BACKGROUND:Current quantitative chest CT techniques improve chronic obstructive pulmonary disease (COPD) phenotyping but do not capture spatial variability and potentially reversible disease in local lung parenchyma. METHODS:Applying elastic principal graphing to CT scans from Genetic Epidemiology of COPD study participants (age 45-80 years; ≥10 pack-years), we developed elastic parametric response mapping (ePRM), a tiered scoring system (tiers 0-3 and tier Op, ie, lung opacities) that classifies lung subvolumes based on their relative composition of normal lung, emphysema, small airways disease and parenchymal disease. For 3631 participants with longitudinal data, we evaluated how relative tier assignment and mean tier position of subvolumes changed over 5 years and how they associated with forced expiratory volume in 1 s (FEV1) change. We stratified analyses by baseline spirometry: no airflow obstruction, Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-2 and GOLD 3-4. RESULTS:The proportion of tier 0 subvolumes decreased with worsening airflow obstruction, while tier 2 and 3 proportions increased. Tier 1 proportions were similar in GOLD 1-2 (25.7%) and GOLD 3-4 (28.1%), with over half of subvolumes remaining in tier 1 or reverting to tier 0 at year 5. In contrast to tiers 0 and 2, baseline mean tier 1 position was strongly predictive of reassignment to more advanced tiers at year 5 in participants without airflow obstruction, GOLD 1-2 and GOLD 3-4 (area under the curves (95% CIs) 0.86 (0.85 to 0.87), 0.90 (0.89 to 0.91) and 0.92 (0.90 to 0.93), respectively). A higher per cent volume of lung retained in tier 1 was associated with less FEV1 decline in all groups. CONCLUSION:CT ePRM categorises local lung tissue into distinct and potentially reversible tiers of disease severity.

RATIONALE –:Airway mucus plugging is a clinically relevant manifestation of airway pathology in chronic obstructive pulmonary disease (COPD) and is associated with increased mortality even in early disease; however, visual computed tomography (CT) assessment is subjective and labor intensive. OBJECTIVES –:To develop an AI-based quantitative CT method for automated detection of airway mucus plugging and evaluate associations with physiologic impairment and clinical outcomes. METHODS –:Inspiratory CT scans from 8,971 COPDGene Phase 1 (GOLD 0-4 and PRISm) participants were analyzed. An AI-based framework combining 3D airway segmentation discontinuities and convolutional neural network classification identified mucus plug obstructions, yielding mucus plug burden (total plug count). Associations with outcomes were evaluated using covariate-adjusted models. MEASUREMENTS AND MAIN RESULTS –:Higher mucus plug burden was associated with lower post-bronchodilator FEV1 % predicted (ρ = -0.41; P < 0.001), greater air trapping (LAA < -856 HU; ρ = 0.33; P < 0.001), worse health status (SGRQ; ρ = 0.31; P < 0.001), and shorter 6-minute walk distance (ρ = -0.26; P < 0.001). Among GOLD 1-4 participants, mucus plug presence was independently associated with increased all-cause mortality (adjusted hazard ratio, 1.28; P < 0.005) and exacerbation frequency (adjusted incidence rate ratio, 1.32; P < 0.005). Plug presence was also associated with increased respiratory mortality across GOLD categories and cardiovascular mortality in GOLD 1-2. CONCLUSIONS –:AI-based quantitative CT assessment of airway mucus plugging provides a scalable, reproducible measure associated with physiologic impairment and adverse outcomes in COPD, supporting its role in risk stratification and future therapeutic studies.

