Back-to-Africa introductions of Mycobacterium tuberculosis as the main cause of tuberculosis in Dar es Salaam, Tanzania.

In settings with high tuberculosis (TB) endemicity, distinct genotypes of the Mycobacterium tuberculosis complex (MTBC) often differ in prevalence. However, the factors leading to these differences remain poorly understood. Here we studied the MTBC population in Dar es Salaam, Tanzania over a six-year period, using 1,082 unique patient-derived MTBC whole-genome sequences (WGS) and associated clinical data. We show that the TB epidemic in Dar es Salaam is dominated by multiple MTBC genotypes introduced to Tanzania from different parts of the world during the last 300 years. The most common MTBC genotypes deriving from these introductions exhibited differences in transmission rates and in the duration of the infectious period, but little differences in overall fitness, as measured by the effective reproductive number. Moreover, measures of disease severity and bacterial load indicated no differences in virulence between these genotypes during active TB. Instead, the combination of an early introduction and a high transmission rate accounted for the high prevalence of L3.1.1, the most dominant MTBC genotype in this setting. Yet, a longer co-existence with the host population did not always result in a higher transmission rate, suggesting that distinct life-history traits have evolved in the different MTBC genotypes. Taken together, our results point to bacterial factors as important determinants of the TB epidemic in Dar es Salaam.

Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium.

BACKGROUND: Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising accuracy or reliability. Rather than deploying a fully automatic black-box, we propose to incorporate the automated LA volumetry into a human-centric interactive image-analysis process. METHODS AND RESULTS: Atri-U, an automated data analysis pipeline for long-axis cardiac cine images, computes the atrial volume by: (i) detecting the end-systolic frame, (ii) outlining the endocardial borders of the LA, (iii) localizing the mitral annular hinge points and constructing the longitudinal atrial diameters, equivalent to the usual workup done by clinicians. In every step human interaction is possible, such that the results provided by the algorithm can be accepted, corrected, or re-done from scratch. Atri-U was trained and evaluated retrospectively on a sample of 300 patients and then applied to a consecutive clinical sample of 150 patients with various heart conditions. The agreement of the indexed LA volume between Atri-U and two experts was similar to the inter-rater agreement between clinicians (average overestimation of 0.8 mL/m2 with upper and lower limits of agreement of - 7.5 and 5.8 mL/m2, respectively). An expert cardiologist blinded to the origin of the annotations rated the outputs produced by Atri-U as acceptable in 97% of cases for step (i), 94% for step (ii) and 95% for step (iii), which was slightly lower than the acceptance rate of the outputs produced by a human expert radiologist in the same cases (92%, 100% and 100%, respectively). The assistance of Atri-U lead to an expected reduction in reading time of 66%-from 105 to 34 s, in our in-house clinical setting. CONCLUSIONS: Our proposal enables automated calculation of the maximum LA volume approaching human accuracy and precision. The optional user interaction is possible at each processing step. As such, the assisted process sped up the routine CMR workflow by providing accurate, precise, and validated measurement results.

Automated detection of pulmonary embolism in CT pulmonary angiograms using an AI-powered algorithm.

