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A novel technique to guide a subjects' breathing pattern using a respiratory biofeedback (rBF) "game" to improve respiratory efficiency is presented. The continuously adaptive windowing strategy, a fully automatic and highly effic...
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A novel technique to guide a subjects' breathing pattern using a respiratory biofeedback (rBF) "game" to improve respiratory efficiency is presented. The continuously adaptive windowing strategy, a fully automatic and highly efficient free-breathing navigator gated technique, is used to acquire the data as it ensures that all potential navigator acceptance windows are possible. This enables the rBF to be fully adaptable to a subject's respiratory pattern. Images of the thoracic aorta acquired using balanced steady-state free precession with continuously adaptive windowing strategy respiratory motion control, with and without rBF, were compared in 10 healthy subjects. Total scan time was reduced by using rBF. The mean scan time was reduced from 7 min 44 s (463 cardiac cycles, ±127cc) without rBF to 5 min 43 s (380 cardiac cycles, ±118cc) with the use of rBF (P < 0.05). Respiratory efficiency was increased from 45% without rBF to 56% with rBF (P < 0.01). Image quality was the same for both techniques (P = ns). In conclusion, rBF significantly improved respiratory efficiency and reduced acquisition duration without affecting image quality.
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Abstract Purpose of Review Motion artifacts, due to cardiac and respiratory cycles, myocardial cardiac creep, or gross patient movements, have been extensively investigated in the context of relative myocardial perfusion imaging w...
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Abstract Purpose of Review Motion artifacts, due to cardiac and respiratory cycles, myocardial cardiac creep, or gross patient movements, have been extensively investigated in the context of relative myocardial perfusion imaging with SPECT and PET. These movements have been identified as a major source of errors in image quantification and diagnosis. Recently, as dynamic PET quantification for myocardial blood flow assessment has entered clinical practice, similar questions have arisen on the impact of motion on final blood flow values. Recent Findings While preliminary investigations have underlined the potential impact of these motions on MBF quantification, their correction on dynamic acquisition remains challenging and limited to research studies. Gross patient’s body movements occur in a consistent number of cases, particularly during stress acquisition, typically involving a limited number of image frames. If undetected, these movements can lead to great differences in flow values and consequently misdiagnosis. Quality control routines can be applied to automatically inspect the shape of time activity curves and to help identify motion artifacts. Summary Cyclic cardiac and respiratory motion may have a considerable impact on final flow values. Correction of gross body motion represents a priority in the context of optimizing absolute flow clinical routine utilization and protocol standardization.
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Purpose: The purpose was to compare hepatic arterial phase (HAP) respiratory motion artifact (RMA) between gadoxetate, gadobutrol, gadopentetate, and gadobenate.
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The quality of MRI time series data, which allows the study of dynamic processes, is often affected by confounding sources of signal fluctuation, including the cardiac and respiratory cycle. An adaptive filter is described, reduci...
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The quality of MRI time series data, which allows the study of dynamic processes, is often affected by confounding sources of signal fluctuation, including the cardiac and respiratory cycle. An adaptive filter is described, reducing these signal fluctuations as long as they are repetitive and their timing is known. The filter, applied in image domain, does not require temporal oversampling of the artifact-related fluctuations. Performance is demonstrated for suppression of cardiac and respiratory artifacts in 10-minute brain scans on 6 normal volunteers. Experimental parameters resemble a typical fMRI experiment (17 slices; 1700 ms TR). A second dataset was acquired at a rate well above the Nyquist frequency for both cardiac and respiratory cycle (single slice; 100 ms TR), allowing identification of artifacts specific to the cardiac and respiratory cycles, aiding assessment of filtering performance. Results show significant reduction in temporal standard deviation (SD(t)) in all subjects. For all 6 datasets with 1700 ms TR combined, the filtering method resulted in an average reduction in SD(t) of 9.2% in 2046 voxels substantially affected by respiratory artifacts, and 12.5% for the 864 voxels containing substantial cardiac artifacts. The maximal SD(t) reduction achieved was 52.7% for respiratory and 55.3% for cardiac filtering. Performance was found to be at least equivalent to the previously published RETROICOR method. Furthermore, the interaction between the filter and fMRI activity detection was investigated using Monte Carlo simulations, demonstrating that filtering algorithms introduce a systematic error in the detected BOLD-related signal change if applied sequentially. It is demonstrated that this can be overcome by combining physiological artifact filtering and detection of BOLD-related signal changes simultaneously. Visual fMRI data from 6 volunteers were analyzed with and without the filter proposed here. Inclusion of the cardio-respiratory regressors in the design matrix yielded a 4.6% t-score increase and 4.0% increase in the number of significantly activated voxels.
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PET-CT scanners allow generation of transmission maps from CT. The use of CT attenuation correction (CTAC) instead of germanium-68 attenuation correction (Ge AC) might be expected to cause artifacts on reconstructed emission image...