Accurate airway segmentation from chest computed tomography (CT) scans is essential for quantitative lung analysis, yet manual annotation is impractical and many automated U-Net-based methods yield disconnected components that hinder reliable biomarker extraction. We present RepAir, a three-stage framework for robust 3D airway segmentation that combines an nnU-Net-based network with anatomically informed topology correction. The segmentation network produces an initial airway mask, after which a skeleton-based algorithm identifies potential discontinuities and proposes reconnections. A 1D convolutional classifier then determines which candidate links correspond to true anatomical branches versus false or obstructed paths. We evaluate RepAir on two distinct datasets: ATM'22, comprising annotated CT scans from predominantly healthy subjects and AeroPath, encompassing annotated scans with severe airway pathology. Across both datasets, RepAir outperforms existing 3D U-Net-based approaches such as Bronchinet and NaviAirway on both voxel-level and topological metrics, and produces more complete and anatomically consistent airway trees while maintaining high segmentation accuracy.

Abstract Background Dordaviprone (ONC201) is the first FDA-approved therapy for H3K27M-mutant diffuse midline glioma (DMG). However, therapeutic response varies among patients, and no imaging biomarkers currently predict long-term benefit. Diffusion-weighted MRI and apparent diffusion coefficient (ADC) obtained during standard clinical imaging reflects tissue cellularity and necrosis, offering potential as an early response indicator. Methods Clinical and imaging data from patients treated with dordaviprone (n = 83) on completed studies underwent centralized neuroradiology review, ADC histogram analysis, and Functional Diffusion Mapping (fDM), which were compared and correlated to CSF tumor DNA (tDNA) and metabolomics when available. Final analysis included 38 patients receiving standardized dordaviprone dosing and treatment schedule. Results In the pre-progression cohort, early changes in volumetric or cross-sectional RAPNO criteria were not predictive of overall (OS) or progression-free survival (PFS). Importantly, in patients treated pre-progression with dordaviprone post-radiation, tumors with increased diffusion ADC (reduced cellularity) at cycle 3 demonstrated median OS of 732 days versus 360 days for those with decreased ADC (increased cellularity, p = 0.0149); with corresponding PFS of 334 versus 110 days (p = 0.0132). fDM analysis of spatio-temporal changes of ADC did not correlate with outcomes in a sub-cohort analysis but demonstrated predictive patterns in individual patient time course series. CSF metabolomics stratified by ADC change revealed differences in reductive stress metabolites among dordaviprone responders, linking ADC elevation to dordaviprone -driven metabolic effects. Conclusion Early increase in ADC correlates with improved response to dordaviprone in H3K27M-DMG and correlates with CSF metabolic changes, suggesting ADC warrants prospective validation as a biomarker.

Chronic obstructive pulmonary disease (COPD) is complex, and its course is difficult to predict due to its diverse pathophysiology. Small airway disease (SAD), a key component of COPD and potential target for emerging therapeutics, may be reversible in mild COPD, but left unchecked, may worsen, leading to airway loss and emphysema. The dual nature of SAD complicates clinical management of COPD patients, necessitating more accurate monitoring methods. To meet this need, we developed elastic Parametric Response Mapping (ePRM), a tiered scoring system that classifies local lung volumes by the degree of PRM-derived SAD, normal, and emphysematous tissue. In individuals with or at risk for COPD, we demonstrate that chest CT ePRM can categorize local lung tissue into distinct tiers of disease severity that distinguish between tissue characterized by early reversible SAD and progressive destruction. This level of characterization is crucial to developing personalized treatment strategies for COPD.

