Lennart Thurfjell

Image registration: an essential tool for nuclear medicine

Abstract. There is increasing interest in being able to automatically register medical images from either the same or different modalities. Registered images are proving useful in a range of applications, not only providing more correlative information to aid in diagnosis, but also assisting with the planning and monitoring of therapy, both surgery and radiotherapy. The practising nuclear medicine specialist is faced with a dilemma in choosing an appropriate method since the literature in the field is extensive, with conflicting evidence as to what methods are optimal. Although most barriers to implementing registration in routine practice have been removed, there remains a lack of commercial, validated software. The alternative is to install a dual-modality instrument. The objective of this review is to present a general overview of medical image registration with emphasis on the application and issues relevant to nuclear medicine.

Implementation and validation of a fully automatic system for intra- and interindividual registration of PET brain scans

PURPOSEnStereotactic coordinate spaces and methods to adapt subjects to that space are required when performing averaging of functional studies across subjects.nnnMETHODSnA rapid and fully automatic method to perform intersubject registration and adaptation to a previously defined coordinate space has been developed and implemented. The implementation has been performed within an existing software developed to facilitate manual registration and adaptation, thus offering a versatile combination of automatic and manual tools. Furthermore, a novel measure, based on the F-statistic for intersubject (block) differences, for the assessment of intersubject goodness of fit was suggested and validated.nnnRESULTSnThe intra- and intersubject registration was validated by its application to data from six human subjects participating in an activation study. The registration was performed both manually and automatically, and the results indicated that the automatic method performed at least as well as the manual. The block F-statistic was lower for the automatic method, and the z-scores were not significantly different for the methods. The localization of activated regions showed good agreement and differed by an average of 6 mm between the methods.nnnCONCLUSIONnIt is concluded that the suggested method is a valuable alternative to the current manual approach.

CBA—an atlas-based software tool used to facilitate the interpretation of neuroimaging data

CBA, a software tool used to improve quantification and evaluation of neuroimaging data has been developed. It uses a detailed 3-dimensional brain atlas that can be adapted to fit the brain of an individual patient represented by a series of displayed images. Anatomical information from the atlas can then be introduced into the images. If the patient has been imaged in different modalities, adaptation of the atlas to the different images will provide the transformation that brings the images into registration. CBA can thus be used as a tool for fusing multimodality information from the same patient. Furthermore, by applying the inverse atlas transformation, images from a patient can be transformed to conform to the anatomy of the atlas brain. This anatomical standardization, where the atlas brain itself serves as the anatomy standard, brings data from different individuals into a compatible form providing possibilities to perform individual-group and group-by-group comparisons between patients and normal controls.

Transformations and algorithms in a computerized brain atlas

The computerized brain atlas constructed at the Karolinska Hospital, Stockholm, Sweden has been further developed. This atlas was designed to be employed in different fields of neuro imaging such as positron emission tomography (PET), single photon emission computed tomography (SPECT), computerized tomography (CT), and magnetic resonance imaging (MR). The main objectives with the atlas are to aid the interpretation of functional images by introducing anatomical information, to serve as a tool in the merging of data from different imaging modalities, and to facilitate the comparisons of data from different individuals by allowing for anatomical standardization of individual data. The authors describe the algorithms and transformations used in the implementation of the atlas software. >

Improved efficiency for MRI-SPET registration based on mutual information

Abstract.Mutual information has been proposed as a criterion for image registration. The criterion is calculated from a two-dimensional grey-scale histogram of the image pair being registered. In this paper we study how sparse sampling can be used to increase speed performance using the registration algorithm of Maes et al. (IEEE Trans Med Imaging 1997; 16: 187–198) with a focus on registration of MRI-SPET brain images. In particular we investigate how sparse sampling and parameters such as the number of bins used for the grey-scale histograms and smoothing of the data prior to registration affect accuracy and robustness of the registration. The method was validated using both simulated and human data. Our results show that sparse sampling introduced local maxima into the mutual information similarity function when the number of bins used for the histograms was large. To speed up registration while retaining robustness, smoothing of the data prior to registration was used and a coarse to fine subsampling protocol, where the number of bins in the histograms were dependent on the subsampling factor, was employed. For the simulated data, the method was able to recover known transformations with an accuracy of about 1 mm. Using the human data, there were no significant differences in the recovered transformation parameters when the suggested subsampling scheme was used compared with when no subsampling was used, but there was a more than tenfold increase in speed. Our results show that, with the appropriate choice of parameters, the method can accurately register MRI-SPET brain images even when very efficient sampling protocols are used.

