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Abstract
Fluorescence lifetime (FLT) imaging has been shown to distinguish tumors from normal tissue with high accuracy. However, the practical utility of FLT imaging is hindered by slow acquisition speeds and depth-dependent inaccuracies. To address these challenges, we introduce FLT-SLAM, a novel algorithm that combines rapid FLT imaging with simultaneous localization and mapping (SLAM) for real-time 3D surface reconstruction and depth-corrected FLT estimation. Using a stereo laparoscope, our approach extracts real-time depth information to improve accuracy, while achieving acquisition speeds exceeding 5 Hz. FLT maps are overlaid onto large-scale 3D surface models generated by SLAM, improving visualization and spatial awareness. We validate FLT-SLAM through phantom and ex-vivo tissue measurements, and show that it reduces FLT estimation errors by nearly 20%, thereby demonstrating its potential to enhance real-time, depth-corrected FLT imaging for surgical applications.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2025/paper/2939_paper.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to the Code Repository
N/A
Link to the Dataset(s)
N/A
BibTex
@InProceedings{KriMur_RealTime_MICCAI2025,
author = { Krishnamoorthy, Murali and Zhou, Haoyin and Frazee, Katherine and Pal, Rahul and Jagadeesan, Jayender and Kumar, Anand T. N.},
title = { { Real-Time SLAM-Based Correction and 3D Visualization for Fluorescence Lifetime Imaging } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
year = {2025},
publisher = {Springer Nature Switzerland},
volume = {LNCS 15970},
month = {September},
page = {492 -- 501}
}
Reviews
Review #1
- Please describe the contribution of the paper
The paper proposes a method to integrate FLT imaging with a 3D reconstruction of the target organ obtained from stereo SLAM in the context of tumor identification. The proposed method achieves more accurate FLT maps than those obtained without the integration of SLAM.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
- The idea of integrating SLAM with FLT is novel and, as shown in the experimental validation, leads to significant improvements in speed, when compared to the standard approach, and in accuracy, when compared to the non-SLAM solution.
- Experiments both in phantom and ex-vivo tissue are performed
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
- In my opinion, the description of the method lacks clarity. In particular, it mentions the “calibrated transformation between the FLT camera and the laparoscope” but does not explain how it is obtained. Then, it is stated that “the SLAM algorithm aligns FLT images” but my understanding is that SLAM is performed without any information about FLT and the alignment is performed afterwards. Also, “the stereo laparoscope is co-registered to the field using fiducials”. How is this done? Which type of fiducials? Could this be performed in a real application scenario?
- The paper claims (even in the title) that it is real-time. However, the reported frame rate of the proposed method is 4.9FPS, which cannot be considered real-time
- Why wasn’t a quantitative evaluation similar to the one performed in the experiment of Fig. 2 done for the experiments corresponding to Figs 3 and 4?
- Stating that the proposed method yields 20% improvement in accuracy from a single experiment performed in phantom seems to be an overstatement.
- Please rate the clarity and organization of this paper
Satisfactory
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission does not provide sufficient information for reproducibility.
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2025/en/REVIEWER-GUIDELINES.html
Minor comments:
- The paper mentions the supplementary material but reviewers do not have access to it.
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(3) Weak Reject — could be rejected, dependent on rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
I am not fully confident in my assessment of this paper because I am not familiar with the technology or literature of FL. Nevertheless, from my analysis, I consider that the paper makes statements (regarding its speed and accuracy) that are not properly justified and the method’s description lacks clarity.
- Reviewer confidence
Somewhat confident (2)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
Accept
- [Post rebuttal] Please justify your final decision from above.
The authors addressed most of my concerns in the rebuttal, and considering that Reviewer 2 feels very positively about this submission, I am changing my recommendation to Accept.
Review #2
- Please describe the contribution of the paper
The authors propose a method to correct depth-dependent error in Fluorescence Lifetime Imaging in the context of laparoscopic surgery. FLT exhibits error depending on the variation in the distance between the camera and the source of flourescence. Knowledge of this distance would help reduce this error. The authors employ laparoscopic SLAM to this end. The model of the scene created in this process is then employed to provide a better visualization of the acquired data with respect to simple 2D imaging. The method is compatible with any commercially available stereo laparoscope, in contrast to other approaches attempting to make FLT imaging real-time.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
The method can make FLT imaging feasible, with significant potential impact on the outcome of cancer resection procedures. The proposed solution is compatible with commercial hardware and does not pose further barriers to its translation to clinical practice. The document is of high quality and the validation is thorough.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
None noted
- Please rate the clarity and organization of this paper
Good
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission does not mention open access to source code or data but provides a clear and detailed description of the algorithm to ensure reproducibility.
