Dynamic Optical Tomography Research Articles

Optimization and correction of breast dynamic optical imaging projection data based on deep learning

Breast cancer poses a significant health threat to women, necessitating advancements in diagnostic technologies. Breast dynamic optical imaging (DOI) technology, recognized for its non-invasive and radiation-free properties, is extensively utilized for the early screening and quantitative analysis of breast tumors. The integration of deep learning, a robust technology for automatic image feature extraction, with breast DOI has the potential to enhance tumor detection and diagnosis significantly. This paper introduces a deep learning-enhanced image optimization approach to overcome challenges such as poor image quality and distorted projection data commonly encountered in existing DOI methods. The approach utilizes convolutional neural networks (CNNs) to extract features from raw images and employs generative adversarial networks (GANs) to enhance these images, thereby improving their quality and contrast. Additionally, a novel correction algorithm is developed to address projection data distortion, enabling the reconstruction and correction of this data for more accurate and reliable imaging results. Experimental findings confirm that the proposed method markedly enhances both image quality and projection data accuracy in breast DOI, offering a reliable foundation for clinical diagnosis. This study not only provides a new perspective and methodology for the early screening and diagnosis of breast cancer but also holds substantial clinical importance and prospective applications.

A bionic self-driven retinomorphic eye with ionogel photosynaptic retina.

Bioinspired bionic eyes should be self-driving, repairable and conformal to arbitrary geometries. Such eye would enable wide-field detection and efficient visual signal processing without requiring external energy, along with retinal transplantation by replacing dysfunctional photoreceptors with healthy ones for vision restoration. A variety of artificial eyes have been constructed with hemispherical silicon, perovskite and heterostructure photoreceptors, but creating zero-powered retinomorphic system with transplantable conformal features remains elusive. By combining neuromorphic principle with retinal and ionoelastomer engineering, we demonstrate a self-driven hemispherical retinomorphic eye with elastomeric retina made of ionogel heterojunction as photoreceptors. The receptor driven by photothermoelectric effect shows photoperception with broadband light detection (365 to 970 nm), wide field-of-view (180°) and photosynaptic (paired-pulse facilitation index, 153%) behaviors for biosimilar visual learning. The retinal photoreceptors are transplantable and conformal to any complex surface, enabling visual restoration for dynamic optical imaging and motion tracking.

Intelligent scoring system based on dynamic optical breast imaging for early detection of breast cancer.

Early detection of breast cancer can significantly improve patient outcomes and five-year survival in clinical screening. Dynamic optical breast imaging (DOBI) technology reflects the blood oxygen metabolism level of tumors based on the theory of tumor neovascularization, which offers a technical possibility for early detection of breast cancer. In this paper, we propose an intelligent scoring system integrating DOBI features assessment and a malignancy score grading reporting system for early detection of breast cancer. Specifically, we build six intelligent feature definition models to depict characteristics of regions of interest (ROIs) from location, space, time and context separately. Similar to the breast imaging-reporting and data system (BI-RADS), we conclude the malignancy score grading reporting system to score and evaluate ROIs as follows: Malignant (≥ 80 score), Likely Malignant (60-80 score), Intermediate (35-60 score), Likely Benign (10-35 score), and Benign (<10 score). This system eliminates the influence of subjective physician judgments on the assessment of the malignant probability of ROIs. Extensive experiments on 352 Chinese patients demonstrate the effectiveness of the proposed system compared to state-of-the-art methods.

Abstract B013: Development of oropharyngeal tumor organoids for disease modeling and high throughput drug screening

