Department of Radiology - Open Access Policy Deposits

This series is automatically populated with publications deposited by UC San Diego School of Medicine Department of Radiation Medicine & Applied Science researchers in accordance with the University of California’s open access policies. For more information see Open Access Policy Deposits and the UC Publication Management System.

Sort By:

Show:

$Cover page of Fast dual-echo estimation of apparent long T2 fraction using ultrashort echo time magnetic resonance imaging in tibialis tendons and its osteoporosis-related differences in women$

Fast dual-echo estimation of apparent long T2 fraction using ultrashort echo time magnetic resonance imaging in tibialis tendons and its osteoporosis-related differences in women

(2024)

Background

Tendon and bone comprise a critical interrelating unit. Bone loss, including that seen with osteopenia (OPe) or osteoporosis (OPo), may be associated with a reduction in tendon quality, though this remains incompletely investigated. Clinical magnetic resonance imaging (MRI) sequences cannot directly detect signals from tendons because of the very short T2. Clinical MRI may detect high-graded abnormalities by changes in the adjacent structures like bone. However, ultrashort echo time MRI (UTE-MRI) can capture high signals from all tendons. To determine if the long T2 fraction, as measured by a dual-echo UTE-MRI sequence, is a sensitive quantitative technique to the age- and bone-loss-related changes of the lower leg tendons.

Methods

This is a cross-sectional study conducted between January 2018 to February 2020 in the lower legs of 14 female patients with OPe [72±6 years old, body mass index (BMI) =25.8±6.2 kg/m²] and 31 female patients with OPo (73±6 years old, BMI=22.0±3.8 kg/m²), as well as 30 female subjects with normal bone (Normal, 35±18 years old, BMI =23.2±4.3 kg/m²), were imaged on a 3T clinical scanner using a dual-echo 3D Cones UTE sequence. We defined the apparent long T2 signal fraction (aFrac-LongT2) of tendons as the ratio between the signal at the second echo time (TE =2.2 ms) to the UTE signal. The average aFrac-LongT2 and the cross-sectional area were calculated for the anterior tibialis tendons (ATTs) and the posterior tibialis tendons (PTTs). The Kruskal-Wallis rank test was used to compare the differences in aFrac-LongT2 and the cross-sectional area of the tendons between the groups.

Results

The aFrac-LongT2 of the ATTs and PTTs were significantly higher in the OPo group compared with the Normal group (22.2% and 34.8% in the ATT and PTT, respectively, P<0.01). The cross-sectional area in the ATTs was significantly higher for the OPo group than in the Normal group (Normal/OPo difference was 28.7, P<0.01). Such a difference for PTTs did not reach the significance level. Mean aFrac-LongT2 and cross-sectional area in the OPe group were higher than the Normal group and lower than the OPo group. However, the differences did not show statistical significance, likely due to the higher BMI in the OPe group.

Conclusions

Dual-echo UTE-MRI is a rapid quantification technique, and aFrac-LongT2 values showed significant differences in tendons between Normal and OPo patients.

Whole knee joint mapping using a phase modulated UTE adiabatic T1ρ (PM‐UTE‐AdiabT1ρ) sequence

(2024)

Purpose

To develop a 3D phase modulated UTE adiabatic T_1ρ (PM-UTE-AdiabT_1ρ ) sequence for whole knee joint mapping on a clinical 3 T scanner.

Methods

This new sequence includes six major features: (1) a magnetization reset module, (2) a train of adiabatic full passage pulses for spin locking, (3) a phase modulation scheme (i.e., RF cycling pair), (4) a fat saturation module, (5) a variable flip angle scheme, and (6) a 3D UTE Cones sequence for data acquisition. A simple exponential fitting was used for T_1ρ quantification. Phantom studies were performed to investigate PM-UTE-AdiabT_1ρ 's sensitivity to compositional changes and reproducibility as well as its correlation with continuous wave-T_1ρ measurement. The PM-UTE-AdiabT_1ρ technique was then applied to five ex vivo and five in vivo normal knees to measure T_1ρ values of femoral cartilage, meniscus, posterior cruciate ligament, anterior cruciate ligament, patellar tendon, and muscle.

Results

The phantom study demonstrated PM-UTE-AdiabT_1ρ 's high sensitivity to compositional changes, its high reproducibility, and its strong linear correlation with continuous wave-T_1ρ measurement. The ex vivo and in vivo knee studies demonstrated average T_1ρ values of 105.6 ± 8.4 and 77.9 ± 3.9 ms for the femoral cartilage, 39.2 ± 5.1 and 30.1 ± 2.2 ms for the meniscus, 51.6 ± 5.3 and 29.2 ± 2.4 ms for the posterior cruciate ligament, 79.0 ± 9.3 and 52.0 ± 3.1 ms for the anterior cruciate ligament, 19.8 ± 4.5 and 17.0 ± 1.8 ms for the patellar tendon, and 91.1 ± 8.8 and 57.6 ± 2.8 ms for the muscle, respectively.