Validation of fully automatic brain SPET to MR co-registration

Abstract.Fully automatic co-registration of functional to anatomical brain images using information intrinsic to the scans has been validated in a clinical setting for positron emission tomography (PET), but not for single-photon emission tomography (SPET). In this paper we evaluate technetium-99m hexamethylpropylene amine oxime to magnetic resonance (MR) co-registration for five fully automatic methods. We attached six small fiducial markers, visible in both SPET and MR, to the skin of 13 subjects. No increase in the radius of SPET acquisition was necessary. Distortion of the fiducial marker distribution observed in the SPET and MR studies was characterised by a measure independent of registration and three subjects were excluded on the basis of excessive distortion. The location of each fiducial marker was determined in each modality to sub-pixel precision and the inter-modality distance was averaged over all markers to give a fiducial registration error (FRE). The component of FRE excluding the variability inherent in the validation method was estimated by computing the error transformation between the arrays of MR marker locations and registered SPET marker locations. When applied to the fiducial marker locations this yielded the surface registration error (SRE), and when applied to a representative set of locations within the brain it yielded the intrinsic registration error (IRE). For the best method, mean IRE was 1.2 mm, SRE 1.5 mm and FRE 2.4 mm (with corresponding maxima of 3.3, 4.3 and 5.0 mm). All methods yielded a mean IRE <3 mm. The accuracy of the most accurate fully automatic SPET to MR co-registration was comparable with that published for PET to MR. With high standards of calibration and instrumentation, intra-subject cerebral SPET to MR registration accuracy of <2 mm is attainable.

A new three-dimensional connected components labeling algorithm with simultaneous object feature extraction capability

Abstract A new algorithm for performing connected component labeling of volume data is presented in this paper. The algorithm uses a table that combines efficient handling of label equivalences with the flexibility to add the calculation of features for each labeled component as well as to set various feature thresholds. The volume of each component is calculated in our implementation and it is possible to set a volume threshold for discarding small regions. The reuse of storage in the table is implemented in a simple but natural way.

Mapping pathological (99m)Tc-d,l-hexamethylpropylene amine oxime uptake in Alzheimer's disease and frontal lobe dementia with SPECT.

Seventeen patients with probable Alzheimer’s disease (AD), 7 patients with frontal lobe dementia (FLD) and 19 control subjects (NOR) were examined by 99mTc-d,l- hexamethylpropylene amine oxime (99mTc-HMPAO) SPECT. Images were standardised in the same 3D space and averaged within each group. After normalisation, the three sets of images were analysed in all cerebral lobes, hippocampus, thalamus and basal ganglia. In AD, the 99mTc-HMPAO uptake values were significantly reduced, as compared to NOR, in the parietal, temporal and insular lobes. In patients with FLD, the uptake was altered in all lobes with the exception of the parietal lobe. The uptake in the nucleus caudatus decreased significantly in both AD and FLD as compared to NOR. The uptake in the anterior cingulate cortex was significantly reduced in FLD. Subtraction images highlighted all significantly decreased areas. In conclusion, standardising SPECT in a common space and subtracting data from a control group improves the visual interpretation of images. In this study, the typical temporo-parietal and fronto-parietal 99mTc-HMPAO uptake reductions were found in AD and FLD, respectively. The uptake in the nucleus caudatus was found to decrease significantly in AD and FLD and the one in the anterior cingulate cortex was reduced in FLD.

Reproducibility and repeatability of 99Tcm-HMPAO rCBF SPET in normal subjects at rest using brain atlas matching.

The aim of this study was to assess regional cerebral blood flow (rCBF) in normal subjects at rest using 99Tcm-HMPAO single photon emission tomography (SPET). Analysis of reproducibility and repeatability was performed both before and after normalization of flow data. Six healthy volunteers were examined, three times each, according to a routine rCBF protocol. A computerized brain atlas was used to evaluate flow data in eight selected regions. The overall reproducibility of rCBF was evaluated from two scans performed at an average interval of 3 months. Repeatability was evaluated from two scans, 3 h apart and without re-injection of 99Tcm-HMPAO. For the normalized (relative) flow data, the reproducibility was ±1.3% and the repeatability ±2.2% (i.e. methodological errors dominate). For the non-normalized flow data, the corresponding values were ±14.8% and ±5.9%. rCBF SPET with 99Tcm-HMPAO is highly reproducible provided that the flow data are normalized. The variation in flow between individuals at one point in time and 3 months later was less than ±5% for all brain regions.

Resting state rCBF mapping with single-photon emission tomography and positron emission tomography : magnitude and origin of differences

Abstract. Single-photon emission tomography (SPET), using technetium-99m hexamethylpropylene amine oxime, and positron emission tomography (PET), using oxygen-15 butanol were compared in six healthy male volunteers with regard to the mapping of resting state regional cerebral blood flow (rCBF). A computerized brain atlas was utilized for 3D regional analyses and comparison of 64 selected and normalized volumes of interest (VOIs). The normalized mean rCBF values in SPET, as compared to PET, were higher in most of the Brodmann areas in the frontal and parietal lobes (4.8% and 8.7% respectively). The average differences were small in the temporal (2.3%) and occipital (1.1%) lobes. PET values were clearly higher in small VOIs like the thalamus (12.3%), hippocampus (12.3%) and basal ganglia (9.9%). A resolution phantom study showed that the in-plane SPET/PET system resolution was 11.0/7.5xa0mm. In conclusion, SPET and PET data demonstrated a fairly good agreement despite the superior spatial resolution of PET. The differences between SPET and PET rCBF are mainly due to physiological and physical factors, the data processing, normalization and co-registration methods. In order to further improve mapping of rCBF with SPET it is imperative not only to improve the spatial resolution but also to apply accurate correction techniques for scatter, attenuation and non-linear extraction.