Background An anterior cruciate ligament (ACL) injury greatly increases the risk for premature knee osteoarthritis (OA). Improved diagnosis and staging of early disease are needed to develop strategies to delay or prevent disabling OA. Purpose Novel magnetic resonance imaging (MRI) ultrashort echo time (UTE)–T2* mapping was evaluated against clinical metrics of cartilage health in cross-sectional and longitudinal studies of human participants before and after ACL reconstruction (ACLR) to show reversible deep subsurface cartilage and meniscus matrix changes. Study Design Cohort study (diagnosis/prognosis); Level of evidence, 2. Methods Forty-two participants (31 undergoing anatomic ACLR; 11 uninjured) underwent 3-T MRI inclusive of a sequence capturing short and ultrashort T2 signals. An arthroscopic examination of the medial meniscus was performed, and modified Outerbridge grades were assigned to the central and posterior medial femoral condyle (cMFC and pMFC, respectively) of ACL-reconstructed patients. Two years after ACLR, 16 patients underwent the same 3-T MRI. UTE-T2* maps were generated for the posterior medial meniscus (pMM), cMFC, pMFC, and medial tibial plateau (MTP). Cross-sectional evaluations of UTE-T2* and arthroscopic data along with longitudinal analyses of UTE-T2* changes were performed. Results Arthroscopic grades showed that 74% (23/31) of ACL-reconstructed patients had intact cMFC cartilage (Outerbridge grade 0 and 1) and that 90% (28/31) were Outerbridge grade 0 to 2. UTE-T2* values in deep cMFC and pMFC cartilage varied significantly with injury status and arthroscopic grade (Outerbridge grade 0–2: n = 39; P = .03 and .04, respectively). Pairwise comparisons showed UTE-T2* differences between uninjured controls (n = 11) and patients with arthroscopic Outerbridge grade 0 for the cMFC (n = 12; P = .01) and arthroscopic Outerbridge grade 1 for the pMFC (n = 11; P = .01) only and not individually between arthroscopic Outerbridge grade 0, 1, and 2 of ACL-reconstructed patients (P > .05). Before ACLR, UTE-T2* values of deep cMFC and pMFC cartilage of ACL-reconstructed patients were a respective 43% and 46% higher than those of uninjured controls (14.1 ± 5.5 vs 9.9 ± 2.3 milliseconds [cMFC] and 17.4 ± 7.0 vs 11.9 ± 2.4 milliseconds [pMFC], respectively; P = .02 for both). In longitudinal analyses, preoperative elevations in UTE-T2* values in deep pMFC cartilage and the pMM in those with clinically intact menisci decreased to levels similar to those in uninjured controls (P = .02 and .005, respectively), suggestive of healing. No decrease in UTE-T2* values for the MFC and new elevation in UTE-T2* values for the submeniscus MTP were observed in those with meniscus tears. Conclusion This study shows that novel UTE-T2* mapping demonstrates changes in cartilage deep tissue health according to joint injury status as well as a potential for articular cartilage and menisci to heal deep tissue injuries. Further clinical studies of UTE-T2* mapping are needed to determine if it can be used to ident...

Rationale and Objectives To compare the effectiveness of MAVRIC SL with conventional 2D-FSE MR techniques at 3T in imaging patients with a variety of metallic implants. Materials and Methods Twenty-one 3T MR studies were obtained in 19 patients with different types of metal implants. Paired MAVRIC SL and 2D-FSE sequences were reviewed by 2 radiologists, and compared for in-plane and through-plane metal artifact, visualization of the bone implant interface and surrounding soft tissues, blurring, and overall image quality using a 2-tailed Wilcoxon signed rank test. The area of artifact on paired images was measured and compared using a paired Wilcoxon signed rank test. Changes in patient management resulting from MAVRIC SL imaging were documented. Results Significantly less in-plane and through-plane artifact was seen with MAVRIC SL, with improved visualization of the bone-implant interface and surrounding soft tissues, and superior overall image quality (p = 0.0001). Increased blurring was seen with MAVRIC SL (p=0.0016). MAVRIC SL significantly decreased the image artifact compared to 2D-FSE (p=0.0001). Inclusion of MAVRIC SL to the imaging protocol determined the need for surgery or type of surgery in 5 patients, and ruled out the need for surgery in 13 patients. In 3 patients the area of interest was well seen on both MAVRIC SL and 2D-FSE images, so the addition of MAVRIC had no effect on patient management. Conclusion Imaging around metal implants with MAVRIC SL at 3T significantly improved image quality and decreased image artifact compared to conventional 2D-FSE imaging techniques, and directly impacted patient management.