Conclusion

The 3D PM-UTE-AdiabT_1ρ sequence allows volumetric T_1ρ assessment for both short and long T₂ tissues in the knee joint on a clinical 3 T scanner.

FEMA: Fast and efficient mixed‐effects algorithm for large sample whole‐brain imaging data

(2024)

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive Development^SM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

White matter and literacy: A dynamic system in flux

(2024)

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

Focal radiotherapy boost to MR-visible tumor for prostate cancer: a systematic review.

(2024)

PURPOSE: The FLAME trial provides strong evidence that MR-guided external beam radiation therapy (EBRT) focal boost for localized prostate cancer increases biochemical disease-free survival (bDFS) without increasing toxicity. Yet, there are many barriers to implementation of focal boost. Our objectives are to systemically review clinical outcomes for MR-guided EBRT focal boost and to consider approaches to increase implementation of this technique. METHODS: We conducted literature searches in four databases according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guideline. We included prospective phase II/III trials of patients with localized prostate cancer underdoing definitive EBRT with MR-guided focal boost. The outcomes of interest were bDFS and acute/late gastrointestinal and genitourinary toxicity. RESULTS: Seven studies were included. All studies had a median follow-up of greater than 4 years. There were heterogeneities in fractionation, treatment planning, and delivery. Studies demonstrated effectiveness, feasibility, and tolerability of focal boost. Based on the Phoenix criteria for biochemical recurrence, the reported 5-year biochemical recurrence-free survival rates ranged 69.7-100% across included studies. All studies reported good safety profiles. The reported ranges of acute/late grade 3 + gastrointestinal toxicities were 0%/1-10%. The reported ranges of acute/late grade 3 + genitourinary toxicities were 0-13%/0-5.6%. CONCLUSIONS: There is strong evidence that it is possible to improve oncologic outcomes without substantially increasing toxicity through MR-guided focal boost, at least in the setting of a 35-fraction radiotherapy regimen. Barriers to clinical practice implementation are addressable through additional investigation and new technologies.

Bi-Exponential 3D UTE-T1ρ Relaxation Mapping of Ex Vivo Human Knee Patellar Tendon at 3T.

(2024)

Introduction: The objective of this study was to assess the bi-exponential relaxation times and fractions of the short and long components of the human patellar tendon ex vivo using three-dimensional ultrashort echo time T1ρ (3D UTE-T1ρ) imaging. Materials and Methods: Five cadaveric human knee specimens were scanned using a 3D UTE-T1ρ imaging sequence on a 3T MR scanner. A series of 3D UTE-T1ρ images were acquired and fitted using single-component and bi-component models. Single-component exponential fitting was performed to measure the UTE-T1ρ value of the patellar tendon. Bi-component analysis was performed to measure the short and long UTE-T1ρ values and fractions. Results: The single-component analysis showed a mean single-component UTE-T1ρ value of 8.4 ± 1.7 ms for the five knee patellar tendon samples. Improved fitting was achieved with bi-component analysis, which showed a mean short UTE-T1ρ value of 5.5 ± 0.8 ms with a fraction of 77.6 ± 4.8%, and a mean long UTE-T1ρ value of 27.4 ± 3.8 ms with a fraction of 22.4 ± 4.8%. Conclusion: The 3D UTE-T1ρ sequence can detect the single- and bi-exponential decay in the patellar tendon. Bi-component fitting was superior to single-component fitting.

spatialHeatmap: visualizing spatial bulk and single-cell assays in anatomical images

(2024)

Visualizing spatial assay data in anatomical images is vital for understanding biological processes in cell, tissue, and organ organizations. Technologies requiring this functionality include traditional one-at-a-time assays, and bulk and single-cell omics experiments, including RNA-seq and proteomics. The spatialHeatmap software provides a series of powerful new methods for these needs, and allows users to work with adequately formatted anatomical images from public collections or custom images. It colors the spatial features (e.g. tissues) annotated in the images according to the measured or predicted abundance levels of biomolecules (e.g. mRNAs) using a color key. This core functionality of the package is called a spatial heatmap plot. Single-cell data can be co-visualized in composite plots that combine spatial heatmaps with embedding plots of high-dimensional data. The resulting spatial context information is essential for gaining insights into the tissue-level organization of single-cell data, or vice versa. Additional core functionalities include the automated identification of biomolecules with spatially selective abundance patterns and clusters of biomolecules sharing similar abundance profiles. To appeal to both non-expert and computational users, spatialHeatmap provides a graphical and a command-line interface, respectively. It is distributed as a free, open-source Bioconductor package (https://bioconductor.org/packages/spatialHeatmap) that users can install on personal computers, shared servers, or cloud systems.