Abstract Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide with approximately 550,000 new cases diagnosed each year. The oropharynx is one of the most common sites of occurrence, and disease in this region most often presents in the palatine tonsil, followed by the base of tongue and soft palate. Human papilloma virus (HPV) is a common cause of HNSCC, giving rise to a distinct subset of tumors with a different clinical presentation, disease course, and response to treatment. In order to further characterize HPV+ HNSCC tumors and their therapy response, we have leveraged our high throughput 3D tumor organoid screening platform. Our approach entails seeding tumor cells in extracellular matrix gels deposited around the perimeter of tissue culture wells in ring-like structures, with screening results available within a week from surgery (Phan et al, 2019; Al Shihabi et al, 2022; Nguyen et al, 2022; Al Shihabi et al 2023). We particularly focused on tonsil tumors, and develop organoids from both HNSCC and contralateral normal tonsil tissue. Thus far, we have procured tonsil samples from 31 patients to develop organoids, perform high throughput drug screening, and immunohistochemical characterization including H&amp;E staining, Ki67, p16 and p53 immunohistochemical staining. We have leveraged dynamic optical contrast imaging (DOCI), a label-free tissue autofluorescence imaging method that is being investigated clinically (Kim et al, 2017), to assist in distinguishing tumor organoids from normal tissue organoids. Lastly, given the importance of radiation as a treatment modality for HPV+ HNSCC, we have also optimized a chemoradiation pipeline to test possible combinatorial effects. Taken together, our results demonstrate that 3D organoids of HNSCC generated from patient samples can successfully preserve native tissue characteristics and can be used for high throughput drug screening and radiation de-escalation testing, affording critical insight into the management of HNSCC in patients. Citation Format: Krishna K. Bommakanti, Luda Lin, Yazeed Alhiyari, Andreanne Sannajust, Brandon Mo, Lauran Evans, Peyton Tebon, Maie St. John, Alice Soragni. Development of oropharyngeal tumor organoids for disease modeling and high throughput drug screening [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B013.

Binocular Positioning Method Based on Dynamic Optical Center Imaging Model

Binocular Positioning Method Based on Dynamic Optical Center Imaging Model

A SPECT/NIR Fluorescence Dual-Modality Imaging Agent Composed of Drugs and Hospital Available Isotope for Preoperative Sentinel Lymph Node Mapping and Intraoperative Biopsy.

Sentinel lymph node (SLN) mapping-guided biopsy is crucial for cancer staging and treatment. Optical/nuclide dual-modality imaging agents for mapping SLN are ideal for preoperative planning and intraoperative biopsy, which are enabled by penetration-depth unlimited nuclide imaging and dynamic real-time optical imaging, respectively. However, commonly reported dual-modality imaging agents are composed of novel but safety-unproven materials, making their quick clinical translation challenging. Herein, we report a novel nanoparticle composed of facile hospital-available drugs and isotope for single-photon emission computed tomography (SPECT)/near-infrared (NIR) fluorescence imaging to detect SLNs. Indocyanine green-human serum albumin (ICG-HSA) nanoparticles (NPs) were synthesized by ICG-induced HSA self-assembly and further 99mTc-labeling via a one-step, facile hospital-available method. After injecting 99mTc-ICG-HSA into the rats' forepaw pads, the rats' draining axillary lymph nodes were visualized by preoperative mapping with SPECT/CT and intraoperative biopsy with NIR fluorescence. The axillary lymph nodes of rats were identified by pathology and fluorescent staining after execution. Additionally, its toxicity testing and comparison with 99mTc-sulfur colloid imaging were also explored. The study reported a self-assembled 99mTc-ICG-HSA with a high radiochemical yield (85.6 ± 3.8%). Compared with conventional 99mTc-sulfur colloid, 99mTc-ICG-HSA NPs showed faster SLN identification, higher renal clearance, and lower hepatic retention. Furthermore, NIRF imaging allowed for the accurate visualization of the SLN and guided SLN biopsy intraoperatively. Notably, the 99mTc-ICG-HSA NPs were composed of hospital-available drugs and isotope, which are safe for acute toxicity evaluation by a certified institute. The proposed 99mTc-ICG-HSA NPs are safe and capable of noninvasive SLN identification and biopsy guidance with multi-modal imaging strategies and could be a promising tool for clinically assisted SLN biopsy.

Real-Time Intraoperative Detection of Margins for Oral Cavity Squamous Cell Carcinoma.

Obtaining negative surgical cancer margins is the strongest predictor for the long-term survival of oral cavity squamous cell carcinoma patients. To verify that the tumor has been completely removed, surgeons rely on pathologic evaluation of frozen sections to determine surgical margins, which can be time-consuming and subjective. Herein, we detail the real-time intraoperative use of dynamic optical contrast imaging (DOCI), a novel imaging modality that rapidly distinguishes head and neck cancer from healthy adjacent tissues based on fluorescence decay information from spectral bands in the UV-VIS range. Analysis of DOCI revealed microscopic characterization sufficient for tissue type identification consistent with histology (p < .05). DOCI delivers a clinically relevant tool that may better inform and drive precision surgery, directly impacting surgical outcomes and improving overall survival for our patients.

