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Vivek Gupta

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5 papers

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YNICL Journal 2026 Journal Article

Enhancing 7T MRI for deep brain stimulation with deep-learning based image reconstruction and dynamic parallel transmission

Justyna O. Ekert
Vishal Patel
Xiangzhi Zhou
Shengzhen Tao
Patrick Liebig
Jürgen Herrler
Thomas Yu
Dominik Nickel

OBJECTIVE: Precise targeting of subcortical structures is crucial for deep brain stimulation (DBS). Although 7T MRI provides superior resolution and contrast, its clinical adoption remains limited by B1+ transmit inhomogeneity, prolonged scan times, and motion sensitivity. This study applied deep learning (DL)-based image reconstruction and dynamic parallel transmission (pTx) to optimize DBS protocols and improve image quality. METHODS: Thirteen patients scanned using a conventional 7T DBS protocol were compared to 13 imaged after implementing DL reconstruction and dynamic pTx. Two readers scored image quality, motion artifact, and target conspicuity on 5-point Likert scales. Ordinal logistic regression was used to calculate odds ratios (OR) for improvements with the enhanced protocol, adjusted for multiple comparisons. RESULTS: Enhanced MP2RAGE reduced voxel volume by 65.8% and scan time by 32.9%, with improved image quality (OR = 4.4;p = 0.003), target conspicuity (OR = 3.4;p = 0.011), and reduced motion artifacts (OR = 3.8;p = 0.006). Fast gray matter acquisition T1 inversion recovery (FGATIR) scan time decreased by 45.2% with improved target delineation of both globus pallidus interna (OR = 22.9;p < 0.001) and dentato-rubro-thalamic tract (OR = 8.8;p < 0.001). T2-weighted sampling perfection with application-optimized contrasts using different flip angle evolutions (SPACE) improved subthalamic nucleus (STN) delineation (OR = 25.3;p < 0.001). Susceptibility-weighted imaging (SWI) improved image quality (OR = 17.4;p < 0.001), STN delineation (OR = 16.9;p < 0.001), and reduced scan time by 42.6%. Enhanced 3D spoiled gradient recall echo improved image quality (OR = 17.4;p < 0.001) and vessel visualization (OR = 26.1;p < 0.001) with reduced motion artifact (OR = 8.8;p < 0.001). Scan time decreased from 4:33 to 1:35, reducing protocol duration from 42:16 to 26:40 (36.9%). CONCLUSIONS: DL reconstruction and dynamic pTx improved image quality, target definition, and motion robustness while shortening 7T DBS protocol time.

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AAAI Conference 2025 Conference Paper

Advancements in AI for Reasoning with Complex Data

Vivek Gupta

Artificial intelligence has made remarkable progress in reasoning over complex, structured, multimodal, and multilingual data, addressing critical challenges in domains such as finance and healthcare. This abstract underscores key advancements in tabular reasoning, temporal analysis, and structured multimodal reasoning. Key contributions include the development of TempTabQA, a benchmark for temporal question answering, along with novel methods for enhancing temporal reasoning in large language models (LLMs). Additionally, a framework for evaluating mathematical reasoning in financial documents has been introduced, establishing robust techniques for interpreting time-sensitive and quantitative data. Building on these foundations, we have developed hybrid SQL-text adaptive reasoning models (H-STAR) and knowledge-aware reasoning techniques for semi-structured tables (MMTabQA), enabling precise and efficient handling of complex queries. In the vision-language domain, our contributions include advancements in spatial reasoning for geographic data (MAPWise), methods to improve robustness in chart interpretation (FlowVQA), and evaluations of LLMs’ ability to understand visual data, such as charts. Furthermore, we have addressed challenges in multilingual and cross-modal robustness through innovations such as multilingual table synchronization (InfoSync), concurrent robustness evaluations across languages and modalities, and numerical reasoning in tabular data. Our work aims to enhance reasoning on dynamically evolving data using hybrid LLM-SQL queries, symbolic query generation, and multi-table retrieval techniques. We also plan to tackle challenges in interpreting hierarchical table structures, analyzing multiple complex chart types, and exploring diverse map types, while advancing real-world multimodal data analysis. Additionally, we plan to improve table generation in both closed/open-book scenarios and refine evaluation frameworks for structured tasks. These advancements demonstrate the potential of AI in tackling complex, multimodal data and delivering impactful real-world solutions.

