Dr. Alice C. Wei Profile

Dr. Alice C. Wei

at Memorial Sloan Kettering Cancer Ctr

SPIE Involvement:

Author

Publications (3)

SPIE Journal Paper | 4 March 2024

Comparison study of intraoperative surface acquisition methods on registration accuracy for soft-tissue surgical navigation

Bowen Xiang, Jon Heiselman, Winona Richey, Michael D’Angelica, Alice Wei, T. Peter Kingham, Frankangel Servin, Kyvia Pereira, Sunil Geevarghese, William Jarnagin, Michael Miga

JMI, Vol. 11, Issue 02, 025001, (March 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.2.025001

KEYWORDS: Liver, Natural surfaces, Deformation, Image registration, Surgery, Data acquisition, Data modeling, Rigid registration, Optical tracking, Image segmentation

Read Abstract +

PurposeTo study the difference between rigid registration and nonrigid registration using two forms of digitization (contact and noncontact) in human in vivo liver surgery.ApproachA Conoprobe device attachment and sterilization process was developed to enable prospective noncontact intraoperative acquisition of organ surface data in the operating room (OR). The noncontact Conoprobe digitization method was compared against stylus-based acquisition in the context of image-to-physical registration for image-guided surgical navigation. Data from n=10 patients undergoing liver resection were analyzed under an Institutional Review Board-approved study at Memorial Sloan Kettering Cancer Center. Organ surface coverage of each surface acquisition method was compared. Registration accuracies resulting from the acquisition techniques were compared for (1) rigid registration method (RRM), (2) model-based nonrigid registration method (NRM) using surface data only, and (3) NRM with one subsurface feature (vena cava) from tracked intraoperative ultrasound (NRM-VC). Novel vessel centerline and tumor targets were segmented and compared to their registered preoperative counterparts for accuracy validation.ResultsSurface data coverage collected by stylus and Conoprobe were 24.6%±6.4% and 19.6%±5.0%, respectively. The average difference between stylus data and Conoprobe data using NRM was −1.05 mm and using NRM-VC was −1.42 mm, indicating the registrations to Conoprobe data performed worse than to stylus data with both NRM approaches. However, using the stylus and Conoprobe acquisition methods led to significant improvement of NRM-VC over RRM by average differences of 4.48 and 3.66 mm, respectively.ConclusionThe first use of a sterile-field amenable Conoprobe surface acquisition strategy in the OR is reported for open liver surgery. Under clinical conditions, the nonrigid registration significantly outperformed standard-of-care rigid registration, and acquisition by contact-based stylus and noncontact-based Conoprobe produced similar registration results. The accuracy benefits of noncontact surface acquisition with a Conoprobe are likely obscured by inferior data coverage and intrinsic noise within acquisition systems.

SPIE Journal Paper | 2 June 2023

Registration-based biomarkers for neoadjuvant treatment response of pancreatic cancer via longitudinal image registration

Jon Heiselman, Brett Ecker, Liana Langdon-Embry, Eileen O’Reilly, Michael Miga, William R. Jarnagin, Richard K. Do, Natally Horvat, Alice Wei, Jayasree Chakraborty

JMI, Vol. 10, Issue 03, 036002, (June 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.3.036002

KEYWORDS: Tumors, Image registration, Pancreas, Image segmentation, Deformation, Radiography, Chemotherapy, Resection, Error analysis, Pancreatic cancer

Read Abstract +

PurposePancreatic ductal adenocarcinoma (PDAC) frequently presents as hypo- or iso-dense masses with poor contrast delineation from surrounding parenchyma, which decreases reproducibility of manual dimensional measurements obtained during conventional radiographic assessment of treatment response. Longitudinal registration between pre- and post-treatment images may produce imaging biomarkers that more reliably quantify treatment response across serial imaging.ApproachThirty patients who prospectively underwent a neoadjuvant chemotherapy regimen as part of a clinical trial were retrospectively analyzed in this study. Two image registration methods were applied to quantitatively assess longitudinal changes in tumor volume and tumor burden across the neoadjuvant treatment interval. Longitudinal registration errors of the pancreas were characterized, and registration-based treatment response measures were correlated to overall survival (OS) and recurrence-free survival (RFS) outcomes over 5-year follow-up. Corresponding biomarker assessments via manual tumor segmentation, the standardized response evaluation criteria in solid tumors (RECIST), and pathological examination of post-resection tissue samples were analyzed as clinical comparators.ResultsAverage target registration errors were 2.56 ± 2.45 mm for a biomechanical image registration algorithm and 4.15 ± 3.63 mm for a diffeomorphic intensity-based algorithm, corresponding to 1–2 times voxel resolution. Cox proportional hazards analysis showed that registration-derived changes in tumor burden were significant predictors of OS and RFS, while none of the alternative comparators, including manual tumor segmentation, RECIST, or pathological variables were associated with consequential hazard ratios. Additional ROC analysis at 1-, 2-, 3-, and 5-year follow-up revealed that registration-derived changes in tumor burden between pre- and post-treatment imaging were better long-term predictors for OS and RFS than the clinical comparators.ConclusionsVolumetric changes measured by longitudinal deformable image registration may yield imaging biomarkers to discriminate neoadjuvant treatment response in ill-defined tumors characteristic of PDAC. Registration-based biomarkers may help to overcome visual limits of radiographic evaluation to improve clinical outcome prediction and inform treatment selection.

Proceedings Article | 4 April 2022 Presentation + Paper

CT radiomics to predict early hepatic recurrence after resection for intrahepatic cholangiocarcinoma

Jayasree Chakraborty, Joshua Jolissaint, Tiegong Wang, Kevin Soares, Mithat Gonen, Linda Pak, Thomas Boerner, Richard K. Do, Vinod Balachandran, Michael D'Angelica, Jeffrey Drebin, T. Peter Kingham, Alice Wei, William Jarnagin

Proceedings Volume 12033, 120333C (2022) https://doi.org/10.1117/12.2612889

KEYWORDS: Tumors, Surgery, Liver, Computed tomography, Liver cancer, Feature extraction, Cancer, Oncology

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years