In dynamic magnetic resonance imaging (MRI) studies, the motion kinetics or the contrast variability are often hard to predict, hampering an appropriate choice of the image update rate or the temporal resolution. A constant azimuthal profile spacing (111.246 degrees), based on the Golden Ratio, is investigated as optimal for image reconstruction from an arbitrary number of profiles in radial MRI. The profile order is evaluated and compared with a uniform profile distribution in terms of signal-to-noise ratio (SNR) and artifact level. The favorable characteristics of such a profile order are exemplified in two applications on healthy volunteers. First, an advanced sliding window reconstruction scheme is applied to dynamic cardiac imaging, with a reconstruction window that can be flexibly adjusted according to the extent of cardiac motion that is acceptable. Second, a contrast-enhancing k-space filter is presented that permits reconstructing an arbitrary number of images at arbitrary time points from one raw data set. The filter was utilized to depict the T1-relaxation in the brain after a single inversion prepulse. While a uniform profile distribution with a constant angle increment is optimal for a fixed and predetermined number of profiles, a profile distribution based on the Golden Ratio proved to be an appropriate solution for an arbitrary number of profiles.

The problem of respiratory motion has proved a serious obstacle in developing techniques to acquire images or guide interventions in abdominal and thoracic organs. Motion models offer a possible solution to these problems, and as a result the field of respiratory motion modelling has become an active one over the past 15 years. A motion model can be defined as a process that takes some surrogate data as input and produces a motion estimate as output. Many techniques have been proposed in the literature, differing in the data used to form the models, the type of model employed, how this model is computed, the type of surrogate data used as input to the model in order to make motion estimates and what form this output should take. In addition, a wide range of different application areas have been proposed. In this paper we summarise the state of the art in this important field and in the process highlight the key papers that have driven its advance. The intention is that this will serve as a timely review and comparison of the different techniques proposed to date and as a basis to inform future research in this area.

This study demonstrates that native and post-contrast T1 values provide indexes with high diagnostic accuracy for the discrimination of normal and diffusely diseased myocardium.

Electrocardiography (ECG) is a key non-invasive diagnostic tool for cardiovascular diseases which is increasingly supported by algorithms based on machine learning. Major obstacles for the development of automatic ECG interpretation algorithms are both the lack of public datasets and well-defined benchmarking procedures to allow comparison s of different algorithms. To address these issues, we put forward PTB-XL, the to-date largest freely accessible clinical 12-lead ECG-waveform dataset comprising 21837 records from 18885 patients of 10 seconds length. The ECG-waveform data was annotated by up to two cardiologists as a multi-label dataset, where diagnostic labels were further aggregated into super and subclasses. the dataset covers a broad range of diagnostic classes including, in particular, a large fraction of healthy records. the combination with additional metadata on demographics, additional diagnostic statements, diagnosis likelihoods, manually annotated signal properties as well as suggested folds for splitting training and test sets turns the dataset into a rich resource for the development and the evaluation of automatic ECG interpretation algorithms.

Atherosclerosis and its consequences remain the main cause of mortality in industrialized and developing nations. Plaque burden and progression have been shown to be independent predictors for future cardiac events by intravascular ultrasound. Routine prospective imaging is hampered by the invasive nature of intravascular ultrasound. A noninvasive technique would therefore be more suitable for screening of atherosclerosis in large populations. Here we introduce an elastin-specific magnetic resonance contrast agent (ESMA) for noninvasive quantification of plaque burden in a mouse model of atherosclerosis. The strong signal provided by ESMA allows for imaging with high spatial resolution, resulting in accurate assessment of plaque burden. Additionally, plaque characterization by quantifying intraplaque elastin content using signal intensity measurements is possible. Changes in elastin content and the high abundance of elastin during plaque development, in combination with the imaging properties of ESMA, provide potential for noninvasive assessment of plaque burden by molecular magnetic resonance imaging (MRI).

BackgroundLate Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop.MethodsThe image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King’s College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study.ResultsSome algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72.ConclusionsThe study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface.

