Qmass software

Manufactured by Medis Medical Imaging Systems

QMass software is a comprehensive medical imaging analysis tool developed by Medis Medical Imaging Systems. It provides a suite of functionalities for the visualization, quantification, and analysis of medical images, including cardiac, vascular, and general radiological imaging data.

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Lab products found in correlation

Achieva, by Philips (1 mentions) Advanced 400 mr system, by Bruker (1 mentions)

7 protocols using qmass software

Cardiac MRI Segmentation and Normalization Protocol

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The three SAX images in each dataset were segmented by a faculty in radiology with 13 years of experience in cardiac MRI using the QMass software (Medis medical imaging systems, Leiden, Netherlands) based on the AHA 17-segment model [11 (link)]. The apical cap was removed as no useful segmentation results are available from this segment. The normalized wall thickness values of all contour points within each segment were averaged to represent each segment by one normalized wall thickness (NWT) value. That is, each segment is represented by a NWT vector, t_i,j representing the average thickness of the ith segment (i = 1 : 16) at different 23 cardiac phases (or timeframes). The subscript j represents the jth patient (j = 1 : 27). Figure 2 shows an example of normal and abnormal NWT patterns in a midventricular SAX slice.

Wael M., Ibrahim E.S, & Fahmy A.S. (2016). Detection of Cardiac Function Abnormality from MRI Images Using Normalized Wall Thickness Temporal Patterns. International Journal of Biomedical Imaging, 2016, 4301087.

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Cardiac MRI Ventricular Volume Quantification

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All image datasets were de-identified on the scanner, and then subsequently post-processed using commercial QMass software (Medis Medical Imaging Systems, Leiden, Netherlands). Left ventricular endocardial and epicardial borders were traced for measurement of EDV, ESV, and end-diastolic mass using the summation of disks technique, with papillary muscles included in the ventricular volume. Two observers (RC and LO) with 11 and 7 years of cardiac MRI experience independently performed endocardial tracings.

Cross R., Olivieri L., O’Brien K., Kellman P., Xue H, & Hansen M. (2016). Improved workflow for quantification of left ventricular volumes and mass using free-breathing motion corrected cine imaging. Journal of Cardiovascular Magnetic Resonance, 18, 10.

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Ventricular Volume Quantification Using CMR

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QMass software (Medis Medical Imaging Systems, Leiden, The Netherlands) was used for post-processing of right ventricular volumes after all images had been anonymized prior to transfer from the scanner. Summation of disks was used to determine RV volume and function after tracing of endocardium in end-diastole (EDV) and end-systole (ESV). Two observers with 2 and 9 years of CMR experience (AM and LO) independently performed endocardial tracings.

Merlocco A., Olivieri L., Kellman P., Xue H, & Cross R. (2018). Improved Workflow for Quantification of Right Ventricular Volumes Using Free-Breathing Motion Corrected Cine Imaging. Pediatric cardiology, 40(1), 79-88.

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Quantifying USPIO Uptake in Tissue

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All T2*-weighted multigradient-echo images for each patient were analysed using Circle CVI software (Circle CVI42, Canada). An experimentally determined threshold used in previous work7 (link) for the coefficient of determination (r²>0.85) was used to exclude data that did not have an acceptable exponential decay when signal intensity was plotted against echo time. The relaxation rate, R2*, is the inverse of the mean T2*, and was calculated to assess the uptake of USPIO for each region of interest (ROI), where the higher the value, the greater the USPIO accumulation.
Late gadolinium enhancement (LGE), ventricular volume and functional analyses were determined manually using QMass software (Medis Medical Imaging Systems, Leiden, The Netherlands).

Stirrat C.G., Alam S.R., MacGillivray T.J., Gray C.D., Dweck M.R., Raftis J., Jenkins W.S., Wallace W.A., Pessotto R., Lim K.H., Mirsadraee S., Henriksen P.A., Semple S.I, & Newby D.E. (2017). Ferumoxytol-enhanced magnetic resonance imaging assessing inflammation after myocardial infarction. Heart, 103(19), 1528-1535.