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YNICL Journal 2020 Journal Article

Improved detection of focal cortical dysplasia using a novel 3D imaging sequence: Edge-Enhancing Gradient Echo (3D-EDGE) MRI

Erik H. Middlebrooks
Chen Lin
Erin Westerhold
Lela Okromelidze
Prasanna Vibhute
Sanjeet S. Grewal
Vivek Gupta

Epilepsy is a common neurological disorder with focal cortical dysplasia (FCD) being one of the most common lesional causes. Detection of FCD by MRI is a major determinant of surgical outcome. Evolution of MRI sequences and hardware has greatly increased the detection rate of FCD, but these gains have largely been related to the more visible Type IIb FCD, with Type I and IIa remaining elusive. While most sequence improvements have relied on increasing contrast between gray and white matter, we propose a novel imaging approach, 3D Edge-Enhancing Gradient Echo (3D-EDGE), to directly image the gray-white boundary. By acquiring images at an inversion time where gray and white matter have equal signal but opposite phases, voxels with a mixture of gray and white matter (e.g., at the gray-white boundary) will have cancellation of longitudinal magnetization producing a thin area of signal void at the normal boundary. By creating greater sensitivity for minor changes in T1 relaxation, microarchitectural abnormalities present in FCD produce greater contrast than on other common MRI sequences. 3D-EDGE had a significantly greater contrast ratio between lesion and white matter for FCD compared to MP2RAGE (98% vs 17%; p = 0.0006) and FLAIR (98% vs 19%; p = 0.0006), which highlights its potential to improve outcomes in epilepsy. We present a discussion of the framework for 3D-EDGE, optimization strategies, and analysis of a series of FCDs to highlight the benefit of 3D-EDGE in FCD detection compared to commonly used sequences in epilepsy.

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AAAI Conference 2020 Conference Paper

P-SIF: Document Embeddings Using Partition Averaging

Vivek Gupta
Ankit Saw
Pegah Nokhiz
Praneeth Netrapalli
Piyush Rai
Partha Talukdar

Simple weighted averaging of word vectors often yields effective representations for sentences which outperform sophisticated seq2seq neural models in many tasks. While it is desirable to use the same method to represent documents as well, unfortunately, the effectiveness is lost when representing long documents involving multiple sentences. One of the key reasons is that a longer document is likely to contain words from many different topics; hence, creating a single vector while ignoring all the topical structure is unlikely to yield an effective document representation. This problem is less acute in single sentences and other short text fragments where the presence of a single topic is most likely. To alleviate this problem, we present P-SIF, a partitioned word averaging model to represent long documents. P-SIF retains the simplicity of simple weighted word averaging while taking a document’s topical structure into account. In particular, P- SIF learns topic-speciﬁc vectors from a document and ﬁnally concatenates them all to represent the overall document. We provide theoretical justiﬁcations on the correctness of P-SIF. Through a comprehensive set of experiments, we demonstrate P-SIF’s effectiveness compared to simple weighted averaging and many other baselines.

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AAAI Conference 2019 Conference Paper

Distributional Semantics Meets Multi-Label Learning

Vivek Gupta
Rahul Wadbude
Nagarajan Natarajan
Harish Karnick
Prateek Jain
Piyush Rai

We present a label embedding based approach to large-scale multi-label learning, drawing inspiration from ideas rooted in distributional semantics, specifically the Skip Gram Negative Sampling (SGNS) approach, widely used to learn word embeddings. Besides leading to a highly scalable model for multi-label learning, our approach highlights interesting connections between label embedding methods commonly used for multi-label learning and paragraph embedding methods commonly used for learning representations of text data. The framework easily extends to incorporating auxiliary information such as label-label correlations; this is crucial especially when many training instances are only partially annotated. To facilitate end-to-end learning, we develop a joint learning algorithm that can learn the embeddings as well as a regression model that predicts these embeddings for the new input to be annotated, via efficient gradient based methods. We demonstrate the effectiveness of our approach through an extensive set of experiments on a variety of benchmark datasets, and show that the proposed models perform favorably as compared to state-of-the-art methods for large-scale multi-label learning.

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