Interview Questions for In vivo imaging

My experience with in vivo imaging spans several modalities, each offering unique advantages and challenges. I’ve extensively worked with fluorescence imaging, leveraging its high sensitivity and diverse fluorophore options for tracking cellular processes and drug delivery. Bioluminescence imaging, with its inherent low background signal, has been invaluable in longitudinal studies assessing tumor growth and metastasis. In the realm of nuclear imaging, I’m proficient in both PET (Positron Emission Tomography) and SPECT (Single-Photon Emission Computed Tomography), utilizing them to study metabolic activity and receptor expression in preclinical models. Finally, I have significant experience with MRI (Magnetic Resonance Imaging), particularly for its high-resolution anatomical imaging capabilities, allowing for precise co-registration with other modalities.

• Fluorescence: Used extensively in studying gene expression, protein localization, and drug targeting.
• Bioluminescence: Ideal for monitoring long-term changes in living subjects due to its non-invasive nature.
• PET: Excellent for assessing metabolic processes and identifying cancer cells.
• SPECT: Offers good sensitivity with readily available radiotracers but lower resolution than PET.
• MRI: Provides high-resolution anatomical images, vital for anatomical context in functional studies.

My experience includes optimizing imaging protocols for each modality, considering factors such as probe selection, injection routes, and image acquisition parameters to achieve optimal signal-to-noise ratios and minimize artifacts.

Fluorescence-guided surgery (FGS) uses fluorescent probes to identify and remove cancerous tissues during surgical procedures. Imagine it like highlighting the tumor with a special glowing marker, making it easier to see and remove. The process involves administering a fluorescently labeled molecule that specifically targets cancer cells. During surgery, a specialized imaging system, often an intraoperative near-infrared (NIR) fluorescence imaging system, detects this signal, allowing the surgeon to differentiate between healthy and cancerous tissues in real-time. This provides a significant advantage over traditional methods by offering increased precision and reducing the need for extensive resection, minimizing damage to healthy tissue. This precision can lead to improved patient outcomes by achieving more complete tumor removal and reducing the need for additional treatments.

For example, a surgeon might use a fluorescent antibody that binds specifically to a cancer cell surface marker. The antibody is labeled with a fluorophore that emits light in the near-infrared spectrum, minimizing interference from tissue autofluorescence. The surgeon then uses a special camera that detects this near-infrared light to visualize the tumor boundaries during surgery, guiding the resection process.

Bioluminescence and fluorescence imaging both offer powerful ways to visualize biological processes in vivo, but they differ significantly in their mechanisms and applications. Bioluminescence involves light emission from a bioluminescent protein, such as luciferase, which catalyzes a reaction producing light without the need for external excitation light. In contrast, fluorescence imaging requires an external light source to excite a fluorophore, leading to the emission of light at a longer wavelength. Therefore, bioluminescence typically offers lower background signal compared to fluorescence imaging because it doesn’t require an excitation light source, thus reducing the chances of interference from other sources of light within the organism. Consequently, bioluminescence imaging provides better signal-to-noise ratio, especially for deeper tissue penetration. However, bioluminescence requires genetic modification of cells to express the bioluminescent protein, while fluorescence allows for the use of various dyes and probes without genetic manipulation.

• Bioluminescence Advantages: Higher signal-to-noise ratio, deeper tissue penetration, less background noise.
• Bioluminescence Disadvantages: Requires genetic modification, less versatility in probe selection.
• Fluorescence Advantages: Greater probe versatility, allows for multiplexing (using different fluorophores simultaneously), no need for genetic modification.
• Fluorescence Disadvantages: Lower signal-to-noise ratio compared to bioluminescence, can suffer from photobleaching and autofluorescence.

The choice between the two depends largely on the research question and the specific biological system under study. For example, long-term monitoring of tumor growth would benefit from the lower background noise of bioluminescence, whereas studying different cellular compartments simultaneously might require the versatility of fluorescence imaging.

Ensuring proper animal alignment and positioning is crucial for obtaining high-quality in vivo images and minimizing artifacts. We employ several strategies to achieve this. Anesthesia is essential to maintain immobility, and we carefully monitor vital signs throughout the procedure. Specialized animal holders are used, designed to gently restrain the animal in a consistent and reproducible manner, optimizing image acquisition and minimizing stress on the animals. For small animals like mice, we use specialized platforms and adaptors that are compatible with the imaging system. Precise positioning is often guided by real-time imaging, allowing for fine adjustments before image acquisition. Furthermore, high-resolution anatomical scans, like MRI, can be used to co-register functional images, ensuring accurate spatial mapping. These techniques minimize motion artifacts and improve data quality and reproducibility across experiments.