Perfusion CT of the liver typically involves scanning the liver at least 20 times, resulting in a large radiation dose. We developed and validated a simplified model of tumor blood supply that can be applied to standard triphasic scans and evaluated whether this can be used to distinguish benign and malignant liver lesions. Triphasic CTs of 46 malignant and 32 benign liver lesions were analyzed. For each phase, regions of interest were drawn in the arterially enhancing portion of each lesion, as well as the background liver, aorta, and portal vein. Hepatic artery and portal vein blood supply coefficients for each lesion were then calculated by expressing the enhancement curve of the lesion as a linear combination of the enhancement curves of the aorta and portal vein. Hepatocellular carcinoma (HCC) and hypervascular metastases, on average, both had increased hepatic artery coefficients compared to the background liver. Compared to HCC, benign lesions, on average, had either a greater hepatic artery coefficient (hemangioma) or a greater portal vein coefficient (focal nodular hyperplasia or transient hepatic attenuation difference). Hypervascularity with washout is a key diagnostic criterion for HCC, but it had a sensitivity of 72 % and specificity of 81 % for diagnosing malignancy in our diverse set of liver lesions. The sensitivity for malignancy was increased to 89 % by including enhancing lesions that were hypodense on all phases. The specificity for malignancy was increased to 97 % (p=0.039) by also examining hepatic artery and portal vein blood supply coefficients, while maintaining a sensitivity of 76 %.

The pattern of 18F-FDG uptake within the spinal cord, observed in patients with non-central nervous system malignancy, may be helpful in understanding glucose physiology of spinal cord diseases and warrants further research.

Because many bone tumors have a variety of appearances and are uncommon, few radiologists develop sufficient expertise to guide optimal management. Bayesian inference can guide decision-making by computing probabilities of multiple diagnoses to generate a differential. We built and validated a naïve Bayes machine (NBM) that processes 18 demographic and radiographic features. We reviewed over 1664 analog radiographic cases of bone tumors and selected 811 cases (66 diagnoses) for annotation using a quantitative imaging platform. Leave-one-out cross validation was performed. Primary accuracy was defined as the correct pathological diagnosis as the top machine prediction. Differential accuracy was defined as whether the correct pathological diagnosis was within the top three predictions. For the 29 most common diagnoses (710 cases), primary accuracy was 44%, and differential accuracy was 60%. For the top 10 most common diagnoses (478 cases), primary accuracy was 62%, and differential accuracy was 80%. The machine returned relevant diagnoses for the majority of unknown test cases and may be a feasible alternative to machine learning approaches such as deep neural networks or support vector machines that typically require larger training data (our model required a minimum of five samples per diagnosis) and are "black boxes" (our model can provide details of probability calculations to identify features that most significantly contribute to truth diagnoses). Finally, our Bayes model was designed to scale and "learn" from external data, enabling incorporation of outside knowledge such as Dahlin's Bone Tumors, a reference of anatomic and demographic statistics of more than 10,000 tumors.

Mid-sagittal single slice 2-D UTE-T2* mapping may be an efficient means to assess medial femoral cartilage for subsurface matrix changes early after ACL reconstruction while 3-D assessments provide additional sensitivity to changes in the medial tibial plateau. This article is protected by copyright. All rights reserved.

Natural language processing (NLP) techniques to extract data from unstructured text into formal computer representations are valuable for creating robust, scalable methods to mine data in medical documents and radiology reports. As voice recognition (VR) becomes more prevalent in radiology practice, there is opportunity for implementing NLP in real time for decision-support applications such as context-aware information retrieval. For example, as the radiologist dictates a report, an NLP algorithm can extract concepts from the text and retrieve relevant classification or diagnosis criteria or calculate disease probability. NLP can work in parallel with VR to potentially facilitate evidence-based reporting (for example, automatically retrieving the Bosniak classification when the radiologist describes a kidney cyst). For these reasons, we developed and validated an NLP system which extracts fracture and anatomy concepts from unstructured text and retrieves relevant bone fracture knowledge. We implement our NLP in an HTML5 web application to demonstrate a proof-of-concept feedback NLP system which retrieves bone fracture knowledge in real time.

Our proposed framework addresses some limitations of existing CBIR systems by incorporating user feedback and simultaneously predicting the semantic features of the query image. This obviates the need for the user to provide those terms and makes CBIR search more efficient for inexperience users/trainees. Encouraging results achieved in the current study highlight possible new directions in radiological image interpretation employing semantic CBIR combined with relevance feedback of visual similarity.