Abstract ONC201 is the first monotherapy to improve outcomes in H3K27M- diffuse midline glioma (DMG) beyond radiation. Despite impressive early efficacy in H3K27M-DMG, individual response to ONC201 is variable and no radiographic biomarkers predict long term response. Previous studies demonstrated baseline MRI diffusion apparent diffusion coefficient (ADC) is lower in DMG with H3K27M compared to wildtype but change after radiation therapy was not correlated with improved OS or PFS. We hypothesize ADC will enable stratification of patients more likely to respond to ONC201. Imaging and clinical data were abstracted from chart review of patients treated with ONC201 at University of Michigan. Two neuroradiologists performed centralized review for RAPNO measurements and mean ADC was assessed using ROI measured in consistent anatomic locations, with areas of cystic degeneration or necrosis excluded. Sixty-one patients were identified for review. Preliminary analysis was performed on twenty-three patients, with upfront ONC201 therapy, median age 14.8 years old (5-25yo), primary site includes pontine (n=15), thalamic (n=7). Baseline characteristics of age, ADC and size at diagnosis or follow-up RAPNO scored size were not statistically significant. Patients with longer than median survival (334 days) demonstrated increased ADC (+58.25x10-6mm2/s) from baseline to cycle 2 whereas patients with shorter than median survival demonstrated decreased ADC (-174.8x10-6mm2/s) (p = 0.0076). Further, patients with decreased ADC demonstrated mean OS of 302.8 days and patients with increased ADC had mean OS of 588.0 (p = 0.0054). In H3K27M-DMG patients treated with ONC201, increased ADC after two cycles of ONC201 was strongly predictive of longer overall survival. Our recent work demonstrated that ONC201 disrupts metabolic and epigenetic pathways to restore pathognomonic H3K27me3 reduction, which may underline greater change in ADC. Ongoing work will validate these findings in an independent external cohort and integrate histogram analysis, parametric assessments, MRI perfusion and liquid biopsy biomarkers (cf-tDNA and metabolomics).

Background Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline. Methods PRM metrics of normal lung (PRM Norm ) and functional SAD (PRM fSAD ) were generated from CT scans collected as part of the COPDGene study (n = 8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRM Norm and PRM fSAD . Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV 1 decline using a machine learning model. Results Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRM fSAD and PRM Norm were independently associated with the amount of emphysema. Readouts χ fSAD (β of 0.106, p < 0.001) and V fSAD (β of 0.065, p = 0.004) were also independently associated with FEV 1 % predicted. The machine learning model using PRM topologies as inputs predicted FEV 1 decline over five years with an AUC of 0.69. Conclusions We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRM fSAD and PRM Norm may show promise as an early indicator of emphysema onset and COPD progression.

ABSTRACT Rationale and Objectives Small airways disease (SAD) and emphysema are significant components of COPD, a heterogenous disease where predicting progression is difficult. SAD, a principal cause of airflow obstruction in mild COPD, has been identified as a precursor to emphysema. Parametric Response Mapping (PRM) of chest computed tomography (CT) can help distinguish SAD from emphysema. Specifically, topologic PRM can define local patterns of both diseases to characterize how and in whom COPD progresses. We aimed to determine if distribution of CT-based PRM of functional SAD (fSAD) is associated with emphysema progression. Materials and Methods We analyzed paired inspiratory-expiratory chest CT scans at baseline and 5-year follow up in 1495 COPDGene subjects using topological analyses of PRM classifications. By spatially aligning temporal scans, we mapped local emphysema at year 5 to baseline lobar PRM-derived topological readouts. K-means clustering was applied to all observations. Subjects were subtyped based on predominant PRM cluster assignments and assessed using non-parametric statistical tests to determine differences in PRM values, pulmonary function metrics and clinical measures. Results We identified distinct lobar imaging patterns and classified subjects into three radiologic subtypes: emphysema-dominant (ED), fSAD-dominant (FD), and fSAD-transition (FT: transition from healthy lung to fSAD). Relative to year 5 emphysema, FT showed rapid local emphysema progression (−57.5% ± 1.1) compared to FD (−49.9% ± 0.5) and ED (−33.1% ± 0.4). FT consisted primarily of at-risk subjects (roughly 60%) with normal spirometry. Conclusion The FT subtype of COPD may allow earlier identification of individuals without spirometrically-defined COPD at-risk for developing emphysema.

