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Biomedical subjects

Daniel J Tozer

Publications and source records attributed to Daniel J Tozer.

6 recordsLinked to original sources

Quantitative analysis of whole-tumor Gd enhancement histograms predicts malignant transformation in low-grade gliomas.

PURPOSE: To quantify subtle gadolinium (Gd) enhancement (signal increase) in whole-tumor histograms and optimize their ability to predict subsequent malignant transformation in low-grade gliomas (LGGs). MATERIALS AND METHODS: We analyzed histograms from 21 adult subjects with LGGs (eight nontransformers and 13 transformers) who had been imaged every six months for periods of two to five years. Before transformation these tumors were reported as radiologically non-enhancing. Imaging included a T(1)-weighted volume sequence before and after a double dose of Gd-DTPA contrast agent. Image data sets were spatially registered and subtracted to obtain maps of percent enhancement (%E). Tumor outlines were defined on fluid-attenuated inversion recovery (FLAIR) images, and the volumes were calculated. Histogram tails were analyzed to obtain the volume (mL) of subtly enhancing tissue (%E > 10%). RESULTS: Baseline enhancing volumes were higher for Ts than for NTs (P < 0.005). Kaplan-Meier survival curves for a threshold of 4 mL showed clear differences at five years (P < 0.04). Pretransformation examinations predicted transformation (corrected threshold = 3.0 mL, P = 0.011). CONCLUSION: Clear histogram differences at presentation suggest that the process of transformation starts very early. It is now possible to identify individuals at high risk for transformation at baseline by quantifying the volume of subtly enhancing tumor tissue, and such findings could have an impact on patient management.

Adult↗

Apparent diffusion coefficient histograms may predict low-grade glioma subtype.

The subtypes of glioma are known to have different prognosis and response to treatment. The purpose of this work was to investigate whether apparent diffusion coefficient (ADC) histograms of untreated low-grade astrocytomas and oligodendrogliomas exhibit different characteristics due to their biological differences, and whether a diagnosis of tumour subtype can be made at presentation using the histogram alone, which, if possible, would have an impact on clinical practise. Fifteen patients with astrocytoma (AC) [11 male (mean age +/- standard deviation) 40 +/- 11 years], nine with oligodendroglioma (OD) (four male, 45 +/- 13 years) and three with oligoastrocytoma (OA) (two male, 60 +/- 11 years) were recruited and diffusion-weighted images (b = 0 and 1000 s mm(-2)) were acquired every 6 months to date or until malignant transformation. Whole tumour ADC histograms were calculated, a multiple discriminant analysis was performed and quantitative morphological parameters extracted, the AC and OD subtypes were then compared using Student's unpaired t-test. Classification of the histograms was also performed. ODs had significantly lower group ADC values than ACs and up to 83% of the subjects could be correctly classified into the OD and AC groups by reference to the histogram. The group differences were most significant for the multiple discriminant analysis (p = 1 x 10(-5)) and at the 10th centile point [AC = 1170 +/- 170, OD = (1030 +/- 80) x 10(-6) mm(2) s(-1)] (p = 0.01). ODs have a lower ADC than ACs with differences throughout the histogram. Both tumour types show similar intra-tumour heterogeneity, as seen from the equal group peak heights, but ACs shows more intra-group heterogeneity. ADC histogram analysis may aid non-invasive sub-classification of low-grade glioma histological subtypes.

Adult↗

Principal component and linear discriminant analysis of T1 histograms of white and grey matter in multiple sclerosis.

Twenty-three relapsing remitting multiple sclerosis (RRMS) patients and 14 controls were imaged to produce normal-appearing white and grey matter T1 histograms. These were used to assess whether histogram measures from principal component analysis (PCA) and linear discriminant analysis (LDA) out-perform traditional histogram metrics in classification of T1 histograms into control and RRMS subject groups and in correlation with the expanded disability status score (EDSS). The histograms were classified into one of two groups using a leave-one-out analysis. In addition, the patients were scanned serially, and the calculated parameters correlated with the EDSS. The classification results showed that the more complex techniques were at least as good at classifying the subjects as histogram mean, peak height and peak location, with PCA/LDA having success rates of 76% for white matter and 68%/65% for grey matter. No significant correlations were found with EDSS for any histogram parameter. These results indicate that there is much information contained within the grey matter as well as the white matter histograms. Although in these histograms PCA and LDA did not add greatly to the discriminatory power of traditional histogram parameters, they provide marginally better performance, while relying only on data-driven feature selection.

Brain↗

A simple correction for B1 field errors in magnetization transfer ratio measurements.

