Subset Of Scans Research Articles

LGG-15. DEEP LEARNING ENABLES LONGITUDINAL RISK PREDICTION FOR PEDIATRIC LOW-GRADE GLIOMAS AFTER SURGERY

Abstract BACKGROUND Disease progression is challenging to predict following surgery for pediatric low-grade glioma (pLGG), and early progression indicators on MRI surveillance imaging would help guide management. Longitudinal surveillance imaging data may capture subtle temporal tumor changes and patterns that could inform recurrence risk but are difficult to synthesize clinically. We applied deep, self-supervised learning to longitudinal surveillance MRI to predict short-interval event-free-survival (EFS) risk from time-of-scan. METHODS We retrospectively collected data from 278 patients (2,174 scans) who underwent surgical resection for pLGG at Dana-Farber Cancer Institute/Boston Children’s Hospital (DFCI/BCH) from 1990-2022. A deep-learning model was pretrained with T2-FLAIR sequences to determine the chronological scan order for each patient (temporal pretraining). The model was fine-tuned to predict 1-year EFS from point-of-scan using a subset of scans from DFCI/BCH and then validated on a hold-out dataset (511 scans, 67 patients). Separately, the model was fine-tuned on an external dataset from the Children’s Brain Tumor Network (CBTN) and validated on a hold-out set (204 scans, 61 patients). Results were compared to a model trained without temporal pretraining. RESULTS Median follow-up and interval between scans were 73 and 6.3 months for DFCI/BCH and 20 and 5.8 months for CBTN. Temporal pretraining improved performance for 1-year EFS, for DFCI/BCH (AUC: 0.83 [0.71-0.90] vs. 0.78 [0.62-0.91]; F1-score: 0.80 vs. 0.67), and CBTN hold-out sets (AUC: 0.75 [0.58-0.90] vs. 0.53 [0.35-0.70], p=0.01; F1-score: 0.65 vs. 0.55). Increasing the number of longitudinal scans available to the model incrementally improved performance up to 7 scans/patient for DFCI/BCH and 10 scans/patient for CBTN. CONCLUSIONS Deep-learning with temporal pretraining improves the predictive analysis of tumor growth patterns in postoperative pLGG surveillance MRI, enabling accurate short-interval EFS prediction. This model could help inform patients and clinicians regarding surveillance regimen and initiation of adjuvant therapy.

Harmonization of brain PET images in multi-center PET studies using Hoffman phantom scan

BackgroundImage harmonization has been proposed to minimize heterogeneity in brain PET scans acquired in multi-center studies. However, standard validated methods and software tools are lacking. Here, we assessed the performance of a framework for the harmonization of brain PET scans in a multi-center European clinical trial.MethodHoffman 3D brain phantoms were acquired in 28 PET systems and reconstructed using site-specific settings. Full Width at Half Maximum (FWHM) of the Effective Image Resolution (EIR) and harmonization kernels were estimated for each scan. The target EIR was selected as the coarsest EIR in the imaging network. Using “Hoffman 3D brain Analysis tool,” indicators of image quality were calculated before and after the harmonization: The Coefficient of Variance (COV%), Gray Matter Recovery Coefficient (GMRC), Contrast, Cold-Spot RC, and left-to-right GMRC ratio. A COV% ≤ 15% and Contrast ≥ 2.2 were set as acceptance criteria. The procedure was repeated to achieve a 6-mm target EIR in a subset of scans. The method’s robustness against typical dose-calibrator-based errors was assessed.ResultsThe EIR across systems ranged from 3.3 to 8.1 mm, and an EIR of 8 mm was selected as the target resolution. After harmonization, all scans met acceptable image quality criteria, while only 13 (39.4%) did before. The harmonization procedure resulted in lower inter-system variability indicators: Mean ± SD COV% (from 16.97 ± 6.03 to 7.86 ± 1.47%), GMRC Inter-Quartile Range (0.040–0.012), and Contrast SD (0.14–0.05). Similar results were obtained with a 6-mm FWHM target EIR. Errors of ± 10% in the DRO activity resulted in differences below 1 mm in the estimated EIR.ConclusionHarmonizing the EIR of brain PET scans significantly reduced image quality variability while minimally affecting quantitative accuracy. This method can be used prospectively for harmonizing scans to target sharper resolutions and is robust against dose-calibrator errors. Comparable image quality is attainable in brain PET multi-center studies while maintaining quantitative accuracy.

Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases.

Demand for head and neck cancer (HNC) radiotherapy data in algorithmic development has prompted increased image dataset sharing. Medical images must comply with data protection requirements so that re-use is enabled without disclosing patient identifiers. Defacing, i.e., the removal of facial features from images, is often considered a reasonable compromise between data protection and re-usability for neuroimaging data. While defacing tools have been developed by the neuroimaging community, their acceptability for radiotherapy applications have not been explored. Therefore, this study systematically investigated the impact of available defacing algorithms on HNC organs at risk (OARs). A publicly available dataset of magnetic resonance imaging scans for 55 HNC patients with eight segmented OARs (bilateral submandibular glands, parotid glands, level II neck lymph nodes, level III neck lymph nodes) was utilized. Eight publicly available defacing algorithms were investigated: afni_refacer, DeepDefacer, defacer, fsl_deface, mask_face, mri_deface, pydeface, and quickshear. Using a subset of scans where defacing succeeded (N=29), a 5-fold cross-validation 3D U-net based OAR auto-segmentation model was utilized to perform two main experiments: 1.) comparing original and defaced data for training when evaluated on original data; 2.) using original data for training and comparing the model evaluation on original and defaced data. Models were primarily assessed using the Dice similarity coefficient (DSC). Most defacing methods were unable to produce any usable images for evaluation, while mask_face, fsl_deface, and pydeface were unable to remove the face for 29%, 18%, and 24% of subjects, respectively. When using the original data for evaluation, the composite OAR DSC was statistically higher (p ≤ 0.05) for the model trained with the original data with a DSC of 0.760 compared to the mask_face, fsl_deface, and pydeface models with DSCs of 0.742, 0.736, and 0.449, respectively. Moreover, the model trained with original data had decreased performance (p ≤ 0.05) when evaluated on the defaced data with DSCs of 0.673, 0.693, and 0.406 for mask_face, fsl_deface, and pydeface, respectively. Defacing algorithms may have a significant impact on HNC OAR auto-segmentation model training and testing. This work highlights the need for further development of HNC-specific image anonymization methods.

Incidental findings on coronary computed tomography in women with selected reproductive disorders

ObjectiveTo determine the prevalence of incidental findings (IFs) on coronary computed tomography (CCT) in women aged 45–55 years and previously diagnosed with reproductive disorders such as polycystic ovary syndrome (PCOS), premature ovarian insufficiency (POI) or preeclampsia (PE).MethodsA total of 486 middle-aged women with PCOS (n = 101), POI (n = 97) or a history of PE (n = 288) underwent a CCT as part of a prior prospective study. IFs were categorized by their significance (minor, moderate and major). Follow-up information was collected from patients’ records. To investigate the impact of different field of views (FOVs), a subset of scans was analyzed in full FOV and small FOV.ResultsIn 96/486 (19.8%) women, one or more IFs were detected, of which 54/486 (11.1%) were classified as moderate/major and 48/486 (9.9%) required follow-up. A moderate/major IF was detected in 16/101 (15.9%) women with PCOS, 13/97 (13.4%) women with POI and 25/288 (8.7%) women with a history of PE. In 78 women with an IF detected in the full FOV, the IF was still visible in 60 (76.9%) women in the small FOV. In the full FOV, 46 women required follow-up, but using the small FOV this was reduced to 30 women.ConclusionUsing CCT as a cardiovascular disease screening tool in women with selected reproductive disorders increases the probability of detecting IFs that can cause anxiety and may generate extra costs, but can also reveal clinically relevant findings. Using a small FOV centered around the heart resulted in a lower prevalence of IFs and required less follow-up.

Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI

With modern management of primary liver cancer shifting towards non-invasive diagnostics, accurate tumor classification on medical imaging is increasingly critical for disease surveillance and appropriate targeting of therapy. Recent advancements in machine learning raise the possibility of automated tools that can accelerate workflow, enhance performance, and increase the accessibility of artificial intelligence to clinical researchers. We explore the use of an automated Tree-Based Optimization Tool that leverages a genetic programming algorithm for differentiation of the two common primary liver cancers on multiphasic MRI. Manual and automated analyses were performed to select an optimal machine learning model, with an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 70–75% (95% CI 0.48–0.89), and specificity of 71–79% (95% CI 0.52–0.90) on manual optimization, and an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 65–75% (95% CI 0.43–0.89) and specificity of 75–79% (95% CI 0.56–0.90) for automated machine learning. We found that automated machine learning performance was similar to that of manual optimization, and it could classify hepatocellular carcinoma and intrahepatic cholangiocarcinoma with an sensitivity and specificity comparable to that of radiologists. However, automated machine learning performance was poor on a subset of scans that met LI-RADS criteria for LR-M. Exploration of additional feature selection and classifier methods with automated machine learning to improve performance on LR-M cases as well as prospective validation in the clinical setting are needed prior to implementation.

Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets.

PurposeTo develop a three-dimensional (3D) deep learning algorithm to detect glaucoma using spectral-domain optical coherence tomography (SD-OCT) optic nerve head (ONH) cube scans and validate its performance on ethnically diverse real-world datasets and on cropped ONH scans.MethodsIn total, 2461 Cirrus SD-OCT ONH scans of 1012 eyes were obtained from the Glaucoma Clinic Imaging Database at the Byers Eye Institute, Stanford University, from March 2010 to December 2017. A 3D deep neural network was trained and tested on this unique raw OCT cube dataset to identify a multimodal definition of glaucoma excluding other concomitant retinal disease and optic neuropathies. A total of 1022 scans of 363 glaucomatous eyes (207 patients) and 542 scans of 291 normal eyes (167 patients) from Stanford were included in training, and 142 scans of 48 glaucomatous eyes (27 patients) and 61 scans of 39 normal eyes (23 patients) were included in the validation set. A total of 3371 scans (Cirrus SD-OCT) from four different countries were used for evaluation of the model: the non overlapping test dataset from Stanford (USA) consisted of 694 scans: 241 scans from 113 normal eyes of 66 patients and 453 scans of 157 glaucomatous eyes of 89 patients. The datasets from Hong Kong (total of 1625 scans; 666 OCT scans from 196 normal eyes of 99 patients and 959 scans of 277 glaucomatous eyes of 155 patients), India (total of 672 scans; 211 scans from 147 normal eyes of 98 patients and 461 scans from 171 glaucomatous eyes of 101 patients), and Nepal (total of 380 scans; 158 scans from 143 normal eyes of 89 patients and 222 scans from 174 glaucomatous eyes of 109 patients) were used for external evaluation. The performance of the model was then evaluated on manually cropped scans from Stanford using a new algorithm called DiagFind. The ONH region was cropped by identifying the appropriate zone of the image in the expected location relative to Bruch's Membrane Opening (BMO) using a commercially available imaging software. Subgroup analyses were performed in groups stratified by eyes, myopia severity of glaucoma, and on a set of glaucoma cases without field defects. Saliency maps were generated to highlight the areas the model used to make a prediction. The model's performance was compared to that of a glaucoma specialist using all available information on a subset of cases.ResultsThe 3D deep learning system achieved area under the curve (AUC) values of 0.91 (95% CI, 0.90–0.92), 0.80 (95% CI, 0.78–0.82), 0.94 (95% CI, 0.93–0.96), and 0.87 (95% CI, 0.85–0.90) on Stanford, Hong Kong, India, and Nepal datasets, respectively, to detect perimetric glaucoma and AUC values of 0.99 (95% CI, 0.97–1.00), 0.96 (95% CI, 0.93–1.00), and 0.92 (95% CI, 0.89–0.95) on severe, moderate, and mild myopia cases, respectively, and an AUC of 0.77 on cropped scans. The model achieved an AUC value of 0.92 (95% CI, 0.90–0.93) versus that of the human grader with an AUC value of 0.91 on the same subset of scans (). The performance of the model in terms of recall on glaucoma cases without field defects was found to be 0.76 (0.68–0.85). Saliency maps highlighted the lamina cribrosa in glaucomatous eyes versus superficial retina in normal eyes as the regions associated with classification.ConclusionsA 3D convolutional neural network (CNN) trained on SD-OCT ONH cubes can distinguish glaucoma from normal cases in diverse datasets obtained from four different countries. The model trained on additional random cropping data augmentation performed reasonably on manually cropped scans, indicating the importance of lamina cribrosa in glaucoma detection.Translational RelevanceA 3D CNN trained on SD-OCT ONH cubes was developed to detect glaucoma in diverse datasets obtained from four different countries and on cropped scans. The model identified lamina cribrosa as the region associated with glaucoma detection.