For example, when imaging a mouse brain, we might use a stereotaxic frame to fix the head in place, ensuring consistent positioning for longitudinal studies. For larger animals, custom-designed cradles and body supports may be used.

My experience encompasses a wide range of image acquisition and processing software. I’m proficient in using specialized software packages for each imaging modality, including those provided by manufacturers of imaging systems. For example, I have extensive experience with In-Vivo imaging systems such as IVIS Lumina and Spectrum, and different MRI reconstruction and analysis packages. These packages allow for image acquisition, quality control, and basic processing steps like background subtraction, region of interest (ROI) definition, and image registration. Beyond this, I have expertise in image analysis software like ImageJ/Fiji, which offers advanced capabilities for quantification, co-localization analysis, and 3D reconstruction. Finally, I have experience with programming languages like Python (with packages like scikit-image, OpenCV) for advanced image processing tasks requiring customized algorithms and statistical analysis.

The choice of software depends heavily on the modality and the specifics of the analysis. For example, simple quantification of fluorescence intensity might use ImageJ/Fiji, while complex 3D reconstructions and motion correction might require more specialized software and programming skills.

Quantifying data from in vivo imaging experiments involves extracting meaningful metrics from the images. This process depends heavily on the imaging modality and the specific research question. For example, in bioluminescence imaging, we often quantify the total photon flux (total light emitted) from the region of interest (ROI) encompassing the tumor. This provides a measure of the tumor’s size and metabolic activity. In fluorescence imaging, we often quantify the mean fluorescence intensity within the ROI, providing a measure of the concentration of the fluorophore and, by extension, the target molecule. For PET and SPECT, we typically quantify the uptake of the radiotracer using standardized uptake values (SUVs), which normalize for injected dose and body weight. The choice of quantitative method depends on the specific application and the imaging modality.

Statistical analysis is then employed to compare different experimental groups and draw meaningful conclusions. This often involves using statistical tests like t-tests, ANOVA, and regression analysis. Proper experimental design, including appropriate controls and sample size, is crucial to ensure the validity of the quantitative results.

In vivo imaging is prone to various artifacts that can compromise data quality and interpretation. Common artifacts include motion blur (caused by animal movement), light scattering (especially in deeper tissues), autofluorescence (unwanted fluorescence from the tissue itself), and variations in tissue absorption and penetration of excitation light. We employ several mitigation strategies: Careful anesthesia and animal immobilization minimize motion blur. For light scattering, choosing appropriate imaging wavelengths (e.g., near-infrared) improves penetration. Spectral unmixing techniques help to separate autofluorescence from the signal of interest. Careful calibration and standardization of imaging parameters, such as light source intensity and detector sensitivity, help to minimize inconsistencies in data acquisition. Furthermore, advanced image processing techniques like deconvolution and correction algorithms can help to partially correct for these artifacts.

For example, spectral unmixing can separate signals from multiple fluorophores by analyzing their emission spectra. This is particularly useful when imaging cells that express multiple fluorescent proteins or when the tissue autofluorescence overlaps with the signal of interest. Careful data preprocessing and quality control steps are essential to identify and potentially correct for these artifacts. It’s always important to critically assess the quality of the images and to understand the potential impact of artifacts on the quantitative results.

Designing an in vivo imaging experiment starts with a well-defined research question. Think of it like building a house – you need a solid blueprint. First, you clearly articulate your hypothesis and the specific biological processes you want to investigate. Then, you select the appropriate imaging modality based on the tissue depth, temporal resolution, and the type of contrast you need (e.g., fluorescent proteins, radiotracers). Next, you define your experimental groups (control and treatment) and sample size, ensuring adequate statistical power. Consider potential confounding factors and implement controls to mitigate them. For example, if studying a drug’s effect on tumor growth, you’d need a control group receiving a placebo and a treatment group receiving the drug, with enough animals in each to ensure statistically significant results. The experimental design should also include details about animal handling, image acquisition parameters (e.g., exposure time, scanning parameters), and the data analysis plan.

Let’s say our research question is: ‘Does drug X inhibit tumor growth in mice?’. Our experimental design would involve two groups of mice: one receiving drug X and one receiving a placebo. We would use bioluminescence imaging to monitor tumor growth over several weeks, acquiring images at regular intervals using standardized protocols. Our analysis plan would involve quantitative measurements of bioluminescence signal from defined regions of interest (ROIs) within each tumor, and statistical analysis to compare the two groups. This whole process needs to be meticulously documented for reproducibility.