Abstract In chronic obstructive pulmonary disease (COPD) functional small airways disease (fSAD) has been identified as a transitional state between healthy lung and irremediable emphysema. Topological analysis of the CT-based parametric response map (PRM) spatially quantifies distribution and arrangement of fSAD and emphysema. We present a cross sectional analysis of topological PRM (tPRM) in 8,972 participants from the COPDGene study, covering a complete spectrum of COPD severity. We aimed to evaluate tPRM dynamics with respect to GOLD and test association of tPRM with pulmonary function using Spearman correlation and stepwise linear regression. Baseline CT and clinical data were included, and tPRM was computed as whole lung averages summarizing disease volume density, surface area, curvature and perforation (measured by Ⲭ). Strong correlation (ρ = -0.74, p < 0.001) was determined between degree of perforation in healthy lung (ⲬNorm) and fSAD regions (ⲬfSAD), transposing from predominantly healthy lung perforated with fSAD (GOLD 2, ⲬNorm = -0.84, ⲬfSAD = 0.47) to fSAD perforated with healthy lung (GOLD 4, ⲬNorm = 0.39, ⲬfSAD = -0.36). tPRM significantly (p < 0.05) contributed to regression modelling of FEV1/FVC (R2 = 0.71), where surface area of emphysema had the most notable effect (β = -0.49, p < 0.01). Thus, tPRM provided insight into topology of fSAD and emphysema in COPD and associated statistically with clinical lung function measures.

Chronic rejection of lung allografts has two major subtypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), which present radiologically either as air trapping with small airways disease or with persistent pleuroparenchymal opacities. Parametric response mapping (PRM), a computed tomography (CT) methodology, has been demonstrated as an objective readout of BOS and RAS and bears prognostic importance, but has yet to be correlated to biological measures. Using a topological technique, we evaluate the distribution and arrangement of PRM-derived classifications of pulmonary abnormalities from lung transplant recipients undergoing redo-transplantation for end-stage BOS (N = 6) or RAS (N = 6). Topological metrics were determined from each PRM classification and compared to structural and biological markers determined from microCT and histopathology of lung core samples. Whole-lung measurements of PRM-defined functional small airways disease (fSAD), which serves as a readout of BOS, were significantly elevated in BOS versus RAS patients (p = 0.01). At the core-level, PRM-defined parenchymal disease, a potential readout of RAS, was found to correlate to neutrophil and collagen I levels (p < 0.05). We demonstrate the relationship of structural and biological markers to the CT-based distribution and arrangement of PRM-derived readouts of BOS and RAS.

Background Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression. Objective To investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques. Materials and methods Paired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model. Results QAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach. Conclusion The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.

Recent characterization of spatiotemporal genomic architecture of IDH -wild-type multifocal glioblastomas (M-GBMs) suggests a clinically unobserved common-ancestor (CA) with a less aggressive phenotype, generating highly genetically divergent malignant gliomas/GBMs in distant brain regions. Using serial MRI/3D-reconstruction, whole-genome sequencing and spectral karyotyping-based single-cell phylogenetic tree building, we show two distinct types of tumor evolution in p53-mutant driven mouse models. Malignant gliomas/GBMs grow as a single mass (Type 1) and multifocal masses (Type 2), respectively, despite both exhibiting loss of Pten /chromosome 19 (chr19) and PI3K/Akt activation with sub-tetraploid/4N genomes. Analysis of early biopsied and multi-segment tumor tissues reveals no evidence of less proliferative diploid/2N lesions in Type 1 tumors. Strikingly, CA-derived relatively quiescent tumor precursors with ancestral diploid/2N genomes and normal Pten /chr19 are observed in the subventricular zone (SVZ), but are distantly segregated from multi focal Type 2 tumors. Importantly, PI3K/Akt inhibition by Rictor /mTORC2 deletion blocks distant dispersal, restricting glioma growth in the SVZ.

