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Both methods have been used for emission computed tomography. In the underlying linear model\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$Ax\\sim b$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mi>A<\/mml:mi>\n                            <mml:mi>x<\/mml:mi>\n                            <mml:mo>\u223c<\/mml:mo>\n                            <mml:mi>b<\/mml:mi>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    the noise can be in the data vector\n                    <jats:italic>b<\/jats:italic>\n                    and\/or in the matrix\n                    <jats:italic>A<\/jats:italic>\n                    . Under certain conditions it is shown that the noise error asymptotically grows like\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$O(\\sqrt{k})$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mi>O<\/mml:mi>\n                            <mml:mo>(<\/mml:mo>\n                            <mml:msqrt>\n                              <mml:mi>k<\/mml:mi>\n                            <\/mml:msqrt>\n                            <mml:mo>)<\/mml:mo>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    , with\n                    <jats:italic>k<\/jats:italic>\n                    the iteration index. 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