Advantages of Optical Coherence Tomography as a High Dynamic Range Imaging Modality in Subretinal Hyperreflective Material.

We aim to describe the utility of high dynamic range optical coherence tomography (OCT) imaging to study subretinal hyperreflective material (SHRM) in patients with age-related macular degeneration (AMD). We retrospectively reviewed clinical information including visual acuity (VA) and OCT images (Heidelberg Engineering Inc., Germany) of patients undergoing antiangiogenic treatment for neovascular AMD and showing SHRM at baseline. We increased contrast between strong signal structures (high dynamic range image) re-classifying SHRM as hyperreflective, isoreflective and hyporeflective. We evaluated the patients at baseline, 3, 6 and 12-months follow-up. Forty-four eyes were classified as 15 hyperreflective (34.1%); 21 as isoreflective (47.7%); and 8 as hyporeflective (18.2%). During follow-up, hyporeflective SHRM disappeared in all cases, isoreflective SHRM faded in 16 cases (76,2%); hyperreflective SHRM remained in all cases. Hyporreflective SHRM showed a greater VA improvement than hyperreflective SHRM group (p=0,033). After 12-month follow-up only the hyporeflective and isoreflective groups significantly reduced the presence of fluid in 37,5% (p=0,250) and 46,62% (p=0,006) of the patients respectively; and outer retinal layers were disrupted more frequently in the presence of hyperreflective SHRM (ellipsoid zone, p=0,16; external limiting membrane, p=0,007). Contrast-enhanced OCT images enabled us to classify SHRM according to its reflectivity showing groups with different disappearance rates, visual acuity improvement and outer retinal layer disruption. This easy to access tool may be helpful as a prognostic factor in neovascular AMD cases.

Label-free, real-time detection of perineural invasion and cancer margins in a murine model of head and neck cancer surgery

Surgical management of head and neck cancer requires a careful balance between complete resection of malignancy and preservation of function. Surgeons must also determine whether to resect important cranial nerves that harbor perineural invasion (PNI), as sacrificing nerves can result in significant morbidity including facial paralysis. Our group has previously reported that Dynamic Optical Contrast Imaging (DOCI), a novel non-invasive imaging system, can determine margins between malignant and healthy tissues. Herein, we use an in vivo murine model to demonstrate that DOCI can accurately identify cancer margins and perineural invasion, concordant with companion histology. Eight C3H/HeJ male mice were injected subcutaneously into the bilateral flanks with SCCVIISF, a murine head and neck cancer cell line. DOCI imaging was performed prior to resection to determine margins. Both tumor and margins were sent for histologic sectioning. After validating that DOCI can delineate HNSCC margins, we investigated whether DOCI can identify PNI. In six C3H/HeJ male mice, the left sciatic nerve was injected with PBS and the right with SCCVIISF. After DOCI imaging, the sciatic nerves were harvested for histologic analysis. All DOCI images were acquired intraoperatively and in real-time (10 s per channel), with an operatively relevant wide field of view. DOCI values distinguishing cancer from adjacent healthy tissue types were statistically significant (P < 0.05). DOCI imaging was also able to detect perineural invasion with 100% accuracy compared to control (P < 0.05). DOCI allows for intraoperative, real-time visualization of malignant and healthy tissue margins and perineural invasion to help guide tumor resection.

Deep UV Led Dynamic Optical Imaging and Fluorescence Spectroscopy of the Protein Corona in a Plasmonic Solution and the Effect of Near-Infrared Laser Heating

Deep UV Led Dynamic Optical Imaging and Fluorescence Spectroscopy of the Protein Corona in a Plasmonic Solution and the Effect of Near-Infrared Laser Heating

Dynamic Tomographic Phase Microscopy by Double Six-Pack Holography.

Three-dimensional (3D) optical imaging of rapidly moving biological cells is difficult to achieve as such samples cannot be scanned over time. Here, we present a dynamic scan-free optical tomography approach for stain-free 3D imaging of biological cells using our new double six-pack tomography technique, whereby 12 off-axis holograms are captured in a single camera exposure without sacrificing resolution or field of view. The proposed system illuminates the sample from 12 angles simultaneously, and 3D refractive index (RI) tomograms are reconstructed from each recorded video frame of the dynamic sample. The technique is verified experimentally by recording flowing silica beads, 3 μm in diameter, with the resulting tomogram RI accuracy being 98.5%. A live swimming sperm cell is also imaged, and dynamic 3D imaging results for both beads and sperm cell are presented. The proposed technique represents a 12-fold increase in dynamic holographic data for tomography.

