Dr. Parvathy Sudhir Pillai Profile

Dr. Parvathy Sudhir Pillai

at Univ of Texas MD Anderson Cancer Ctr

SPIE Involvement:

Author

Postdoctoral research fellow working with the Department of Radiology at Mayo Clinic. Ph.D. in Computer Science from the National University of Singapore.

Proceedings Article | 7 May 2024 Presentation + Paper

Patient-specific NEQ(f): automatic local and global determination of spatial-frequency-dependent SNR and task performance in clinical CT

Parvathy Pillai, Jeffrey Siewerdsen, Tatiana Rypinski, Anshuj Deva, Bhavin Soni, A. Kyle Jones, Moiz Ahmad

Proceedings Volume 12925, 1292517 (2024) https://doi.org/10.1117/12.3008465

KEYWORDS: Modulation transfer functions, Computed tomography, Image quality, Quality control, Imaging systems, Spatial resolution, Signal detection, Cancer, Abdomen, 3D image processing

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Proceedings Article | 29 March 2024 Presentation + Paper

Surgical process modeling of image-guided spine surgery

A. Deva, T. Rypinski, B. Soni, P. Pillai, L. Rhines, C. Tatsui, C. Alvarez-Breckenridge, R. North, J. Bird, J. Siewerdsen

Proceedings Volume 12928, 1292813 (2024) https://doi.org/10.1117/12.3008825

KEYWORDS: Scanning probe microscopy, Surgery, Fluoroscopy, Spine, Process modeling, Image registration, Computed tomography, Statistical modeling, Image guided intervention, Cancer

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Purpose. Spine surgery involves complex workflows and disparate levels of system integration that challenge the introduction of emerging technologies. This work develops a computational simulation framework based on statistical surgical process models (SPM) to quantitatively evaluate variations in the workflow and implementation of image guidance systems in terms of key outcome measures in spine surgery. Method. A statistical SPM was developed for spine surgery to describe the effects of various intraoperative technologies (viz., fluoroscopy, CT, image-to-world registration, and planning methods) and a range of procedural variables (e.g., surgeon skill, patient body mass index (BMI), target vertebrae, and fusion length) on key outcome measures, including cycle time, radiation dose and the quality of surgical product (geometric accuracy in pedicle screw placement). The model was parameterized by statistical distributions informed by clinical observation, expert feedback, literature review, and clinical data. Results. The results quantify the advantages of intraoperative CT and/or long-length scout radiography for reduced cycle time in vertebral localization – (4.8-7.2) min, compared to (5.8-12.4) min by fluoroscopy. The models further demonstrate the cycle time for imaging, registration, and planning in surgical guidance: the mean procedure cycle time for 11-level fusion was 540 min by fluoroscopy compared to 441 min for CT + navigation. Analysis of radiation dose quantified the effective dose to the patient (and operating room) between fluoroscopy and CT. The geometric accuracy of pedicle screw placement showed median error of 2.7 mm for fluoroscopy compared to 1.8 mm for CT+navigation and a corresponding reduction in frequency of pedicle breach for the latter. Conclusions. A statistical SPM provides a powerful framework for procedure simulation, evaluation of emerging technologies, and optimization of procedural workflow. Such modeling provides a quantitative basis to evidence the value of emerging technologies and identify optimal means of integration / implementation in clinical workflow.

Proceedings Article | 3 April 2023 Presentation + Paper

A training program to reduce reader search errors for liver metastasis detection in CT

Scott Hsieh, Akitoshi Inoue, Mariana Yalon, David Cook, Jeff Fidler, Hao Gong, Parvathy Sudhir Pillai, Andrew Vercnocke, Matthew Johnson, Shuai Leng, Lifeng Yu, David Holmes, Rickey Carter, Cynthia McCollough, Joel Fletcher

Proceedings Volume 12467, 1246704 (2023) https://doi.org/10.1117/12.2654007

KEYWORDS: Education and training, Liver, Cancer detection, Eye tracking, Eye, Error analysis, Calibration

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SPIE Journal Paper | 13 September 2022

Individualized and generalized models for predicting observer performance on liver metastasis detection using CT

Parvathy Sudhir Pillai, David Holmes, Rickey Carter, Akitoshi Inoue, David Cook, Ron Karwoski, Jeff Fidler, Joel Fletcher, Shuai Leng, Lifeng Yu, Cynthia McCollough, Scott Hsieh

JMI, Vol. 9, Issue 05, 055501, (September 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.5.055501

KEYWORDS: Performance modeling, Liver, Computed tomography, Feature extraction, Data modeling, 3D modeling, Radiology, RGB color model, Reconstruction algorithms, Convolutional neural networks

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Purpose: Radiologists exhibit wide inter-reader variability in diagnostic performance. This work aimed to compare different feature sets to predict if a radiologist could detect a specific liver metastasis in contrast-enhanced computed tomography (CT) images and to evaluate possible improvements in individualizing models to specific radiologists.Approach: Abdominal CT images from 102 patients, including 124 liver metastases in 51 patients were reconstructed at five different kernels/doses using projection domain noise insertion to yield 510 image sets. Ten abdominal radiologists marked suspected metastases in all image sets. Potentially salient features predicting metastasis detection were identified in three ways: (i) logistic regression based on human annotations (semantic), (ii) random forests based on radiologic features (radiomic), and (iii) inductive derivation using convolutional neural networks (CNN). For all three approaches, generalized models were trained using metastases that were detected by at least two radiologists. Conversely, individualized models were trained using each radiologist’s markings to predict reader-specific metastases detection.Results: In fivefold cross-validation, both individualized and generalized CNN models achieved higher area under the receiver operating characteristic curves (AUCs) than semantic and radiomic models in predicting reader-specific metastases detection ability (p < 0.001). The individualized CNN with an AUC of mean (SD) 0.85(0.04) outperformed the generalized one [AUC = 0.78 ( 0.06 ) , p = 0.004]. The individualized semantic [AUC = 0.70 ( 0.05 ) ] and radiomic models [AUC = 0.68 ( 0.06 ) ] outperformed the respective generalized versions [semantic AUC = 0.66 ( 0.03 ) , p = 0.009; radiomic AUC = 0.64 ( 0.06 ) , p = 0.03].Conclusions: Individualized models slightly outperformed generalized models for all three feature sets. Inductive CNNs were better at predicting metastases detection than semantic or radiomic features. Generalized models have implementation advantages when individualized data are unavailable.

Proceedings Article | 4 April 2022 Presentation + Paper

A 25-reader performance study for hepatic metastasis detection: lessons from unsupervised learning

Scott Hsieh, Akitoshi Inoue, Parvathy Sudhir Pillai, Hao Gong, David Holmes, David Cook, Shuai Leng, Lifeng Yu, Rickey Carter, Joel Fletcher, Cynthia McCollough

Proceedings Volume 12031, 1203116 (2022) https://doi.org/10.1117/12.2611543

KEYWORDS: Machine learning, Liver, Visualization, MATLAB, Abdomen, Temporal resolution, Spatial resolution, Scanners, Radiology

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Proceedings Article | 4 April 2022 Presentation + Paper

Individualized and generalized learner models for predicting missed hepatic metastases

Parvathy Sudhir Pillai, Scott Hsieh, David Holmes, Rickey Carter, Joel Fletcher, Cynthia McCollough

Proceedings Volume 12035, 120350C (2022) https://doi.org/10.1117/12.2612745

KEYWORDS: Computed tomography, 3D image processing, Feature extraction, Diagnostics, Liver, Convolutional neural networks

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