Virtual monoenergetic imaging in photon-counting CT of the head and neck.

PURPOSE: Advantages of virtual monoenergetic images (VMI) have been reported for dual energy CT of the head and neck, and more recently VMIs derived from photon-counting (PCCT) angiography of the head and neck. We report image quality metrics of VMI in a PCCT angiography dataset, expanding the anatomical regions evaluated and extending observer-based qualitative methods further than previously reported. METHODS: In a prospective study, asymptomatic subjects underwent contrast enhanced PCCT of the head and neck using an investigational scanner. Image sets of low, high, and full spectrum (Threshold-1) energies; linear mix of low and high energies (Mix); and 23 VMIs (40-150 keV, 5 keV increments) were generated. In 8 anatomical locations, SNR and radiologists' preferences for VMI energy levels were measured using a forced-choice rank method (4 observers) and ratings of image quality using visual grading characteristic (VGC) analysis (2 observers) comparing VMI to Mix and Threshold-1 images. RESULTS: Fifteen subjects were included (7 men, 8 women, mean 57 years, range 46-75). Among all VMIs, SNRs varied by anatomic location. The highest SNRs were observed in VMIs. Radiologists preferred 50-60 keV VMIs for vascular structures and 75-85 keV for all other structures. Cumulative ratings of image quality averaged across all locations were higher for VMIs with areas under the curve of VMI vs Mix and VMI vs Threshold-1 of 0.67 and 0.68 for the first reader and 0.72 and 0.76 for the second, respectively. CONCLUSION: Preferred keV level and quality ratings of VMI compared to mixed and Threshold-1 images varied by anatomical location.

A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging.

BACKGROUND: The development of adipose tissue during adolescence may provide valuable insights into obesity-associated diseases. We propose an automated convolutional neural network (CNN) approach using Dixon-based magnetic resonance imaging (MRI) to quantity abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in children and adolescents. METHODS: 474 abdominal Dixon MRI scans of 136 young healthy volunteers (aged 8-18) were included in this study. For each scan, an axial fat-only Dixon image located at the L2-L3 disc space and another image at the L4-L5 disc space were selected for quantification. For each image, an outer and an inner region around the abdomen wall, as well as SAT and VAT pixel masks, were generated by expert readers as reference standards. A standard U-Net CNN architecture was then used to train two models: one for region segmentation and one for fat pixel classification. The performance was evaluated using the dice similarity coefficient (DSC) with fivefold cross-validation, and by Pearson correlation and the Student's t-test against the reference standards. RESULTS: For the DSC results, means and standard deviations of the outer region, inner region, SAT, and VAT comparisons were 0.974 ± 0.026, 0.997 ± 0.003, 0.981 ± 0.025, and 0.932 ± 0.047, respectively. Pearson coefficients were 1.000 for both outer and inner regions, and 1.000 and 0.982 for SAT and VAT comparisons, respectively (all p = NS). CONCLUSION: These results show that our method not only provides excellent agreement with the reference SAT and VAT measurements, but also accurate abdominal wall region segmentation. The proposed combined region- and pixel-based CNN approach provides automated abdominal wall segmentation as well as SAT and VAT quantification with Dixon MRI and enables objective longitudinal assessment of adipose tissues in children during adolescence.

CT Evaluation of Lymph Nodes That Merge or Split during the Course of a Clinical Trial: Limitations of RECIST 1.1.

Purpose To compare Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 with volumetric measurement in the setting of target lymph nodes that split into two or more nodes or merge into one conglomerate node. Materials and Methods In this retrospective study, target lymph nodes were evaluated on CT scans from 166 patients with different types of cancer; 158 of the scans came from The Cancer Imaging Archive. Each target node was measured using RECIST 1.1 criteria before and after merging or splitting, followed by volumetric segmentation. To compare RECIST 1.1 with volume, a single-dimension hypothetical diameter (HD) was determined from the nodal volume. The nodes were divided into three groups: (a) one-target merged (one target node merged with other nodes); (b) two-target merged (two neighboring target nodes merged); and (c) split node (a conglomerate node cleaved into smaller fragments). Bland-Altman analysis and t test were applied to compare RECIST 1.1 with HD. On the basis of the RECIST 1.1 concept, we compared response category changes between RECIST 1.1 and HD. Results The data set consisted of 30 merged nodes (19 one-target merged and 11 two-target merged) and 20 split nodes (mean age for all 50 included patients, 50 years ± 7 [standard deviation]; 38 men). RECIST 1.1, volumetric, and HD measurements indicated an increase in size in all one-target merged nodes. While volume and HD indicated an increase in size for nodes in the two-target merged group, RECIST 1.1 showed a decrease in size in all two-target merged nodes. Although volume and HD demonstrated a decrease in size of all split nodes, RECIST 1.1 indicated an increase in size in 60% (12 of 20) of the nodes. Discrepancy of the response categories between RECIST 1.1 and HD was observed in 5% (one of 19) in one-target merged, 82% (nine of 11) in two-target merged, and 55% (11 of 20) in split nodes. Conclusion RECIST 1.1 does not optimally reflect size changes when lymph nodes merge or split. Keywords: CT, Lymphatic, Tumor Response Supplemental material is available for this article. © RSNA, 2021.