Signal-to-noise ratio (SNR) is crucial in in vivo imaging. It’s simply the ratio of the signal (the information we want, like the fluorescence from a labeled protein) to the noise (unwanted variations in the signal, such as background fluorescence or electronic noise). A high SNR means the signal is strong and easily distinguishable from the background, resulting in clear, high-quality images. A low SNR makes it difficult to discern the true signal, leading to inaccurate results and uncertainty. Think of it like listening to a radio; a high SNR means you clearly hear your station, while a low SNR results in static and interference. We strive for a high SNR through various methods, including using appropriate imaging modalities, optimizing imaging parameters, and employing advanced noise reduction techniques during image processing.

For instance, using a sensitive camera with low dark current (electronic noise) will improve the SNR. Similarly, careful background subtraction and using appropriate filters during image acquisition significantly enhances the SNR. Quantitatively, SNR is often calculated as the ratio of the mean signal intensity to the standard deviation of the background noise. A higher number indicates a better SNR.

My experience with image analysis techniques is extensive. I routinely use region of interest (ROI) analysis to quantify signal intensity within specific areas of an image, for example, measuring the fluorescence intensity within a tumor or an organ. This is essential for quantifying changes in biological processes over time or comparing different experimental groups. Colocalization analysis is another vital technique I employ to determine if two different signals (e.g., from two different fluorescent proteins) are located in the same cellular compartments or structures. This technique helps to understand the interactions between different biological molecules. Furthermore, I’m proficient in using advanced image processing and analysis software (e.g., ImageJ, Imaris) to perform tasks like background subtraction, image registration, and 3D reconstruction.

For example, in a study involving dual-labeled cells, one labeled with a marker for cell nuclei and the other for a specific protein, colocalization analysis helps determine if the protein is localized to the nucleus. Using ImageJ, we can create masks around ROIs, measure intensity values, and apply colocalization algorithms to determine the degree of overlap between the two signals, quantifying the level of colocalization.

Reproducibility and reliability are paramount. We achieve this through rigorous standardization of all aspects of the experiment, from animal handling and imaging protocols to data analysis. This includes detailed documentation of all experimental procedures, including the exact settings used for each imaging modality. We utilize standardized image acquisition parameters, ensuring consistent illumination, exposure times, and other relevant settings. We also implement strict quality control measures during image acquisition and analysis, using automated processes where possible to minimize human error. Blind analysis, where the person analyzing the data is unaware of the experimental group assignments, helps to avoid bias. Finally, we use appropriate statistical methods to analyze the data and report the results clearly and transparently, including measures of variability and uncertainty.

For instance, we create detailed standard operating procedures (SOPs) for every step of the experiment. This ensures that the experiment can be replicated accurately by others. We perform multiple independent experiments to demonstrate consistency and generate statistically robust data. Utilizing appropriate positive and negative controls further strengthens the reliability of our results.

Ethical considerations are central to in vivo imaging experiments involving animals. We adhere strictly to the 3Rs principle: Replacement (using alternative methods whenever possible), Reduction (minimizing the number of animals used), and Refinement (minimizing pain, suffering, and distress). All our protocols are reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) to ensure ethical compliance. We prioritize proper animal housing, handling, and anesthesia, using humane endpoints to minimize suffering. Pain management strategies are implemented when necessary. Detailed records are maintained for every animal, including their health status and procedures performed. We must always balance the potential scientific benefit with the welfare of the animals, making responsible choices and carefully weighing the costs and benefits.

For example, we carefully evaluate the necessity of using animals in the study and explore alternatives before proceeding. When animals are used, we use appropriate analgesics and anesthetics and closely monitor their health throughout the experiment. We also establish clear humane endpoints to minimize any suffering.

My experience encompasses a range of animal models, including mice, rats, zebrafish, and Drosophila. The choice of animal model depends heavily on the research question and the specific biological process being studied. Mice and rats are common mammalian models due to their genetic tractability and the availability of numerous transgenic strains. Zebrafish are particularly useful for developmental biology studies because of their transparency and external development. Drosophila, with its well-characterized genetics, offers advantages for studying specific gene functions. The selection of the model considers factors like the ease of genetic manipulation, the availability of established protocols, and the cost and practicality of the model. The characteristics of each model necessitate specific adaptations in imaging protocols and data analysis strategies.

