Rolf Bippus

Quantile based noise estimation for spectral subtraction and Wiener filtering

Elimination of additive noise from a speech signal is a fundamental problem in audio signal processing. In this paper we restrict our considerations to the case where only a single microphone recording of the noisy signal is available. The algorithms which we investigate proceed in two steps. First, the noise power spectrum is estimated. A method based on temporal quantiles in the power spectral domain is proposed and compared with pause detection and recursive averaging. The second step is to eliminate the estimated noise from the observed signal by spectral subtraction or Wiener filtering. The database used in the experiments comprises 6034 utterances of German digits and digit strings by 770 speakers in 10 different cars. Without noise reduction, we obtain an error rate of 11.7%. Quantile based noise estimation and Wiener filtering reduce the error rate to 8.6%. Similar improvements are achieved in an experiment with artificial, non-stationary noise.

Acoustic synthesis of training data for speech recognition in living room environments

Despite continuous progress in robust automatic speech recognition acoustic mismatch between training and test conditions is still a major problem. Consequently, large speech collections must be conducted in many environments. An alternative approach is to generate training data synthetically by filtering clean speech with impulse responses and/or adding noise signals from the target domain. We compare the performance of a speech recognizer trained on recorded speech in the target domain with a system trained on suitably transformed clean speech. In order to obtain comparable results, our experiments are based on two channel recordings with a close talk and a distant microphone which produce the clean signal and the target domain signal respectively. By filtering and adding noise we obtain error rates which are only 10% higher for natural number recognition and 30% higher for a command recognition task compared to training with target domain data.

Feasibility of [18F]-2-Fluoro-A85380-PET imaging of human vascular nicotinic acetylcholine receptors in vivo.

OBJECTIVES The aim of this feasibility study was to evaluate [(18)F]-2-Fluoro-A85380 for in vivo imaging of arterial nicotinic acetylcholine receptors (nAChRs) in humans. Furthermore, potentially different vascular uptake patterns of this new tracer were evaluated in healthy volunteers and in patients with neurodegenerative disorders. BACKGROUND [(18)F]-2-Fluoro-A85380 was developed for in vivo positron emission tomography (PET) imaging of nAChR subunits in the human brain. These nAChRs are also found in arteries and seem to mediate the deleterious effects of nicotine as a part of tobacco smoke in the vasculature. It has been previously shown that uptake patterns of the radiotracer in the brain differs in patients with neurodegenerative disorders compared with healthy controls. METHODS [(18)F]-2-Fluoro-A85380 uptake was quantified in the ascending and descending aorta, the aortic arch, and the carotids in 5 healthy volunteers and in 6 patients with either Parkinsons disease or multiple system atrophy, respectively, as the maximum target-to-background ratio. The maximal standardized uptake value values, the single hottest segment, and the percent active segments of the [(18)F]-2-Fluoro-A85380 uptake in the arteries were also assessed. RESULTS [(18)F]-2-Fluoro-A85380 uptake was clearly visualized and maximum target-to-background ratio uptake values corrected for the background activity of the tracer showed specific tracer uptake in the arterial walls. Significantly higher uptake values were found in the descending aorta. Comparison between volunteers and patients revealed significant differences, with lower [(18)F]-2-Fluoro-A85380 uptake in the patient group when comparing single arterial territories but not when all arterial territories were pooled together. CONCLUSIONS [(18)F]-2-Fluoro-A85380 can provide specific information on the nAChR distribution in human arteries. Vascular nAChR density seems to be lower in patients with Parkinsons disease or multiple system atrophy. Once confirmed in larger study populations and in the experimental setting, this approach might provide insights into the pathogenic role of nAChRs in the human vasculature.

Is multidetector CT-based bone mineral density and quantitative bone microstructure assessment at the spine still feasible using ultra-low tube current and sparse sampling?