OBJECTIVES: To evaluate the performance of an AI-powered algorithm for the automatic detection of pulmonary embolism (PE) on chest computed tomography pulmonary angiograms (CTPAs) on a large dataset. METHODS: We retrospectively identified all CTPAs conducted at our institution in 2017 (n = 1499). Exams with clinical questions other than PE were excluded from the analysis (n = 34). The remaining exams were classified into positive (n = 232) and negative (n = 1233) for PE based on the final written reports, which defined the reference standard. The fully anonymized 1-mm series in soft tissue reconstruction served as input for the PE detection prototype algorithm that was based on a deep convolutional neural network comprising a Resnet architecture. It was trained and validated on 28,000 CTPAs acquired at other institutions. The result series were reviewed using a web-based feedback platform. Measures of diagnostic performance were calculated on a per patient and a per finding level. RESULTS: The algorithm correctly identified 215 of 232 exams positive for pulmonary embolism (sensitivity 92.7%; 95% confidence interval [CI] 88.3-95.5%) and 1178 of 1233 exams negative for pulmonary embolism (specificity 95.5%; 95% CI 94.2-96.6%). On a per finding level, 1174 of 1352 findings marked as embolus by the algorithm were true emboli. Most of the false positive findings were due to contrast agent-related flow artifacts, pulmonary veins, and lymph nodes. CONCLUSION: The AI prototype algorithm we tested has a high degree of diagnostic accuracy for the detection of PE on CTPAs. Sensitivity and specificity are balanced, which is a prerequisite for its clinical usefulness. KEY POINTS: • An AI-based prototype algorithm showed a high degree of diagnostic accuracy for the detection of pulmonary embolism on CTPAs. • It can therefore help clinicians to automatically prioritize exams with a high suspection of pulmonary embolism and serve as secondary reading tool. • By complementing traditional ways of worklist prioritization in radiology departments, this can speed up the diagnostic and therapeutic workup of patients with pulmonary embolism and help to avoid false negative calls.

Cost-effectiveness of lung MRI in lung cancer screening.

OBJECTIVES: Recent studies with lung MRI (MRI) have shown high sensitivity (Sn) and specificity (Sp) for lung nodule detection and characterization relative to low-dose CT (LDCT). Using this background data, we sought to compare the potential screening performance of MRI vs. LDCT using a Markov model of lung cancer screening. METHODS: We created a Markov cohort model of lung cancer screening which incorporated lung cancer incidence, progression, and mortality based on gender, age, and smoking burden. Sensitivity (Sn) and Sp for LDCT were taken from the MISCAN Lung Microsimulation and Sn/Sp for MRI was estimated from a published substudy of the German Lung Cancer Screening and Intervention Trial. Screening, work-up, and treatment costs were estimated from published data. Screening with MRI and LDCT was simulated for a cohort of male and female smokers (2 packs per day; 36 pack/years of smoking history) starting at age 60. We calculated the screening performance and cost-effectiveness of MRI screening and performed a sensitivity analysis on MRI Sn/Sp and cost. RESULTS: There was no difference in life expectancy between MRI and LDCT screening (males 13.28 vs. 13.29 life-years; females 14.22 vs. 14.22 life-years). MRI had a favorable cost-effectiveness ratio of $258,169 in men and $403,888 in women driven by fewer false-positive screens. On sensitivity analysis, MRI remained cost effective at screening costs < $396 dollars and Sp > 81%. CONCLUSIONS: In this Markov model of lung cancer screening, MRI has a near-equivalent life expectancy benefit and has superior cost-effectiveness relative to LDCT. KEY POINTS: • In this Markov model of lung cancer screening, there is no difference in mortality between yearly screening with MRI and low-dose CT. • Compared to low-dose CT, screening with MRI led to a reduction in false-positive studies from 26 to 2.8% in men and 26 to 2.6% in women. • Due to similar life-expectancy and reduced false-positive rate, we found a favorable cost-effectiveness ratio of $258,169 in men and $403,888 in women of MRI relative to low-dose CT.

Impact of Modified Frozen Elephant Trunk Procedure on Downstream Aorta Remodeling in Acute Aortic Dissection: CT Scan Follow-Up.

