9 papers
An energy optimization method based on mixed-integer model and variational quantum computing algorithm for faster IMPT
Ya-Nan Zhu, Nimita Shinde, Bowen Lin +1
Intensity-modulated proton therapy (IMPT) offers superior dose conformity with reduced exposure to surrounding healthy tissues compared to conventional photon therapy. Improving IM…
A quantum computing approach to beam angle optimization
Nimita Shinde, Ya-Nan Zhu, Haozheng Shen +1
Background: Beam angle optimization (BAO) is a critical component of radiation therapy (RT) treatment planning, where small changes in beam configuration can significantly impact t…
A mixed integer programming approach to minibeam aperture optimization for multi-collimator proton minibeam radiotherapy
Nimita Shinde, Weijie Zhang, Yuting Lin +1
Background: Multi-collimator proton minibeam radiation therapy (MC-pMBRT) has recently emerged as a versatile technique for dose shaping, enabling peak-valley dose patterns in orga…
Optimizing normal tissue sparing via spatiotemporal optimization under equivalent tumor-radical efficacy
Nimita Shinde, Wangyao Li, Ronald C Chen +1
Objective: Spatiotemporal optimization in radiation therapy involves determining the optimal number of dose delivery fractions (temporal) and the optimal dose per fraction (spatial…
Minibeam-pLATTICE: A novel proton LATTICE modality using minibeams
Nimita Shinde, Weijie Zhang, Yuting Lin +1
Purpose: LATTICE, a form of spatially fractionated radiation therapy that delivers high-dose peaks and low-dose valleys within the target, has been clinically utilized for treating…
Multi-IMPT: a biologically equivalent approach to proton ARC therapy
Nimita Shinde, Yanan Zhu, Wei Wang +7
Objective: Proton spot-scanning arc therapy (ARC) is an emerging modality that can improve the high-dose conformity to targets compared with standard intensity-modulated proton the…
An Inexact Frank-Wolfe Algorithm for Composite Convex Optimization Involving a Self-Concordant Function
Nimita Shinde, Vishnu Narayanan, James Saunderson
In this paper, we consider Frank-Wolfe-based algorithms for composite convex optimization problems with objective involving a logarithmically-homogeneous, self-concordant functions…
Memory-Efficient Approximation Algorithms for Max-k-Cut and Correlation Clustering
Nimita Shinde, Vishnu Narayanan, James Saunderson
Max-k-Cut and correlation clustering are fundamental graph partitioning problems. For a graph with G=(V,E) with n vertices, the methods with the best approximation guarantees for M…
Memory-efficient structured convex optimization via extreme point sampling
Nimita Shinde, Vishnu Narayanan, James Saunderson
Memory is a key computational bottleneck when solving large-scale convex optimization problems such as semidefinite programs (SDPs). In this paper, we focus on the regime in which…