3D-perfusion analysis of burn wounds using hyperspectral imaging

Non-invasive medical imaging technology for the diagnosis of burn depth

Currently, the clinical diagnosis of burn depth primarily relies on physicians' judgements based on patients' symptoms and physical signs, particularly the morphological characteristics of the wound. This method highly depends on individual doctors' clinical experience, proving challenging for less experienced or primary care physicians, with results often varying from one practitioner to another. Therefore, scholars have been exploring an objective and quantitative auxiliary examination technique to enhance the accuracy and consistency of burn depth diagnosis. Non-invasive medical imaging technology, with its significant advantages in examining tissue surface morphology, blood flow in deep and changes in structure and composition, has become a hot topic in burn diagnostic technology research in recent years. This paper reviews various non-invasive medical imaging technologies that have shown potential in burn depth diagnosis. These technologies are summarized and synthesized in terms of imaging principles, current research status, advantages and limitations, aiming to provide a reference for clinical application or research for burn specialists.

Diagnostical accuracy of hyperspectral imaging after free flap surgery

Microsurgical free-tissue transfer has been a safe option for tissue reconstruction. This study aimed to analyze the diagnostic accuracy of hyperspectral imaging (HSI) after free-tissue transfer surgery. From January 2017 to October 2019, 42 consecutive free-flap surgeries were performed, and their outcomes were analyzed via HSI. Clinical examination of free-flap perfusion was initially performed. Clinical examination findings were subsequently compared with those of HSI. Potential venous congestion with subsequent necrosis was defined as a tissue hemoglobin index of ≥53%. Student's t-test was used to compare the results of the analysis. The evaluation of sensitivity and specificity for flap failure detection was time dependent using the Fisher's exact test. A p-value of ≤0.05 was considered statistically significant. Microsurgical tissue transfer success rate was 84%. Seven patients presented with venous congestion that caused total flap necrosis. Overall, 124 assessments were made. HSI accurately identified 12 out of 19 pathological images: four as false positive and seven as false negative. The sensitivity and specificity of HSI were 57 and 94%, respectively, compared to those of clinical examination that were 28 and 100%, respectively, within 24 h following tissue transfer. The sensitivity and specificity of HSI were 63 and 96%, respectively, compared to those of clinical examination that were 63 and 100%, respectively, within the first 72 h. A tissue hemoglobin index of ≥53% could predict venous congestion after free-flap surgery. HSI demonstrated higher sensitivity than clinical examination within the first 24 h; however, it was not superior compared to clinical findings within 72 h.

Hyperspectral imaging to accurately segment skin erythema and hyperpigmentation in cutaneous chronic graft-versus-host disease

In 51 lesions from 15 patients with the inflammatory skin condition chronic graft-versus-host-disease, hyperspectral imaging accurately delineated active erythema and post-inflammatory hyperpigmentation. The method was validated by dermatologist-approved confident delineations of only definitely affected and definitely unaffected areas in photographs. A prototype hyperspectral imaging system acquired a 2.5 × 3.5 cm2 area of skin at 120 wavelengths in the 450-850 nm range. Unsupervised extraction of unknown absorbers by endmember analysis achieved a comparable accuracy to that of supervised extraction of known absorbers (melanin, hemoglobin) by chromophore mapping: 0.78 (IQR: 0.39-0.85) vs. 0.83 (0.53-0.91) to delineate erythema and 0.74 (0.57-0.87) vs. 0.73 (0.52-0.84) to delineate hyperpigmentation. Both algorithms achieved higher specificity than sensitivity. Whereas a trained human confidently marked a median of 7% of image pixels, unsupervised and supervised algorithms delineated a median of 14% and 27% pixels. Hyperspectral imaging could overcome a fundamental practice gap of distinguishing active from inactive manifestations of inflammatory skin disease.

Compact and ultracompact spectral imagers: technology and applications in biomedical imaging

Significance

Spectral imaging, which includes hyperspectral and multispectral imaging, can provide images in numerous wavelength bands within and beyond the visible light spectrum. Emerging technologies that enable compact, portable spectral imaging cameras can facilitate new applications in biomedical imaging.

Aim

With this review paper, researchers will (1) understand the technological trends of upcoming spectral cameras, (2) understand new specific applications that portable spectral imaging unlocked, and (3) evaluate proper spectral imaging systems for their specific applications.