For example, using transgenic mice expressing fluorescent proteins allows us to visualize specific cellular processes in vivo. In zebrafish, the transparency allows for direct imaging of developing organs, whereas Drosophila’s simpler body plan simplifies imaging and data analysis.

Choosing the right in vivo imaging modality is crucial. The optimal modality depends on several factors, including the research question, the tissue depth being studied, the desired temporal and spatial resolution, and the type of contrast needed. For superficial structures, optical imaging techniques like fluorescence or bioluminescence imaging may suffice. For deeper tissues, techniques like MRI, PET, or SPECT offer better penetration depth. If high temporal resolution is needed to track rapid dynamic processes, techniques like fluorescence lifetime imaging microscopy (FLIM) or optical coherence tomography (OCT) may be preferred. If you need to assess metabolic activity, PET or SPECT could be the appropriate choice. A detailed understanding of the strengths and limitations of each modality is crucial to make an informed selection.

For instance, if we are studying the spread of cancer cells, bioluminescence imaging might be appropriate for tracking the growth of superficial tumors, but PET imaging would be better suited for visualizing the spread of metastases to distant organs. Similarly, if we want to study the dynamics of blood flow in real time, optical coherence tomography (OCT) could be used. Careful consideration of these factors ensures the chosen method provides the necessary information to answer the research question effectively.

Data visualization and reporting are crucial for effectively communicating in vivo imaging results. My approach involves a multi-step process. First, I select appropriate visualization techniques based on the data type and the intended audience. For quantitative data, I often use heatmaps, 3D renderings, and line graphs to highlight changes in signal intensity or distribution over time or across different treatment groups. For qualitative data, I might use maximum intensity projections (MIPs) or other image processing techniques to enhance features of interest.

Second, I utilize software like ImageJ, Imaris, or dedicated packages within programming languages like Python (with libraries like matplotlib, seaborn, or plotly) and MATLAB to generate publication-quality figures and generate reports. I ensure that all figures include clear scales, legends, and annotations to ensure clarity. Importantly, I always adhere to scientific principles in preparing figures and carefully avoiding any misrepresentation of the data. For example, I might use a combination of a 3D reconstruction of a brain region and a corresponding heatmap overlaid on a 2D slice to accurately represent the location of a signal. Finally, I use a standardized report structure including an introduction, materials and methods, results section with clear figures and captions, and discussion. This ensures consistency and facilitates easier understanding of the complex datasets generated in in vivo imaging.

Troubleshooting in vivo imaging experiments requires a systematic approach. I begin by carefully reviewing the experimental protocol and identifying potential sources of error. This includes checking equipment settings, verifying the quality of reagents, and ensuring proper animal handling. For example, if signal intensity is unexpectedly low, I would investigate potential issues such as improper probe placement, insufficient excitation light, or photobleaching. If there’s significant image noise, I might consider optimizing the imaging parameters (e.g., reducing exposure time, increasing gain), adjusting data acquisition methods, or using denoising algorithms during post-processing.

Next, I’ll conduct control experiments to isolate the problem. This could involve imaging known positive controls or comparing the results obtained with different settings or equipment. A methodical approach to troubleshooting is essential and sometimes includes consulting with colleagues or specialists, reviewing relevant publications, and analyzing error messages. It’s always important to properly document every step taken for troubleshooting so issues can be resolved efficiently and efficiently.

Image registration and fusion are essential for integrating data from multiple imaging modalities or aligning images acquired at different time points. I have extensive experience with both rigid and deformable registration techniques. Rigid registration is suitable for aligning images with minimal deformation, often using algorithms like mutual information or image correlation. Deformable registration handles larger deformations, often using algorithms based on elastic or spline models, and is critical for aligning images obtained from longitudinal studies or multimodal imaging, where tissue deformation occurs.

For instance, I’ve successfully used deformable registration to align functional MRI (fMRI) data with high-resolution anatomical MRI data, enabling precise localization of brain activity. Fusion techniques involve combining registered images to create a composite image containing information from all modalities. This approach is vital in translational research, where we integrate microscopic data obtained from ex vivo studies to understand macroscopic level observations from in vivo imaging. Software packages like ANTs, Elastix, and SPM are regularly used in my workflows for both registration and fusion procedures.

Each in vivo imaging modality has its own set of limitations. For example, optical imaging techniques like confocal and multiphoton microscopy offer high resolution but have limited tissue penetration depth. This means they are best suited for superficial tissues or small animal models. Conversely, Magnetic Resonance Imaging (MRI) provides excellent soft tissue contrast and deep tissue penetration but can be relatively slow and expensive. Positron Emission Tomography (PET) excels at quantifying metabolic activity but offers relatively lower spatial resolution compared to MRI.