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2025/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(5) Accept — should be accepted, independent of rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
The potential impact, the elegance of the solution, the quality of the document
- Reviewer confidence
Confident but not absolutely certain (3)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
N/A
- [Post rebuttal] Please justify your final decision from above.
N/A
Author Feedback
We thank the reviewers for their thoughtful comments and address each point below:
- Method clarity and fiducial use: As described on Page 7, the fluorescence lifetime (FLT) image and the SLAM-based 3D model are registered using the fiducial-based registration that employs the P3P algorithm to compute the 6-DoF transformation. After constructing 3D models with the SLAM algorithm, we manually select visually identifiable points between the FLT image and the 3D SLAM model using the 3D Slicer software. The P3P algorithm then calculates the rigid transformation using the corresponding 2D points on FLT images and 3D points on the SLAM model. In the actual clinical scenario, the registration of the FLT and SLAM-based reconstruction could be done using natural landmarks using the same approach proposed in this paper with an intuitive user interface. While the current setup serves as a proof-of-concept, our eventual goal is to integrate the FLT system with a stereo laparoscope using a light guide. For this setup, a one-time only affine calibration would eliminate the need for intraoperative registration of the FLT and SLAM models.
- Claim of real-time performance: For image-guidance, 5 FPS is sufficient to provide feedback to the clinician to localize the tumor and provide accurate guidance on the tumor boundaries. While the SLAM algorithm itself runs in real time (>25 FPS), the current bottleneck in speed is the FLT acquisition, which operates at ~5 FPS. Although this may be sufficient for navigation during typical image-guided surgery applications, the update rate can be further improved to 10–15 FPS using a camera with higher quantum efficiency, gain, or laser power within ANSI safety limits, which are part of our ongoing efforts.
- Lack of quantitative data in Figs. 3 and 4: Figs. 1 and 2 present validation experiments, while Figs. 3 and 4 demonstrate applications of the algorithm; hence, we did not include quantitative evaluation for the latter. However, we appreciate the reviewer’s point and have performed additional analysis. In liver tissue, when the height was varied by ~3 cm, the measured FLT changed from the expected 1.01 ns to 1.34 ns. After correcting for this height difference using the stereo laparoscope, the estimated lifetime improved to 1.08 ns. Similar results were observed in kidney phantom experiments. We will include these results in the camera-ready version.
- 20% improvement from a single experiment: We agree that the 20% improvement is based on a single phantom experiment; however, we note that simulations and theoretical analysis also support the observed improvement. We have shown that at a depth of ~40 mm (as shown in Fig. 2), light transport introduces a delay of ~255 ps, significantly affecting FLT estimates. Incorporating stereo-derived depth corrects this error, leading to improved accuracy in the FLT measurement.
Meta-Review
Meta-review #1
- Your recommendation
Invite for Rebuttal
- If your recommendation is “Provisional Reject”, then summarize the factors that went into this decision. In case you deviate from the reviewers’ recommendations, explain in detail the reasons why. You do not need to provide a justification for a recommendation of “Provisional Accept” or “Invite for Rebuttal”.
This manuscript received 2 reviews; a third reviewer accepted the review invitation but did not supply a review in time. Based on the 2 received reviews and my personal reading, the decision of ‘Invite for Rebuttal’ was made.
Indeed this work is of great interest to the MICCAI community. However, and perhaps due to the page-limit of the MICCAI submission, many details such as the co-calibration between the FLT and stereo-cameras were left out. In the rebuttal, authors are asked to kindly provide these missing details to satisfy these queries from the reviewers.
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Accept
- Please justify your recommendation. You may optionally write justifications for ‘accepts’, but are expected to write a justification for ‘rejects’
N/A
Meta-review #2
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Accept
- Please justify your recommendation. You may optionally write justifications for ‘accepts’, but are expected to write a justification for ‘rejects’
Both reviewers vote for paper acceptance.
Meta-review #3
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Reject
- Please justify your recommendation. You may optionally write justifications for ‘accepts’, but are expected to write a justification for ‘rejects’
N/A