Morphometric and Functional Brain Connectivity Differentiates Chess Masters From Amateur Players.

A common task in brain image analysis includes diagnosis of a certain medical condition wherein groups of healthy controls and diseased subjects are analyzed and compared. On the other hand, for two groups of healthy participants with different proficiency in a certain skill, a distinctive analysis of the brain function remains a challenging problem. In this study, we develop new computational tools to explore the functional and anatomical differences that could exist between the brain of healthy individuals identified on the basis of different levels of task experience/proficiency. Toward this end, we look at a dataset of amateur and professional chess players, where we utilize resting-state functional magnetic resonance images to generate functional connectivity (FC) information. In addition, we utilize T1-weighted magnetic resonance imaging to estimate morphometric connectivity (MC) information. We combine functional and anatomical features into a new connectivity matrix, which we term as the functional morphometric similarity connectome (FMSC). Since, both the FC and MC information is susceptible to redundancy, the size of this information is reduced using statistical feature selection. We employ off-the-shelf machine learning classifier, support vector machine, for both single- and multi-modality classifications. From our experiments, we establish that the saliency and ventral attention network of the brain is functionally and anatomically different between two groups of healthy subjects (chess players). We argue that, since chess involves many aspects of higher order cognition such as systematic thinking and spatial reasoning and the identified network is task-positive to cognition tasks requiring a response, our results are valid and supporting the feasibility of the proposed computational pipeline. Moreover, we quantitatively validate an existing neuroscience hypothesis that learning a certain skill could cause a change in the brain (functional connectivity and anatomy) and this can be tested via our novel FMSC algorithm.

Lens Dose Reduction by Patient Posture Modification During Neck CT.

OBJECTIVE: Radiation exposure of the lens during neck CT may increase a patient's risk of developing cataracts. Radiologists at the National Institutes of Health worked with technicians to modify the neck CT scanning procedure to include a reduction in the scanning range, a reduction in the tube potential (kilovoltage), and a change in neck positioning using a head tilt. We objectively quantified the organ dose changes after this procedure modification using a computer simulation. MATERIALS AND METHODS: We retrospectively analyzed CT images of 40 patients (20 men and 20 women) scanned before and after the procedure change. Radiation dose to the lens delivered before and after the procedure change was calculated using an in-house CT dose calculator combined with computational human phantoms deformed to match head tilt angles. We also calculated the doses to other radiosensitive organs including the brain, pituitary gland, eye globes, and salivary glands before and after the procedure change. RESULTS: Our dose calculations showed that modifying the neck position, shortening the scanning range, and reducing the tube potential reduced the dose to the lens by 89% (p < 0.0001). The median brain, pituitary gland, globes, and salivary gland doses also decreased by 59%, 52%, 66%, and 29%, respectively. We found that overranging significantly affects the lens dose. CONCLUSION: Combining head tilt and scanning range reduction is an easy and effective method that significantly reduces radiation dose to the lens and other radiosensitive head and neck organs.

Unrelated umbilical cord blood transplant for juvenile metachromatic leukodystrophy: a 5-year follow-up in three affected siblings.

Unrelated umbilical cord blood transplantation (UCBT) was used to treat three siblings with juvenile metachromatic leukodystrophy (jMLD). The efficacy of this therapy was measured over a 5-year period with serial neurological examinations, neuroimaging, nerve conduction studies (NCS), and neuropsychological evaluations (NPE). Outcomes were a function of disease stage at time of UCBT with alteration of disease course occurring in the first 2 years after UCBT and then subsequent halting of progression and stabilization of symptoms and disease.

ECoG gamma activity during a language task: differentiating expressive and receptive speech areas.

Electrocorticographic (ECoG) spectral patterns obtained during language tasks from 12 epilepsy patients (age: 12-44 years) were analysed in order to identify and characterize cortical language areas. ECoG from 63 subdural electrodes (500 Hz/channel) chronically implanted over frontal, parietal and temporal lobes were examined. Two language tasks were performed. During the first language task, patients listened to a series of 50 words preceded by warning tones, and were asked to repeat each word. During a second memory task, subjects heard the 50 words from the first task randomly mixed with 50 new words and were asked to repeat the word only if it was a new word. Increases in ECoG gamma power (70-100 Hz) were observed in response to hearing tones (primary auditory cortex), hearing words (posterior temporal and parietal cortex) and repeating words (lateral frontal and anterior parietal cortex). These findings were compared to direct electrical stimulation and separate analysis of ECoG gamma changes during spontaneous inter-personal conversations. The results indicate that high-frequency ECoG reliably differentiates cortical areas associated with receptive and expressive speech processes for individual patients. Compared to listening to words, greater frontal lobe and decreased temporal lobe gamma activity was observed while speaking. The data support the concept of distributed functionally specific language modules interacting to serve receptive and expressive speech, with frontal lobe 'corollary discharges' suppressing low-level receptive cortical language areas in the temporal lobe during speaking.