Similarly, Single Photon Emission Computed Tomography (SPECT) is also used for functional imaging and offers a balance between cost and resolution, but suffers from lower sensitivity compared to PET. Understanding these limitations is crucial for selecting the most appropriate modality for a given research question. For example, if high spatial resolution is needed for imaging brain microvasculature, we would utilize multiphoton microscopy. If we want to assess the whole brain activity, fMRI or PET would be more suitable options, while SPECT would represent a more cost-effective alternative for clinical applications.

Validating the accuracy and precision of in vivo imaging data is critical for ensuring the reliability of research findings. Several strategies are employed. First, we compare results obtained with different imaging modalities whenever possible. Second, we incorporate rigorous quality control measures during data acquisition and processing and utilize phantom studies for quantifying instrumental performance and data stability. Phantoms mimic biological tissues and serve as a reliable reference point for accuracy checks.

For example, in PET imaging, standardized phantoms are used to calibrate the scanner and assess its resolution and sensitivity. In addition, we employ appropriate statistical methods to analyze the data and quantify uncertainties. This includes assessing the signal-to-noise ratio (SNR), determining the limits of detection (LOD), and performing appropriate statistical analyses, such as repeated measures ANOVA to account for within-subject variability. Finally, comparison with established gold standard methods, where possible, further validates the results. For example, the results from a novel fluorescent probe might be compared with results obtained from a standard histological analysis.

My experience with optical imaging systems encompasses both confocal and multiphoton microscopy. Confocal microscopy uses a pinhole to reject out-of-focus light, resulting in high-resolution images of thin sections. This is suitable for detailed examination of cellular structures. I have used confocal microscopy extensively to study cellular changes in response to drug treatments or disease states, for example observing morphological changes in cells following drug treatment.

Multiphoton microscopy uses longer wavelengths of light that penetrate deeper into tissue with less scattering and photobleaching than confocal microscopy and allows for imaging of deeper tissue structures. I’ve leveraged multiphoton microscopy for in vivo imaging of brain dynamics, capturing real-time changes in neuronal activity and microvasculature, for example. Both modalities require careful optimization of imaging parameters (e.g., laser power, scanning speed) and post-processing steps (e.g., image deconvolution, noise reduction) to achieve high-quality data. My workflow often involves using appropriate image analysis software (e.g., ImageJ, Imaris) to quantify features of interest within the microscopy images.

My experience with radioactive isotopes in PET and SPECT imaging includes both preclinical and clinical applications. This includes using radiotracers, which are radioactive molecules designed to target specific biological processes or structures. Working with radioactive materials requires strict adherence to safety protocols to minimize radiation exposure. This involves wearing appropriate personal protective equipment (PPE), working in designated radiation safety areas, and following proper waste disposal procedures. I have experience in handling various radioisotopes, such as ¹⁸F-FDG (fluorodeoxyglucose) commonly used for PET imaging of glucose metabolism, and ^99mTc-labeled agents, used frequently in SPECT.

In addition to radiation safety, data analysis in PET and SPECT involves reconstructing 3D images from the detected radiation counts. This often involves using specialized software packages, and careful attention to correction for attenuation, scatter, and other artifacts is crucial. For example, attenuation correction is essential to compensate for the attenuation of radiation as it passes through tissues. I have extensive experience interpreting the obtained images to detect and quantify the concentration of the radiotracers and correlating these values to functional and biological parameters. Quality assurance is vital and includes regular calibration and testing of the equipment to ensure accuracy and reliability of the obtained results.

Interpreting and reporting in vivo imaging results is a multi-step process requiring meticulous attention to detail and a deep understanding of the imaging modality used. It begins with rigorous quality control of the acquired data, checking for artifacts, motion blur, and other technical issues. Next, we perform quantitative analysis, often involving specialized software to extract meaningful measurements, such as tumor volume, bioluminescence intensity, or fluorescent signal. This quantitative data is then statistically analyzed to determine significant differences between experimental groups. Finally, the results are presented in a clear and concise manner, usually including representative images, graphs, and tables, along with a detailed description of the methodology and statistical analysis. For example, in a study assessing tumor growth using bioluminescence imaging, we might report the average bioluminescence intensity over time for each treatment group, along with statistical significance values (p-values) to demonstrate differences between groups. The report would also include representative images showcasing the bioluminescence signal in the animals over time. The clarity of the presentation is paramount to ensure that the findings are easily understood by a broad scientific audience.

