Toward Vision Language Model-based Assessment of Clinical Quality and Usability of LGE-MR Images for Cardiac Ablation Planning

• URL: http://arxiv.org/abs/2608.21180v1
• Date: Fri, 21 Aug 2026 14:51:48 GMT
• Title: Toward Vision Language Model-based Assessment of Clinical Quality and Usability of LGE-MR Images for Cardiac Ablation Planning
• Abstract summary: We propose a two-stage vision language model (VLM) framework for clinically grounded image quality assessment of left atrial LGE-MRI.<n>In this work, we propose a two-stage vision language model (VLM) framework for clinically grounded image quality assessment of left atrial LGE-MRI.
• Score: 0.7394388288509157
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LGE cardiac MRI is widely used for left atrial fibrosis assessment and ablation planning in atrial fibrillation patients as knowledge of fibrotic tissue regions identified from LGE-MRI is critical for catheter ablation. Often, poor quality images used during ablation planning can cause mis-localization of ablation targets, directly impacting procedure safety and outcome. The decision of whether a scan meets the minimum quality threshold for ablation planning is currently made informally by the reviewing radiologist and is not captured by any automated system, yet it is arguably the most safety-critical output of the image quality assessment (IQA) process. However, variations in image quality caused by noise, motion artifacts, and poor boundary definition significantly compromise the reliability of downstream segmentation and clinical decision-making tasks. Manual quality assessment by expert radiologists is subjective and difficult to scale, while existing automated methods produce scalar scores without interpretable clinical reasoning. In this work, we propose a two-stage vision language model (VLM) framework for clinically grounded image quality assessment of left atrial LGE-MRI. In the first stage, a fine-tuned VLM generates structured radiology-style quality reports predicting five radiologist-defined criteria: Noise, Motion Artifact, LA Boundary Accuracy, PV Region Accuracy, and Under-segmentation Severity. In the second stage, a GPT-based reasoning module maps the predicted quality and reports to a structured quality scores and binary clinical usability decision for ablation planning. We curate a dataset of 60 annotated image slice-text pairs from 20 patients and benchmark four state-of-the-art VLM architectures. InternVL2 achieves the highest criterion-level accuracy (Avg ACC=0.65, PLCC=0.79), while DeepSeek achieves perfect clinical usability agreement (Acc=1.00, kappa=1.00).