ObjectiveOsteoporosis diagnosis using multidetector CT (MDCT) is limited to relatively high radiation exposure. We investigated the effect of simulated ultra-low-dose protocols on in-vivo bone mineral density (BMD) and quantitative trabecular bone assessment.Materials and methodsInstitutional review board approval was obtained. Twelve subjects with osteoporotic vertebral fractures and 12 age- and gender-matched controls undergoing routine thoracic and abdominal MDCT were included (average effective dose: 10 mSv). Ultra-low radiation examinations were achieved by simulating lower tube currents and sparse samplings at 50%, 25% and 10% of the original dose. BMD and trabecular bone parameters were extracted in T10–L5.ResultsExcept for BMD measurements in sparse sampling data, absolute values of all parameters derived from ultra-low-dose data were significantly different from those derived from original dose images (p<0.05). BMD, apparent bone fraction and trabecular thickness were still consistently lower in subjects with than in those without fractures (p<0.05).ConclusionIn ultra-low-dose scans, BMD and microstructure parameters were able to differentiate subjects with and without vertebral fractures, suggesting osteoporosis diagnosis is feasible. However, absolute values differed from original values. BMD from sparse sampling appeared to be more robust. This dose-dependency of parameters should be considered for future clinical use.Key Points• BMD and quantitative bone parameters are assessable in ultra-low-dose in vivo MDCT scans.• Bone mineral density does not change significantly when sparse sampling is applied.• Quantitative trabecular bone microstructure measurements are sensitive to dose reduction.• Osteoporosis subjects could be differentiated even at 10% of original dose.• Radiation exposure should be considered when comparing quantitative bone parameters.

Role of Compressive Sensing Technique in Dose Reduction for Chest Computed Tomography: A Prospective Blinded Clinical Study

Purpose The purpose of this study was to assess pulmonary lesion detection, diagnostic confidence, and noise reduction in sparse-sampled (SpS) computed tomographic (CT) data of submillisievert (SubmSv) chest CT reconstructed with iterative reconstruction technique (IRT). Materials and Methods This Human Insurance Portability and Accountability–compliant, institutional review board–approved prospective study was performed using SpS-SubmSv IRT chest CT in 10 non–obese patients (body-mass index, 21–35 kg/m2; age range, 26–90 years). Written informed consent was obtained. The patients were scanned at standard-dose CT (mean [SD] volumetric CT dose index, 6 [0.9] mGy; mean [SD] dose-length product, 208 ± 44 mGy·cm; and mean [SD] effective dose, 3 [0.6] mSv) and at SubmSv dose (1.8 [0.2] mGy, 67 [2] mGy·cm, 0.9 [0.03] mSv, respectively) on a Philips 128-slice CT scanner with double z-sampling. Sparse angular sampling data were reconstructed using 25% of the angular projections from the SubmSv sinogram to reduce the number of views and radiation dose by approximately 4-fold. Hence, the patients were scanned and then, simulation-based sparse sampling was performed with a resultant dose hypothetical SpS scan estimated mathematically (0.2 mSv). From each patient data, 3 digital imaging and communications in medicine series were generated: SpS-SubmSv with IRT, fully sampled SubmSv filtered back projection (FBP), and fully sampled standard-dose FBP (SD-FBP). Two radiologists independently assessed these image series for detection of lung lesions, visibility of small structures, and diagnostic acceptability. Objective noise was measured in the thoracic aorta, and noise spectral density was obtained for SpS-SubmSv IRT, SubmSv-FBP, and SD-FBP. Results The SpS-SubmSv IRT resulted in 75% (0.2/0.9 mSv) and 92% (0.2/2.9 mSv) dose reduction, when compared with the fully sampled SubmSv-FBP and SD-FBP, respectively. Images of SpS-SubmSv displayed all 46 lesions (most <1 cm, 30 lung nodules, 7 ground glass opacities, 9 emphysema) seen on the SubmSv-FBP and SD-FBP data sets. Lesion margins with sparse-sampled data were deemed acceptable compared with both SubmSv-FBP and SD-FBP. Overall diagnostic confidence was maintained with SpS-SubmSv IRT despite the presence of minor pixilation artifacts in 3 of 10 cases. The SpS-SubmSv IRT showed 63% and 38% noise reduction when compared with SubmSv-FBP (P < 0.0001) and SD-FBP (P < 0.01), respectively, with no significant change in Hounsfield unit values (P > 0.05). Noise-spectral density showed that SpS-SubmSv IRT gives a linear decrease over frequency in the semilog plot and an exponential decrease of noise power over frequency compared with SubmSv-FBP and SD-FBP. Conclusions More than 90% dose reduction could be achieved with one-fourth sparse-sampled and SubmSv chest CT examination when reconstructed with IRT. Chest CT dose at one fourth of a millisievert with SpS is possible with optimal lesion detection and diagnostic confidence for the evaluation of pulmonary findings.