My experience with regulatory requirements for in vivo imaging studies is extensive. I’m intimately familiar with the guidelines set forth by institutions like the NIH, FDA, and IACUC (Institutional Animal Care and Use Committee). This includes understanding and adhering to protocols related to animal welfare, experimental design, data management, and reporting. For instance, I have firsthand experience with preparing and submitting IACUC protocols detailing the justification for the study, the experimental procedures, the methods for minimizing animal distress, and the appropriate endpoints for humane euthanasia. Further, I understand the importance of maintaining detailed records, including animal identification, imaging parameters, and data analysis methods, to ensure compliance and reproducibility. Moreover, my work often involves navigating the complexities of GLP (Good Laboratory Practice) guidelines when conducting studies intended for regulatory submissions for drug development.

In vivo imaging allows us to study a wide range of biological processes in real-time within living organisms. My work has focused on several key areas. For example, I’ve extensively used bioluminescence imaging to monitor tumor growth and metastasis, assessing the efficacy of anticancer therapies. In these studies, we genetically engineer cells to express luciferase, an enzyme that produces light, which allows us to track their location and proliferation in vivo. Another area of focus is using fluorescence imaging to visualize blood vessel formation (angiogenesis) and inflammatory responses. We can utilize fluorescently labeled antibodies or molecular probes to visualize specific cells or molecules involved in these processes. Furthermore, I’ve employed techniques such as PET and SPECT imaging to study drug distribution and metabolism in preclinical models, which is critical in drug development. Essentially, these imaging techniques allow for non-invasive longitudinal monitoring of dynamic biological processes, providing invaluable insights that are difficult or impossible to obtain using traditional methods.

Collaboration is crucial in in vivo imaging projects. My approach involves establishing clear communication channels and shared responsibilities from the outset. I work closely with biologists to design appropriate experiments, ensuring the imaging techniques align with the biological questions being addressed. I also collaborate extensively with biostatisticians to ensure appropriate statistical analysis of the imaging data. With veterinary staff, I coordinate animal handling and welfare throughout the study. Finally, I work with engineers and physicists when necessary for instrument maintenance and optimization. Effective collaboration relies on regular meetings, transparent data sharing, and a commitment to constructive feedback. For instance, in a recent project involving the development of a novel imaging agent, I collaborated with chemists to optimize the agent’s properties, with biologists to validate its efficacy, and with biostatisticians to analyze the resulting imaging data, culminating in a robust and impactful publication.

My strengths lie in my comprehensive understanding of various in vivo imaging modalities, my proficiency in image analysis and quantitative data interpretation, and my experience in designing and executing complex in vivo studies. I am adept at problem-solving, troubleshooting technical issues, and adapting to unexpected challenges. A weakness, however, is the ever-evolving nature of this field. Keeping up-to-date with the latest technological advancements and analytical methods requires continuous learning and dedication. I actively combat this by attending conferences, reading relevant literature, and seeking mentorship opportunities from leading experts in the field.

In five years, I envision myself leading innovative research projects in in vivo imaging, potentially focusing on the development and application of novel imaging techniques for earlier disease detection and more personalized medicine. I also aim to mentor junior scientists and contribute to the advancement of the field through publications, collaborations, and teaching. My goal is to bridge the gap between basic research and clinical translation, ultimately leading to improvements in human health. I see myself as a sought-after expert, recognized for my contributions to both methodological advancements and the translation of imaging discoveries into practical applications. I aspire to lead or participate in large-scale, multi-institutional collaborations that tackle challenging questions in biomedical research.

During a study using multiphoton microscopy to visualize neuronal activity in a freely moving mouse, we encountered significant challenges with motion artifacts. The mouse’s movement caused blurry images, rendering quantitative analysis extremely difficult. We initially tried several strategies, including using more robust anesthesia protocols, which proved unsuccessful and compromised the physiological relevance of our data. Finally, we developed a custom-built motion correction algorithm using advanced image processing techniques, based on cross-correlation analysis of successive image frames. This algorithm significantly improved image quality, allowing us to accurately quantify neuronal activity. This experience highlighted the importance of adaptability and creative problem-solving in overcoming unforeseen obstacles in in vivo imaging experiments. It also reinforced the value of collaborating with engineers and computer scientists to develop innovative solutions to complex challenges. This experience led to a publication detailing the algorithm, contributing to the field’s methodological advancements.