual PE or PCE data for the left and the right hGAW within each subject resulted in asymmetry measures for the regularity of vocal fold vibration. The PCE-based asymmetry measure revealed significant differences between the healthy group and the paralysis group ( P = 0.03). Conclusions Quantitative phasegram analysis of GAW and hGAW data is a promising tool for the automated processing of HSV data in research and in clinical practice.

About two thirds of laryngeal cancers originate at the vocal cords. Early-stage detection of malignant vocal fold alterations, including a discrimination of premalignant lesions, represents a major challenge in laryngology as precancerous vocal fold lesions and small carcinomas are difficult to distinguish by means of regular endoscopy only. We report a procedure to discriminate between malignant and precancerous lesions by measuring the characteristics of vocal fold dynamics by means of a computerized analysis of laryngeal high-speed videos. Ten patients with squamous cell T1a carcinoma, ten with precancerous lesions with hyperkeratosis, and ten subjects without laryngeal disease underwent high-speed laryngoscopy yielding 4,000 images per second. By means of wavelet-based phonovibrographic analysis, a set of three clinically meaningful vibratory measures was extracted from the videos comprising a total number of 15,000 video frames. Statistical analysis (ANOVA with post hoc two-sided t tests, P < 0.05) revealed that vocal fold dynamics is significantly affected in the presence of precancerous lesions and T1a carcinoma. On the basis of the three measures, a discriminating pattern was extracted using a support vector machine-learning algorithm performing an individual classification in respect to the different clinical groups. By applying a leave-one-out cross-validation strategy, we could show that the proposed measures discriminate with a very high performance between precancerous lesions and T1a carcinoma (sensitivity, 100%; specificity, 100%). Although a large-scale study will be necessary to confirm clinical significance, the set of vibratory measures derived in this study may be applicable to improve the accuracy and reliability of noninvasive diagnostics of vocal fold lesions. Cancer Res; 75(1); 31–39. ©2014 AACR.

Direct observation of vocal fold vibration is indispensable for a clinical diagnosis of voice disorders. Among current imaging techniques, high-speed videoendoscopy constitutes a state-of-the-art method capturing several thousand frames per second of the vocal folds during phonation. Recently, a method for extracting descriptive features from phonovibrograms, a two-dimensional image containing the spatio-temporal pattern of vocal fold dynamics, was presented. The derived features are closely related to a clinically established protocol for functional assessment of pathologic voices. The discriminative power of these features for different pathologic findings and configurations has not been assessed yet. In the current study, a collective of 220 subjects is considered for two- and multi-class problems of healthy and pathologic findings. The performance of the proposed feature set is compared to conventional feature reduction routines and was found to clearly outperform these. As such, the proposed procedure shows great potential for diagnostical issues of vocal fold disorders.

Recently, endoscopic high-speed laryngoscopy has been established for commercial use as a state-of-the-art technique to examine vocal fold kinematics. Since modern cameras provide sampling rates of several thousand frames per second, a high volume of data has to be considered for visual and objective analysis. A method for visualizing endoscopic high speed videos in three-dimensional cycle-based graphs combining and extending the approaches of phonovibrograms and electroglottographic wavegrams is presented. To build a phonovibrographic wavegram, individual cycles of a phonovibrogram are segmented, normalized in cycle duration, and concatenated over time. For analyzing purposes, the emerging three-dimensional scalar field is visualized with different rendering techniques providing information of different aspects of vocal fold kinematics. The phonovibrographic wavegram incorporates information about the glottal closure type, size, and location of the amplitudes, symmetry, periodicity, and phase information. The potential of the approach to visualize the characteristics of vocal fold vibration in a compact and intuitive way is demonstrated within two healthy and three pathologic subjects. The phonovibrographic wavegram allows a comprehensive analysis of vocal fold kinematics and reveals information that remains hidden with other visualization techniques.

Recently, endoscopic high-speed laryngoscopy has been established for commercial use and constitutes a state-of-the-art technique to examine vocal fold dynamics. Despite overcoming many limitations of commonly applied stroboscopy it has not gained widespread clinical application, yet. A major drawback is a missing methodology of extracting valuable features to support visual assessment or computer-aided diagnosis. In this paper a compact and descriptive feature set is presented. The feature extraction routines are based on two-dimensional color graphs called phonovibrograms (PVG). These graphs contain the full spatio-temporal pattern of vocal fold dynamics and are therefore suited to derive features that comprehensively describe the vibration pattern of vocal folds. Within our approach, clinically relevant features such as glottal closure type, symmetry and periodicity are quantified in a set of 10 descriptive features. The suitability for classification tasks is shown using a clinical data set comprising 50 healthy and 50 paralytic subjects. A classification accuracy of 93.2% has been achieved.