OBJECTIVES: The aim was to evaluate the impact of a modified frozen elephant trunk procedure (mFET) on remodeling of the downstream aorta following acute aortic dissections. METHODS: Over a period of 8 years, 205 patients (mean age 62.6 ± 12.6 years) underwent a mFET (n = 69, 33.7%) or isolated ascending aorta replacement (n = 136, 66.3%) (iAoA). Aortic diameter was assessed at the aortic arch (AoA), at the mid of the thoracic aorta (mThA), at the thoracoabdominal transition (ThAbd) and at the celiac trunk level (AbdA). RESULTS: Mean follow-up was 3.3 ± 2.6 years. In-hospital mortality was 14% (n = 28), 7% in mFET and 17% in the iAoA group (p = 0.08). At the end of the follow-up, overall survival was 84% (95% CI 70-92%) and 75% (65-82%) and freedom from aorta-related reoperation was 100% and 95% (88-98%) for mFET and iAoA, respectively. At iAoA, the average difference in diameter change per year between mFET and iAoA was for total lumen 0 mm (- 0.95 to 0.94 mm, p = 0.99), and for true lumen, it was 1.23 mm (- 0.09 to 2.55 mm) per year, p = 0.067. False lumen demonstrated a decrease in diameter in mFET as compared to iAoA by - 1.43 mm (- 2.75 to - 0.11 mm), p = 0.034. In mFET, at the aortic arch level the total lumen diameter decreased from 30.7 ± 4.8 mm to 30.1 ± 2.5 mm (Δr + 2.90 ± 3.64 mm) and in iAoA it increased from 31.8 ± 4.9 to 34.6 ± 5.9 mm (Δr + 2.88 ± 4.18 mm). CONCLUSION: The mFET procedure provides satisfactory clinical outcome at short term and mid-term and has a positive impact on aorta remodeling, especially at the level of the aortic arch.

[Assessment of lung impairment in patients with cystic fibrosis : Novel magnetic resonance imaging methods]. / Detektion von Lungenveränderungen bei Patienten mit Mukoviszidose : Innovationen der Magnetresonanztomographie des Thorax.

CLINICAL/METHODOLOGICAL ISSUE: The differentiated assessment of respiratory mechanics, gas exchange and pulmonary circulation, as well as structural impairment of the lung are essential for the treatment of patients with cystic fibrosis (CF). Clinical lung function measurements are often not sufficiently specific and are often difficult to perform. STANDARD RADIOLOGICAL METHODS: The standard procedures for pulmonary imaging are chest X­ray and computed tomography (CT) for assessing lung morphology. In more recent studies, an increasing number of centers are using magnetic resonance imaging (MRI) to assess lung structure and function. However, functional imaging is currently limited to specialized centers. METHODOLOGICAL INNOVATIONS: In patients with CF, studies showed that MRI with hyperpolarized gases and Fourier decomposition/matrix pencil MRI (FD/MP-MRI) are feasible for assessing pulmonary ventilation. For pulmonary perfusion, dynamic contrast-enhanced MRI (DCE-MRI) or contrast-free methods, e.g., FD-MRI, can be used. PERFORMANCE: Functional MRI provides more accurate insight into the pathophysiology of pulmonary function at the regional level. Advantages of MRI over X­ray are its lack of ionizing radiation, the large number of lung function parameters that can be extracted using different contrast mechanisms, and ability to be used repeatedly over time. ACHIEVEMENTS: Early assessment of lung function impairment is needed as the structural changes usually occur later in the course of the disease. However, sufficient experience in clinical application exist only for certain functional lung MRI procedures. PRACTICAL RECOMMENDATIONS: Clinical application of the aforementioned techniques, except for DCE-MRI, should be restricted to scientific studies.

Signal enhancement ratio imaging of the lung parenchyma with ultra-fast steady-state free precession MRI at 1.5T.

