Estimation of Spatiotemporal Sensitivity Using Band-limited Signals with No Additional Acquisitions for k-t Parallel Imaging.

PURPOSE: Dynamic MR techniques, such as cardiac cine imaging, benefit from shorter acquisition times. The goal of the present study was to develop a method that achieves short acquisition times, while maintaining a cost-effective reconstruction, for dynamic MRI. k - t sensitivity encoding (SENSE) was identified as the base method to be enhanced meeting these two requirements. METHODS: The proposed method achieves a reduction in acquisition time by estimating the spatiotemporal (x - f) sensitivity without requiring the acquisition of the alias-free signals, typical of the k - t SENSE technique. The cost-effective reconstruction, in turn, is achieved by a computationally efficient estimation of the x - f sensitivity from the band-limited signals of the aliased inputs. Such band-limited signals are suitable for sensitivity estimation because the strongly aliased signals have been removed. RESULTS: For the same reduction factor 4, the net reduction factor 4 for the proposed method was significantly higher than the factor 2.29 achieved by k - t SENSE. The processing time is reduced from 4.1 s for k - t SENSE to 1.7 s for the proposed method. The image quality obtained using the proposed method proved to be superior (mean squared error [MSE] ± standard deviation [SD] = 6.85 ± 2.73) compared to the k - t SENSE case (MSE ± SD = 12.73 ± 3.60) for the vertical long-axis (VLA) view, as well as other views. CONCLUSION: In the present study, k - t SENSE was identified as a suitable base method to be improved achieving both short acquisition times and a cost-effective reconstruction. To enhance these characteristics of base method, a novel implementation is proposed, estimating the x - f sensitivity without the need for an explicit scan of the reference signals. Experimental results showed that the acquisition, computational times and image quality for the proposed method were improved compared to the standard k - t SENSE method.

Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge.

RATIONALE AND OBJECTIVES: Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA). MATERIALS AND METHODS: The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type. RESULTS: Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall. CONCLUSION: The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.

Clinical application of an automatic slice-alignment method for cardiac MR imaging.

PURPOSE: We evaluated the usefulness of an automatic slice-alignment method to simplify planning of cardiac magnetic resonance (MR) scans with a 3-tesla scanner. METHODS: We obtained 2-dimensional (2D) axial multislice images using steady-state free precession (SSFP) sequences covering the whole heart at the end-diastole phase with electrocardiography (ECG) gating in 38 patients. We detected several anatomical feature points of the heart and calculated all planes required for cardiac imaging based on those points. We visually evaluated the acceptability of an acquired imaging plane and measured the angular differences of each view between the results obtained by this method and by a conventional manual pointing approach. RESULTS: The average visual scores were 3.4 ± 1.0 for short-axis images, 3.2 ± 0.9 for 4-chamber images, 3.2 ± 0.8 for 2-chamber images, and 3.3 ± 0.8 for 3-chamber images; average angular differences were 5.8 ± 5.1 (short axis), 7.7 ± 5.7 (4-chamber), 11.5 ± 6.7 (2-chamber), and 9.1 ± 4.6 degrees (3-chamber). Processing time was within 1.8 s in all subjects. CONCLUSION: The proposed method can provide planes within the clinically acceptable range and within a short time in cardiac imaging of patients with various cardiac shapes and diseases without the need for high level operator proficiency in performing the examination and interpreting results.

Automated flow quantification for spin labeling MR imaging.

OBJECTIVE: The Time-Spatial Labeling Inversion Pulse (Time-SLIP) technique enables tracing of regional fluid flows without the use of contrast medium. The objective of this study is to quantify automatically slow and complex fluid flows using the Time-SLIP technique. MATERIALS AND METHODS: Series images were acquired with a 1.5-T MRI scanner using the Time-SLIP technique with half-Fourier fast spin-echo (FSE) and balanced steady-state free precession (bSSFP) sequences. In this method, labeled fluid regions in images were automatically detected based on image processing techniques for a given point. The flow velocity of the labeled fluid region was calculated using regression fitting for the region's position. To evaluate our method, constant and non-constant laminar flows in a water phantom were studied. In addition, volunteer experiments were conducted to quantify the flow of cerebrospinal fluid. RESULTS: In the constant flow experiments the correlation factor r (2) between the flow velocity calculated from our method and the laminar peak velocity calculated from the volumetric flow rate was 0.9992 for the FSE sequence and 0.9982 for the bSSFP sequence. In the non-constant flow study, the flow velocity was calculated accurately for any period inversion time even when the flow velocity was changed, and the quantification error was negligible. In the volunteer experiments, r (2) between the flow velocity calculated by the proposed method and that obtained by manual annotation was 0.9383. CONCLUSION: The experimental results showed that our proposed method can quickly and accurately provide information on flow velocities especially for slower and complex flows. Our method is, therefore, expected to be useful in diagnostic support systems.