Rationale and Objectives: Glioblastoma image evaluation utilizes Magnetic Resonance Imaging contrast-enhanced, T1-weighted, and noncontrast T2-weighted fluid-attenuated inversion recovery (FLAIR) acquisitions. Disease progression assessment relies on changes in tumor diameter, which correlate poorly with survival. To improve treatment monitoring in glioblastoma, we investigated serial voxel-wise comparison of anatomically-aligned FLAIR signal as an early predictor of GBM progression. Materials and Methods: We analyzed longitudinal normalized FLAIR images (rFLAIR) from 52 subjects using voxel-wise Parametric Response Mapping (PRM) to monitor volume fractions of increased (PRMrFLAIR+), decreased (PRMrFLAIR-), or unchanged (PRMrFLAIR0) rFLAIR intensity. We determined response by rFLAIR between pretreatment and 10 weeks posttreatment. Risk of disease progression in a subset of subjects (N = 26) with stable disease or partial response as defined by Response Assessment in Neuro-Oncology (RANO) criteria was assessed by PRMrFLAIR between weeks 10 and 20 and continuously until the PRMrFLAIR+ exceeded a defined threshold. RANO defined criteria were compared with PRM-derived outcomes for tumor progression detection. Results: Patient stratification for progression-free survival (PFS) and overall survival (OS) was achieved at week 10 using RANO criteria (PFS: p <0.0001; OS: p <0.0001), relative change in FLAIR-hyperintense volume (PFS: p = 0.0011; OS: p <0.0001), and PRMrFLAIR+ (PFS: p <0.01; OS: p <0.001). PRMrFLAIR+ also stratified responding patients' progression between weeks 10 and 20 (PFS: p <0.05; OS: p = 0.01) while changes in FLAIR-volume measurements were not predictive. As a continuous evaluation, PRMrFLAIR+ exceeding 10% stratified patients for PFA after 5.6 months (p<0.0001), while RANO criteria did not stratify patients until 15.4 months (p <0.0001). Conclusion: PRMrFLAIR may provide an early biomarker of disease progression in glioblastoma.

Rationale and Objectives: The aim of this study was to assess variability in quantitative air trapping (QAT) measurements derived from spatially aligned expiration CT scans. Materials and Methods: Sixty-four paired CT examinations, from 16 school-age cystic fibrosis subjects examined at four separate time intervals, were used in this study. For each pair, visually inspected lobe segmentation maps were generated and expiration CT data were registered to the inspiration CT frame. Measurements of QAT, the percentage of voxels on the expiration CT scan below a set threshold were calculated for each lobe and whole-lung from the registered expiration CT and compared to the true values from the unregistered data. Results: A mathematical model, which simulates the effect of variable regions of lung deformation on QAT values calculated from aligned to those from unaligned data, showed the potential for large bias. Assessment of experimental QAT measurements using Bland-Altman plots corroborated the model simulations, demonstrating biases greater than 5% when QAT was approximately 40% of lung volume. These biases were removed when calculating QAT from aligned expiration CT data using the determinant of the Jacobian matrix. We found, by Dice coefficient analysis, good agreement between aligned expiration and inspiration segmentation maps for the whole-lung and all but one lobe (Dice coefficient > 0.9), with only the lingula generating a value below 0.9 (mean and standard deviation of 0.85 +/- 0.06). Conclusion: The subtle and predictable variability in corrected QAT observed in this study suggests that image registration is reliable in preserving the accuracy of the quantitative metrics.