Fat Content Quantification Using Dual-Energy CT for Differentiation of Anterior Mediastinal Lesions from Normal or Hyperplastic Thymus

BackgroundDetection of fat content in thymic lesions is crucial to differentiate thymic hyperplasia from thymic tumors or other anterior mediastinal pathologies. PurposeTo assess the feasibility of dual-energy CT (DECT) fat content quantification for the differentiation of anterior mediastinal lesions from benign thymic lesions and the normal spectrum of the thymus. Materials and MethodsChest DECT images of 465 patients (median 61 years, 63% female) were visually evaluated by two radiologists and semiquantitatively scored based on the degree of fatty degeneration ranging from completely fatty (score 0) to predominantly soft-tissue (score 3), and anterior mediastinal mass (score 4). A subset of scans (n =134 including all cases with scores 2-4 and 20 randomly-selected cases from scores 0 and 1) underwent quantitative DECT analysis (fat fraction, iodine density, and conventional CT value). DECT values were compared across the semiquantitative scores. ResultsResults of visual evaluation included 35 with predominantly solid thymus (score 3) and 15 with anterior mediastinal mass (score 4). The most common clinical diagnoses of the 15 masses (including 8 with pathologic confirmation) were metastases (n = 10) and lymphoma (n = 4). CT values in the abnormal thymus were significantly higher than those in score 3 (median: 69.7 HU versus 19.9 HU, P <0.001). There was no significant difference in iodine density values (median: 1.7 mg/ml versus 1 mg/ml, P = 0.09). However, the fat fraction value was significantly lower in the abnormal thymus (score 4) than in the predominantly soft-tissue attenuation thymuses (score 3) (median: 12.8% versus 38.7%, P <0.001). ROC curve analysis showed that fat fraction had an AUC of 0.96 (P <0.001), with a cutoff of <39.2% fat fraction yielding 100% sensitivity and 85% specificity. ConclusionDECT fat fraction measurements of the thymus may provide additional value in distinguishing anterior mediastinal lesions from benign thymus. Use of DECT may reduce the need for subsequent imaging evaluation.

Automatic segmentation, classification, and follow-up of optic pathway gliomas using deep learning and fuzzy c-means clustering based on MRI.