BACKGROUND: Lung perfusion MRI after i.v. gadolinium (Gd) contrast administration is commonly based on spoiled gradient-echo acquisitions, such as volume-interpolated breath-hold examinations (VIBE), suffering from low signal-to-noise in the parenchyma. PURPOSE: To investigate the lung signal enhancement ratio (SER) with ultra-fast steady-state free precession (ufSSFP) after Gd-administration. STUDY TYPE: Retrospective. SUBJECTS: Ten subjects with healthy lungs; nine patients with pulmonary diseases (chronic obstructive pulmonary disease [COPD], lung cancer, pulmonary fibrosis, lung contusion). FIELD STRENGTH/SEQUENCE: VIBE and ufSSFP imaging of the chest was performed at 1.5T before and 3 minutes after i.v. gadobenate dimeglumine. ASSESSMENT: A workflow including deformable image registration and median filtering was used to compute 3D SER maps. SER was analyzed in the lung, blood pool, liver, muscles, and fat. The artifacts were assessed by a radiologist. In the COPD patients, ufSSFP-SER was compared to 99m Tc-MAA-SPECT/CT by visual scoring of lung enhancement deficits. STATISTICAL TESTS: Mean signal, standard deviation (SD), intersubject SD, and coefficient of variation (CV) were calculated for SER. Statistical significance of differences in signal and artifacts were determined using Wilcoxon signed-rank paired test. Intermodality agreement between ufSSFP-SER and SPECT/CT was calculated by Cohen's kappa (κq ). RESULTS: In healthy lungs, ufSSFP-SER (99% ± 23%, mean ± pooled intrasubject SD, CV = 23%) was significantly higher (P < 10-3 ) and more homogeneous (P < 10-3 ) than VIBE (47% ± 26%, CV = 57%). UfSSFP-SER was significantly higher (P < 10-3 ) for the lungs (99% ± 9%, mean ± intersubject SD) than for the blood (81% ± 7%) and other tissues (liver 33% ± 8%, muscle 26% ± 5%, fat 2% ± 1%). In the lung ufSSFP-SER exhibits homogeneity on iso-gravitational planes, and an anterior-posterior gradient. In COPD patients, ufSSFP-SER was reduced and less homogeneous compared to the control group (73% ± 33%, mean ± pooled intrasubject SD, CV = 42%). ufSSFP-SER had moderate intermodality agreement with SPECT/CT (κq = 0.64). DATA CONCLUSION: UfSSFP-SER of the lung is a rapid and simple method. Our preliminary data show plausible results in different pulmonary diseases, motivating further evaluation in larger cohorts. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Novel magnetic resonance technique for functional imaging of cystic fibrosis lung disease.

Lung function tests are commonly used to monitor lung disease in cystic fibrosis (CF). While practical, they cannot locate the exact origin of functional impairment. Contemporary magnetic resonance imaging (MRI) techniques provide information on the location of disease but the need for contrast agents constrains their repeated application. We examined the correlation between functional MRI, performed without administration of contrast agent, and lung clearance index (LCI) from nitrogen multiple-breath washout (N2-MBW).40 children with CF (median (range) age 12.0 (6-18) years) and 12 healthy age-matched controls underwent functional and structural MRI and lung function tests on the same day. Functional MRI provided semiquantitative measures of perfusion (RQ) and ventilation (RFV) impairment as percentages of affected lung volume. Morphological MRI was evaluated using CF-specific scores. LCI measured global ventilation inhomogeneity.MRI detected functional impairment in CF: RFV 19-38% and RQ 16-35%. RFV and RQ correlated strongly with LCI (r=0.76, p<0.0001 and r=0.85, p<0.0001, respectively), as did total morphology score (r=0.81, p<0.0001). All indices differed significantly between patients with CF and healthy controls (p<0.001).Noninvasive functional MRI is a promising method to detect and visualise perfusion and ventilation impairment in CF without the need for contrast agents.

Ultrafast 3D balanced steady-state free precession MRI of the lung: Assessment of anatomic details in comparison to low-dose CT.

PURPOSE: To evaluate the anatomical details offered by a new single breath-hold ultrafast 3D balanced steady-state free precession (uf-bSSFP) sequence in comparison to low-dose chest computed tomography (CT). MATERIALS AND METHODS: This was an Institutional Review Board (IRB)-approved, Health Insurance Portability and Accountability Act (HIPAA)-compliant prospective study. A total of 20 consecutive patients enrolled in a lung cancer screening trial underwent same-day low-dose chest CT and 1.5T MRI. The presence of pulmonary nodules and anatomical details on 1.9 mm isotropic uf-bSSFP images was compared to 2 mm lung window reconstructions by two readers. The number of branching points on six predefined pulmonary arteries and the distance between the most peripheral visible vessel segment to the pleural surface on thin slices and 50 mm maximum intensity projections (MIP) were assessed. Image quality and sharpness of the pulmonary vasculature were rated on a 5-point scale. RESULTS: The uf-bSSFP detection rate of pulmonary nodules (32 nodules visible on CT and MRI, median diameter 3.9 mm) was 45.5% with 21 false-positive findings (pooled data of both readers). Uf-bSSFP detected 71.2% of branching points visible on CT data. The mean distance between peripheral vasculature and pleural surface was 13.0 ± 4.2 mm (MRI) versus 8.5 ± 3.3 mm (CT) on thin slices and 8.6 ± 3.9 mm (MRI) versus 4.6 ± 2.5 mm (CT) on MIPs. Median image quality and sharpness were rated 4 each. CONCLUSION: Although CT is superior to MRI, uf-bSSFP imaging provides good anatomical details with sufficient image quality and sharpness obtainable in a single breath-hold covering the entire chest.

