Hippocampal subfields and thalamic nuclei associations with clinical outcomes in multiple sclerosis: An ultrahigh field MRI study.

BACKGROUND: Previous studies have shown that thalamic and hippocampal neurodegeneration is associated with clinical decline in Multiple Sclerosis (MS). However, contributions of the specific thalamic nuclei and hippocampal subfields require further examination. OBJECTIVE: Using 7 Tesla (7T) magnetic resonance imaging (MRI), we investigated the cross-sectional associations between functionally grouped thalamic nuclei and hippocampal subfields volumes and T1 relaxation times (T1-RT) and subsequent clinical outcomes in MS. METHODS: High-resolution T1-weighted and T2-weighted images were acquired at 7T (n=31), preprocessed, and segmented using the Thalamus Optimized Multi Atlas Segmentation (THOMAS, for thalamic nuclei) and the Automatic Segmentation of Hippocampal Subfields (ASHS, for hippocampal subfields) packages. We calculated Pearson correlations between hippocampal subfields and thalamic nuclei volumes and T1-RT and subsequent multi-modal rater-determined and patient-reported clinical outcomes (∼2.5 years after imaging acquisition), correcting for confounders and multiple tests. RESULTS: Smaller volume bilaterally in the anterior thalamus region correlated with worse performance in gait function, as measured by the Patient Determined Disease Steps (PDDS). Additionally, larger volume in most functional groups of thalamic nuclei correlated with better visual information processing and cognitive function, as measured by the Symbol Digit Modalities Test (SDMT). In bilateral medial and left posterior thalamic regions, there was an inverse association between volumes and T1-RT, potentially indicating higher tissue degeneration in these regions. We also observed marginal associations between the right hippocampal subfields (both volumes and T1-RT) and subsequent clinical outcomes, though they did not survive correction for multiple testing. CONCLUSION: Ultrahigh field MRI identified markers of structural damage in the thalamic nuclei associated with subsequently worse clinical outcomes in individuals with MS. Longitudinal studies will enable better understanding of the role of microstructural integrity in these brain regions in influencing MS outcomes.

Photoacoustic detection of circulating melanoma cells in late stage patients.

Melanoma is the deadliest skin cancer and is responsible for over 7000 deaths in the US annually. The spread of cancer, or metastasis, is responsible for these deaths, as secondary tumors interrupt normal organ function. Circulating tumor cells, or those cells that spread throughout the body from the primary tumor, are thought to be responsible for metastasis. We developed an optical method, photoacoustic flow cytometry, in order to detect and enumerate circulating melanoma cells (CMCs) from blood samples of patients. We tested the blood of Stage IV melanoma patients to show the ability of the photoacoustic flow cytometer to detect these rare cells in blood. We then tested the system on archived blood samples from Stage III melanoma patients with known outcomes to determine if detection of CMCs can predict future metastasis. We detected between 0 and 66 CMCs in Stage IV patients. For the Stage III study, we found that of those samples with CMCs, 2 remained disease free and 5 developed metastasis. Of those without CMCs, 6 remained disease free and 1 developed metastasis. We believe that photoacoustic detection of CMCs provides valuable information for the prediction of metastasis and we postulate a system for more accurate prognosis.

Bacteriophage-mediated identification of bacteria using photoacoustic flow cytometry.

Infection with resistant bacteria has become an ever increasing problem in modern medical practice. Currently, broad spectrum antibiotics are prescribed until bacteria can be identified through blood cultures, a process that can take two to three days and is unable to provide quantitative information. To detect and quantify bacteria rapidly in blood samples, we designed a method using labeled bacteriophage in conjunction with photoacoustic flow cytometry (PAFC). PAFC is the generation of ultrasonic waves created by the absorption of laser light in particles under flow. Bacteriophage is a virus that infects bacteria and possesses the ability to discriminate bacterial surface antigens, allowing the bacteriophage to bind only to their target bacteria. Bacteria can be tagged with dyed phage and processed through a photoacoustic flow cytometer where they are detected by the acoustic response. We demonstrate that E. coli; can be detected and discriminated from Salmonella; using this method. Our goal is to develop a method to determine bacterial content in blood samples. We hope to develop this technology into future clinical use and decrease the time required to identify bacterial species from 3 to 4 days to less than 1 hour.

Ultrashort echo time imaging for quantification of hepatic iron overload: Comparison of acquisition and fitting methods via simulations, phantoms, and in vivo data.

BACKGROUND: Current R2*-MRI techniques for measuring hepatic iron content (HIC) use various acquisition types and fitting models. PURPOSE: To evaluate the accuracy and precision of R2*-HIC acquisition and fitting methods. STUDY TYPE: Signal simulations, phantom study, and prospective in vivo cohort. POPULATION: In all, 132 patients (58/74 male/female, mean age 17.7 years). FIELD STRENGTH/SEQUENCE: 2D-multiecho gradient-echo (GRE) and ultrashort echo time (UTE) acquisitions at 1.5T. ASSESSMENT: Synthetic MR signals were created to mimic published GRE and UTE methods, using different R2* values (25-2000 s-1 ) and signal-to-noise ratios (SNR). Phantoms with varying iron concentrations were scanned at 1.5T. In vivo data were analyzed from 132 patients acquired at 1.5T. R2* was estimated by fitting using three signal models. Accuracy and precision of R2* measurements for UTE acquisition parameters (SNR, echo spacing [ΔTE], maximum echo time [TEmax ]) and fitting methods were compared for simulated, phantom, and in vivo datasets. STATISTICAL TESTS: R2* accuracy was determined from the relative error and by linear regression analysis. Precision was evaluated using coefficient of variation (CoV) analysis. RESULTS: In simulations, all models had high R2* accuracy (error <5%) and precision (CoV <10%) for all SNRs, shorter ΔTE (≤0.5 msec), and longer TEmax (≥10.1 msec); except the constant offset model overestimated R2* at the lowest SNR. In phantoms and in vivo, all models produced similar R2* values for different SNRs and shorter ΔTEs (slopes: 0.99-1.06, R2 > 0.99, P < 0.001). In all experiments, R2* results degraded for high R2* values with longer ΔTE (≥1 msec). In vivo, shorter and longer TEmax gave similar R2* results (slopes: 1.02-1.06, R2 > 0.99, P < 0.001) for the noise subtraction model for 25≤R2*≤2000 s-1 . However, both quadratic and constant offset models, using shorter TEmax (≤4.7 msec) overestimated R2* and yielded high CoVs up to ∼170% for low R2* (<250 s-1 ). DATA CONCLUSION: UTE with TEmax ≥ 10.1 msec and ΔTE ≤ 0.5 msec yields accurate R2* estimates over the entire clinical HIC range. Monoexponential fitting with noise subtraction is the most robust signal model to changes in UTE parameters and achieves the highest R2* accuracy and precision. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1475-1488.