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PET-CT scanners allow generation of transmission maps from CT. The use of CT attenuation correction (CTAC) instead of germanium-68 attenuation correction (Ge AC) might be expected to cause artifacts on reconstructed emission images if differences in respiratory status exist between the two methods of attenuation correction. The aim of this study was to evaluate for possible respiratory motion artifacts (RMA) in PET images attenuation corrected with CT from PET-CT in clinical patients. PET-CT scans were performed using a Discovery LS PET-CT system in 50 consecutive patients (23 males, 27 females; mean age 58.2 years) with known or suspected malignancy. Both CTAC and Ge AC transmission data obtained during free tidal breathing were used to correct PET emission images. Cold artifacts at the interface of the lungs and diaphragm, believed to be due to respiratory motion (RMA), that were seen on CTAC images but not on the Ge AC images were evaluated qualitatively on a four-point scale (0, no artifact; 1, mild artifact; 2, moderate artifact; 3, severe artifact). RMA was also measured for height. Curvilinear cold artifacts paralleling the dome of the diaphragm at the lung/diaphragm interface were noted on 84% of PET-CT image acquisitions and were not seen on the ~(68)Ge-corrected images; however, these artifacts were infrequently severe. In conclusion, RMA of varying magnitude were noted in most of our patients as a curvilinear cold area at the lung/diaphragm interface, but were not diagnostically problematic in these patients.
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On-line measurement of respiration plays an important role in monitoring human physical activities. Such measurement commonly employs sensing belts secured around the rib cage and abdomen of the test object. Affected by the moveme...
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On-line measurement of respiration plays an important role in monitoring human physical activities. Such measurement commonly employs sensing belts secured around the rib cage and abdomen of the test object. Affected by the movement of body tissues, respiratory signals typically have a low signal-to-noise ratio. Removing tissue artifacts therefore is critical to ensuring effective respiration analysis. This paper presents a signal decomposition technique for tissue artifact removal from respiratory signals, based on the empirical mode decomposition (EMD). An algorithm based on the mutual information and power criteria was devised to automatically select appropriate intrinsic mode functions for tissue artifact removal and respiratory signal reconstruction. Performance of the EMD-algorithm was evaluated through simulations and real-life experiments (N = 105). Comparison with low-pass filtering that has been conventionally applied confirmed the effectiveness of the technique in tissue artifacts removal.
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Cine average CT (CACT) and interpolated average CT (IACT) have been proposed to improve attenuation correction (AC) for PET/CT in oncologic and cardiac studies. This study aims to evaluate their effectiveness on myocardial perfusi...
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Cine average CT (CACT) and interpolated average CT (IACT) have been proposed to improve attenuation correction (AC) for PET/CT in oncologic and cardiac studies. This study aims to evaluate their effectiveness on myocardial perfusion SPECT/CT using computer simulation and physical phantom experiments.
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The forced oscillation technique (FOT) can provide unique and clinically relevant lung function information with little cooperation with subjects. However, FOT has higher variability than spirometry, possibly because strategies fo...
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The forced oscillation technique (FOT) can provide unique and clinically relevant lung function information with little cooperation with subjects. However, FOT has higher variability than spirometry, possibly because strategies for quality control and reducing artifacts in FOT measurements have yet to be standardized or validated. Many quality control procedures rely on either simple statistical filters or subjective evaluation by a human operator. In this study, we propose an automated artifact removal approach based on the resistance against flow profile, applied to complete breaths. We report results obtained from data recorded from children and adults, with and without asthma. Our proposed method has 76% agreement with a human operator for the adult data set and 79% for the pediatric data set. Furthermore, we assessed the variability of respiratory resistance measured by FOT using within-session variation (wCV) and between-session variation (bCV). In the asthmatic adults test data set, our method was again similar to that of the manual operator for wCV (6.5 vs. 6.9%) and significantly improved bCV (8.2 vs. 8.9%). Our combined automated breath removal approach based on advanced feature extraction offers better or equivalent quality control of FOT measurements compared with an expert operator and computationally more intensive methods in terms of accuracy and reducing intrasubject variability.
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Unobtrusive and wearable devices are gaining large acceptance in the continuous monitoring of physiological parameters. Among the five vital signs, respiratory rate (f(R)) can be used to detect physiological abnormalities and heal...
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Unobtrusive and wearable devices are gaining large acceptance in the continuous monitoring of physiological parameters. Among the five vital signs, respiratory rate (f(R)) can be used to detect physiological abnormalities and health status changes. The purpose of this work was to investigate the performances of a multi-sensor smart garment in estimating the f(R) during walking and running activities. Bespoke algorithms have been implemented to retrieve f(R) values from raw data. Experiments were carried out on ten male volunteers during walking and running activities at selected speeds controlled by a treadmill (i.e., from 1.6 km.h(-1) to 8.0 km.h(-1)). Data were analysed in both frequency and time domains. In the frequency domain, f(R) was analyzed considering a time window of 20 s. The 97% of f(R) estimated by the garment agreed with the reference (i.e., flowmeter) values in the range +/- 3 breaths per minute (bpm). In the time domain, breath-by-breath f(R) analysis was carried out. The garment performance was evaluated in terms of mean absolute error (MAE), standard error (SE), mean percentage error (mean %E[i]) and by the Bland-Altman analysis. Good agreement with the reference device was testified by low MAE (<1.86 bpm), SE (<0.21 bpm), mean %E[i] (<2.83%), and by the Bland-Altman analysis (Mean of Differences = 0.22 bpm, Limits of Agreement = 6.06 bpm). Summing up, the garment based on six sensing elements and related bespoke algorithms are able to provide robust information about f(R) on both average and breath-by-breath bases even during physical activities.
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Previously, we proposed interpolated averaged CT (IACT) for improved attenuation correction (AC) in thoracic PET/CT. This study aims to evaluate its feasibility and effectiveness on cardiac PET/CT.
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