Optic pathway gliomas (OPG) are low-grade pilocytic astrocytomas accounting for 3-5% of pediatric intracranial tumors. Accurate and quantitative follow-up of OPG using magnetic resonance imaging (MRI) is crucial for therapeutic decision making, yet is challenging due to the complex shape and heterogeneous tissue pattern which characterizes these tumors. The aim of this study was to implement automatic methods for segmentation and classification of OPG and its components, based on MRI. A total of 202 MRI scans from 29 patients with chiasmatic OPG scanned longitudinally were retrospectively collected and included in this study. Data included T2 and post-contrast T1 weighted images. The entire tumor volume and its components were manually annotated by a senior neuro-radiologist, and inter- and intra-rater variability of the entire tumor volume was assessed in a subset of scans. Automatic tumor segmentation was performed using deep-learning method with U-Net+ResNet architecture. A fivefold cross-validation scheme was used to evaluate the automatic results relative to manual segmentation. Voxel-based classification of the tumor into enhanced, non-enhanced, and cystic components was performed using fuzzy c-means clustering. The results of the automatic tumor segmentation were: mean dice score=0.736±0.025, precision=0.918±0.014, and recall=0.635±0.039 for the validation data, and dice score=0.761±0.011, precision=0.794±0.028, and recall=0.742±0.012 for the test data. The accuracy of the voxel-based classification of tumor components was 0.94, with precision=0.89, 0.97, and 0.85, and recall=1.00, 0.79, and 0.94 for the non-enhanced, enhanced, and cystic components, respectively. This study presents methods for automatic segmentation of chiasmatic OPG tumors and classification into the different components of the tumor, based on conventional MRI. Automatic quantitative longitudinal assessment of these tumors may improve radiological monitoring, facilitate early detection of disease progression and optimize therapy management.

Derivation and validation of thoracic sarcopenia assessment in patients undergoing thoracic endovascular aortic repair

ObjectiveSarcopenia, as assessed by computed tomography (CT)-based measurements of muscle mass, is an objective and patient-specific indicator of frailty, which is an important predictor of operative morbidity and mortality. Studies to date have primarily focused on psoas-defined sarcopenia, which may not be valid among patients with thoracic aortic disease. Using psoas sarcopenia as the reference for sarcopenia, the purpose of this study was to create and to validate a new thoracic-level method of measuring sarcopenia as a novel method to assess frailty among patients undergoing thoracic endovascular aortic repair. MethodsProspectively collected data of patients undergoing thoracic endovascular aortic repair for thoracic aortic dissection, aneurysm, or injury using a conformable thoracic graft were reviewed. Patients with preoperative abdominal and thoracic CT imaging were included. Thoracic muscle mass was measured on axial images at the T12 level using our newly established standardized computer-assisted protocol. Psoas sarcopenia was measured at the L3 level using standard methods. Optimal sex-specific diagnostic T12 measurements were determined by receiver operating characteristic (ROC) curve analysis. A subset of scans were reviewed in duplicate by two trained observers and intraobserver and interobserver reliability tested by intraclass correlation coefficient. Agreement between T12 and L3 sarcopenia was tested by Cohen κ (scale, 0-1). ResultsThere were 147 patients included for analysis, including 34 dissection, 80 trauma, and 33 aneurysm patients. ROC curve analysis yielded sarcopenic cutoff values of 106.00 cm2/m2 for women and 110.00 cm2/m2 for men at the T12 level. Based on ROC curve analysis, overall accuracy of T12 measurements was high (area under ROC curve, 0.91 for men and 0.90 for women). Quantitative interobserver and intraobserver reliability yielded excellent intraclass correlation coefficient values (>0.95). Qualitative interobserver reliability yielded nearly perfect Cohen κ values (>0.85). Qualitative intraobserver reliability of calculating sarcopenia at both the T12 and L3 levels was fair for both readers (0.361 and 0.288). There was additionally a general correlation between changes in muscle area at L3 with changes at T12 during 48 months. ConclusionsThoracic sarcopenia can be readily and reliably reproduced from CT-derived measurement of T12-level muscle area. This approach may be used as an alternative method to objectively define sarcopenia in patients without abdominal CT imaging. Future studies to assess the predictability of thoracic vs abdominal sarcopenia on postoperative outcomes will enhance the utility of these tools.