Ex vivo hypercellular parathyroid gland differentiation using dynamic optical contrast imaging (DOCI).

Primary hyperparathyroidism, often caused by a single adenoma (80-85%) or four-gland hyperplasia (10-15%), can lead to elevated parathyroid hormone (PTH) levels and resultant hypercalcemia. Surgical excision of offending lesions is the standard of care, as the removal of pathologic adenomas reduces PTH and calcium values to baseline. The small size, variable location, and indistinct external features of parathyroid glands can make their identification quite challenging intraoperatively. Our group has developed the dynamic optical contrast imaging (DOCI) technique, a novel realization of dynamic temporally dependent measurements of tissue autofluorescence. In this study, we evaluated the efficacy of using the DOCI technique and normalized steady-state fluorescence intensity data for differentiating types of human parathyroid and thyroid tissues. We demonstrate that the DOCI technique has the capability to distinguish normal parathyroid tissue from diseased parathyroid glands as well as from adjacent healthy thyroid and adipose tissue across 8 different spectral channels between 405nm-600nm (p<0.05). Patient tissue DOCI data was further analyzed with a logistic regression classifier trained across the 8 spectral channels. After computer training, the computer-aided identification was able to accurately locate hypercellular parathyroid tissue with 100% sensitivity and 98.8% specificity within the captured DOCI image.

Neoadjuvant approach in patients with early breast cancer: patient assessment, staging, and planning

Neoadjuvant treatment (NAT) has become an option in early stage (stage I-II) breast cancer (EBC). New advances in systemic and targeted therapies have increased rates of pathologic complete response increasing the number of patients undergoing NAT. Clear benefits of NAT are downstaging the tumor and the axillary nodes to de-escalate surgery and to evaluate response to treatment. Selection of patients for NAT in EBC rely in several factors that are related to patient characteristics (i.e, age and comorbidities), to tumor histology, to stage at diagnosis and to the potential changes in surgical or adjuvant treatments when NAT is administered.Imaging and histologic confirmation is performed to assess extent of disease y to confirm diagnosis. Besides mammogram and ultrasound, functional breast imaging MRI has been incorporated to better predict treatment response and residual disease. Contrast enhanced mammogram (CEM), shear wave elastography (SWE), or Dynamic Optical Breast Imaging (DOBI) are emerging techniques under investigation for assessment of response to neoadjuvant therapy as well as for predicting response. Surgical plan should be delineated after NAT taking into account baseline characteristics, tumor response and patient desire.In the COVID era, we have witnessed also the increasing use of NAT in patients who may be directed to surgery, unable to have it performed as surgery has been reserved for emergency cases only.

Single shot real-time high-resolution imaging through dynamic turbid media based on deep learning

Low signal-to-noise ratio (SNR) measurement is conceivable the primary obstruction to real-time, high-resolution through dynamic turbid media optical imaging. To break this restriction, by individualizing and employing these low SNR measurement data, the spectrum estimation theory is procured a noise model for scatter imaging. The noise model proposed is exploited to synthesize data set training to settle the related problems of noise phase without knowing the experimental scenes. We verify the robustness of the resulting deep correlography method to noise, outdistance the capabilities of the existing Fourier-domain shower-curtain effect (FDSE) system in terms of spatial resolution and total acquisition time, in addition, the targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure.

A Tool to Locate Parathyroid Glands Using Dynamic Optical Contrast Imaging.

Identification of parathyroid glands and adjacent tissues intraoperatively can be quite challenging because of their small size, variable locations, and indistinct external features. The objective of this study is to test the efficacy of the dynamic optical contrast imaging (DOCI) technique as a tool in specifically differentiating parathyroid tissue and adjacent structures, facilitating efficient and reliable tissue differentiation. Prospective study. Both animal and human tissues were included in this study. Fresh specimens were imaged with DOCI and subsequently processed for hematoxylin and eosin (H&E) stain. The DOCI images were analyzed and compared to the H&E results as ground truth. In both animal and human experiments, significant DOCI contrast was observed between parathyroid glands and adjacent tissue of all types. Region of interest analysis revealed most distinct DOCI values for each tissue when using 494 and 572 nm-specific band pass filter for signal detection (P < .005 for porcine tissues, and P=.02 for human specimens). Linear discriminant classifier for tissue type prediction based on DOCI also matched the underlying histology. We demonstrate that the DOCI technique reliably facilitates specific parathyroid gland localization. The DOCI technique constitutes important groundwork for in vivo precision endocrine surgery. 4 Laryngoscope, 131:2391-2397, 2021.