Imaging to predict early relapses after treatment discontinuation in patients with large vessel giant cell arteritis - A cohort study.

OBJECTIVES: To investigate the value of [18F]fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) in predicting relapse after treatment discontinuation in patients with large-vessel giant cell arteritis (LV-GCA). METHODS: This study included patients with LV-GCA whose treatment was discontinued between 2018 and 2023. All patients underwent PET/CT and/or MRI at the time of treatment discontinuation in clinical remission. Qualitative and quantitative PET/CT scores, by measuring standardized uptake values (SUV), and semiquantitative MRI scores of the aorta and supraaortic vessels were compared between patients who relapsed within 4 months after treatment discontinuation and those who did not. RESULTS: Forty patients were included (median age 67.4 years, interquartile range (IQR) 60.8-74.0; 77.5 % females). Eleven patients (27.5 %) relapsed after treatment discontinuation (time to relapse 1.9 months, IQR 1.4-3.3). Patients who relapsed were comparable to those who remained in remission with respect to the presence of active vasculitis on MRI and/or PET/CT (54.5% vs. 58.6 %, p = 1.0), the number of segments with vasculitic findings on MRI (0, IQR 0.0-1.5, vs. 2, IQR 0.0-3.0, p = 0.221) or the highest SUV artery/liver ratio on PET/CT (1.5, IQR 1.4-1.6, vs. 1.3, IQR 1.2-1.6, p = 0.505). The median number of vasculitic segments on PET/CT was 2.5 (IQR 0.5-4.5) in those with vs. 0 (IQR 0.0-1.5, p = 0.085) in those without relapse, and the PET/CT scores 4.5 (IQR 0.75-8.25) vs. 0 (IQR 0.0-3.0, p = 0.172). CONCLUSION: PET/CT or MRI at treatment stop did not predict relapse and may not be suited to guide treatment decisions in patients with LV-GCA in remission.

Magnetic Resonance Imaging Findings Corresponding to Vasculitis as Defined via [18F]FDG Positron Emission Tomography or Ultrasound.

Background: We sought to investigate magnetic resonance imaging (MRI) parameters that correspond to vasculitis observed via [18F]FDG positron emission tomography/computed tomography (PET/CT) and ultrasound in patients with large-vessel giant cell arteritis (LV-GCA). Methods: We performed a cross-sectional analysis of patients diagnosed with LV-GCA. Patients were selected if MRI, PET/CT, and vascular ultrasound were performed at the time of LV-GCA diagnosis. Imaging findings in vessel segments (axillary segment per side, thoracic aorta) assessed using at least two methods were compared. Vessel wall thickening, oedema, and contrast agent enhancement were each assessed via MRI. Results: Twelve patients with newly diagnosed LV-GCA were included (seven females, 58%; median age 72.1, IQR 65.5-74.2 years). The MRI results showed mural thickening in 9/24 axillary artery segments. All but 1 segment showed concomitant oedema, and additional contrast enhancement was found in 3/9 segments. In total, 8 of these 9 segments corresponded to vasculitic findings in the respective segments as observed via PET/CT, and 2/9 corresponded to vasculitis in the respective ultrasound images. If MRI was performed more than 6 days after starting prednisone treatment, thickening and oedema were seen in only 1/24 segments, which was also pathologic according to ultrasound findings but not those obtained via PET/CT. Four patients had mural thickening, oedema, and contrast enhancement in the aorta, among whom three patients also had vasculitic findings observed via PET/CT. Isolated mural thickening in one patient corresponded to a negative PET/CT result. Conclusions: In the MRI results, mural thickening due to oedema corresponded to vasculitic PET/CT findings but not vasculitic ultrasound findings. The duration of steroid treatment may reduce the sensitivity of MRI.