A mathematical model for velocity‐selective arterial spin labeling

(2024)

To present a theoretical framework that rigorously defines and analyzes key concepts and quantities for velocity selective arterial spin labeling (VSASL). An expression for the VSASL arterial delivery function is derived based on (1) labeling and saturation profiles as a function of velocity and (2) physiologically plausible approximations of changes in acceleration and velocity across the vascular system. The dependence of labeling efficiency on the amplitude and effective bolus width of the arterial delivery function is defined. Factors that affect the effective bolus width are examined, and timing requirements to minimize quantitation errors are derived. The model predicts that a flow-dependent negative bias in the effective bolus width can occur when velocity selective inversion (VSI) is used for the labeling module and velocity selective saturation (VSS) is used for the vascular crushing module. The bias can be minimized by choosing a nominal labeling cutoff velocity that is lower than the nominal cutoff velocity of the vascular crushing module. The elements of the model are specified in a general fashion such that future advances can be readily integrated. The model can facilitate further efforts to understand and characterize the performance of VSASL and provide critical theoretical insights that can be used to design future experiments and develop novel VSASL approaches.

1 supplemental PDF

Brain structural covariance network features are robust markers of early heavy alcohol use

(2024)

Background and aims

Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.

Design and setting

Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years).

Cases

Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.

Measurements

Graph theory metrics of segregation and integration were used to summarize SCN.

Findings

Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.

Conclusion

Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

$Cover page of Marked difference in liver fat measured by histology vs. magnetic resonance-proton density fat fraction: A meta-analysis$

Marked difference in liver fat measured by histology vs. magnetic resonance-proton density fat fraction: A meta-analysis

(2024)

Background & aims

Pathologists quantify liver steatosis as the fraction of lipid droplet-containing hepatocytes out of all hepatocytes, whereas the magnetic resonance-determined proton density fat fraction (PDFF) reflects the tissue triacylglycerol concentration. We investigated the linearity, agreement, and correspondence thresholds between histological steatosis and PDFF across the full clinical spectrum of liver fat content associated with non-alcoholic fatty liver disease.

Methods

Using individual patient-level measurements, we conducted a systematic review and meta-analysis of studies comparing histological steatosis with PDFF determined by magnetic resonance spectroscopy or imaging in adults with suspected non-alcoholic fatty liver disease. Linearity was assessed by meta-analysis of correlation coefficients and by linear mixed modelling of pooled data, agreement by Bland-Altman analysis, and thresholds by receiver operating characteristic analysis. To explain observed differences between the methods, we used RNA-seq to determine the fraction of hepatocytes in human liver biopsies.

Results

Eligible studies numbered 9 (N = 597). The relationship between PDFF and histology was predominantly linear (r = 0.85 [95% CI, 0.80-0.89]), and their values approximately coincided at 5% steatosis. Above 5% and towards higher levels of steatosis, absolute values of the methods diverged markedly, with histology exceeding PDFF by up to 3.4-fold. On average, 100% histological steatosis corresponded to a PDFF of 33.0% (29.5-36.7%). Targeting at a specificity of 90%, optimal PDFF thresholds to predict histological steatosis grades were ≥5.75% for ≥S1, ≥15.50% for ≥S2, and ≥21.35% for S3. Hepatocytes comprised 58 ± 5% of liver cells, which may partly explain the lower values of PDFF vs. histology.

Conclusions

Histological steatosis and PDFF have non-perfect linearity and fundamentally different scales of measurement. Liver fat values obtained using these methods may be rendered comparable by conversion equations or threshold values.

Impact and implications

Magnetic resonance-proton density fat fraction (PDFF) is increasingly being used to measure liver fat in place of the invasive liver biopsy. Understanding the relationship between PDFF and histological steatosis fraction is important for preventing misjudgement of clinical status or treatment effects in patient care. Our analysis revealed that histological steatosis fraction is often significantly higher than PDFF, and their association varies across the spectrum of fatty liver severity. These findings are particularly important for physicians and clinical researchers, who may use these data to interpret PDFF measurements in the context of histologically evaluated liver fat content.