BACKGROUND AND PURPOSE:Standard assessment criteria for brain tumors that only include anatomic imaging continue to be insufficient. While numerous studies have demonstrated the value of DSC-MR imaging perfusion metrics for this purpose, they have not been incorporated due to a lack of confidence in the consistency of DSC-MR imaging metrics across sites and platforms. This study addresses this limitation with a comparison of multisite/multiplatform analyses of shared DSC-MR imaging datasets of patients with brain tumors. MATERIALS AND METHODS:DSC-MR imaging data were collected after a preload and during a bolus injection of gadolinium contrast agent using a gradient recalled-echo-EPI sequence (TE/TR = 30/1200 ms; flip angle = 72°). Forty-nine low-grade (n = 13) and high-grade (n = 36) glioma datasets were uploaded to The Cancer Imaging Archive. Datasets included a predetermined arterial input function, enhancing tumor ROIs, and ROIs necessary to create normalized relative CBV and CBF maps. Seven sites computed 20 different perfusion metrics. Pair-wise agreement among sites was assessed with the Lin concordance correlation coefficient. Distinction of low- from high-grade tumors was evaluated with the Wilcoxon rank sum test followed by receiver operating characteristic analysis to identify the optimal thresholds based on sensitivity and specificity. RESULTS:For normalized relative CBV and normalized CBF, 93% and 94% of entries showed good or excellent cross-site agreement (0.8 ≤ Lin concordance correlation coefficient ≤ 1.0). All metrics could distinguish low- from high-grade tumors. Optimum thresholds were determined for pooled data (normalized relative CBV = 1.4, sensitivity/specificity = 90%:77%; normalized CBF = 1.58, sensitivity/specificity = 86%:77%). CONCLUSIONS:By means of DSC-MR imaging data obtained after a preload of contrast agent, substantial consistency resulted across sites for brain tumor perfusion metrics with a common threshold discoverable for distinguishing low- from high-grade tumors.

3D printing is a rapidly evolving technology at the forefront of biomedical innovation and cardiovascular 3D printing is one of the most common medical applications (1). Technical advances in biomedical 3D printing have been driven by improved printer technology and image segmentation tools, however, clinical and research applications of cardiovascular are still catching up to such rapid technical advances.

