Deep-learning for automated detection of MSU deposits on DECT: evaluating impact on efficiency and reader confidence.

Introduction: Dual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Manually identifying these foci (most commonly labeled green) is tedious, and an automated detection system could streamline the process. This study aims to evaluate the impact of a deep-learning (DL) algorithm developed for detecting green pixelations on DECT on reader time, accuracy, and confidence. Methods: We collected a sample of positive and negative DECTs, reviewed twice-once with and once without the DL tool-with a 2-week washout period. An attending musculoskeletal radiologist and a fellow separately reviewed the cases, simulating clinical workflow. Metrics such as time taken, confidence in diagnosis, and the tool's helpfulness were recorded and statistically analyzed. Results: We included thirty DECTs from different patients. The DL tool significantly reduced the reading time for the trainee radiologist (p = 0.02), but not for the attending radiologist (p = 0.15). Diagnostic confidence remained unchanged for both (p = 0.45). However, the DL model identified tiny MSU deposits that led to a change in diagnosis in two cases for the in-training radiologist and one case for the attending radiologist. In 3/3 of these cases, the diagnosis was correct when using DL. Conclusions: The implementation of the developed DL model slightly reduced reading time for our less experienced reader and led to improved diagnostic accuracy. There was no statistically significant difference in diagnostic confidence when studies were interpreted without and with the DL model.

Abnormal Brachial Plexus Differentiation from Routine Magnetic Resonance Imaging: An AI-based Approach.

Automatic abnormality identification of brachial plexus (BP) from normal magnetic resonance imaging to localize and identify a neurologic injury in clinical practice (MRI) is still a novel topic in brachial plexopathy. This study developed and evaluated an approach to differentiate abnormal BP with artificial intelligence (AI) over three commonly used MRI sequences, i.e. T1, FLUID sensitive and post-gadolinium sequences. A BP dataset was collected by radiological experts and a semi-supervised artificial intelligence method was used to segment the BP (based on nnU-net). Hereafter, a radiomics method was utilized to extract 107 shape and texture features from these ROIs. From various machine learning methods, we selected six widely recognized classifiers for training our Brachial plexus (BP) models and assessing their efficacy. To optimize these models, we introduced a dynamic feature selection approach aimed at discarding redundant and less informative features. Our experimental findings demonstrated that, in the context of identifying abnormal BP cases, shape features displayed heightened sensitivity compared to texture features. Notably, both the Logistic classifier and Bagging classifier outperformed other methods in our study. These evaluations illuminated the exceptional performance of our model trained on FLUID-sensitive sequences, which notably exceeded the results of both T1 and post-gadolinium sequences. Crucially, our analysis highlighted that both its classification accuracies and AUC score (area under the curve of receiver operating characteristics) over FLUID-sensitive sequence exceeded 90%. This outcome served as a robust experimental validation, affirming the substantial potential and strong feasibility of integrating AI into clinical practice.

A review of epidural and non-epidural contrast flow patterns during fluoroscopic and CT-guided epidural steroid injections.

Epidural steroid injections are commonly performed using fluoroscopic or CT guidance. With both modalities, the injection of contrast material is necessary before steroid administration to ensure adequate epidural flow and exclude non-epidural flow. While fluoroscopic guidance is conventional, CT is utilized at some centers and can be particularly helpful in the setting of challenging or postoperative anatomy. It is important for proceduralists to be adept at evaluating contrast media flow patterns under both modalities. The goal of this review article is to describe and provide examples of epidural and non-epidural flow patterns on both conventional fluoroscopy and CT. Specific non-epidural patterns discussed include intrathecal flow, intradural/subdural flow, vascular uptake, flow into the retrodural space of Okada, inadvertent facet joint flow, and intradiscal flow.

A Machine Learning Model Demonstrates Excellent Performance in Predicting Subscapularis Tears Based on Pre-Operative Imaging Parameters Alone.

