3-D Microvessel-Mimicking Ultrasound Phantoms Produced With a Scanning Motion System

Ultrasound techniques are currently being developed which can assess the vascularization of tissue as a marker for therapeutic response. Some of these ultrasound imaging techniques seek to extract quantitative features about vessel networks, while high-frequency imaging also allows individual vessels to be resolved. The development of these new techniques, and subsequent imaging analysis strategies, necessitates an understanding of their sensitivities to vessel and vessel network structural abnormalities. Constructing in-vitro flow phantoms for this purpose can be prohibitively challenging, as simulating precise flow environments with non-trivial structures is often impossible using conventional methods of construction for flow phantoms. Presented in this manuscript is a method to create predefined structures with < 10 μm precision using a three-axis motion system. The application of this technique is demonstrated for the creation of individual vessel and vessel networks, which can easily be made to simulate the development of structural abnormalities typical of diseased vasculature in-vivo. Additionally, beyond facilitating the creation of phantoms which would be otherwise very challenging to construct, the method presented herein enables one to precisely simulate very slow blood flow, respiration artifacts, and to measure imaging resolution

An In Vivo Validation of the Application of Acoustic Radiation Force to Enhance the Diagnostic Utility of Molecular Imaging Using 3-D Ultrasound

For over a decade, the application of acoustic radiation force (ARF) has been proposed as a mechanism to increase ultrasonic molecular imaging (MI) sensitivity in vivo. Presented herein is the first noninvasive in vivo validation of ARF-enhanced MI with an unmodified clinical system. First, an in vitro optical-acoustical setup was used to optimize system parameters and ensure sufficient microbubble translation when exposed to ARF. 3D ARF-enhanced MI was then performed on 7 rat fibrosarcoma tumors using microbubbles targeted to αvβ3 and non-targeted microbubbles. Low-amplitude (< 25 kPa) 3D ARF pulse sequences were tested and compared to passive targeting studies in the same animal. Our results demonstrate that a 78% increase in image intensity from targeted microbubbles can be achieved when using ARF relative to the passive targeting studies. Furthermore, ARF did not significantly increase image contrast when applied to non-targeted agents, suggesting that ARF did not increase non-specific adhesion

“Worst case” result from a qualitative standpoint.

<p>Original (<b>A</b>) and re-digitized (<b>B</b>) 12-lead ECG tracings from patient 2H as interpreted by the Leuven automated diagnostic algorithm when a Cardiax ADC was used to collect the original data and a CorScience ADC the re-digitized data. This was the only file amongst the 10 tested wherein a minor change was elicited in the automated interpretation of the re-digitized compared to the original file. This minor change occurred only when using the Leuven algorithm (a corresponding change did not occur for the automated interpretation when using the Cardiax algorithm under any circumstances), and occurred regardless of whether the re-digitized data were collected on a CorScience or Cardiax ADC. Note also the modest change in DC offset (which may have been a key contributor to the slight change in the automated interpretation) as well as the very minor differences between (A) and (B) in some intervals, axes and voltages as automatically determined.</p

RMS difference values for all 10 patients' original versus re-digitized files when both the original and re-digitized files were collected on the same model of Cardiax ADC.

<p>RMS: Root mean square, with RMS difference values expressed in analog to digital converter (ADC) counts, and with 1 ADC count = 2.44 µV.</p><p>Channel: the equivalent of leads I, II and the precordial electrodes as referenced to the right arm electrode (CR1-CR6).</p><p>H and D: Healthy and Diseased patients, respectively.</p><p>LBBB and RBBB: left and right bundle branch block (BBB), respectively.</p

New System for Digital to Analog Transformation and Reconstruction of 12-Lead ECGs