Related papers

• Beyond Fluency: A Clinical Benchmark and Anomaly-Enhanced Baseline for Spine MRI Report Generation [11.89113336167412]
  We benchmark state-of-the-art Vision-Language Models (VLMs) on lumbar spine MRI with a focus on diagnostic accuracy.<n>We propose an architecture-agnostic framework that augments VLM inputs with spatially localized, disc-level anomaly heatmaps.
  arXiv  Detail & Related papers  (2026-08-07T11:21:33Z)
• ClinReadNet: A clinical reading-inspired network for low-dose abdominal CT image quality assessment [20.78704478280648]
  In abdominal CT imaging, developing a low-dose, no-reference image quality assessment model mimics doctors' reading habits for evaluating CT image quality.<n>This paper proposes a novel deep learning-based framework, ClinReadNet, whose design aligns with the clinical reading logic of radiologists.<n> Experiments conducted on the LDCTIQAG2023 dataset show that the proposed method achieves the current state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2026-06-09T03:34:11Z)
• Look-Closer-Then-Diagnose: Confidence-Aware Ultrasound VQA via Active Zooming [41.40761036883243]
  Vision-Language Models (VLMs) have significantly advanced medical visual question answering, yet their performance in ultrasound remains suboptimal.<n>We propose a framework specifically designed to consider the sonographer's cognitive workflow.<n>We first introduce a structured Zoom-then-Diagnose paradigm, which replicates the interactive search process to enable lesion-focused reasoning.
  arXiv  Detail & Related papers  (2026-05-20T19:06:34Z)
• Radiology Report Generation for Low-Quality X-Ray Images [47.455316078409]
  Vision-Language Models (VLMs) have significantly advanced automated Radiology Report Generation (RRG)<n>Existing methods implicitly assume high-quality inputs, overlooking the noise and artifacts prevalent in real-world clinical environments.<n>We propose a robust report generation framework explicitly designed for image quality variations.
  arXiv  Detail & Related papers  (2026-04-11T12:42:37Z)
• ODySSeI: An Open-Source End-to-End Framework for Automated Detection, Segmentation, and Severity Estimation of Lesions in Invasive Coronary Angiography Images [23.05175906366438]
  Invasive Coronary Angiography (ICA) is the clinical gold standard for the assessment of coronary artery disease.<n>In this work, we introduce ODySSeI: an Open-source framework for automated Detection, lesions, and Severity estimation in ICA images.<n> ODySSeI integrates deep learning-based lesion detection and lesion segmentation models trained using a novel Pyramidal Augmentation Scheme (PAS)<n>Our LSE technique achieves high accuracy, with predicted MLD values differing by only $pm$ 2-3 pixels from the corresponding ground truths.
  arXiv  Detail & Related papers  (2026-03-20T15:07:15Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Neural Discrete Representation Learning for Sparse-View CBCT Reconstruction: From Algorithm Design to Prospective Multicenter Clinical Evaluation [64.42236775544579]
  Cone beam computed tomography (CBCT)-guided puncture has become an established approach for diagnosing and treating thoracic tumours.<n>DeepPriorCBCT is a three-stage deep learning framework that achieves diagnostic-grade reconstruction using only one-sixth of the conventional radiation dose.
  arXiv  Detail & Related papers  (2025-11-30T12:45:02Z)
• LGE-Guided Cross-Modality Contrastive Learning for Gadolinium-Free Cardiomyopathy Screening in Cine CMR [51.11296719862485]
  We propose a Contrastive Learning and Cross-Modal alignment framework for gadolinium-free cardiomyopathy screening using cine CMR sequences.<n>By aligning the latent spaces of cine CMR and Late Gadolinium Enhancement (LGE) sequences, our model encodes fibrosis-specific pathology into cine CMR embeddings.
  arXiv  Detail & Related papers  (2025-08-23T07:21:23Z)
• A Clinically-Grounded Two-Stage Framework for Renal CT Report Generation [4.408787333571913]
  We propose a framework for automatic renal CT report generation.<n>In Stage 1, a multi-task learning model detects structured clinical features from each 2D image.<n>In Stage 2, a vision-language model generates free-text reports conditioned on the image and the detected features.
  arXiv  Detail & Related papers  (2025-06-30T07:45:02Z)
• Metrics that matter: Evaluating image quality metrics for medical image generation [48.85783422900129]
  This study comprehensively assesses commonly used no-reference image quality metrics using brain MRI data.<n>We evaluate metric sensitivity to a range of challenges, including noise, distribution shifts, and, critically, morphological alterations designed to mimic clinically relevant inaccuracies.
  arXiv  Detail & Related papers  (2025-05-12T01:57:25Z)
• Class-Aware Cartilage Segmentation for Autonomous US-CT Registration in Robotic Intercostal Ultrasound Imaging [39.597735935731386]
  A class-aware cartilage bone segmentation network with geometry-constraint post-processing is presented to capture patient-specific rib skeletons. A dense skeleton graph-based non-rigid registration is presented to map the intercostal scanning path from a generic template to individual patients. Results demonstrate that the proposed graph-based registration method can robustly and precisely map the path from CT template to individual patients.
  arXiv  Detail & Related papers  (2024-06-06T14:15:15Z)
• Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images [0.22585387137796725]
  We propose a two-stage deep-learning approach for automated LGE-MRI image diagnostic quality assessment. The method includes a left atrium detector to focus on relevant regions and a deep network to evaluate diagnostic quality.
  arXiv  Detail & Related papers  (2023-10-13T01:27:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.