PURPOSE: To develop a machine learning model capable of identifying subscapularis tears before surgery based on imaging and physical examination findings. METHODS: Between 2010 and 2020, 202 consecutive shoulders underwent arthroscopic rotator cuff repair by a single surgeon. Patient demographics, physical examination findings (including range of motion, weakness with internal rotation, lift/push-off test, belly press test, and bear hug test), and imaging (including direct and indirect signs of tearing, biceps status, fatty atrophy, cystic changes, and other similar findings) were included for model creation. RESULTS: Sixty percent of the shoulders had partial or full thickness tears of the subscapularis verified during surgery (83% of these were upper third). Using only preoperative imaging-related parameters, the XGBoost model demonstrated excellent performance at predicting subscapularis tears (c-statistic, 0.84; accuracy, 0.85; F1 score, 0.87). The top 5 features included direct signs related to the presence of tearing as evidenced on magnetic resonance imaging (MRI) (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. CONCLUSIONS: In this study, machine learning was successful in predicting subscapularis tears by MRI alone in 85% of patients, and this accuracy did not decrease by isolating the model to the top features. The top five features included direct signs related to the presence of tearing as evidenced on MRI (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Last, in advanced modeling, the addition of physical examination or patient characteristics did not make a significant difference in the predictive ability of this model. LEVEL OF EVIDENCE: Level III, diagnostic case-control study.

Accuracy of MR arthrography in the detection of posterior glenoid labral injuries of the shoulder.

OBJECTIVE: The purpose of this study is to evaluate the accuracy of MR arthrography in detecting isolated posterior glenoid labral injuries using arthroscopy as the reference standard. METHODS: MR arthrograms of 97 patients with isolated posterior glenoid labral tears by arthroscopy and those of 96 age and gender-matched controls with intact posterior labra were reviewed by two blinded radiologists for the presence and location of posterior labral abnormalities. The sensitivity and specificity of detection of posterior labral tears were calculated as well as the prevalence of associated pathologies. Medical records were reviewed for demographics, history and direction of shoulder instability, and prior surgery. RESULTS: Posterior labral pathology was detected by MR arthrography with sensitivities of 76% and 84% for readers 1 and 2, and a specificity of 88% for both readers. Kappa value for interreader agreement was 0.91. Twenty-two of twenty-three (96%) tears isolated to the posteroinferior quadrant on arthroscopy were correctly identified on MRI. Commonly associated pathologies included paralabral cyst (38%), humeral fracture (7%), and glenoid fracture (2%). Fifteen of ninety-seven (16%) patients with posterior tears on both arthroscopy and MRI had glenoid rim deficiency on imaging versus no patients with intact posterior labra (p < 0.001). Forty of ninety-seven (41%) patients with posterior tears on arthroscopy had a history of posterior instability versus none without posterior tears. There was no significant difference in tear length on MRI between those with a history of instability and those without (p = 0.56). CONCLUSION: MR arthrography is accurate in detecting posterior glenoid labroligamentous injuries.

Modality and terminology changes for behavioral health service delivery during the COVID-19 pandemic: a systematic review.

Introduction: The COVID-19 pandemic prompted healthcare professionals to implement service delivery adaptations to remain in compliance with safety regulations. Though many adaptations in service delivery were reported throughout the literature, a wide variety of terminology and definitions were used. Methods: To address this, we conducted a PRISMA review to identify service delivery adaptations across behavioral healthcare services in the United States from March 2020 to May 2022 and to identify variations in terminology used to describe these adaptations. We identified 445 initial articles for our review across eight databases using predetermined keywords. Using a two-round screening process, authors used a team approach to identify the most appropriate articles for this review. Results: Our results suggested that a total of 14 different terms were used to describe service modality changes, with the most frequent term being telehealth (63%). Each term found in our review and the frequency of use across identified articles is described in detail. Discussion: Implications of this review such as understanding modality changes during the COVID-19 pandemic and beyond are discussed. Our findings illustrate the importance of standardizing terminology to enhance communication and understanding among professionals.

Deep learning-based virtual noncalcium imaging in multiple myeloma using dual-energy CT.

BACKGROUND: Dual-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. OBJECTIVES: In 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 METHODS: AGATE 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. RESULTS: AGATE 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]). CONCLUSIONS: AGATE demonstrated reduced noise and artifacts in VNCa images and ability to improve visualization of bone marrow lesions for assessing multiple myeloma.

Radiology of Intra-Aortic Balloon Pump Catheters.

