Rhodes, Nicholas G.

AbstractBackgroundDual‐energy CT with virtual noncalcium (VNCa) images allows the evaluation of focal intramedullary bone marrow involvement in patients with multiple myeloma. However, current commercial VNCa techniques suffer from excessive image noise and artifacts due to material decomposition used in synthesizing VNCa images.ObjectivesIn this work, we aim to improve VNCa image quality for the assessment of focal multiple myeloma, using an Artificial intelligence based Generalizable Algorithm for mulTi‐Energy CT (AGATE) method.Materials and methodsAGATE method used a custom dual‐task convolutional neural network (CNN) that concurrently carries out material classification and quantification. The material classification task provided an auxiliary regularization to the material quantification task. CNN parameters were optimized using custom loss functions that involved cross‐entropy, physics‐informed constraints, structural redundancy in spectral and material images, and texture information in spectral images. For training data, CT phantoms (diameters 30 to 45 cm) with tissue‐mimicking inserts were scanned on a third generation dual‐source CT system. Scans were performed at routine dose and half of the routine dose. Small image patches (i.e., 40 × 40 pixels) of tissue‐mimicking inserts with known basis material densities were extracted for training samples. Numerically simulated insert materials with various shapes increased diversity of training samples. Generalizability of AGATE was evaluated using CT images from phantoms and patients. In phantoms, material decomposition accuracy was estimated using mean‐absolute‐percent‐error (MAPE), using physical inserts that were not used during the training. Noise power spectrum (NPS) and modulation transfer function (MTF) were compared across phantom sizes and radiation dose levels. Five patients with multiple myeloma underwent dual‐energy CT, with VNCa images generated using a commercial method and AGATE. Two fellowship‐trained musculoskeletal radiologists reviewed the VNCa images (commercial and AGATE) side‐by‐side using a dual‐monitor display, blinded to VNCa type, rating the image quality for focal multiple myeloma lesion visualization using a 5‐level Likert comparison scale (−2 = worse visualization and diagnostic confidence, −1 = worse visualization but equivalent diagnostic confidence, 0 = equivalent visualization and diagnostic confidence, 1 = improved visualization but equivalent diagnostic confidence, 2 = improved visualization and diagnostic confidence). A post hoc assignment of comparison ratings was performed to rank AGATE images in comparison to commercial ones.ResultsAGATE demonstrated consistent material quantification accuracy across phantom sizes and radiation dose levels, with MAPE ranging from 0.7% to 4.4% across all testing materials. Compared to commercial VNCa images, the AGATE‐synthesized VNCa images yielded considerably lower image noise (50–77% noise reduction) without compromising noise texture or spatial resolution across different phantom sizes and two radiation doses. AGATE VNCa images had markedly reduced area under NPS curves and maintained NPS peak frequency (0.7 lp/cm to 1.0 lp/cm), with similar MTF curves (50% MTF at 3.0 lp/cm). In patients, AGATE demonstrated reduced image noise and artifacts with improved delineation of focal multiple myeloma lesions (all readers comparison scores indicating improved overall diagnostic image quality [scores 1 or 2]).ConclusionsAGATE demonstrated reduced noise and artifacts in VNCa images and ability to improve visualization of bone marrow lesions for assessing multiple myeloma.

Background: Meniscus root tears are increasingly being recognized. Meniscal extrusion has previously been associated with medial root tears; however, the relationship between secondary meniscal restraints, such as the meniscotibial (MT) ligament, extrusion, and root tears has yet to be formally evaluated. Purpose: To better understand the association between MT ligament competence, medial meniscal extrusion, and medial meniscus posterior root tears (MMPRTs) as well as to determine the progression of meniscal extrusion over time. Study Design: Case series; Level of evidence, 4. Methods: Serial magnetic resonance imaging (MRI) scans were reviewed for patients who showed evidence of medial meniscal extrusion and MMPRTs on at least 1 of ≥2 available MRI scans. All patients were symptomatic at the time of diagnosis. All MRI scans were analyzed independently by 2 board-certified musculoskeletal radiologists. MT ligament disruption, medial meniscal extrusion, and MMPRTs were recorded for each MRI scan. The time between MRI scans, presence of insufficiency fractures, and Outerbridge classification for the medial femur and tibia were also evaluated. Results: Overall, 27 knees in 26 patients were included in this study, with a total of 63 MRI scans analyzed (21 knees with 2 MRI scans, 3 with 3 MRI scans, and 3 with 4 MRI scans). All patients demonstrated clear medial meniscal extrusion and MT ligament disruption before the subsequent development of MMPRTs ( P < .001). Mean extrusion at the time of initial MRI was 3.3 ± 1.1 mm and increased significantly to 5.5 ± 1.8 mm at the time of first imaging with an identified MMPRT ( P < .001). The mean time between initial MRI and the first identification of an MMPRT on later MRI was 1.7 ± 1.6 years. Conclusion: In a sample of 27 symptomatic knees with serial MRI scans both before and after an MMPRT diagnosis, all patients demonstrated MT ligament disruption and associated meniscal extrusion before the development of subsequent medial meniscus root tears. These findings suggest that MT ligament disruption and medial meniscal extrusion represent early and predisposing events contributing to MMPRTs. Therefore, this provides a possible explanation of why meniscal extrusion is not corrected with medial meniscus root repair.