T 2 * relaxometry for quantitative MR imaging is strongly hampered by large-scale field inhomogeneities, which lead to signal losses and an overestimation of the relaxation rate R 2 *. This is of particular importance for the sensitive detection of iron oxide contrast agent distributions. To derive an accurate measurement of T 2 *, a main field inhomogeneity correction is applied: the main field inhomogeneity is derived from multislice T 2 * relaxometry data and used as an initial value for an iterative optimization, by which the relaxation signal is corrected for each voxel. These corrected T 2 * maps show reduced influence of the local field variation and contain information about the local SPIO concentration. The method was tested on phantoms and the limit of detection of SPIO labeled cells using T 2 * relaxometry was estimated in volunteers to be 120 Quantitative imaging by means of T 2 * relaxometry is becoming an increasingly requested tool in many areas of MRI. It is applied for BOLD contrast studies (1,2) as well as for the detection of changes in cancellous bone structure (3). The evaluation of perfusion by means of bolus transit methods (4) and the assessment of iron content in brain and liver can also profit from accurate T 2 * mapping methods (5,6). The sensitive detection of iron oxide particlebased contrast agents has become a growing application, especially within the scope of molecular imaging MR. The distribution of these iron oxides is usually determined by acquiring T 2 and T 2 *-weighted images. In Ref. (7) it was already suggested that EPI sequences could be used to study localized magnetic inhomogeneities, since they are introduced by SPIO particles. Hence, a multigradient readout approach can be utilized to come one step closer toward quantification of iron oxide particle distributions by measuring the T 2 * relaxation time.SPIO particles are phagocytosed by macrophages in the liver or lymph nodes. They are also used to label cells ex vivo and track them throughout the body after injection (8,9). For cellularly compartmentalized SPIO particles T 2 is less influenced than T 2 *, which is explained by the static dephasing theory (10). To gain high sensitivity for the detection of compartmentalized SPIOs, the reversible relaxation rate T 2 * should be measured (11). Therefore, this study focuses on the measurement of T 2 *. This includes the challenge that the T 2 * relaxation rate is not only influenced by SPIO contrast agents, but also by macroscopic susceptibilities that arise from air/tissue interfaces, like the lung/liver or the sinus/brain interface. These disturbances cause enhanced signal decay and scale linearly with the field strength, which intensifies their influence at 3.0 T. Therefore, susceptibility artifacts lead to overestimated relaxation rates or obscure small concentrations of SPIO-labeled cells.Several methods have been proposed to correct for the macroscopic magnetic susceptibility influence. The most obvious method involves increasing the spatial resolution (12). Compen...

In this paper we present a benchmarking framework for the validation of cardiac motion analysis algorithms. The reported methods are the response to an open challenge that was put to the medical imaging community through a MICCAI workshop. The database included magnetic resonance (MR) and 3D ultrasound (3DUS) datasets from a dynamic phantom and 15 healthy volunteers. Participants processed 3D tagged MR datasets (3DTAG), cine steady state free precession MR datasets (SSFP) and 3DUS datasets, amounting to 1158 image volumes. Ground-truth for motion tracking was based on 12 landmarks (4 walls at 3 ventricular levels). They were manually tracked by two observers in the 3DTAG data over the whole cardiac cycle, using an in-house application with 4D visualization capabilities. The median of the inter-observer variability was computed for the phantom dataset (0.77mm) and for the volunteer datasets (0.84mm). The ground-truth was registered to 3DUS coordinates using a point based similarity transform. Four institutions responded to the challenge by providing motion estimates for the data: Fraunhofer MEVIS (MEVIS), Bremen, Germany; Imperial College London -University College London (IUCL), UK; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Inria-Asclepios project (INRIA), France. Details on the implementation and evaluation of the four methodologies are presented in this manuscript. The manually tracked landmarks were used to evaluate tracking accuracy of all methodologies. For 3DTAG, median values were computed over all time frames for the phantom dataset (MEVIS=1.20mm, IUCL=0.73mm, UPF=1.10mm, INRIA=1.09mm) and for the volunteer datasets (MEVIS=1.33mm, IUCL=1.52mm, UPF=1.09mm, INRIA=1.32mm). For 3DUS, median values were computed at end diastole and end systole for the phantom dataset (MEVIS=4.40mm, UPF=3.48mm, INRIA=4.78mm) and for the volunteer datasets (MEVIS=3.51mm, UPF=3.71mm, INRIA=4.07mm). For SSFP, median values were computed at end diastole and end systole for the phantom dataset (UPF=6.18mm, INRIA=3.93mm) and for the volunteer datasets (UPF=3.09mm, INRIA=4.78mm). Finally, strain curves were generated and qualitatively compared. Good agreement was found between the different modalities and methodologies, except for radial strain that showed a high variability in cases of lower image quality.