PD-1 Blockade Aggravates Epstein-Barr Virus+ Post-Transplant Lymphoproliferative Disorder in Humanized Mice Resulting in Central Nervous System Involvement and CD4+ T Cell Dysregulations.

Post-transplant lymphoproliferative disorder (PTLD) is one of the most common malignancies after solid organ or allogeneic stem cell transplantation. Most PTLD cases are B cell neoplasias carrying Epstein-Barr virus (EBV). A therapeutic approach is reduction of immunosuppression to allow T cells to develop and combat EBV. If this is not effective, approaches include immunotherapies such as monoclonal antibodies targeting CD20 and adoptive T cells. Immune checkpoint inhibition (ICI) to treat EBV+ PTLD was not established clinically due to the risks of organ rejection and graft-versus-host disease. Previously, blockade of the programmed death receptor (PD)-1 by a monoclonal antibody (mAb) during ex vivo infection of mononuclear cells with the EBV/M81+ strain showed lower xenografted lymphoma development in mice. Subsequently, fully humanized mice infected with the EBV/B95-8 strain and treated in vivo with a PD-1 blocking mAb showed aggravation of PTLD and lymphoma development. Here, we evaluated vis-a-vis in fully humanized mice after EBV/B95-8 or EBV/M81 infections the effects of a clinically used PD-1 blocker. Fifteen to 17 weeks after human CD34+ stem cell transplantation, Nod.Rag.Gamma mice were infected with two types of EBV laboratory strains expressing firefly luciferase. Dynamic optical imaging analyses showed systemic EBV infections and this triggered vigorous human CD8+ T cell expansion. Pembrolizumab administered from 2 to 5 weeks post-infections significantly aggravated EBV systemic spread and, for the M81 model, significantly increased the mortality of mice. ICI promoted Ki67+CD30+CD20+EBER+PD-L1+ PTLD with central nervous system (CNS) involvement, mirroring EBV+ CNS PTLD in humans. PD-1 blockade was associated with lower frequencies of circulating T cells in blood and with a profound collapse of CD4+ T cells in lymphatic tissues. Mice treated with pembrolizumab showed an escalation of exhausted T cells expressing TIM-3, and LAG-3 in tissues, higher levels of several human cytokines in plasma and high densities of FoxP3+ regulatory CD4+ and CD8+ T cells in the tumor microenvironment. We conclude that PD-1 blockade during acute EBV infections driving strong CD8+ T cell priming decompensates T cell development towards immunosuppression. Given the variety of preclinical models available, our models conferred a cautionary note indicating that PD-1 blockade aggravated the progression of EBV+ PTLD.

Machine learning-based automatic segmentation of region of interest in dynamic optical imaging

Compared with static optical imaging, dynamic optical imaging technology can obtain quantitative pharmacokinetics information, such as the probe metabolism curve, removal rate, and binding potential of the receptor. Accurate segmentation of the region of interest (ROI) is an important step in dynamic optical imaging. Generally, the ROI is manually labeled by researchers based on experience. This will lead to two unavoidable problems. First, manual segmentation of the ROI is very time consuming, especially when there are many sequential dynamic optical images. Second, manual segmentation cannot ensure accuracy when the optical signal gradually decays to a point at which it is difficult to distinguish by using the naked eyes. These problems will inevitably lead to inaccuracy of quantitative results of dynamic optical imaging. Here, we presented a machine learning-based automatic segmentation method to avoid these time-consuming and inaccuracy problems caused by manual segmentation. The K-means clustering algorithm and fuzzy c-means clustering algorithm were implemented to separate the ROI from the background of sequential dynamic optical images. Automatic selection of clustering results was completed by mathematical methods. The accuracy and feasibility of machine learning-based methods were verified by comparing their results with the manual segmentation results. The preliminary results demonstrated that the machine learning-based automatic segmentation has coherent performance with the manual one.