A validated clinical MRI injury scoring system in neonatal hypoxic-ischemic encephalopathy.

Deep nuclear gray matter injury in neonatal hypoxic-ischemic encephalopathy (HIE) is associated with worse neurodevelopmental outcomes. We previously published a qualitative MRI injury scoring system utilizing serial T1-weighted, T2-weighted and diffusion-weighted imaging (DWI), weighted for deep nuclear gray matter injury. To establish the validity of the MRI scoring system with neurodevelopmental outcome at 18-24months. MRI scans from neonates with moderate to severe HIE treated with therapeutic hypothermia were evaluated. Signal abnormality was scored on T1-weighted, T2-weighted and DWI sequences and assessed using an established system in five regions: (a) subcortical: caudate nucleus, globus pallidus and putamen, thalamus and the posterior limb of the internal capsule; (b) white matter; (c) cortex, (d) cerebellum and (e) brainstem. MRI injury was graded as none, mild, moderate or severe. Inter-rater reliability was tested on a subset of scans by two independent and blinded neuroradiologists. Surviving infants underwent the Bayley Scales of Infant and Toddler Development-III (Bayley-III) at 18-24months. Data were analyzed using univariate and multivariate linear and logistic regression. Fifty-seven eligible neonates underwent at least one MRI scan in the first 2weeks of life. Mean postnatal age at scan 1 was 4±2days in 50/57 (88%) neonates and 48/54 (89%) surviving infants underwent scan 2 at 10±2days. In 54/57 (95%) survivors, higher MRI injury grades were significantly associated with worse outcomes in the cognitive, motor and language domains of the Bayley-III. A qualitative MRI injury scoring system weighted for deep nuclear gray matter injury is a significant predictor of neurodevelopmental outcome at 18-24months in neonates with HIE.

Calibration analysis software for the ATLAS Pixel Detector

The calibration of the ATLAS Pixel Detector at LHC fulfils two main purposes: to tune the front-end configuration parameters for establishing the best operational settings and to measure the tuning performance through a subset of scans. An analysis framework has been set up in order to take actions on the detector given the outcome of a calibration scan (e.g. to create a mask for disabling noisy pixels). The software framework to control all aspects of the Pixel Detector scans and analyses is called calibration console. The introduction of a new layer, equipped with new FE-I4 chips, required an update of the console architecture. It now handles scans and scan analyses applied together to chips with different characteristics. An overview of the newly developed calibration analysis software will be presented, together with some preliminary results.

Wavelet‐based blind deconvolution of near‐field ultrasound scans

A wavelet-based technique for blind deconvolution and denoising of ultrasound scans is introduced. The target application is near-field ultrasound imaging for non-destructive testing. Existing blind deconvolution techniques for ultrasound such as cepstrum-based methods and the work of Adam and Michailovich – based on discrete wavelet transform (DWT) shrinkage of the log-spectrum – estimate the pulse by exploiting the pulse log-spectrum smoothness relative to the material reflectivity function. In the proposed technique, the log-spectrum is localised with respect to time as the continuous wavelet transform (CWT) log-scalogram to deal with the non-stationarity of the near-field ultrasound signals in both the pulse estimation and deconvolution. The pulse is estimated in the wavelet domain via DWT shrinkage of the log-scalogram and is deconvolved by wavelet-domain Wiener filtering. Extensions of the proposed technique include: using separate CWT domains for estimation and deconvolution, as inspired by the WienerChop denoising method; and training the algorithm parameters on a subset of scans.