Motion compensated reconstruction of the aortic valve for computed tomography

Motion compensated cardiac reconstruction in computed tomography (CT) has traditionally been focused on coronary arteries. However, with the increasing number of cardiac CT scans being performed for the diagnosis and treatment planning of valvular diseases, there is a clear need for motion correction of the aortic valve region to assist with the reproducibility of aortic annulus measurements. A second pass approach for aortic valve motion compensation on retrospective ECG-gated CT scans is introduced here. The processing chain is comprised of four steps. A gated multi-phase cardiac reconstruction is first performed, followed by a gradient based filter to enhance the edges in the resulting time series of volume images. Subsequently these normalized filtered results are made to undergo an elastic registration and finally followed by a motion compensated reconstruction that includes the estimated motion vector fields. The method was applied to twelve clinical cases and tested for systolic (30% R-R interval) and diastolic (70% R-R interval) imaging of the aortic valve. This second pass approach leads to a significant reduction of motion artifacts especially in late systole.

Motion compensation for non-gated helical CT: application to lung imaging

Computed tomography (CT) imaging of the thorax is a common application of CT in radiology. Most of these scans are performed with a helical scan protocol. A significant number of images suffer from motion artefacts due to the inability of the patients to hold their breath or due to hiccups or coughing. Some images become nondiagnostic while others are simply degraded in quality. In order to correct for these artefacts a motion compensated reconstruction for non-periodic motion is required. For helical CT scans with a pitch smaller or equal to one the redundancy in the helical projection data can be used to generate images at the identical spatial position for multiple time points. As the scanner moves across the thorax during the scan, these images do not have a fixed time point, but a well-defined temporal distance inbetween the images. Using image based registration a motion vector field can be estimated based on these images. The motion artefacts are corrected in a subsequent motion compensated reconstruction. The method is tested on mathematical phantom data (reconstruction) and clinical lung scans (motion estimation and reconstruction).

Diagnostic value of sparse sampling computed tomography for radiation dose reduction: initial results

Computed Tomography (CT) is one of the most important imaging modalities in the medical domain. Ongoing demand for reduction of the X-ray radiation dose and advanced reconstruction algorithms induce ultra-low dose CT acquisitions more and more. However, though advanced reconstructions lead to improved image quality, the ratio between electronic detector noise and incoming signal decreases in ultra-low dose scans causing a degradation of the image quality and, therefore, building a boundary for radiation dose reduction. Future generations of CT scanners may allow sparse sampled data acquisitions, where the source can be switched on and off at any source position. Sparse sampled CT acquisitions could reduce photon starvation in ultra-low dose scans by distributing the energy of skipped projections to the remaining ones. In this work, we simulated sparse sampled CT acquisitions from clinical projection raw data and evaluated the diagnostic value of the reconstructions compared to conventional CT. Therefore, we simulated radiation dose reduction with different degrees of sparse sampling and with a tube current simulator. Up to four experienced radiologists rated the diagnostic quality of each dataset. By a dose reduction to 25% of the clinical dosage, images generated with 4-times sparse sampling – meaning a gap of three projections between two sampling positions – were consistently rated as diagnostic, while about 20% of the ratings for conventional CT were non-diagnostic. Therefore, our data give an initial indication that with sparse sampling a reduction to 25% of the clinical dose is feasible without loss of diagnostic value.