B1 errors are a problem in magnetization transfer ratio (MTR) measurements because the MTR value is dependent on the amplitude of the magnetization transfer (MT) pulse. B1 errors can arise from radiofrequency (RF) nonuniformity (caused by the RF coil, or skin effect and dielectric resonance in the subject's head) and also from inaccurate setting of the transmitter output when compensating for varying amounts of loading of the RF coil. B1 errors, and hence MTR errors, may be up to 5-10%, a large source of error in quantitative MR measurements. Radiofrequency nonuniformity may cause MTR histograms to be broadened. The dependence of MTR on B1 was modeled using binary spin bath theory, with a continuous wave (CW) approximation. For B1 reductions of up to 20%, normalized plots for different brain tissue types could be approximated by a single line, indicating that a systematic correction could be applied to MTR measurements with a known B1 error, regardless of tissue type. On a 1.5-T scanner with a birdcage coil, MTR was measured in 18 tissue types in five controls. The MT pulse amplitude was reduced in steps from its nominal value by up to 20%. Averaging data over all controls and tissue types resulted in a line fitting mtr(normalized)=0.812b(1normalized)+0.193, where mtr(normalized) is the normalized value of MTR (relative to its value at the nominal B1) and b(1normalized) is the normalized value of B1 (relative to its nominal value). For a 20% reduction in MT pulse amplitude (i.e., b(1normalized)=0.80), the mean MTR value for the 18 tissue types was 7.0 percent units (pu) below the correct value. After correction using the single equation above for all tissue types, all MTR values were within 1.5 pu of their correct value [root mean square (rms) error=0.7 pu]. Magnetization transfer ratio values tended to be slightly overcorrected because the simple linear correction scheme is only an approximation to the true MTR dependence on B1. A B1 field mapping technique was implemented, based on the double angle method (DAM), with fast spin-echo (FSE) readout, and TR=15 s; this took a total of 6 min of imaging time. This was used to quantify B(1) errors and correct MTR maps and histograms. However, the cerebrospinal fluid (CSF) T1 is very long (approximately 4.2 s); thus, to achieve complete longitudinal relaxation (a requirement of the DAM B1 mapping method), an increase in TR and, hence, acquisition time would be required. In general, however, we are not interested in calculating the B1 in the CSF, although it is important that the B1 is determined in partial volume voxels around the CSF. Using our birdcage head coil, whole-brain B1 histograms were found to have full-width half maximums (FWHMs) ranging from just 6.8% to 11.5% of the nominal B1 value. The FSE DAM B1 field mapping technique was shown to be robust, although a longer TR time may be desirable to ensure complete elimination of CSF partial volume errors. The procedure can be applied on any scanner where the Euro-MT sequence is available, or alternatively, where the amplitude of B1 or of the MT pulse can be manually reduced in order to perform this type of "calibration" experiment for the particular MTR sequence used. The MTR is known to be highly dependent on the parameters of the sequence used, in particular, the MT pulse shape, flip angle, duration, and offset frequency, and the repetition time TR' between successive MT pulses. Therefore, correction schemes will differ for different MTR sequences, and new data sets would be required to calculate these different correction schemes.

Algorithms↗

Three-dimensional quantitative magnetisation transfer imaging of the human brain.

Quantitative magnetisation transfer (MT) analysis is based on a two-pool model of magnetisation transfer and allows important physical properties of the two proton pools to be assessed. A good signal-to-noise ratio (SNR) for the measured signal is essential in order to estimate reliably the parameters from a small number of samples, thus prompting the use of a sequence with high SNR, such as a three-dimensional spoiled gradient acquisition. Here, we show how full brain coverage can be accomplished efficiently, using a three-dimensional acquisition, in a clinically acceptable time, and without the use of large numbers of slice-selective radio-frequency pulses which could otherwise confound analysis. This acquisition was first compared in post mortem human brain tissue to established two-dimensional acquisition protocols with differing SNR levels and then used to collect data from six healthy subjects. Image data were fitted using the two pool model and showed negligible residual deviations. Quantitative results were assessed in several brain locations. Results were consistent with previous single-slice data, and parametric maps were of good quality. Further investigations are needed to interpret the regional variation of quantitative MT quantities.

Adult↗

Removing spikes caused by quantization noise from high-resolution histograms.

A novel method is presented for the removal of spikes, caused by the division of two series of integers, from high-resolution histograms. When two series of integers are divided the results take the form of a nonuniform distribution. Such a division is often used in medical imaging, due to the storage of most images as integers. An example of this is the division of the saturated and unsaturated signal intensities to obtain a magnetization transfer ratio. Histograms produced using these methods often contain spikes relating to the nonuniform distribution mentioned above. These spikes can have serious implications for certain histogram characteristics. Most commonly, peak height and location can be seriously distorted by these spikes, which have predictable locations. These spikes can be removed by the addition of uniformly distributed noise to the integer signal intensities before division.

Magnetic Resonance Imaging↗