The sphingosine 1-phosphate receptor 1 causes tissue retention by inhibiting the entry of peripheral tissue T lymphocytes into afferent lymphatics.

Although much is known about the migration of T cells from blood to lymph nodes, less is known about the mechanisms regulating the migration of T cells from tissues into lymph nodes through afferent lymphatics. Here we investigated T cell egress from nonlymphoid tissues into afferent lymph in vivo and developed an experimental model to recapitulate this process in vitro. Agonism of sphingosine 1-phosphate receptor 1 inhibited the entry of tissue T cells into afferent lymphatics in homeostatic and inflammatory conditions and caused the arrest, mediated at least partially by interactions of the integrin LFA-1 with its ligand ICAM-1 and of the integrin VLA-4 with its ligand VCAM-1, of polarized T cells at the basal surface of lymphatic but not blood vessel endothelium. Thus, the increased sphingosine 1-phosphate present in inflamed peripheral tissues may induce T cell retention and suppress T cell egress.

Automated image quality evaluation of T2 -weighted liver MRI utilizing deep learning architecture.

PURPOSE: To develop and test a deep learning approach named Convolutional Neural Network (CNN) for automated screening of T2 -weighted (T2 WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists. MATERIALS AND METHODS: We evaluated 522 liver magnetic resonance imaging (MRI) exams performed at 1.5T and 3T at our institution between November 2014 and May 2016 for CNN training and validation. The CNN consisted of an input layer, convolutional layer, fully connected layer, and output layer. 351 T2 WI were anonymized for training. Each case was annotated with a label of being diagnostic or nondiagnostic for detecting lesions and assessing liver morphology. Another independently collected 171 cases were sequestered for a blind test. These 171 T2 WI were assessed independently by two radiologists and annotated as being diagnostic or nondiagnostic. These 171 T2 WI were presented to the CNN algorithm and image quality (IQ) output of the algorithm was compared to that of two radiologists. RESULTS: There was concordance in IQ label between Reader 1 and CNN in 79% of cases and between Reader 2 and CNN in 73%. The sensitivity and the specificity of the CNN algorithm in identifying nondiagnostic IQ was 67% and 81% with respect to Reader 1 and 47% and 80% with respect to Reader 2. The negative predictive value of the algorithm for identifying nondiagnostic IQ was 94% and 86% (relative to Readers 1 and 2). CONCLUSION: We demonstrate a CNN algorithm that yields a high negative predictive value when screening for nondiagnostic T2 WI of the liver. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:723-728.

Clinical applications of physical 3D models derived from MDCT data and created by rapid prototyping.

OBJECTIVE: In this article, we describe the production of physical models from CT data using rapid prototyping and present their clinical application. MDCT data acquisition of isotropic voxels and modern postprocessing techniques provide exquisite detail for clinicians and radiologists. CONCLUSION: In recent years, rapid prototyping technologies have provided new possibilities to visualize complex anatomic structures through the generation of physical models that can be used to assist with diagnosis, surgical planning, prosthesis design, and patient communication.

Imaging Facilities' Adherence to PI-RADS v2 Minimum Technical Standards for the Performance of Prostate MRI.

PURPOSE: This study aimed to assess variability in imaging facilities' adherence to the minimum technical standards for prostate magnetic resonance imaging acquisition established by Prostate Imaging-Reporting and Data System (PI-RADS) version 2 (v2). METHODS: A total of 107 prostate magnetic resonance imaging examinations performed at 107 unique imaging facilities after the release of PI-RADS v2 and that were referred to a tertiary care center for secondary interpretation were included. Image sets, DICOM headers, and outside reports were reviewed to assess adherence to 21 selected PI-RADS v2 minimum technical standards. RESULTS: Hardware arrangements were 23.1%, 1.5T without endorectal coil; 7.7%, 1.5T with endorectal coil; 63.5%, 3T without endorectal coil; and 5.8%, 3T with endorectal coil. Adherence to minimum standards was lowest on T2 weighted imaging (T2WI) for frequency resolution ≤0.4 mm (16.8%) and phase resolution ≤0.7 mm (48.6%), lowest on diffusion-weighted imaging (DWI) for field of view (FOV) 120-220 mm (30.0%), and lowest on dynamic contrast-enhanced (DCE) imaging for slice thickness 3 mm (33.3%) and temporal resolution <10 s (31.5%). High b-value (≥1400 s/mm2) images were included in 58.0% (calculated in 25.9%). Adherence to T2WI phase resolution and DWI inter-slice gap were greater (P < .05) at 3T than at 1.5T. Adherence did not differ (P > .05) for any parameter between examinations performed with and without an endorectal coil. Adherence was greater for examinations performed at teaching facilities for T2WI slice thickness and DCE temporal resolution (P < .05). Adherence was not better for examinations performed in 2016 than in 2015 for any parameter (P > .05). CONCLUSION: Facilities' adherence to PI-RADS v2 minimum technical standards was variable, being particularly poor for T2WI frequency resolution and DCE temporal resolution. The standards warrant greater community education. Certain technical standards may be too stringent, and revisions should be considered.

Imaging of extraosseous intracranial and intraspinal multiple myeloma, including central nervous system involvement.

Involvement of the central nervous system by multiple myeloma is rare and has a very poor prognosis. Magnetic resonance imaging of the head and spine plays an important role in diagnosing central nervous system involvement with multiple myeloma. We retrospectively analyzed the imaging features of pathology-proven extraosseous intracranial and intraspinal multiple myeloma from 2002 to 2013. The most common imaging manifestations were extraaxial nonosseous spinal lesions, cranial nerve involvement, and intracranial extraaxial nonosseous lesions. Different sites in the central nervous system may be affected by multiple myeloma, often simultaneously, producing a variety of imaging appearances.

Blind spots at oncological CT: lessons learned from PET/CT.

Improved accuracy in oncological computed tomography (CT) could lead to a decrease in morbidity and improved survival for oncology patients. Visualization of metabolic activity using the glucose analogue [(18)F]fluorodeoxyglucose (FDG) in combination with the high anatomic resolution of CT in an integrated positron emission tomography (PET)/CT examination has the highest sensitivity and specificity for the detection of primary and metastatic lesions. However, PET/CT costs are high and patient access is limited; thus CT remains the primary imaging modality in oncology patients. We have noted that subtle lesions are more easily detected on CT by radiologists with PET/CT experience. We aimed to provide a brief review of the literature with comparisons of multi-detector computed tomography (MDCT) and PET/CT in primary and metastatic disease with an emphasis on findings that may be overlooked on MDCT in cancer of the breast, lung, colon, and ovaries, and in melanoma, as well as thrombosis in oncology patients. We further reviewed our experience for illustrative comparisons of PET/CT and MDCT studies. Experience in interpreting conventional CT scans alongside PET/CT can help the reader develop an appreciation for the subtle appearance of some lesions on CT that might otherwise be missed. This could improve detection rates, reduce errors, and improve patient management.