Radiographs play an important role in ascertaining appropriate placement of the intra-aortic balloon pump catheter. This imaging essay highlights correct and incorrect positioning of these catheters, with emphasis on the variability of radiopaque markers used with different catheter models and on axillary versus femoral catheter placement routes. Keywords: Conventional Radiography, CT, Percutaneous, Cardiac, Vascular, Aorta, Anatomy, Cardiac Assist Devices, Catheters © RSNA, 2022.

Clinical development innovation in rare diseases: lessons learned and best practices from the DevelopAKUre consortium.

New opportunities have arisen for development of therapies for rare diseases with the increased focus and progress in the field. However, standardised framework integrating individual initiatives has not been formed. We present lessons learned and best practice from a collaborative success case in developing a treatment for a rare genetic disease. Our unique consortium model incorporated several of the identified developments under one project, DevelopAKUre, truly bringing together academia, industry and patient organisations in clinical drug development. We found that the equal partnership between all parties in our consortium was a key success factor creating a momentum based on a strong organisational culture where all partners had high engagement and taking ownership of the entire programme. With an agreed mutual objective, this provided synergies through connecting the strengths of the individual parties. Another key success factor was the central role of the patient organisation within the management team, and their unique study participants' advocacy role securing the understanding and meeting the needs of the clinical study participants in real-time. This resulted in an accelerated enrolment into the clinical studies with a high retention rate allowing for delivery of the programme with significantly improved timelines. Our project was partly funded through an external EU research grant, enabling our model with equal partnership. Further attention within the community should be given to establishing a functional framework where sustainable funding and risk sharing between private and public organisations allow for our model to be replicated.

Gadolinium retention within multiple rat organs after intra-articular administration of gadolinium-based contrast agents.

OBJECTIVE: To characterize the extent of retention and biodistribution of gadolinium (Gd) following intra-articular (IA) injection of linear and macrocyclic gadolinium-based contrast agents (GBCAs) into the knee joint of a rat model. MATERIALS AND METHODS: Fifteen Wistar rats were divided into five groups and underwent fluoroscopically-guided injections of both knee joints of (1) clinical 1:200 dilution (low dose, LD) gadodiamide (linear GBCA), (2) LD gadobutrol (macrocyclic GBCA), (3) undiluted (high dose, HD) gadodiamide, (4) HD gadobutrol, and (5) saline. Gd concentrations were quantified by inductively coupled plasma mass spectrometry in (1) blood and urine samples obtained over a 72 h period and (2) knee joint tissues, brain, kidney, and bone marrow at 3 days post-injection. RESULTS: Both HD and LD gadodiamide and gadobutrol were rapidly absorbed from the joint with peak serum and urine concentration at 1 h post-injection, with relatively faster clearance of gadobutrol. All GBCA-exposed groups had detectable levels of Gd in the joint tissues, bone marrow, and/or kidneys (median tissue gadolinium range: 0.1-71 µg Gd/g tissue), with higher amounts observed with gadodiamide versus gadobutrol. Retention within brain tissues was only detected following HD gadodiamide administration but not LD gadodiamide nor HD or LD gadobutrol. CONCLUSION: There was rapid systemic absorption, redistribution, and widespread multi-organ retention of Gd following IA injection of both linear and macrocyclic GBCAs, despite substantial amounts of urinary excretion. Higher concentrations of Gd were observed with administration of gadodiamide compared to gadobutrol in most tissues and biofluids.

CT Attenuation Values Do Not Reliably Distinguish Benign Sclerotic Lesions From Osteoblastic Metastases in Patients Undergoing Bone Biopsy.