HomeCirculation: Cardiovascular ImagingVol. 11, No. 8Three-Dimensional Growth Analysis of Thoracic Aortic Aneurysm With Vascular Deformation Mapping Free AccessCase ReportPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissionsDownload Articles + Supplements ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toSupplemental MaterialFree AccessCase ReportPDF/EPUBThree-Dimensional Growth Analysis of Thoracic Aortic Aneurysm With Vascular Deformation Mapping Nicholas S. Burris, MD, Benjamin A. Hoff, PhD, Himanshu J. Patel, MD, Ella A. Kazerooni, MD, MS and Brian D. Ross, PhD Nicholas S. BurrisNicholas S. Burris Nicholas S. Burris, MD, Department of Radiology, University of Michigan, 1500 E Medical Center Dr, TC B1-132, SPC-5030, Ann Arbor, MI 48109-5030. E-mail E-mail Address: [email protected] Department of Radiology (N.S.B., E.A.K.) , Benjamin A. HoffBenjamin A. Hoff University of Michigan, Ann Arbor. Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor. (B.A.H., B.D.R.). , Himanshu J. PatelHimanshu J. Patel Department of Cardiothoracic Surgery (H.J.P.) , Ella A. KazerooniElla A. Kazerooni Department of Radiology (N.S.B., E.A.K.) and Brian D. RossBrian D. Ross Department of Biologic Chemistry (B.D.R.) University of Michigan, Ann Arbor. Department of Radiology, Center for Molecular Imaging, University of Michigan, Ann Arbor. (B.A.H., B.D.R.). Originally published17 Aug 2018https://doi.org/10.1161/CIRCIMAGING.118.008045Circulation: Cardiovascular Imaging. 2018;11:e008045IntroductionPatients with thoracic aortic aneurysm undergo regular imaging surveillance, commonly with computed tomography angiography (CTA). Aortic diameter measurements are the standard metric for assessing aortic growth and risk for adverse events, but are subject to significant measurement variability, on the order of 1 to 5 mm, a problem that is compounded when serial studies are compared.1,2 Vascular deformation mapping (VDM) is a recently developed technique that uses serial CTA examinations to quantify interval aortic growth in a 3-dimensional (3D) manner.3 VDM measures the local change in aortic wall dimensions between 2-time points through registration of clinical CTA data followed by quantification of local aortic wall deformation (ie, growth) between studies using a spatial Jacobian analysis. The determinant of the spatial Jacobian at each voxel is normalized by the time interval to yield a deformation rate (|J|/y) and, this data are superimposed on a 3D model of the aorta to allow for topographical depiction of aortic growth. The spatial Jacobian tensor is a dimensionless parameter, thus paired luminal circumference measurements are used to calculate aortic growth rate (mm/y). We present the case of a young woman with aortitis and a rapidly enlarging ascending aortic aneurysm undergoing presurgical evaluation, studied as part of a Health Insurance Portability and Accountability Act-compliant and Institutional Review Board–approved study at the University of Michigan. Although the tubular ascending aorta met size criteria for surgical repair, an accurate assessment of growth in adjacent segments was desired to determine the extent of repair (ie, how much aorta to resect), as replacement of the aortic root and arch carry added technical challenges and patient risk. Maximal diameter measurements were performed on clinical CTA studies spanning a 2-year period acquired on the same dual-source CT scanner (Siemens SOMATOM Force; Siemens Healthcare AG, Erlangen, Germany) but acquired at difference centers (Figure 1). Rapid growth of the mid-ascending level was clearly detected by diameter measurements with a calculated growth rate of ≈9 mm/y. Although there was ≈1 mm of increase in the maximal aortic diameter at the level of the sinuses, proximal arch, and distal arch, the conclusion of clinical diameter assessment was that these segments were stable within the limits of measurement variability (ie, ±2 mm).4 Subsequently, VDM analysis was performed on the same CTA studies (Figure 2; Video 1), and results were validated by comparison with paired luminal circumference measurements (Figure 3). In agreement with diameter measurements, rapid growth (9.2 mm/y) was noted at the ascending aorta by VDM analysis, and the aortic root dimension were stable over the 2-year interval. VDM analysis demonstrated that growth of the ascending aorta extended proximally to involve the sinotubular junction, from which both the right and left coronary ostia arose, implying the need for coronary reimplantation. In disagreement with diameter assessment low intensity (1.0 mm/y), eccentric growth was noted at the proximal arch, with a higher degree of growth along the greater curvature (yellow arrow) than the lesser curvature (purple arrow). Last, an area of low-intensity growth (0.8 mm/y) was detected at the mid-descending level, which was not clinically suspected but consistent with the patient's aortitis (blue arrowhead). Using VDM results, a surgical repair strategy was devised that maximized resection of diseased aortic tissue while balancing surgical risk (Figure 