Residual vectors for Alzheimer disease diagnosis and prognostication

Alzheimer disease (AD) is an increasingly prevalent neurodegenerative condition and a looming socioeconomic threat. A biomarker for the disease could make the process of diagnosis easier and more accurate, and accelerate drug discovery. The current work describes a method for scoring brain images that is inspired by fundamental principles from information retrieval (IR), a branch of computer science that includes the development of Internet search engines. For this research, a dataset of 254 baseline 18-F fluorodeoxyglucose positron emission tomography (FDG-PET) scans was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For a given contrast, a subset of scans (nine of every 10) was used to compute a residual vector that typified the difference, at each voxel, between the two groups being contrasted. Scans that were not used for computing the residual vector (the remaining one of 10 scans) were then compared to the residual vector using a cosine similarity metric. This process was repeated sequentially, each time generating cosine similarity scores on 10% of the FDG-PET scans for each contrast. Statistical analysis revealed that the scores were significant predictors of functional decline as measured by the Functional Activities Questionnaire (FAQ). When logistic regression models that incorporated these scores were evaluated with leave-one-out cross-validation, cognitively normal controls were discerned from AD with sensitivity and specificity of 94.4% and 84.8%, respectively. Patients who converted from mild cognitive impairment (MCI) to AD were discerned from MCI nonconverters with sensitivity and specificity of 89.7% and 62.9%, respectively, when FAQ scores were brought into the model. Residual vectors are easy to compute and provide a simple method for scoring the similarity between an FDG-PET scan and sets of examples from a given diagnostic group. The method is readily generalizable to any imaging modality. Further interdisciplinary work between IR and clinical neuroscience is warranted.

How the latest evidence from clinical research informs patient care

How the latest evidence from clinical research informs patient care

Entorhinal cortex volume in older adults: Reliability and validity considerations for three published measurement protocols

Measuring the entorhinal cortex (ERC) is challenging due to lateral border discrimination from the perirhinal cortex. From a sample of 39 nondemented older adults who completed volumetric image scans and verbal memory indices, we examined reliability and validity concerns for three ERC protocols with different lateral boundary guidelines (i.e., Goncharova, Dickerson, Stoub, & deToledo-Morrell, 2001; Honeycutt et al., 1998; Insausti et al., 1998). We used three novice raters to assess inter-rater reliability on a subset of scans (216 total ERCs), with the entire dataset measured by one rater with strong intra-rater reliability on each technique (234 total ERCs). We found moderate to strong inter-rater reliability for two techniques with consistent ERC lateral boundary endpoints (Goncharova, Honeycutt), with negligible to moderate reliability for the technique requiring consideration of collateral sulcal depth (Insausti). Left ERC and story memory associations were moderate and positive for two techniques designed to exclude the perirhinal cortex (Insausti, Goncharova), with the Insausti technique continuing to explain 10% of memory score variance after additionally controlling for depression symptom severity. Right ERC-story memory associations were nonexistent after excluding an outlier. Researchers are encouraged to consider challenges of rater training for ERC techniques and how lateral boundary endpoints may impact structure-function associations.

High resolution computed tomography of knee menisci

Sixty four menisci in 22 patients were examined by non-contrast high resolution computed tomography (CT). Fifty four menisci were imaged on a GE 8800 CT scanner. The other 10 were imaged on a GE 9800 CT scanner. Nineteen menisci were shown to be torn on arthrography and/or arthroscopy--12 medially and 7 laterally. The remaining menisci were normal. When the meniscal CTs were reviewed collectively by three observers without knowledge of arthrographic or arthroscopic results, sensitivity was 63%, specificity was 93%, and accuracy was 84%. Although only 10 menisci were imaged on the GE 9800 scanner, the sensitivity in this subset of scans was 83%, the specificity 100%, and the accuracy was 90%. These results suggest that noncontrast CT cannot replace arthrography or arthroscopy, but it is possible that images obtained on the GE 9800 or comparable CT scanner may be accurate enough to be used for screening of meniscal tears in selected patients.