Magnetic resonance imaging in valvular heart disease: clinical application and current role for patient management.

Noninvasive imaging provides important information on cardiac anatomy and function and is a key element in clinical management of valvular heart disease (VHD). Beside echocardiography, which is still considered the first-line modality for assessment of valvular anatomy and longitudinal evaluation of VHD, cardiovascular magnetic resonance (CMR) has evolved during the last two decades as an essential tool for evaluation of cardiac diseases. Today, CMR not only represents the reference standard for measuring cardiac volumes, function, and mass, but also enables accurate assessment of morphology and function of cardiac valves. It can play an important role in clinical decision-making for patients with VHD. This review addresses current applications and limitations of CMR imaging techniques that are used in VHD including cine-balanced steady-state free precession (b-SSFP), phase contrast MR (pcMR), gradient-recalled echo (GRE), and turbo-spin echo (TSE) sequences. Moreover, it describes their application for evaluation of valvular pathologies and gives an overview on the current role of valvular CMR in patient management.

Preoperative staging of non-small-cell lung cancer: comparison of whole-body diffusion-weighted magnetic resonance imaging and 18F-fluorodeoxyglucose-positron emission tomography/computed tomography.

OBJECTIVE: To investigate the diagnostic value of whole-body magnetic resonance imaging (MRI) including diffusion-weighted imaging with background signal suppression (DWIBS) for preoperative assessment of non-small-cell lung cancer (NSCLC) in comparison to (18)F-fluorodeoxyglucose (18)FDG) positron emission tomography/computed tomography (PET/CT). METHODS: Thirty-three patients with suspected NSCLC were enrolled. Patients were examined before surgery with PET/CT and whole-body MRI including T1-weighted turbo spin echo (TSE), T2-weighted short tau inversion recovery (STIR) and DWIBS sequences (b = 0/800). Histological or cytological specimens were taken as standard of reference. RESULTS: Whole-body MRI with DWIBS as well as PET/CT provided diagnostic image quality in all cases. Sensitivity for primary tumour detection: MRI 93%, PET/CT 98%. T-staging accuracy: MRI 63%, PET/CT 56%. N-staging accuracy: MRI 66%, PET/CT 71%. UICC staging accuracy: MRI 66%, PET/CT 74%. Sensitivity for metastatic involvement of individual lymph node groups: MRI 44%, PET/CT 47%. Specificity for individual non-metastatic lymph node groups: MRI 93%, PET/CT 96%. Assessment accuracy for individual lymph node groups: MRI 85%, PET/CT 88%. Observer agreement rate for UICC staging: MRI 74%, PET/CT 90%. CONCLUSION: Whole-body MRI with DWIBS provides comparable results to PET/CT in staging of NSCLC, but shows no superiority. Most relevant challenges for both techniques are T-staging accuracy and sensitivity for metastatic lymph node involvement. KEY POINTS: Numerous radiological methods are available for the crucial staging of lung cancer. Whole-body DWIBS MRI provides comparable results to PET/CT in NSCLC staging. No evident superiority of whole-body DWIBS over PET/CT in NSCLC staging. Challenges for both techniques are T-staging and detection of small metastases.

From cold-blooded reptiles to embryological remnants: Persistent myocardial sinusoids.