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Cardiac MRI Ventricular Volume Protocol

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CMR studies were performed either on a 1.5 Tesla Siemens Avanto (Siemens Medical Solutions, Erlanger, Germany) or a 1.5 Tesla Philips Achieva (Philips Medical Systems, Best The Netherlands) scanner. Five of the youngest subjects were sedated for the CMR. Standard horizontal long axis and short axis images were obtained at end-expiration with a parallel stack of serial images acquired from the base to the apex [16 (link)]. Depending on the size of a subject, the parameters ranged from slice thickness of 4–10 mm, number of averages 1–3, TE 1.1–1.5, TR 2.8–3.5, and in-plane resolution 1.2–1.4 mm. Volumes were measured on Qmass software (MEDIS Medical Imaging Systems, Leiden, The Netherlands) by manually tracing the endocardial border at end diastole and end-systole. From this, data including end-systolic volume indexed to BSA, stroke volume indexed to BSA, and ejection fraction were calculated. Sanz’s approach was used to calculate

V V C R_{m} = \frac{E S V}{S V}

[10 (link)]. RV ejection fraction is calculated as the difference in volume at end-diastole and end-systole, all over the end-diastolic volume × 100.

Truong U., Patel S., Kheyfets V., Dunning J., Fonseca B., Barker A.J., Ivy D., Shandas R, & Hunter K. (2015). Non-invasive determination by cardiovascular magnetic resonance of right ventricular-vascular coupling in children and adolescents with pulmonary hypertension. Journal of Cardiovascular Magnetic Resonance, 17(1), 81.

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Cardiovascular Magnetic Resonance Imaging Protocol

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The CMR protocol has been previously described, and methods of interpretation of LV and RV parameters have been previously reported.16, 17, 18 Images were performed with 1.5 Tesla magnets with a 4‐element phased‐array surface coil positioned anteriorly and posteriorly and electrocardiogram gating. CMR images were transmitted to Johns Hopkins University in Baltimore, Maryland, and image analysis was done on Windows workstations with QMASS software (version 4.2; Medis medical imaging systems, Leiden, the Netherlands). The endocardial and epicardial borders of the RV were traced manually on the short‐axis cine images at the end‐systolic and end‐diastolic phases. The outflow tract was included in the RV volume, and RVEDV and RVESV were calculated using Simpson's rule by summation of areas on each slice multiplied by the sum of the slice thickness and image gap. RV mass was determined at the end‐diastole phase as the difference between the end‐diastolic epicardial and endocardial volumes multiplied by the specific gravity of the myocardium (1.05 g/cm³). RVEF was calculated by dividing the difference of RVESV and RVEDV by RVEDV. The intra‐ and inter‐reader intraclass correlation coefficients from random, blinded rereads of 229 scans were 0.94 for RV mass and from 230 scans were 0.99 for RVEDV and 0.89 for RVEF.18

Al‐Naamani N., Chirinos J.A., Zamani P., Ruthazer R., Paulus J.K., Roberts K.E., Barr R.G., Lima J.A., Bluemke D.A., Kronmal R, & Kawut S.M. (2016). Association of Systemic Arterial Properties With Right Ventricular Morphology: The Multi‐Ethnic Study of Atherosclerosis (MESA)‐Right Ventricle Study. Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, 5(12), e004162.

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Cardiac MRI Assessment in Mice

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Cardiac MRI was performed on anaesthetized mice using a 9.4 T, 89 mm bore size magnet equipped with 1500 mT/m gradients and connected to an advanced 400 MR system (BrukerBiospin, Germany) as previously described 26 (link), 28 (link). Images were reconstructed and left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV) and left ventricular (LV) mass were determined using QMass® software (version MR 6.1.5, Medis Medical Imaging Systems, the Netherlands) and stroke volume (SV) and ejection fraction (EF) were calculated.

Du W., Piek A., Schouten E.M., van de Kolk C.W., Mueller C., Mebazaa A., Voors A.A., de Boer R.A, & Silljé H.H. (2018). Plasma levels of heart failure biomarkers are primarily a reflection of extracardiac production. Theranostics, 8(15), 4155-4169.

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