BACKGROUND. CT attenuation thresholds that accurately distinguish enostoses from untreated osteoblastic metastases have been published. In the Mayo Clinic practices, these thresholds have been applied more broadly to distinguish benign sclerotic bone lesions other than enostoses from osteoblastic metastases. OBJECTIVE. The purpose of this article is to determine if CT attenuation thresholds allow the distinguishing of benign sclerotic bone lesions from osteoblastic metastases in patients undergoing bone biopsy. METHODS. A retrospective search was conducted to identify sclerotic lesions described on CT between October 7, 1998, and July 15, 2018, that underwent subsequent biopsy. Two musculoskeletal radiologists recorded lesions' maximum and mean attenuation. Using previously published attenuation thresholds, sensitivity and specificity for differentiating benign sclerotic lesions from osteoblastic metastases were calculated. ROC curve analysis was performed to determine if more appropriate attenuation thresholds exist. Intraclass correlation coefficients (ICCs) were computed. RESULTS. A total of 280 patients met inclusion criteria. Of those, 162 had malignant biopsy results and 118 had benign biopsy results. Of the 162 malignant lesions, 81 had received prior treatment. Maximum and mean attenuation were not significantly different between benign and malignant lesions for either reader (all p > .05). For reader 1, to distinguish benign from malignant lesions, a maximum attenuation threshold of more than 1060 HU resulted in sensitivity of 23.7%, specificity of 87.0%, and accuracy of 60.6%. A mean attenuation threshold of greater than 885 HU resulted in sensitivity of 19.5%, specificity of 90.7%, and accuracy 60.7%. ROC curve analysis showed AUCs for mean and maximum attenuation thresholds of 51.8% and 54.6%, respectively. Subgroup analyses of benign versus malignant and treated versus untreated lesions had similar results. Similar findings were obtained for reader 2. The two readers' ICC was 0.946 for maximum attenuation and 0.918 for mean attenuation. CONCLUSION. Published attenuation thresholds for distinguishing enostoses from osteoblastic metastases had slightly decreased specificity and markedly decreased sensitivity when applied to the differentiation of benign sclerotic lesions from osteoblastic metastases in our sample of biopsy-proven lesions. ROC analysis showed no high-performing attenuation threshold alternative. CLINICAL IMPACT. Published CT attenuation thresholds intended for distinguishing enostoses from osteoblastic metastases should not be used more broadly. More accurate alternative thresholds could not be derived.

Investigating the Chronology of Meniscus Root Tears: Do Medial Meniscus Posterior Root Tears Cause Extrusion or the Other Way Around?

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.

Efficacy and safety of once-daily nitisinone for patients with alkaptonuria (SONIA 2): an international, multicentre, open-label, randomised controlled trial.

BACKGROUND: Alkaptonuria is a rare, genetic, multisystem disease characterised by the accumulation of homogentisic acid (HGA). No HGA-lowering therapy has been approved to date. The aim of SONIA 2 was to investigate the efficacy and safety of once-daily nitisinone for reducing HGA excretion in patients with alkaptonuria and to evaluate whether nitisinone has a clinical benefit. METHODS: SONIA 2 was a 4-year, open-label, evaluator-blind, randomised, no treatment controlled, parallel-group study done at three sites in the UK, France, and Slovakia. Patients aged 25 years or older with confirmed alkaptonuria and any clinical disease manifestations were randomly assigned (1:1) to receive either oral nitisinone 10 mg daily or no treatment. Patients could not be masked to treatment due to colour changes in the urine, but the study was evaluator-blinded as far as possible. The primary endpoint was daily urinary HGA excretion (u-HGA24) after 12 months. Clinical evaluation Alkaptonuria Severity Score Index (cAKUSSI) score was assessed at 12, 24, 36, and 48 months. Efficacy variables were analysed in all randomly assigned patients with a valid u-HGA24 measurement at baseline. Safety variables were analysed in all randomly assigned patients. The study was registered at ClinicalTrials.gov (NCT01916382). FINDINGS: Between May 7, 2014, and Feb 16, 2015, 139 patients were screened, of whom 138 were included in the study, with 69 patients randomly assigned to each group. 55 patients in the nitisinone group and 53 in the control group completed the study. u-HGA24 at 12 months was significantly decreased by 99·7% in the nitisinone group compared with the control group (adjusted geometric mean ratio of nitisinone/control 0·003 [95% CI 0·003 to 0·004], p<0·0001). At 48 months, the increase in cAKUSSI score from baseline was significantly lower in the nitisinone group compared with the control group (adjusted mean difference -8·6 points [-16·0 to -1·2], p=0·023). 400 adverse events occurred in 59 (86%) patients in the nitisinone group and 284 events occurred in 57 (83%) patients in the control group. No treatment-related deaths occurred. INTERPRETATION: Nitisinone 10 mg daily was well tolerated and effective in reducing urinary excretion of HGA. Nitisinone decreased ochronosis and improved clinical signs, indicating a slower disease progression. FUNDING: European Commission Seventh Framework Programme.

