Department of Informatics, Bioengineering Robotics and System Engineering (DIBRIS), University of Genoa, Viale Causa 13, 16143, Genova, Italy.
Complex Operative Radiology Unit, IRCCS Giannina Gaslini, Genova, Italy.
BMC Med Imaging. 2022 Feb 20;22(1):30. doi: 10.1186/s12880-022-00754-0.
In clinical assessment of Pectus Excavatum (PE), the indication to surgery is based not only on symptoms but also on quantitative markers calculated from Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scans. According to clinical routine, these indexes are measured manually by radiologists with limited computer support. This process is time consuming and potentially subjected to inaccuracy and individual variability in measurements. Moreover, the existing indexes have limitations, since they are based on linear measurements performed on single slices rather than on volumetric data derived from all the thoracic scans.
In this paper we present an image processing pipeline aimed at providing radiologists with a computer-aid tool in support of diagnosis of PE patients developed in MATLAB® and conceived for MRI images. This framework has a dual purpose: (i) to automatize computation of clinical indexes with a view to ease and standardize pre-operative evaluation; (ii) to propose a new marker of pathological severity based on volumetric analysis and overcoming the limitations of existing axial slice-based indexes. Final designed framework is semi-automatic, requiring some user interventions at crucial steps: this is realized through a Graphical User Interface (GUI) that simplifies the interaction between the user and the tools. We tested our pipeline on 50 pediatric patients from Gaslini Children's Hospital and performed manual computation of indexes, comparing the results between the proposed tool and gold-standard clinical practice. Automatic indexes provided by our algorithm have shown good agreement with manual measurements by two independent readers. Moreover, the new proposed Volumetric Correction Index (VCI) has exhibited good correlation with standardized markers of pathological severity, proving to be a potential innovative tool for diagnosis, treatment, and follow-up.
Our pipeline represents an innovative image processing in PE evaluation, based on MRI images (radiation-free) and providing the clinician with a quick and accurate tool for automatically calculating the classical PE severity indexes and a new more comprehensive marker: the Volumetric Correction Index.
在漏斗胸（PE）的临床评估中，手术指征不仅基于症状，还基于从计算机断层扫描（CT）或磁共振成像（MRI）扫描计算得出的定量指标。根据临床常规，这些指标由放射科医生在有限的计算机支持下手动测量。这个过程既耗时又容易出现不准确和测量的个体差异。此外，现有的指标存在局限性，因为它们基于对单个切片的线性测量，而不是基于从所有胸部扫描得出的容积数据。
在本文中，我们提出了一个图像处理管道，旨在为放射科医生提供一种计算机辅助工具，以支持在 MATLAB®中开发的用于 MRI 图像的 PE 患者的诊断。该框架具有双重目的：（i）自动计算临床指标，以简化和标准化术前评估；（ii）提出一种基于容积分析的新的病理严重程度指标，克服了基于现有轴向切片指标的局限性。最终设计的框架是半自动的，在关键步骤需要一些用户干预：这是通过一个图形用户界面（GUI）实现的，它简化了用户与工具之间的交互。我们在 Gaslini 儿童医院的 50 名儿科患者上测试了我们的管道，并对索引进行了手动计算，比较了我们的工具和黄金标准临床实践的结果。我们算法提供的自动索引与两位独立读者的手动测量结果具有良好的一致性。此外，新提出的容积校正指数（VCI）与病理严重程度的标准化指标具有良好的相关性，证明它是一种用于诊断、治疗和随访的潜在创新工具。
我们的管道代表了基于 MRI 图像（无辐射）的漏斗胸评估中的一种创新的图像处理方法，为临床医生提供了一种快速准确的工具，用于自动计算经典的漏斗胸严重程度指数和一个新的更全面的指标：容积校正指数。
