Artificial Intelligence (AI) for Lung Nodules, From the AJR Special Series on AI Applications.

Interest in artificial intelligence (AI) applications for lung nodules continues to grow among radiologists, particularly with the expanding eligibility criteria and clinical utilization of lung cancer screening CT. AI has been heavily investigated for detecting and characterizing lung nodules and for guiding prognostic assessment. AI tools have also been used for image postprocessing (e.g., rib suppression on radiography or vessel suppression on CT) and for noninterpretive aspects of reporting and workflow, including management of nodule follow-up. Despite growing interest in and rapid development of AI tools and FDA approval of AI tools for pulmonary nodule evaluation, integration into clinical practice has been limited. Challenges to clinical adoption have included concerns about generalizability, regulatory issues, technical hurdles in implementation, and human skepticism. Further validation of AI tools for clinical use and demonstration of benefit in terms of patient-oriented outcomes also are needed. This article provides an overview of potential applications of AI tools in the imaging evaluation of lung nodules and discusses the challenges faced by practices interested in clinical implementation of such tools.

Quantification of Myocardial Extracellular Volume Fraction with Cardiac MR Imaging in Thalassemia Major.

Purpose To quantify myocardial extracellular volume (ECV) by using cardiac magnetic resonance (MR) imaging in thalassemia major and to investigate the relationship between ECV and myocardial iron overload. Materials and Methods With institutional review board approval and informed consent, 30 patients with thalassemia major (mean age ± standard deviation, 34.6 years ± 9.5) and 10 healthy control subjects (mean age, 31.5 years ± 4.4) were prospectively recruited (clinicaltrials.gov identification number NCT02090699). Nineteen patients (63.3%) had prior myocardial iron overload (defined as midseptal T2* < 20 msec on any prior cardiac MR images). Cardiac MR imaging at 1.5 T included cine steady-state free precession for ventricular function, T2* for myocardial iron quantification, and unenhanced and contrast material-enhanced T1 mapping. ECV was calculated with input of the patient's hematocrit level. Peak systolic global longitudinal strain by means of speckle tracking was assessed with same-day transthoracic echocardiography. Statistical analysis included use of the two-sample t test, Fisher exact test, and Spearman correlation. Results Unenhanced T1 values were significantly lower in patients with prior myocardial iron overload than in control subjects (850.3 ± 115.1 vs 1006.3 ± 35.4, P < .001) and correlated strongly with T2* values (r = 0.874, P < .001). Patients with prior myocardial iron overload had higher ECV than did patients without iron overload (31.3% ± 2.8 vs 28.2% ± 3.4, P = .030) and healthy control subjects (27.0% ± 3.1, P = .003). There was no difference in ECV between patients without iron overload and control subjects (P = .647). ECV correlated with lowest historical T2* (r = -0.469, P = .010) but did not correlate significantly with left ventricular ejection fraction (r = -0.216, P = .252) or global longitudinal strain (r = -0.164, P = .423). Conclusion ECV is significantly increased in thalassemia major and is associated with myocardial iron overload. These abnormalities may potentially reflect diffuse interstitial myocardial fibrosis. (©) RSNA, 2015 Online supplemental material is available for this article.

Inversion group (IG) fitting: A new T1 mapping method for modified look-locker inversion recovery (MOLLI) that allows arbitrary inversion groupings and rest periods (including no rest period).

PURPOSE: The Modified Look-Locker Inversion Recovery (MOLLI) technique is used for T1 mapping in the heart. However, a drawback of this technique is that it requires lengthy rest periods in between inversion groupings to allow for complete magnetization recovery. In this work, a new MOLLI fitting algorithm (inversion group [IG] fitting) is presented that allows for arbitrary combinations of inversion groupings and rest periods (including no rest period). THEORY AND METHODS: Conventional MOLLI algorithms use a three parameter fitting model. In IG fitting, the number of parameters is two plus the number of inversion groupings. This increased number of parameters permits any inversion grouping/rest period combination. Validation was performed through simulation, phantom, and in vivo experiments. RESULTS: IG fitting provided T1 values with less than 1% discrepancy across a range of inversion grouping/rest period combinations. By comparison, conventional three parameter fits exhibited up to 30% discrepancy for some combinations. The one drawback with IG fitting was a loss of precision-approximately 30% worse than the three parameter fits. CONCLUSION: IG fitting permits arbitrary inversion grouping/rest period combinations (including no rest period). The cost of the algorithm is a loss of precision relative to conventional three parameter fits. Magn Reson Med 75:2332-2340, 2016. © 2015 Wiley Periodicals, Inc.

Fat fraction bias correction using T1 estimates and flip angle mapping.

PURPOSE: To develop a new method of reducing T1 bias in proton density fat fraction (PDFF) measured with iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL). MATERIALS AND METHODS: PDFF maps reconstructed from high flip angle IDEAL measurements were simulated and acquired from phantoms and volunteer L4 vertebrae. T1 bias was corrected using a priori T1 values for water and fat, both with and without flip angle correction. Signal-to-noise ratio (SNR) maps were used to measure precision of the reconstructed PDFF maps. PDFF measurements acquired using small flip angles were then compared to both sets of corrected large flip angle measurements for accuracy and precision. RESULTS: Simulations show similar results in PDFF error between small flip angle measurements and corrected large flip angle measurements as long as T1 estimates were within one standard deviation from the true value. Compared to low flip angle measurements, phantom and in vivo measurements demonstrate better precision and accuracy in PDFF measurements if images were acquired at a high flip angle, with T1 bias corrected using T1 estimates and flip angle mapping. CONCLUSION: T1 bias correction of large flip angle acquisitions using estimated T1 values with flip angle mapping yields fat fraction measurements of similar accuracy and superior precision compared to low flip angle acquisitions.