In nature, basically 2 types of myocardial vascular patterns exist: the sinusoidal and the coronary type. In the sinusoidal type, the sinusoid is completely fed by blood coming directly from the ventricle through a spongy sinusoidal network. This pattern is found in cold-blooded animals and in the early embryologic development of human (warm-blooded) hearts. A 61-year-old man with atrial fibrillation developed severe tachymyopathy with a severely reduced left-ventricular ejection fraction (LVEF) of 20%. The patient had no history of prior heart surgery or other cardiac interventions. He was referred for a computed tomography (CT) scan for assessment of pulmonary vein anatomy prior to their isolation. Incidentally, a focal myocardial defect of the midventricular infero-septal wall with tail-like extension into the right ventricular cavity was detected. In a cardiac magnetic resonance (CMR) scan there was no evidence of a myocardial infarction or fibrosis. In the absence of a ventricular septal defect by CT, CMR and echocardiography the diagnosis of a persistent myocardial sinusoid was evident. In this case, we used state-of-the art methods for pathology visualization, illustrating the effectiveness of CT and CMR in the precise detection and differential diagnosis of myocardial anomalies including a multi-coloured 3D-printed model that may further enhance visuospatial appreciation of those anomalies.

Is sleep disordered breathing a risk factor for COVID-19 or vice versa?

Sleep disordered breathing may be a risk factor or a sequela of COVID-19. https://bit.ly/37v5Gyz.

Associations between fully-automated, 3D-based functional analysis of the left atrium and classification schemes in atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) has been linked to left atrial (LA) enlargement. Whereas most studies focused on 2D-based estimation of static LA volume (LAV), we used a fully-automatic convolutional neural network (CNN) for time-resolved (CINE) volumetry of the whole LA on cardiac MRI (cMRI). Aim was to investigate associations between functional parameters from fully-automated, 3D-based analysis of the LA and current classification schemes in AF. METHODS: We retrospectively analyzed consecutive AF patients who underwent cMRI on 1.5T systems including a stack of oblique-axial CINE series covering the whole LA. The LA was automatically segmented by a validated CNN. In the resulting volume-time curves, maximum, minimum and LAV before atrial contraction were automatically identified. Active, passive and total LA emptying fractions (LAEF) were calculated and compared to clinical classifications (AF Burden score (AFBS), increased stroke risk (CHA2DS2VASc≥2), AF type (paroxysmal/persistent), EHRA score, and AF risk factors). Moreover, multivariable linear regression models (mLRM) were used to identify associations with AF risk factors. RESULTS: Overall, 102 patients (age 61±9 years, 17% female) were analyzed. Active LAEF (LAEF_active) decreased significantly with an increase of AFBS (minimal: 44.0%, mild: 36.2%, moderate: 31.7%, severe: 20.8%, p<0.003) which was primarily caused by an increase of minimum LAV. Likewise, LAEF_active was lower in patients with increased stroke risk (30.7% vs. 38.9%, p = 0.002). AF type and EHRA score did not show significant differences between groups. In mLRM, a decrease of LAEF_active was associated with higher age (per year: -0.3%, p = 0.02), higher AFBS (per category: -4.2%, p<0.03) and heart failure (-12.1%, p<0.04). CONCLUSIONS: Fully-automatic morphometry of the whole LA derived from cMRI showed significant relationships between LAEF_active with increased stroke risk and severity of AFBS. Furthermore, higher age, higher AFBS and presence of heart failure were independent predictors of reduced LAEF_active, indicating its potential usefulness as an imaging biomarker.

Performance of a deep learning tool to detect missed aortic dilatation in a large chest CT cohort.