<div><p>Introduction</p><p>We describe initial validation of a new system for digital to analog conversion (DAC) and reconstruction of 12-lead ECGs. The system utilizes an open and optimized software format with a commensurately optimized DAC hardware configuration to accurately reproduce, from digital files, the original analog electrocardiographic signals of previously instrumented patients. By doing so, the system also ultimately allows for transmission of data collected on one manufacturer's 12-lead ECG hardware/software into that of any other.</p><p>Materials and Methods</p><p>To initially validate the system, we compared original and post-DAC re-digitized 12-lead ECG data files (∼5-minutes long) in two types of validation studies in 10 patients. The first type <i>quantitatively</i> compared the total waveform voltage differences between the original and re-digitized data while the second type <i>qualitatively</i> compared the automated electrocardiographic diagnostic statements generated by the original versus re-digitized data.</p><p>Results</p><p>The grand-averaged difference in root mean squared voltage between the original and re-digitized data was 20.8 µV per channel when re-digitization involved the same manufacturer's analog to digital converter (ADC) as the original digitization, and 28.4 µV per channel when it involved a different manufacturer's ADC. Automated diagnostic statements generated by the original versus reconstructed data did not differ when using the diagnostic algorithm from the same manufacturer on whose device the original data were collected, and differed only slightly for just 1 of 10 patients when using a third-party diagnostic algorithm throughout.</p><p>Conclusion</p><p>Original analog 12-lead ECG signals can be reconstructed from digital data files with accuracy sufficient for clinical use. Such reconstructions can readily enable automated second opinions for difficult-to-interpret 12-lead ECGs, either locally or remotely through the use of dedicated or cloud-based servers.</p></div

RMS difference values for all 10 patients' original versus re-digitized files when the original files were collected on a Cardiax ADC and the re-digitized files on a CorScience ADC.

<p>RMS: Root mean square, with RMS difference values expressed in analog to digital converter (ADC) counts, and with 1 ADC count = 2.44 µV.</p><p>Channel: the equivalent of leads I, II and the precordial electrodes as referenced to the right arm electrode (CR1-CR6).</p><p>H and D: Healthy and Diseased patients, respectively.</p><p>LBBB and RBBB: left and right bundle branch block (BBB), respectively.</p

Schematic summarizing all stages of ECG data processing by the system.

<p>Schematic summarizing all stages of ECG data processing by the system.</p

Automated clinical diagnostic statements outputted by the Cardiax algorithm for the original versus re-digitized files when both files were collected on the same model of Cardiax ADC.

<p>H and D: Healthy and Diseased patients, respectively.</p

Automated clinical diagnostic statement(s) outputted by the Leuven algorithm for the original vs. re-digitized files when the original file was collected on a Cardiax ADC and the re-digitized file on either a Cardiax or CorScience ADC.

<p>H and D: Healthy and Diseased patients, respectively. The single difference noted in the automated diagnostic interpretations (original versus re-digitized) is shown in italics (file 2H).</p

Effect of “true simultaneous” sampling.

<p>(<b>A</b>) The study's standard “round-robin sampled” Cardiax-re-digitized file for the same patient 4D with a left bundle branch block whose original file is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061076#pone-0061076-g004" target="_blank">Figure 4A</a>. Possibly due in part to the higher sampling rate at Cardiax's compared to CorScience's ADC (i.e., 1000 Hz rather than 500 Hz), the visual differences in this patient's leads V1–V3 between the Cardiax re-digitized and original file are perhaps slightly less apparent than those between the CorScience re-digitized and original file as observed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061076#pone-0061076-g004" target="_blank">Figure 4</a>. (<b>B</b>) When using for re-digitization a just-released new Cardiax device briefly loaned to us after our formal study's completion that employs “true simultaneous” sampling via incorporation of Texas Instruments' ADS1298 chip, the visual differences in this same patient's V1–V3 complexes essentially “disappear” in conjunction with a ∼2–3 fold reduction in the RMS difference values for channels CR1, CR2 and CR3 to 9.4, 9.4 and 11.7 ADC counts, respectively. Compare these results to the corresponding results for CR1–CR3 for this patient as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061076#pone-0061076-t001" target="_blank">Tables 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061076#pone-0061076-t002" target="_blank">2</a> when “non true-simultaneous sampling” was used for re-digitization.</p