Automatic slice alignment method for cardiac magnetic resonance imaging.

OBJECTIVES: Automatic slice alignment is important for easier operation and shorter examination times in cardiac magnetic resonance imaging (MRI) examinations. We propose a new automatic slice alignment method for six cardiac planes (short-axis, vertical long-axis, horizontal long-axis, 4-chamber, 2-chamber, and 3-chamber views). MATERIALS AND METHODS: ECG-gated 2D steady-state free precession axial multislice images were acquired using a 1.5-T MRI scanner during a single breath-hold. The scanning time was set to <20 s in 23 volumes from 23 healthy volunteers. In this method, the positions of the mitral valve, cardiac apex, left ventricular outflow tract, tricuspid valve, anterior wall of the heart, and right ventricular corner are detected to determine the positions of six reference planes by combining knowledge-based recognition and image processing techniques. In order to evaluate the results of automatic slice alignment for the short-axis, 4-chamber, 2-chamber, and 3-chamber views, the angular and positional errors between the results obtained by our proposed method and by manual annotation were measured. RESULTS: The average angular errors for the short-axis, 4-chamber, 2-chamber, and 3-chamber views were 3.05°, 4.52°, 7.28°, and 5.79°, respectively. The average positional errors for the short-axis (base), short-axis (apex), 4-chamber, 2-chamber, and 3-chamber views were 6.61°, 3.80°, 1.55°, 1.52°, and 1.48°, respectively. CONCLUSION: The experimental results showed that our proposed method can detect the cardiac planes quickly and accurately. Our method is therefore beneficial to both patients and operators.

Automatic model-based contour detection of left ventricle myocardium from cardiac CT images.

PURPOSE: For accurate evaluation of myocardial perfusion on computed tomography images, precise identification of the myocardial borders of the left ventricle (LV) is mandatory. In this article, we propose a method to detect the contour of LV myocardium automatically and accurately. METHODS: Our detection method is based on active shape model. For precise detection, we estimate the pose and shape parameters separately by three steps: LV coordinate system estimation, myocardial shape estimation, and transformation. In LV coordinate system estimation, we detect heart features followed by the entire LV by introducing machine-learning approach. Since the combination of two types feature detection covers the LV variation, such as pose or shape, we can estimate the LV coordinate system robustly. In myocardial shape estimation, we minimize the energy function including pattern error around myocardium with adjustment of pattern model to input image using estimated concentration of contrast dye. Finally, we detect LV myocardial contours in the input images by transforming the estimated myocardial shape using the matrix composed of the vectors calculated by the LV coordinate system estimation. RESULTS: In our experiments with 211 images from 145 patients, mean myocardial contours point-to-point errors for our method as compared to ground truth were 1.02 mm for LV endocardium and 1.07 mm for LV epicardium. The average computation time was 2.4 s (on a 3.46 GHz processor with 2-multithreading process). CONCLUSIONS: Our method achieved accurate and fast myocardial contour detection which may be sufficient for myocardial perfusion examination.

Practical considerations for a method of rapid cardiac function analysis based on three-dimensional speckle tracking in a three-dimensional diagnostic ultrasound system.

PURPOSE: The purpose of this study was to validate the accuracy of our novel 3D speckle tracking method by using numerical data, and to demonstrate the rapid processing of this method by using data obtained from human subjects. METHODS: In order to create a method that can rapidly assess cardiac function in regional heart wall segments, a 3D speckle tracking algorithm was created that focuses on data quality and performance. Prototype application software based on this algorithm was written to evaluate cardiac wall motion and to calculate indices such as strain. Time series 3D image data were generated for artificial numerical models that simulate the shape of the left ventricle and exhibit various types of motion. We compared observed values returned by the algorithm with expected values yielded by the models. This software was also applied to volume data of the human heart acquired by a 3D ultrasound system. RESULTS: Measurement of the indices was evaluated by using an error ratio that was a residual between an expected value and estimated one divided by the expected value. The average error ratio of the time series volumes was less than 5% for all indices, and no individual error ratio was more than 10% for numerical models. The process was complete within 0.5 s per frame for human heart volumes. CONCLUSION: This application software can provide good estimates of various wall motion indices. The results are similar to data from numerical models, and are provided quickly enough for routine clinical usage.