Approach

We performed a comprehensive literature review in three databases (Scopus, PubMed, and Web of Science). We included only fully realized systems with definable dimensions. To best accommodate many different definitions of "compact," we included a table of dimensions and weights for systems that met our definition.

Results

There is a wide variety of contributions from industry, academic, and hobbyist spaces. A variety of new engineering approaches, such as Fabry-Perot interferometers, spectrally resolved detector array (mosaic array), microelectro-mechanical systems, 3D printing, light-emitting diodes, and smartphones, were used in the construction of compact spectral imaging cameras. In bioimaging applications, these compact devices were used for in vivo and ex vivo diagnosis and surgical settings.

Conclusions

Compact and ultracompact spectral imagers are the future of spectral imaging systems. Researchers in the bioimaging fields are building systems that are low-cost, fast in acquisition time, and mobile enough to be handheld.

Spectral Similarity Measures for In Vivo Human Tissue Discrimination Based on Hyperspectral Imaging

PROBLEM

Similarity measures are widely used as an approved method for spectral discrimination or identification with their applications in different areas of scientific research. Even though a range of works have been presented, only a few showed slightly promising results for human tissue, and these were mostly focused on pathological and non-pathological tissue classification.

METHODS

In this work, several spectral similarity measures on hyperspectral (HS) images of in vivo human tissue were evaluated for tissue discrimination purposes. Moreover, we introduced two new hybrid spectral measures, called SID-JM-TAN(SAM) and SID-JM-TAN(SCA). We analyzed spectral signatures obtained from 13 different human tissue types and two different materials (gauze, instruments), collected from HS images of 100 patients during surgeries.

RESULTS

The quantitative results showed the reliable performance of the different similarity measures and the proposed hybrid measures for tissue discrimination purposes. The latter produced higher discrimination values, up to 6.7 times more than the classical spectral similarity measures. Moreover, an application of the similarity measures was presented to support the annotations of the HS images. We showed that the automatic checking of tissue-annotated thyroid and colon tissues was successful in 73% and 60% of the total spectra, respectively. The hybrid measures showed the highest performance. Furthermore, the automatic labeling of wrongly annotated tissues was similar for all measures, with an accuracy of up to 90%.

CONCLUSION

In future work, the proposed spectral similarity measures will be integrated with tools to support physicians in annotations and tissue labeling of HS images.

Burn characterization using object-oriented hyperspectral image classification

This paper presents a new approach based on hyperspectral imaging combined with an object-oriented classification method that allows the generation of burn depth classification maps facilitating easier characterization of burns. Hyperspectral images of 14 patients diagnosed with burns on the upper and lower limbs were acquired using a pushbroom hyperspectral imaging system. The images were analyzed using an object-oriented classification approach that uses objects with specific spectral, textural and spatial attributes as the minimum unit for classifying information. The method performance was evaluated in terms of overall accuracy, sensitivity, precision and specificity computed from the confusion matrix. The results revealed that the approach proposed in this study performed well in differentiating burn classes with a high level of overall accuracy (95.99% ± 0.60%), precision (97.30% ± 2.46%), sensitivity (97.23% ± 3.02%) and specificity (98.02% ± 1.98%). In conclusion, the object-based approach for burns hyperspectral images classification can provide maps that can help surgeons identify with better precision different depths of burn wounds.

Optimized spectral filter design enables more accurate estimation of oxygen saturation in spectral imaging

Oxygen saturation (SO₂) in tissue is a crucially important physiological parameter with ubiquitous clinical utility in diagnosis, treatment, and monitoring, as well as widespread use as an invaluable preclinical research tool. Multispectral imaging can be used to visualize SO₂ non-invasively, non-destructively and without contact in real-time using narrow spectral filter sets, but typically, these spectral filter sets are poorly suited to a specific clinical task, application, or tissue type. In this work, we demonstrate the merit of optimizing spectral filter sets for more accurate estimation of SO₂. Using tissue modelling and simulated multispectral imaging, we demonstrate filter optimization reduces the root-mean-square-error (RMSE) in estimating SO₂ by up to 37% compared with evenly spaced filters. Moreover, we demonstrate up to a 79% decrease in RMSE for optimized filter sets compared with filter sets chosen to minimize mutual information. Wider adoption of this approach will result in more effective multispectral imaging systems that can address specific clinical needs and consequently, more widespread adoption of multispectral imaging technologies in disease diagnosis and treatment.