Purpose: Thoracic aortic (TA) dilatation (TAD) is a risk factor for acute aortic syndrome and must therefore be reported in every CT report. However, the complex anatomy of the thoracic aorta impedes TAD detection. We investigated the performance of a deep learning (DL) prototype as a secondary reading tool built to measure TA diameters in a large-scale cohort. Material and methods: Consecutive contrast-enhanced (CE) and non-CE chest CT exams with "normal" TA diameters according to their radiology reports were included. The DL-prototype (AIRad, Siemens Healthineers, Germany) measured the TA at nine locations according to AHA guidelines. Dilatation was defined as >45 mm at aortic sinus, sinotubular junction (STJ), ascending aorta (AA) and proximal arch and >40 mm from mid arch to abdominal aorta. A cardiovascular radiologist reviewed all cases with TAD according to AIRad. Multivariable logistic regression (MLR) was used to identify factors (demographics and scan parameters) associated with TAD classification by AIRad. Results: 18,243 CT scans (45.7% female) were successfully analyzed by AIRad. Mean age was 62.3 ± 15.9 years and 12,092 (66.3%) were CE scans. AIRad confirmed normal diameters in 17,239 exams (94.5%) and reported TAD in 1,004/18,243 exams (5.5%). Review confirmed TAD classification in 452/1,004 exams (45.0%, 2.5% total), 552 cases were false-positive but identification was easily possible using visual outputs by AIRad. MLR revealed that the following factors were significantly associated with correct TAD classification by AIRad: TAD reported at AA [odds ratio (OR): 1.12, p < 0.001] and STJ (OR: 1.09, p = 0.002), TAD found at >1 location (OR: 1.42, p = 0.008), in CE exams (OR: 2.1-3.1, p < 0.05), men (OR: 2.4, p = 0.003) and patients presenting with higher BMI (OR: 1.05, p = 0.01). Overall, 17,691/18,243 (97.0%) exams were correctly classified. Conclusions: AIRad correctly assessed the presence or absence of TAD in 17,691 exams (97%), including 452 cases with previously missed TAD independent from contrast protocol. These findings suggest its usefulness as a secondary reading tool by improving report quality and efficiency.

Signal characteristics of focal bone marrow lesions in patients with multiple myeloma using whole body T1w-TSE, T2w-STIR and diffusion-weighted imaging with background suppression.

OBJECTIVE: This study analyses the diagnostic potential of Diffusion-Weighted Imaging with Background Suppression (DWIBS) in the detection of focal bone marrow lesions from multiple myeloma. The signal and contrast properties of DWIBS are evaluated in correlation with the serum concentration of M-component (MC) and compared with established T1- and T2-weighted sequences. METHODS: Data from 103 consecutive studies in 81 patients are analysed retrospectively. Signal intensities and apparent Diffusion Coefficients (ADC) of 79 focal lesions in the lumbar spine or pelvis of 38 patients are determined and contrast-to-noise-ratio (CNR) is calculated. Data from patients with low (<20 g/L) and high (>20 g/dL) MC are evaluated separately. RESULTS: Signal intensities of focal myeloma lesions on T2w-STIR vary significantly depending on the MC, which leads to a loss in CNR in patients with high MC. No signal variation is observed for T1w-TSE and DWIBS. The CNR values provided by DWIBS in patients with high MC are slightly higher than those of T2w-STIR. ADC values in patients with low MC are significantly higher than in patients with high MC. CONCLUSION: Whole-body DWIBS has the potential to improve the conspicuity of focal myeloma lesions and provides additional biological information by ADC quantification.

Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds.

CT patterns of viral pneumonia are usually only qualitatively described in radiology reports. Artificial intelligence enables automated and reliable segmentation of lungs with chest CT. Based on this, the purpose of this study was to derive meaningful imaging biomarkers reflecting CT patterns of viral pneumonia and assess their potential to discriminate between healthy lungs and lungs with viral pneumonia. This study used non-enhanced and CT pulmonary angiograms (CTPAs) of healthy lungs and viral pneumonia (SARS-CoV-2, influenza A/B) identified by radiology reports and RT-PCR results. After deep learning segmentation of the lungs, histogram-based and threshold-based analyses of lung attenuation were performed and compared. The derived imaging biomarkers were correlated with parameters of clinical and biochemical severity (modified WHO severity scale; c-reactive protein). For non-enhanced CTs (n = 526), all imaging biomarkers significantly differed between healthy lungs and lungs with viral pneumonia (all p < 0.001), a finding that was not reproduced for CTPAs (n = 504). Standard deviation (histogram-derived) and relative high attenuation area [600-0 HU] (HU-thresholding) differed most. The strongest correlation with disease severity was found for absolute high attenuation area [600-0 HU] (r = 0.56, 95% CI = 0.46-0.64). Deep-learning segmentation-based histogram and HU threshold analysis could be deployed in chest CT evaluation for the differentiating of healthy lungs from AP lungs.