2; gray dotted line). Aortic growth occurs as a result of failing aortic wall structural integrity; however, diameter-based assessments are often limited for confident detection of slow because of measurement variability and do not depict growth in a 3D manner. VDM is a new imaging analysis technique that overcomes these limitations, whereas harnessing the high-resolution, volumetric data produced by modern CTA techniques, allowing for a more comprehensive depiction of aortic growth that can be applied to inform surgical planning and advance understanding of thoracic aortic aneurysm disease progression.Download figureDownload PowerPointFigure 1. Aortic growth assessment by diameter measurements: Three-dimensional volume rendering of the most recent computed tomography angiography (CTA) examination (left) demonstrating standard locations for clinical aortic diameter measurements, including sinuses of Valsalva (A), mida scending (B), proximal arch (C), and distal arch (D). Maximum aortic diameter measurements (in mm) at each location are shown at baseline (2016) and follow-up (2018) CTA examinations at the matched locations, using double-oblique technique.Download figureDownload PowerPointFigure 2. Result of vascular deformation mapping (VDM) shown in left anterior oblique (LAO; left) and right posterior oblique (RPO; right) projections. The color scale represents the degree of measured aortic wall deformation by spatial Jacobian analysis and is normalized by the interval between computed tomography angiography examinations to yield a growth rate (|J|/y). Based on VDM analysis, growth of the ascending aorta involved the right coronary (RCA) and left main coronary (LM) ostia. (white arrows), and eccentric growth was noted in the proximal arch, which was higher in degree along the greater curvature (yellow arrow) than the lesser curvature (purple arrow). A focal region of growth was detected at the mid-descending level (blue arrowhead). An aberrant origin of the right subclavian artery was incidentally noted.Download figureDownload PowerPointFigure 3. Vascular deformation mapping (VDM) results were validated through measurement of change in aortic perimeter measurements (in mm) between baseline (2016) and follow-up (2018) computed tomography angiography studies at 5 standard locations: sinuses of Valsalva (A), mid-ascending (B), proximal arch (C), distal arch (D), and mid-descending (E). Aortic circumference was used to calculate a derived diameter for growth rate assessment (circumference [in mm]/π].Sources of FundingDr Burris received a Research Fellow Grant from the Radiologic Society of North America (RF1502) and Dr Ross, US National Institutes of Health R35CA197701 and U01CA166104.DisclosuresDrs Burris, Hoff, Kazerooni, and Ross are entitled to royalties from the licensure of intellectual property studied in this research. The Vascular Deformation Mapping technology has been licensed to Imbio, LLC, a company in which Dr Ross has a financial interest. The other author reports no conflicts.Footnoteshttps://www.ahajournals.org/journal/circimagingNicholas S. Burris, MD, Department of Radiology, University of Michigan, 1500 E Medical Center Dr, TC B1-132, SPC-5030, Ann Arbor, MI 48109-5030. E-mail [email protected]umich.eduReferences1. Asch FM, Yuriditsky E, Prakash SK, Roman MJ, Weinsaft JW, Weissman G, Weigold WG, Morris SA, Ravekes WJ, Holmes KW, Silberbach M, Milewski RK, Kroner BL, Whitworth R, Eagle KA, Devereux RB, Weissman NJ; GenTAC Investigators. The need for standardized methods for measuring the aorta: multimodality core labexperiencefrom the GenTAC registry.JACC Cardiovasc Imaging. 2016; 9:219–226. doi: 10.1016/j.jcmg.2015.06.023CrossrefMedlineGoogle Scholar2. Elefteriades JA, Farkas EA. Thoracic aortic aneurysm clinically pertinent controversies and uncertainties.J Am Coll Cardiol. 2010; 55:841–857. doi: 10.1016/j.jacc.2009.08.084CrossrefMedlineGoogle Scholar3. Burris NS, Hoff BA, Kazerooni EA, Ross BD. Vascular deformationmapping (VDM) of thoracicaorticenlargement in aneurysmaldisease and dissection.Tomography. 2017; 3:163–173. doi: 10.18383/j.tom.2017.00015CrossrefMedlineGoogle Scholar4. Quint LE, Liu PS, Booher AM, Watcharotone K, Myles JD. Proximal thoracic aortic diameter measurements at CT: repeatability and reproducibility according to measurement method.Int J Cardiovasc Imaging. 2013; 29:479–488. doi: 10.1007/s10554-012-0102-9CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Burris N, Bian Z, Dominic J, Zhong J, Houben I, van Bakel T, Patel H, Ross B, Christensen G and Hatt C (2022) Vascular Deformation Mapping for CT Surveillance of Thoracic Aortic Aneurysm Growth, Radiology, 10.1148/radiol.2021210658, 302:1, (218-225), Online publication date: 1-Jan-2022. Bian Z, Zhong J, Dominic J, Christensen G, Hatt C and Burris N (2022) Validation of a robust method for quantification of three‐dimensional growth of the thoracic aorta using deformable image registration, Medical Physics, 10.1002/mp.15496, 49:4, (2514-2530), Online publication date: 1-Apr-2022. 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