Blind Source Separation of Retinal Pulsatile Patterns in Optic Nerve Head Video-Recordings.

Dynamic optical imaging of retinal hemodynamics is a rapidly evolving technique in vision and eye-disease research. Video-recording, which may be readily accessible and affordable, captures several distinct functional phenomena such as the spontaneous venous pulsations (SVP) of central vein or local arterial blood supply etc. These phenomena display specific dynamic patterns that have been detected using manual or semi-automated methods. We propose a pioneering concept in retina video-imaging using blind source separation (BSS) serving as an automated localizer of distinct areas with temporally synchronized hemodynamics. The feasibility of BSS techniques (such as spatial principal component analysis and spatial independent component analysis) and K-means based post-processing method were successfully tested on the monocular and binocular video-ophthalmoscopic (VO) recordings of optic nerve head (ONH) in healthy subjects. BSSs automatically detected three spatially distinct reproducible areas, i.e. SVP, optic cup pulsations (OCP) that included areas of larger vessels in the nasal part of ONH, and "other" pulsations (OP). The K-means post-processing reduced a spike noise from the patterns' dynamics while high linear dependence between the non-filtered and post-processed signals was preserved. Although the dynamics of all patterns were heart rate related, the morphology analysis demonstrated significant phase shifts between SVP and OCP, and between SVP and OP. In addition, we detected low frequency oscillations that may represent respiratory-induced effects in time-courses of the VO recordings.

Abstract A01: Clinical detection of melanoma via endogenous fluorophore lifetime imaging

Abstract Objective: The 5-year survival for cutaneous malignant head and neck melanoma is very poor (17%). As proof of principle, we detect variations in endogenous fluorophore lifetimes by illuminating tissue with pulsed ultraviolet (UV) light. We have previously shown that dynamic optical contrast imaging is capable of delineating tumor margins. Our aim herein was to acquire clinical and intraoperative multispectral images that permit selection of regions of interest (ROI) to reliably and noninvasively perform a rapid optical biopsy in the patient. Methods: Patient cases of cutaneous melanoma were acquired with pulsed UV illumination utilizing a 6-diode in-series system. Emitted fluorescence was detected with a nanosecond gated CCD detector. The acquired signal is transformed into a relative difference value representing tissue fluorophore lifetime. Results: From (n=4) patient cases we acquired DOCI images both pre- and post-resection leading to a database of in vivo relative lifetime values that align to histology sections from corresponding locations via pathology. The spectral features of our images enabled classification of clinician-determined regions of interest in DOCI images in order to establish measures of internal and external validity of our novel device. Conclusion: Our device augments native tissue contrast via wide-field imaging to produce spatially resolved and pathology-specific maps of tissue. Our database of histology-matched real-time images is aimed to permit intraoperative guidance for tumor margin delineation in dermatologic surgery. Citation Format: Peter A. Pellionisz, Yong Hu, Jenny Kim, Warren Grundfest, Maie A. St. John. Clinical detection of melanoma via endogenous fluorophore lifetime imaging [abstract]. In: Proceedings of the AACR Special Conference on Melanoma: From Biology to Target; 2019 Jan 15-18; Houston, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(19 Suppl):Abstract nr A01.

Mechanical and hemodynamic responses of breast tissue under mammographic-like compression during functional dynamic optical imaging.

Studying tissue hemodynamics following breast compression has the potential to reveal new contrast mechanisms for evaluating breast cancer. However, how compression will be distributed and, consequently, how hemodynamics will be altered inside the compressed breast remain unclear. To explore the effect of compression, 12 healthy volunteers were studied by applying a step compression increase (4.5-53.4 N) using an optical imaging system capable of concurrently measuring pressure distribution and hemodynamic responses. Finite element analysis was used to predict the distribution of internal fluid pressure (IFP) in breast models. Comparisons between the measured pressure distribution and the reconstructed hemodynamic images for the healthy volunteers indicated significant (p < 0.05) negative correlations. The findings from a breast cancer patient showed that IFP distribution during compression strongly correlates with the observed differential hemodynamic images. We concluded that dynamic breast compression results in non-uniform internal pressure distribution throughout the breast that could potentially drive directed blood flow. The encouraging results obtained highlight the promise of developing dynamic optical imaging biomarkers for breast cancer by interpreting differential hemodynamic images of breast tissue during compression in the context of measured pressure distribution and predicted IFP.