Homogentisic acid is not only eliminated by glomerular filtration and tubular secretion but also produced in the kidney in alkaptonuria.

The clinical effects of alkaptonuria (AKU) are delayed and ageing influences disease progression. Morbidity of AKU is secondary to high circulating homogentisic acid (HGA) and ochronosis. It is not known whether HGA is produced by or processed in the kidney in AKU. Data from AKU patients from four studies were merged to form a single AKU group. A control group of non-AKU subjects was generated by merging data from two non-AKU studies. Data were used to derive renal clearance and fractional excretion (FE) ratios for creatinine, HGA, phenylalanine (PHE) and tyrosine (TYR) using standard calculations, for comparison between the AKU and the control groups. There were 225 AKU patients in the AKU group and 52 in the non-AKU control group. Circulating HGA increased with age (P < 0.001), and was significantly associated with decreased HGA clearance (CLHGA ) (P < 0.001) and FEHGA (P < 0.001). CLHGA and FEHGA were increased beyond the theoretical maximum renal plasma flow, confirming renal production and emphasising the greater contribution of net tubular secretion than glomerular filtration to renal elimination of HGA. The kidneys are crucial to elimination of HGA. Elimination of HGA is impaired with age resulting in worsening disease over time. The kidney is an important site for production of HGA. Tubular secretion of HGA contributes more to elimination of HGA in AKU than glomerular filtration.

SRA Down Under: Cache and Analysis Platform for Infectious Disease.

SRA, NCBI's Sequence Read Archive, is a valuable resource holding a near definitive collection of the world's collective sequenced reads for academic purposes. Increasingly, these reads are being used for both basic research and clinical investigations. When time is a critical factor in analysis, such as during bacterial outbreaks, the geographical separation between Australia and the offshore NCBI SRA servers can result in significant delays that may have adverse clinical outcomes. To address this, Queensland Genomics commissioned a pilot program for the establishment of a local Australian SRA Cache. Utilizing the hosting capabilities of the NeCTAR Research Cloud, QRIScloud's HTC infrastructure and the MeDiCI data fabric as a storage solution, and the software stack of Cromwell for workflow management, PostgreSQL database for sample and job metadata, and a coordinator Python Flask application, a local cache of seventeen bacterial species was established. Furthermore, the workflow capabilities of Cromwell were leveraged to provide analysis solutions for cached sample data, including quality control and taxonomic profiling, and individual and multiple sample analysis. Moving forward to a broader rollout of increased bacterial species, it was found that the initial storage estimation did not keep up with the exponential increase sequencing reads uploaded to NCBI SRA, which while highlighting the increasing availability and importance in modern research, will need to be addressed.

Normal Pacinian corpuscles in the hand: radiology-pathology correlation in a cadaver study.

OBJECTIVE: To provide microdissection and histological confirmation of normal Pacinian corpuscles prospectively identified using MRI in a cadaver model. METHODS: 3-T MRI of a cadaveric hand specimen was performed with fiduciary markers on the skin. Based on previous descriptions, subcutaneous nodules representing presumed Pacinian corpuscles were localized with respect to the skin markers, and their sizes and depths were recorded. Focused ultrasound was performed to attempt to visualize the corpuscles. Subsequent microdissection was then performed and the presence and location of Pacinian corpuscles were recorded and compared with the findings on MRI. Histological evaluation for each identified corpuscle was performed. RESULTS: The MRI demonstrated 11 T2-hyperintense palmar subcutaneous nodules around the second through fifth metacarpophalangeal joints. None was visible sonographically. The first eight were dissected and proved to be normal Pacinian corpuscles histologically. In sites devoid of subcutaneous nodules on MRI, subsequent dissection failed to reveal any corpuscles. CONCLUSION: On MRI, normal Pacinian corpuscles appear as round or oval, T2-hyperintense subcutaneous nodules in the palms, clustered around the metacarpophalangeal joints, and should not be mistaken for pathological conditions.